@prefix bibo: <http://purl.org/ontology/bibo/> .
@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix eufiletype: <http://publications.europa.eu/resource/authority/file-type/> .
@prefix eulang: <http://publications.europa.eu/resource/authority/language/> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix geo: <http://www.opengis.net/ont/geosparql#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix prism: <http://prismstandard.org/namespaces/basic/2.0/> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix schema: <https://schema.org/> .
@prefix spdx: <http://spdx.org/rdf/terms#> .
@prefix sqp: <https://w3id.org/squirrelpapers/ontology#> .
@prefix sqpt: <https://w3id.org/squirrelpapers/type/> .
@prefix wd: <http://www.wikidata.org/entity/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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<https://w3id.org/squirrelpapers/event/31-international-geodetic-students-meeting-2018> a event:Event,
        schema:Event ;
    rdfs:label "31. International Geodetic Students Meeting" ;
    event:place <https://w3id.org/squirrelpapers/place/valencia-spain> ;
    schema:endDate "2018-06-27"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/valencia-spain> ;
    schema:name "31. International Geodetic Students Meeting" ;
    schema:startDate "2018-06-27"^^xsd:date .

<https://w3id.org/squirrelpapers/event/32-international-geodetic-students-meeting-2019> a event:Event,
        schema:Event ;
    rdfs:label "32. International Geodetic Students Meeting" ;
    event:place <https://w3id.org/squirrelpapers/place/warsaw-poland> ;
    schema:endDate "2019-06-25"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/warsaw-poland> ;
    schema:name "32. International Geodetic Students Meeting" ;
    schema:startDate "2019-06-25"^^xsd:date .

<https://w3id.org/squirrelpapers/event/35th-international-geodetic-student-meeting-2023> a event:Event,
        schema:Event ;
    rdfs:label "35th International Geodetic Student Meeting" ;
    event:place <https://w3id.org/squirrelpapers/place/polytechnic-university-of-valencia-universitat> ;
    schema:endDate "2023-07-08"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/polytechnic-university-of-valencia-universitat> ;
    schema:name "35th International Geodetic Student Meeting" ;
    schema:startDate "2023-07-02"^^xsd:date .

<https://w3id.org/squirrelpapers/event/4-mediawiki-users-developer-conference-2025> a event:Event,
        schema:Event ;
    rdfs:label "4. MediaWiki Users & Developer Conference" ;
    event:place <https://w3id.org/squirrelpapers/place/leibnizhaus-hannover-germany> ;
    schema:endDate "2025-10-30"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/leibnizhaus-hannover-germany> ;
    schema:name "4. MediaWiki Users & Developer Conference" ;
    schema:startDate "2025-10-28"^^xsd:date .

<https://w3id.org/squirrelpapers/event/5-workshop-der-ag-caa-2014> a event:Event,
        schema:Event ;
    rdfs:label "5. Workshop der AG CAA" ;
    event:place <https://w3id.org/squirrelpapers/place/tubingen-germany> ;
    schema:endDate "2014-02-15"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/tubingen-germany> ;
    schema:name "5. Workshop der AG CAA" ;
    schema:startDate "2014-02-15"^^xsd:date .

<https://w3id.org/squirrelpapers/event/7-workshop-der-ag-caa-2016> a event:Event,
        schema:Event ;
    rdfs:label "7. Workshop der AG CAA" ;
    event:place <https://w3id.org/squirrelpapers/place/hamburg-germany> ;
    schema:endDate "2016-02-05"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/hamburg-germany> ;
    schema:name "7. Workshop der AG CAA" ;
    schema:startDate "2016-02-05"^^xsd:date .

<https://w3id.org/squirrelpapers/event/8-workshop-der-ag-caa-2017> a event:Event,
        schema:Event ;
    rdfs:label "8. Workshop der AG CAA" ;
    event:place <https://w3id.org/squirrelpapers/place/heidelberg-germany> ;
    schema:endDate "2017-02-11"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/heidelberg-germany> ;
    schema:name "8. Workshop der AG CAA" ;
    schema:startDate "2017-02-11"^^xsd:date .

<https://w3id.org/squirrelpapers/event/9-workshop-der-ag-caa-2019> a event:Event,
        schema:Event ;
    rdfs:label "9. Workshop der AG CAA" ;
    event:place <https://w3id.org/squirrelpapers/place/wilhelmshaven-germany> ;
    schema:endDate "2019-09-23"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/wilhelmshaven-germany> ;
    schema:name "9. Workshop der AG CAA" ;
    schema:startDate "2019-09-23"^^xsd:date .

<https://w3id.org/squirrelpapers/event/ag3d-meeting-2024> a event:Event,
        schema:Event ;
    rdfs:label "AG3D meeting" ;
    schema:endDate "2024-11-13"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "AG3D meeting" ;
    schema:startDate "2024-11-13"^^xsd:date .

<https://w3id.org/squirrelpapers/event/brucken-schlagen-zwischen-terminologien-2024> a event:Event,
        schema:Event ;
    rdfs:label "Brücken schlagen zwischen Terminologien" ;
    schema:endDate "2024-03-20"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "Brücken schlagen zwischen Terminologien" ;
    schema:startDate "2024-03-20"^^xsd:date .

<https://w3id.org/squirrelpapers/event/caa-de-workshop-koln-2025> a event:Event,
        schema:Event ;
    rdfs:label "CAA DE Workshop Köln 2025" ;
    event:place <https://w3id.org/squirrelpapers/place/universitat-zu-koln-koln-deutschland> ;
    schema:endDate "2025-09-18"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/universitat-zu-koln-koln-deutschland> ;
    schema:name "CAA DE Workshop Köln 2025" ;
    schema:startDate "2025-09-17"^^xsd:date .

<https://w3id.org/squirrelpapers/event/clarenet-final-workshop-2023> a event:Event,
        schema:Event ;
    rdfs:label "ClaReNet Final Workshop" ;
    event:place <https://w3id.org/squirrelpapers/place/rgk-frankfurt-am-mainz> ;
    schema:endDate "2023-11-16"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/rgk-frankfurt-am-mainz> ;
    schema:name "ClaReNet Final Workshop" ;
    schema:startDate "2023-11-14"^^xsd:date .

<https://w3id.org/squirrelpapers/event/computer-applications-and-quantitative-methods-in-archaeology-caa-2014> a event:Event,
        schema:Event ;
    rdfs:label "Computer Applications and Quantitative Methods in Archaeology (CAA)" ;
    event:place <https://w3id.org/squirrelpapers/place/paris-france> ;
    schema:endDate "2014-04-24"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/paris-france> ;
    schema:name "Computer Applications and Quantitative Methods in Archaeology (CAA)" ;
    schema:startDate "2014-04-24"^^xsd:date .

<https://w3id.org/squirrelpapers/event/computer-applications-and-quantitative-methods-in-archaeology-caa-2016> a event:Event,
        schema:Event ;
    rdfs:label "Computer Applications and Quantitative Methods in Archaeology (CAA)" ;
    event:place <https://w3id.org/squirrelpapers/place/oslo-norway> ;
    schema:endDate "2016-04-30"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/oslo-norway> ;
    schema:name "Computer Applications and Quantitative Methods in Archaeology (CAA)" ;
    schema:startDate "2016-04-30"^^xsd:date .

<https://w3id.org/squirrelpapers/event/computer-applications-and-quantitative-methods-in-archaeology-caa-2018> a event:Event,
        schema:Event ;
    rdfs:label "Computer Applications and Quantitative Methods in Archaeology (CAA)" ;
    event:place <https://w3id.org/squirrelpapers/place/tubingen-germany> ;
    schema:endDate "2018-03-22"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/tubingen-germany> ;
    schema:name "Computer Applications and Quantitative Methods in Archaeology (CAA)" ;
    schema:startDate "2018-03-22"^^xsd:date .

<https://w3id.org/squirrelpapers/event/derse19-konferenz-fur-forschungssoftwareentwicklerinnen-in-2019> a event:Event,
        schema:Event ;
    rdfs:label "deRSE19 - Konferenz für ForschungssoftwareentwicklerInnen in Deutschland (deRSE19)" ;
    event:place <https://w3id.org/squirrelpapers/place/potsdam-germany> ;
    schema:endDate "2019-06-05"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/potsdam-germany> ;
    schema:name "deRSE19 - Konferenz für ForschungssoftwareentwicklerInnen in Deutschland (deRSE19)" ;
    schema:startDate "2019-06-05"^^xsd:date .

<https://w3id.org/squirrelpapers/event/dh-case-ii-2014> a event:Event,
        schema:Event ;
    rdfs:label "DH-CASE II" ;
    event:place <https://w3id.org/squirrelpapers/place/fort-collins-usa> ;
    schema:endDate "2014-09-10"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/fort-collins-usa> ;
    schema:name "DH-CASE II" ;
    schema:startDate "2014-09-10"^^xsd:date .

<https://w3id.org/squirrelpapers/event/doag-big-data-days-2018> a event:Event,
        schema:Event ;
    rdfs:label "DOAG Big Data Days 2018" ;
    event:place <https://w3id.org/squirrelpapers/place/dresden-germany> ;
    schema:endDate "2018-09-21"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/dresden-germany> ;
    schema:name "DOAG Big Data Days 2018" ;
    schema:startDate "2018-09-21"^^xsd:date .

<https://w3id.org/squirrelpapers/event/e-science-tage-2025> a event:Event,
        schema:Event ;
    rdfs:label "E-Science-Tage 2025" ;
    event:place <https://w3id.org/squirrelpapers/place/alte-universitat-heidelberg> ;
    schema:endDate "2025-03-14"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/alte-universitat-heidelberg> ;
    schema:name "E-Science-Tage 2025" ;
    schema:startDate "2025-03-12"^^xsd:date .

<https://w3id.org/squirrelpapers/event/forum-dh-mainz-2014> a event:Event,
        schema:Event ;
    rdfs:label "Forum DH Mainz" ;
    event:place <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:endDate "2014-07-30"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:name "Forum DH Mainz" ;
    schema:startDate "2014-07-30"^^xsd:date .

<https://w3id.org/squirrelpapers/event/fossgis-konferenz-2026> a event:Event,
        schema:Event ;
    rdfs:label "FOSSGIS-Konferenz 2026" ;
    event:place <https://w3id.org/squirrelpapers/place/georg-august-universitat-gottingen-germany> ;
    schema:endDate "2026-03-28"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/georg-august-universitat-gottingen-germany> ;
    schema:name "FOSSGIS-Konferenz 2026" ;
    schema:startDate "2026-03-25"^^xsd:date .

<https://w3id.org/squirrelpapers/event/get-together-dh-rmu-2017> a event:Event,
        schema:Event ;
    rdfs:label "Get Together DH-RMU" ;
    event:place <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:endDate "2017-09-21"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:name "Get Together DH-RMU" ;
    schema:startDate "2017-09-21"^^xsd:date .

<https://w3id.org/squirrelpapers/event/graphentechnologien-2019> a event:Event,
        schema:Event ;
    rdfs:label "Graphentechnologien 2019" ;
    event:place <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:endDate "2019-01-18"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:name "Graphentechnologien 2019" ;
    schema:startDate "2019-01-18"^^xsd:date .

<https://w3id.org/squirrelpapers/event/intergeo-conference-2019> a event:Event,
        schema:Event ;
    rdfs:label "Intergeo Conference" ;
    event:place <https://w3id.org/squirrelpapers/place/stuttgart-germany> ;
    schema:endDate "2019-09-17"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/stuttgart-germany> ;
    schema:name "Intergeo Conference" ;
    schema:startDate "2019-09-17"^^xsd:date .

<https://w3id.org/squirrelpapers/event/internationale-tagung-der-osterreichischen-gesellschaft-fur-2016> a event:Event,
        schema:Event ;
    rdfs:label "Internationale Tagung der Österreichischen Gesellschaft für Mittelalterarchäologie" ;
    event:place <https://w3id.org/squirrelpapers/place/graz-austria> ;
    schema:endDate "2016-09-20"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/graz-austria> ;
    schema:name "Internationale Tagung der Österreichischen Gesellschaft für Mittelalterarchäologie" ;
    schema:startDate "2016-09-20"^^xsd:date .

<https://w3id.org/squirrelpapers/event/jahrestagung-digital-humanities-im-deutschsprachigen-raum-2015> a event:Event,
        schema:Event ;
    rdfs:label "Jahrestagung Digital Humanities im deutschsprachigen Raum" ;
    event:place <https://w3id.org/squirrelpapers/place/graz-austria> ;
    schema:endDate "2015-02-25"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/graz-austria> ;
    schema:name "Jahrestagung Digital Humanities im deutschsprachigen Raum" ;
    schema:startDate "2015-02-25"^^xsd:date .

<https://w3id.org/squirrelpapers/event/java-user-group-mainz-lightning-talks-2019> a event:Event,
        schema:Event ;
    rdfs:label "Java User Group Mainz Lightning Talks" ;
    event:place <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:endDate "2019-01-16"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:name "Java User Group Mainz Lightning Talks" ;
    schema:startDate "2019-01-16"^^xsd:date .

<https://w3id.org/squirrelpapers/event/kuwiki-tips-tools-15-georeferenzierung-und-mapping-mit-wikimedia-2024> a event:Event,
        schema:Event ;
    rdfs:label "kuwiki: tips&tools, #15 Georeferenzierung und Mapping mit Wikimedia" ;
    schema:endDate "2024-11-28"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "kuwiki: tips&tools, #15 Georeferenzierung und Mapping mit Wikimedia" ;
    schema:startDate "2024-11-28"^^xsd:date .

<https://w3id.org/squirrelpapers/event/linked-pasts-iv-2018> a event:Event,
        schema:Event ;
    rdfs:label "Linked Pasts IV" ;
    event:place <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:endDate "2018-12-12"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/mainz-germany> ;
    schema:name "Linked Pasts IV" ;
    schema:startDate "2018-12-12"^^xsd:date .

<https://w3id.org/squirrelpapers/event/netzwerktreffen-kulturelles-erbe-linked-open-data-und-das-wikiversum-2024> a event:Event,
        schema:Event ;
    rdfs:label "Netzwerktreffen kulturelles Erbe, Linked Open Data und das Wikiversum" ;
    event:place <https://w3id.org/squirrelpapers/place/wikimedia-deutschland-e-v-berlin-germany> ;
    schema:endDate "2024-06-14"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/wikimedia-deutschland-e-v-berlin-germany> ;
    schema:name "Netzwerktreffen kulturelles Erbe, Linked Open Data und das Wikiversum" ;
    schema:startDate "2024-06-14"^^xsd:date .

<https://w3id.org/squirrelpapers/event/nfdi4memory-ag-minimaldatensatz-historische-orte-2025> a event:Event,
        schema:Event ;
    rdfs:label "NFDI4Memory AG Minimaldatensatz Historische Orte" ;
    schema:endDate "2025-02-24"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "NFDI4Memory AG Minimaldatensatz Historische Orte" ;
    schema:startDate "2025-02-24"^^xsd:date .

<https://w3id.org/squirrelpapers/event/nfdi4objects-community-meeting-2025> a event:Event,
        schema:Event ;
    rdfs:label "NFDI4Objects Community Meeting 2025" ;
    event:place <https://w3id.org/squirrelpapers/place/deutsches-bergbau-museum-bochum-germany> ;
    schema:endDate "2025-10-01"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/deutsches-bergbau-museum-bochum-germany> ;
    schema:name "NFDI4Objects Community Meeting 2025" ;
    schema:startDate "2025-09-30"^^xsd:date .

<https://w3id.org/squirrelpapers/event/oldenburger-3d-tage-2024> a event:Event,
        schema:Event ;
    rdfs:label "Oldenburger 3D-Tage" ;
    event:place <https://w3id.org/squirrelpapers/place/jade-hoschschule-oldenburg> ;
    schema:endDate "2024-02-01"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/jade-hoschschule-oldenburg> ;
    schema:name "Oldenburger 3D-Tage" ;
    schema:startDate "2024-01-31"^^xsd:date .

<https://w3id.org/squirrelpapers/event/open-science-festival-mainz-2024-mainz-germany-2024> a event:Event,
        schema:Event ;
    rdfs:label "Open Science Festival Mainz 2024, Mainz, Germany" ;
    event:place <https://w3id.org/squirrelpapers/place/jgu-mainz-germany> ;
    schema:endDate "2024-09-17"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/jgu-mainz-germany> ;
    schema:name "Open Science Festival Mainz 2024, Mainz, Germany" ;
    schema:startDate "2024-09-17"^^xsd:date .

<https://w3id.org/squirrelpapers/event/praxisubung-praktikum-zu-kleinfunddokumentation-und-computergestutzte> a event:Event,
        schema:Event ;
    rdfs:label "Praxisübung / Praktikum zu Kleinfunddokumentation und computergestützte Fotografie am Beispiel römischer Tonlampen bei PD Dr. Matthias Grawehr" ;
    event:place <https://w3id.org/squirrelpapers/place/johannes-gutenberg-universitat-mainz> ;
    schema:location <https://w3id.org/squirrelpapers/place/johannes-gutenberg-universitat-mainz> ;
    schema:name "Praxisübung / Praktikum zu Kleinfunddokumentation und computergestützte Fotografie am Beispiel römischer Tonlampen bei PD Dr. Matthias Grawehr" .

<https://w3id.org/squirrelpapers/event/text-show-and-tell-tools-dienste-projekte-2025> a event:Event,
        schema:Event ;
    rdfs:label "Text+ Show and Tell: Tools, Dienste, Projekte" ;
    schema:endDate "2025-03-27"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "Text+ Show and Tell: Tools, Dienste, Projekte" ;
    schema:startDate "2025-03-27"^^xsd:date .

<https://w3id.org/squirrelpapers/event/text-show-and-tell-tools-dienste-projekte-2025-05-15> a event:Event,
        schema:Event ;
    rdfs:label "Text+ Show and Tell: Tools, Dienste, Projekte" ;
    schema:endDate "2025-05-15"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "Text+ Show and Tell: Tools, Dienste, Projekte" ;
    schema:startDate "2025-05-15"^^xsd:date .

<https://w3id.org/squirrelpapers/event/text-show-and-tell-tools-dienste-projekte-2025-07-23> a event:Event,
        schema:Event ;
    rdfs:label "Text+ Show and Tell: Tools, Dienste, Projekte" ;
    schema:endDate "2025-07-23"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "Text+ Show and Tell: Tools, Dienste, Projekte" ;
    schema:startDate "2025-07-23"^^xsd:date .

<https://w3id.org/squirrelpapers/event/twg-2024-3-prototypische-modellierung-eines-provenienzgazetteers-zur-2025> a event:Event,
        schema:Event ;
    rdfs:label "TWG 2024.3 Prototypische Modellierung eines Provenienzgazetteers zur FAIRen Bereitstellung in einer Wikibase" ;
    schema:endDate "2025-03-20"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
    schema:name "TWG 2024.3 Prototypische Modellierung eines Provenienzgazetteers zur FAIRen Bereitstellung in einer Wikibase" ;
    schema:startDate "2025-03-20"^^xsd:date .

<https://w3id.org/squirrelpapers/event/uk-chapter-of-computer-applications-and-quantitative-methods-in-2018> a event:Event,
        schema:Event ;
    rdfs:label "UK Chapter of Computer Applications and Quantitative Methods in Archaeology (CAA-UK 2018)" ;
    event:place <https://w3id.org/squirrelpapers/place/edinburgh-scotland-uk> ;
    schema:endDate "2018-10-27"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/edinburgh-scotland-uk> ;
    schema:name "UK Chapter of Computer Applications and Quantitative Methods in Archaeology (CAA-UK 2018)" ;
    schema:startDate "2018-10-26"^^xsd:date .

<https://w3id.org/squirrelpapers/event/uk-chapter-of-computer-applications-and-quantitative-methods-in-2019> a event:Event,
        schema:Event ;
    rdfs:label "UK Chapter of Computer Applications and Quantitative Methods in Archaeology (CAA-UK 2019)" ;
    event:place <https://w3id.org/squirrelpapers/place/bournemouth-england> ;
    schema:endDate "2019-10-04"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/bournemouth-england> ;
    schema:name "UK Chapter of Computer Applications and Quantitative Methods in Archaeology (CAA-UK 2019)" ;
    schema:startDate "2019-10-04"^^xsd:date .

<https://w3id.org/squirrelpapers/event/uk-ireland-dh-association-annual-event-2024> a event:Event,
        schema:Event ;
    rdfs:label "UK-Ireland DH Association Annual Event 2024" ;
    event:place <https://w3id.org/squirrelpapers/place/university-college-cork-ucc-cork-ireland> ;
    schema:endDate "2024-06-05"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/university-college-cork-ucc-cork-ireland> ;
    schema:name "UK-Ireland DH Association Annual Event 2024" ;
    schema:startDate "2024-06-04"^^xsd:date .

<https://w3id.org/squirrelpapers/event/wintervortrage-2019> a event:Event,
        schema:Event ;
    rdfs:label "Wintervorträge 2019" ;
    event:place <https://w3id.org/squirrelpapers/place/gesellschaft-der-freunde-des-romisch> ;
    schema:endDate "2019-04-02"^^xsd:date ;
    schema:location <https://w3id.org/squirrelpapers/place/gesellschaft-der-freunde-des-romisch> ;
    schema:name "Wintervorträge 2019" ;
    schema:startDate "2019-04-02"^^xsd:date .

<https://w3id.org/squirrelpapers/event/workshop-objekt-text-bild-2025> a event:Event,
        schema:Event ;
    rdfs:label "Workshop Objekt - Text - Bild" ;
    schema:endDate "2025-04-28"^^xsd:date ;
    schema:eventAttendanceMode schema:OnlineEventAttendanceMode ;
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This modern species of dragons on a (digital) map of the internet as sign for unknown and dangerous areas, the unknown
digital data dragons, have to be resolved by digital techniques. We could use the Semantic Web and all of its ideas for this
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This modern species of dragons on a (digital) map of the internet as sign for unknown and dangerous areas, the unknown
digital data dragons, have to be resolved by digital techniques. We could use the Semantic Web and all of its ideas for this
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lived reality in our digital society.
Archeology has also undergone a digital transformation from an analogous science to an Archaeology 4.0. Starting with an
analogue era, in which research data was kept in books, followed by a digital era in which digitization progresses and data are
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already become established in individual stages in various areas of life. Terms such as Industry 4.0, Work 4.0 and Web 4.0 are
lived reality in our digital society.
Archeology has also undergone a digital transformation from an analogous science to an Archaeology 4.0. Starting with an
analogue era, in which research data was kept in books, followed by a digital era in which digitization progresses and data are
published on the WWW, followed by a semantic era, in which semantic modelling and publication of Linked Data is reality,
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reach that we have to ll the Semantic Web as part of the Web 3.0 with resources and research content. Therefore the 5 star
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access to the AMT world using toponyms as a graph-based vague topology for these toponyms. This paper demonstrates a
very simple example how to model a north/south ontology using AMT."""@en ;
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    bibo:abstract """The Linked Geodesy Research Project topi.link combines geodesy and Linked Data research questions. Using the Academic
Meta Tool (AMT), we have a little minion, which addresses the task of inferencing vague graph data. topi.link will give
access to the AMT world using toponyms as a graph-based vague topology for these toponyms. This paper demonstrates a
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    dct:description "Geodäten leben bereits im Jahr 2019 in einer globalen, digitalen und vernetzten Welt: der Cloud. Wir erleben das Arbeiten 4.0 in einer Industrie 4.0 mit dem Einsatz von cyber-physischen Systemen und der Vernetzung von Maschinen, Geräten, Sensoren, Menschen und Daten. 80% aller Daten besitzen einen Raumbezug, somit sind Geodaten der Treibstoff der digitalen Gesellschaft. Darüber hinaus werden Bestandteile des OOO-Modells immer wichtiger: Open Source Software, Open (Geo-) Data und Open Access. Zur Bereitstellung von offenen und freien (Geo-) Daten in interoperablen Formaten eigenen sich insbesondere Techniken des Web 3.0, sogenannte Linked Open Data (LOD), bzw. Linked Open Geodata. Durch die Bereitstellung von LO(Geo-)D entsteht eine riesige vernetzte Linked Data Cloud verschiedenster Disziplinen (Geodäsie / Geisteswissenschaften / Naturwissenschaften), in der amtliche Geodaten als Bindeglied dienen, um neues Wissen für die Gesellschaft zu erzeugen."@de ;
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    bibo:abstract "Geodäten leben bereits im Jahr 2019 in einer globalen, digitalen und vernetzten Welt: der Cloud. Wir erleben das Arbeiten 4.0 in einer Industrie 4.0 mit dem Einsatz von cyber-physischen Systemen und der Vernetzung von Maschinen, Geräten, Sensoren, Menschen und Daten. 80% aller Daten besitzen einen Raumbezug, somit sind Geodaten der Treibstoff der digitalen Gesellschaft. Darüber hinaus werden Bestandteile des OOO-Modells immer wichtiger: Open Source Software, Open (Geo-) Data und Open Access. Zur Bereitstellung von offenen und freien (Geo-) Daten in interoperablen Formaten eigenen sich insbesondere Techniken des Web 3.0, sogenannte Linked Open Data (LOD), bzw. Linked Open Geodata. Durch die Bereitstellung von LO(Geo-)D entsteht eine riesige vernetzte Linked Data Cloud verschiedenster Disziplinen (Geodäsie / Geisteswissenschaften / Naturwissenschaften), in der amtliche Geodaten als Bindeglied dienen, um neues Wissen für die Gesellschaft zu erzeugen."@de ;
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Wikidata is a secondary database for structured data. Data in it is also enriched with source information, i.e. references about where the information came from. Additionally, connections to other databases, like the Getty AAT, are provided by inclusion of their IDs. In this way, Wikidata not only contains information about e.g. specific archaeological objects, but also helps in providing links to other datasets with further information. Wikidata can be accessed via a dedicated SPARQL endpoint.
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This poster focuses on a short technical insight into our Linked Pipe using the little minions Alligator and AMT. The tools and pipe are demonstrated here with a method verification using the chronology of Roman Emperors and an example of dating Limes parts with Samian (terra sigillata)."""@en ;
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Alligator is developed at the Römisch-Germanisches Zentralmuseum (RGZM). It is a web app for transforming 3D correspondence analyses (CA) with fixed and floating time time intervals into a relative chronology and RDF representation using Allen's interval algebra. Starting from an Alligator CA-File you can select an output mode which can be a visualisation (e.g. matrixes, virtual timelines or graphs) as well as machine readable representations (e.g. Turtle and Cypher) in order to use them in other LOD tools for further research and analyses.
AMT is developed in collaboration with the Mainz Centre for Digitality in the Humanities and Cultural Studies (mainzed) by the RGZM and i3mainz. AMT allows for creating user based ontologies and provides a JavaScript framework for modelling vagueness in graphs, including a vagueness reasoning using different logics. Concerning relative chronologies, results can be modelled as LOD. By using the web app AMT, reasoned chronologies can visualised.
This poster focuses on a short technical insight into our Linked Pipe using the little minions Alligator and AMT. The tools and pipe are demonstrated here with a method verification using the chronology of Roman Emperors and an example of dating Limes parts with Samian (terra sigillata)."""@en ;
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    dct:description """Seit Mitte der 1990er Jahre stellt das RGZM (Römisch-Germanisches Zentralmuseum in Mainz) webbasierte Datenbanken mit hunderttausenden von Datensätzen aus verschiedenen archäologischen Disziplinen der Scientific Community zur Verfügung. Diese historisch gewachsenen, zumeist relationalen Datenbanken wurden in interdisziplinären, transnationalen Projekten erstellt und beinhalten oft „hidden assumptions” hinsichtlich Datierungen. Das Ziel des  Projekts „Dating Mechanism“ ist es, diese impliziten Informationen mittels semantischer Datenmodelle in Graphen sichtbar und verfügbar zu machen und sie als Linked Open Data (LOD) zu teilen. Diese interoperablen Daten sollen im Sinne der „reproducible research“ als Grundlage für Open Science der Forschungscommunity zur Verfügung stehen.
Insbesondere die Samian Research Datenbank[1] des RGZM beinhaltet fast 250'000 identifizierte Töpferstempel, die in der Archäologie auf traditioneller Art datiert werden. Dies wird gewöhnlich dadurch realisiert, dass "absolute Daten" in "from-to tables " erstellt werden, wobei die Realität viel komplexer ist. In der Tat basieren Datierungen von Terra Sigillata oft auf so genannten "dated sites" oder gar ganzen Limes-Abschnitten, die selbst von Terra Sigillata datiert werden. Man trifft also regelmäßig auf Kreisargumentationen. Dazu werden als erster Schritt Zeitintervalle mit offenen Anfangs- oder fehlenden End-Datierungen in eine relative Chronologie transformiert. Darüber hinaus können Zusatzinformationen, wie der Anteil an Töpfern, welche die Limes-Abschnitte gemeinsam haben, als Informationen für den Grad einer Verbindung (hier =Vagheit) zwischen zwei Limes-Abschnitten genutzt werden. In einem Reasoning-Prozess werden diese vagen Informationen ebenfalls mit ausgewertet um neue interferierte Information zu erzeugen, die bei der Beantwortung von Forschungsfragen behilflich sein können.
Zur Modellierung dieser Problematik, wird ein auf RDF (Resource Description Framework) basierendes semantisches Modell entwickelt, das zum Ziel hat, den Terra Sigillata -Datierprozess verifizierbar und transparent zu machen, um so neue Strategien für den maschinenlesbaren Zugriff – hier als LOD - auf archäologische Daten und Informationen in Form von Graphen zu ermöglichen.
Dieser Vortrag zeigt die Modellierung von Datierungsargumenten in einer relativen Chronologie, basierend auf Allens Intervall Algebra [2], in Kombination mit absoluten Datierungen. Dazu werden zwei prototypische Tools benutzt. Zum einen, das vom RGZM erstellte Tool „Alligator“ [3] zur Transformation einer Korrespondenzanalyse in RDF-modellierte Allen -Intervalle und zum anderen das von mainzed in Kooperation mit dem RGZM und dem i3mainz entwickelte „Academic Meta Tool“ (AMT) [4]. AMT ermöglicht die Modellierung von Vagheit in RDF-Graphen und ein „temporal reasoning“  nach Allen, sowie eine Visualisierung des Graphen und Download der Daten zur Verwendung in anderen Tools wie Neo4J.
Die Nutzung von Graphstrukturen ermöglicht es somit, Information und Interpretationen der hochgranulierten Daten zu modellieren, um so „hidden assumptions“ sichtbar zu machen und transparent bereitzustellen. Darüber hinaus können durch die Anwendung der Tools „Alligator“ und „AMT“ heterogene Daten und komplexe digitale Forschungsfragen im Umgang mit relativen Chronologien in den Datenrepositorien des RGZM standardisiert und interoperabel zur Verfügung gestellt werden. Somit können wir zeigen, dass Flexibilität im Datierungsprozess und Interoperabilität bei der Nachhaltigkeit und Wiederverwendung zwei Seiten eines Terra Sigillata–Datensatzes sind.
[1] http://rgzm.de/samian
[2] https://doi.org/10.1145/182.358434
[3] https://rgzm.github.io/alligator/
[4] http://academic-meta-tool.xyz"""@de ;
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    dct:title "Dating Mechanism: Eine Linked Data Strategie zur interoperablen und nachvollziehbaren Modellierung relativer Chronologien am Beispiel südgallischer Terra Sigillata in Limes-Abschnitten"@de ;
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    bibo:abstract """Seit Mitte der 1990er Jahre stellt das RGZM (Römisch-Germanisches Zentralmuseum in Mainz) webbasierte Datenbanken mit hunderttausenden von Datensätzen aus verschiedenen archäologischen Disziplinen der Scientific Community zur Verfügung. Diese historisch gewachsenen, zumeist relationalen Datenbanken wurden in interdisziplinären, transnationalen Projekten erstellt und beinhalten oft „hidden assumptions” hinsichtlich Datierungen. Das Ziel des  Projekts „Dating Mechanism“ ist es, diese impliziten Informationen mittels semantischer Datenmodelle in Graphen sichtbar und verfügbar zu machen und sie als Linked Open Data (LOD) zu teilen. Diese interoperablen Daten sollen im Sinne der „reproducible research“ als Grundlage für Open Science der Forschungscommunity zur Verfügung stehen.
Insbesondere die Samian Research Datenbank[1] des RGZM beinhaltet fast 250'000 identifizierte Töpferstempel, die in der Archäologie auf traditioneller Art datiert werden. Dies wird gewöhnlich dadurch realisiert, dass "absolute Daten" in "from-to tables " erstellt werden, wobei die Realität viel komplexer ist. In der Tat basieren Datierungen von Terra Sigillata oft auf so genannten "dated sites" oder gar ganzen Limes-Abschnitten, die selbst von Terra Sigillata datiert werden. Man trifft also regelmäßig auf Kreisargumentationen. Dazu werden als erster Schritt Zeitintervalle mit offenen Anfangs- oder fehlenden End-Datierungen in eine relative Chronologie transformiert. Darüber hinaus können Zusatzinformationen, wie der Anteil an Töpfern, welche die Limes-Abschnitte gemeinsam haben, als Informationen für den Grad einer Verbindung (hier =Vagheit) zwischen zwei Limes-Abschnitten genutzt werden. In einem Reasoning-Prozess werden diese vagen Informationen ebenfalls mit ausgewertet um neue interferierte Information zu erzeugen, die bei der Beantwortung von Forschungsfragen behilflich sein können.
Zur Modellierung dieser Problematik, wird ein auf RDF (Resource Description Framework) basierendes semantisches Modell entwickelt, das zum Ziel hat, den Terra Sigillata -Datierprozess verifizierbar und transparent zu machen, um so neue Strategien für den maschinenlesbaren Zugriff – hier als LOD - auf archäologische Daten und Informationen in Form von Graphen zu ermöglichen.
Dieser Vortrag zeigt die Modellierung von Datierungsargumenten in einer relativen Chronologie, basierend auf Allens Intervall Algebra [2], in Kombination mit absoluten Datierungen. Dazu werden zwei prototypische Tools benutzt. Zum einen, das vom RGZM erstellte Tool „Alligator“ [3] zur Transformation einer Korrespondenzanalyse in RDF-modellierte Allen -Intervalle und zum anderen das von mainzed in Kooperation mit dem RGZM und dem i3mainz entwickelte „Academic Meta Tool“ (AMT) [4]. AMT ermöglicht die Modellierung von Vagheit in RDF-Graphen und ein „temporal reasoning“  nach Allen, sowie eine Visualisierung des Graphen und Download der Daten zur Verwendung in anderen Tools wie Neo4J.
Die Nutzung von Graphstrukturen ermöglicht es somit, Information und Interpretationen der hochgranulierten Daten zu modellieren, um so „hidden assumptions“ sichtbar zu machen und transparent bereitzustellen. Darüber hinaus können durch die Anwendung der Tools „Alligator“ und „AMT“ heterogene Daten und komplexe digitale Forschungsfragen im Umgang mit relativen Chronologien in den Datenrepositorien des RGZM standardisiert und interoperabel zur Verfügung gestellt werden. Somit können wir zeigen, dass Flexibilität im Datierungsprozess und Interoperabilität bei der Nachhaltigkeit und Wiederverwendung zwei Seiten eines Terra Sigillata–Datensatzes sind.
[1] http://rgzm.de/samian
[2] https://doi.org/10.1145/182.358434
[3] https://rgzm.github.io/alligator/
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    dct:description "Data modelling in relational structures is a major part in geodesist's geodata modelling life. We use PostGIS databases, GeoServer applications using OGC standards, to share interoperable and open geodata via the World Wide Web. In addition, new modelling technologies allow NoSQL modelling, like graphs. Graph data is structured in nodes and edges. Geodesists know these structures if we look deeper into navigation systems technology by using the Dijkstra algorithm. However, to provide interoperable and semantic data, directed edge-coloured graphs, modelled in RDF using subjects, predicates and objects according to the principles of Linked (Open) Data[1] are necessary. LOD are already widely used in geodesy[2]: e.g. GeoSPARQL[3], LinkedGeoData[4] and Britains Ordnance Survey[5]. But what can we do if our data only consists of toponyms which have geographical relations without coordinate information? We could model these spatial relations[6] using the common DE-9IM[7] topological model; but in re- ality this nine relations are not enough. Moreover, these relations are very vague. Furthermore, inference making, e.g. for the property northOf [8], via reasoning[9], to create new knowledge, would be very cool: we need a 'little minion' to all this stuff. For modelling these kinds of vague geographical graph data, the Academic Meta Tool[10] (AMT) can be used: this paper focuses on prototypical examples of the 'topi Ontology'[11] by introducing AMT modelling strategies, the AMT JavaScript framework[12] and the topi.link playground[13]."@en ;
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Das Poster zeigt am Beispiel der RGZM NAVIS Schiffsdatenbanken Schritte und Herausforderungen des digitalen Wandels und zur Veröffentlichung von archäologischen Fachdaten in der Linked Data Cloud auf und stellt Möglichkeiten zur Wissensgenerierung durch die Beschreibung von Abbildungen und die Verschlagwortung jener mit Hilfe des Academic Meta Tool vor. Hierbei wird der Fokus insbesondere auf Möglichkeiten zum Vernetzen von Datenbeständen gelegt, womit aufgezeigt wird, wie hierdurch bereits Analysen implizit vorgenommen werden. Darüber hinaus wird zur Entwicklung einer `Archaeological Linked Data Cloud` die CAA Special Interest Group „Semantics and LOUD in Archaelogy“ vorgestellt, um den Datendrachen Einhalt zu gebieten."""@de ;
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    dct:description "Die räumliche Verteilung und das zeitliche Muster, sowie die Identifizierung von Produktionszentren und die Rekonstruktion von Handelsnetzen der Töpfer, sind zentrale Fragen aktueller archäologischer Forschungen zur ‚Samian Ware‘. Die Studien basieren auf einem Datensatz einer internationalen Gruppe von Wissenschaftlern. Er umfasst detaillierte Daten zu Töpfern, Keramik-Fragmenten und deren Fundorten. Zudem sind Informationen zu Befunden, Töpfermatrizen, Gefäßformen, Produktionsstätten, Formschüsselherstellern und Angaben zur absoluten zeitlichen Datierung erfasst. Der Datensatz ist damit außerordentlich gut geeignet, aktuelle Technologien interoperabler Datenhaltung, insbesondere das Konzept der Linked Open Data (LOD) und der semantischen Modellierung archäologischer Informationen, exemplarisch zu verdeutlichen.  Eines der Prinzipien der Linked Open Data ist die Bereitstellung der Daten als Ressource. Diese ist sowohl menschenlesbar, steht jedoch auch zur computertechnische Verarbeitung zur Verfügung. Ein Gefäßfragment und dessen Fundort wären beispielsweise mit einem Browser im World Wide Web unter einer HTTP-Adresse frei abrufbar. Ein weiterer Informationsgewinn entsteht durch die Nutzung kontrollierter Vokabulare, nicht nur für die Beschreibung der Ressourcen, sondern auch für deren Beziehungen untereinander. In einer Reihe von Wissenschaftsfeldern werden diese Konzepte erfolgreich eingesetzt, so dass mit diesen Konzepten erstmals eine Technologie zur Verfügung steht, welche eine direkte Verknüpfung heterogener Informationen aus unterschiedlichen archäologischen Datenbanken gewährleistet. Der Vortrag führt in die Migration der Daten sowie die Möglichkeiten und die Problematik der Modellierung der Attribute und Beziehungen mit Hilfe bestehender LOD-Konzepte und kontrollierter Vokabularien ein und zeigt eigene Ansätze zur Lösung auf. Insbesondere wird die Verlinkung zu anderen bereits bestehenden Projekten diskutiert. So bietet das Pleiades-Projekt eine Plattform, die geographische Elemente z.B. in Texten mit existierenden Ressourcen verbindet. Die Fund- und Produktionsstätten sind mit den Pleiades Places verknüpft, wodurch eine Vielzahl weiterer archäologischer und historischer Informationen über das Pelagios Projekt eingebunden sind. Zudem wird das Potential der Verlinkung und Abfrage von heterogenen Informationen zwischen Töpfern, Fragmenten und Orten und deren relativ-chronologische Beziehungen über LOD mit einer webbasierten Schnittstelle aufgezeigt. Die Komplexität des Netzes sich bedingender Datierungen der Funde ist im Datenmodell direkt abgebildet. Entsprechend schnell reagiert das System auf Modifikationen und erlaubt so eine Echtzeitvisualisierung der Simulation von Änderungen in diesen Bezügen. Die Ergebnisse zeigen, dass Forschung im Bereich der römischen Keramik vom Konzept der Linked Open Data profitiert. Vielversprechende Perspektiven entstehen durch die Verknüpfung mit weiteren Ressourcen wie Matrizen, Formschlüsseln und Münzen. Die technologische Basis ist eine PostgreSQL/PostGIS Datenbank als Kommunikationsplattform für JAVA Servlets und dem GeoServer, der OGC konforme Webdienste (WMS/WFS) anbietet. Die Daten werden in einer Schnittstelle maschinenlesbar und REST-konform zur Verfügung gestellt, die mit Hilfe von JAVA und den Frameworks Jersey, JAX-B und Hibernate Spatial, sowie dem GeoServer verarbeitet werden. Die beschriebenen Strukturen erlauben somit eine webbasierte Kartenansicht auf dem Client mit der JavaScript Bibliothek Leaflet unter Einbeziehung der Abhängigkeiten zwischen Töpfern, Fragmenten und Fundorten. Darüber hinaus ergibt eine ontologische Modellierung der semantischen Bezüge Potentiale für die Extraktion des in den Daten implizit vorhandenen Wissens. Der Vortrag ist das Ergebnis einer Masterarbeit an der Fachhochschule Mainz und wird die Ergebnisse kritisch reflektieren und damit einen Einstieg in diese neue Technologie erleichtern."@de ;
    dct:identifier "https://doi.org/10.5281/zenodo.292971" ;
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    dct:title "Linked Samian Ware: Potentiale von Linked Data in der Archäologie"@de ;
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    bibo:abstract "Die räumliche Verteilung und das zeitliche Muster, sowie die Identifizierung von Produktionszentren und die Rekonstruktion von Handelsnetzen der Töpfer, sind zentrale Fragen aktueller archäologischer Forschungen zur ‚Samian Ware‘. Die Studien basieren auf einem Datensatz einer internationalen Gruppe von Wissenschaftlern. Er umfasst detaillierte Daten zu Töpfern, Keramik-Fragmenten und deren Fundorten. Zudem sind Informationen zu Befunden, Töpfermatrizen, Gefäßformen, Produktionsstätten, Formschüsselherstellern und Angaben zur absoluten zeitlichen Datierung erfasst. Der Datensatz ist damit außerordentlich gut geeignet, aktuelle Technologien interoperabler Datenhaltung, insbesondere das Konzept der Linked Open Data (LOD) und der semantischen Modellierung archäologischer Informationen, exemplarisch zu verdeutlichen.  Eines der Prinzipien der Linked Open Data ist die Bereitstellung der Daten als Ressource. Diese ist sowohl menschenlesbar, steht jedoch auch zur computertechnische Verarbeitung zur Verfügung. Ein Gefäßfragment und dessen Fundort wären beispielsweise mit einem Browser im World Wide Web unter einer HTTP-Adresse frei abrufbar. Ein weiterer Informationsgewinn entsteht durch die Nutzung kontrollierter Vokabulare, nicht nur für die Beschreibung der Ressourcen, sondern auch für deren Beziehungen untereinander. In einer Reihe von Wissenschaftsfeldern werden diese Konzepte erfolgreich eingesetzt, so dass mit diesen Konzepten erstmals eine Technologie zur Verfügung steht, welche eine direkte Verknüpfung heterogener Informationen aus unterschiedlichen archäologischen Datenbanken gewährleistet. Der Vortrag führt in die Migration der Daten sowie die Möglichkeiten und die Problematik der Modellierung der Attribute und Beziehungen mit Hilfe bestehender LOD-Konzepte und kontrollierter Vokabularien ein und zeigt eigene Ansätze zur Lösung auf. Insbesondere wird die Verlinkung zu anderen bereits bestehenden Projekten diskutiert. So bietet das Pleiades-Projekt eine Plattform, die geographische Elemente z.B. in Texten mit existierenden Ressourcen verbindet. Die Fund- und Produktionsstätten sind mit den Pleiades Places verknüpft, wodurch eine Vielzahl weiterer archäologischer und historischer Informationen über das Pelagios Projekt eingebunden sind. Zudem wird das Potential der Verlinkung und Abfrage von heterogenen Informationen zwischen Töpfern, Fragmenten und Orten und deren relativ-chronologische Beziehungen über LOD mit einer webbasierten Schnittstelle aufgezeigt. Die Komplexität des Netzes sich bedingender Datierungen der Funde ist im Datenmodell direkt abgebildet. Entsprechend schnell reagiert das System auf Modifikationen und erlaubt so eine Echtzeitvisualisierung der Simulation von Änderungen in diesen Bezügen. Die Ergebnisse zeigen, dass Forschung im Bereich der römischen Keramik vom Konzept der Linked Open Data profitiert. Vielversprechende Perspektiven entstehen durch die Verknüpfung mit weiteren Ressourcen wie Matrizen, Formschlüsseln und Münzen. Die technologische Basis ist eine PostgreSQL/PostGIS Datenbank als Kommunikationsplattform für JAVA Servlets und dem GeoServer, der OGC konforme Webdienste (WMS/WFS) anbietet. Die Daten werden in einer Schnittstelle maschinenlesbar und REST-konform zur Verfügung gestellt, die mit Hilfe von JAVA und den Frameworks Jersey, JAX-B und Hibernate Spatial, sowie dem GeoServer verarbeitet werden. Die beschriebenen Strukturen erlauben somit eine webbasierte Kartenansicht auf dem Client mit der JavaScript Bibliothek Leaflet unter Einbeziehung der Abhängigkeiten zwischen Töpfern, Fragmenten und Fundorten. Darüber hinaus ergibt eine ontologische Modellierung der semantischen Bezüge Potentiale für die Extraktion des in den Daten implizit vorhandenen Wissens. Der Vortrag ist das Ergebnis einer Masterarbeit an der Fachhochschule Mainz und wird die Ergebnisse kritisch reflektieren und damit einen Einstieg in diese neue Technologie erleichtern."@de ;
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    prism:number "3" ;
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    dct:creator <https://w3id.org/squirrelpapers/person/0000-0001-8322-1260>,
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    dct:description "In the last years several web services emerged that manage and make accessible place thesauri for the archaeologies and historical sciences. By making use of semantic technologies these applications are able to act as linked data hubs thereby making possibe the linkage of multiple datasets of varying thematic focus and of different structural properties. Another common denominator of archaeological data resources, besides geo-spatial properties, is the temporal classification of research objects. One of the applications that try to assume a role similar to that of gazetteers but for temporal concepts and cultural periods is developed in the chronOntology project. In this project, funded by the German Research Foundation, the German Archaeologial Institute (DAI) together with the i3mainz are developing a system for storing, managing, mapping and making accessible descriptions of temporal concepts. The core of this endeavor is a rich semantic modeling of various existing terminological systems for cultural periods using a data model based on the CIDOC-CRM and its extensions. The use of the rich ontological model provided by the CRM on the one hand permits representing the measurable temporal extent (with the possibility for fuzzy edges) while also making it possible to embed temporal concepts in a network of semantic relationships to other temporal concepts, connected historical regions and thematic contexts. Besides documenting the general architecture and data model of the project the paper will present possibilities of querying different heterogeneous data resources collected throughout various digitization and digging activities inside the DAI with the help of concepts defined in chronOntology. We will also point out the potential and problems of reasoning over geographically and temporally connected datasets."@en ;
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    dct:title "The Labeling System - A New Approach to Overcome the Vocabulary Bottleneck"@en ;
    dct:type sqpt:presentation ;
    bibo:abstract "In the last years several web services emerged that manage and make accessible place thesauri for the archaeologies and historical sciences. By making use of semantic technologies these applications are able to act as linked data hubs thereby making possibe the linkage of multiple datasets of varying thematic focus and of different structural properties. Another common denominator of archaeological data resources, besides geo-spatial properties, is the temporal classification of research objects. One of the applications that try to assume a role similar to that of gazetteers but for temporal concepts and cultural periods is developed in the chronOntology project. In this project, funded by the German Research Foundation, the German Archaeologial Institute (DAI) together with the i3mainz are developing a system for storing, managing, mapping and making accessible descriptions of temporal concepts. The core of this endeavor is a rich semantic modeling of various existing terminological systems for cultural periods using a data model based on the CIDOC-CRM and its extensions. The use of the rich ontological model provided by the CRM on the one hand permits representing the measurable temporal extent (with the possibility for fuzzy edges) while also making it possible to embed temporal concepts in a network of semantic relationships to other temporal concepts, connected historical regions and thematic contexts. Besides documenting the general architecture and data model of the project the paper will present possibilities of querying different heterogeneous data resources collected throughout various digitization and digging activities inside the DAI with the help of concepts defined in chronOntology. We will also point out the potential and problems of reasoning over geographically and temporally connected datasets."@en ;
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    dct:creator <https://w3id.org/squirrelpapers/person/0000-0001-8322-1260>,
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        <https://w3id.org/squirrelpapers/person/0000-0003-3307-5386> ;
    dct:description "Maschinenlesbare Annotationen sind die Voraussetzung für die semantische Verarbeitung von Daten. Diese Aussage gilt unabhängig davon, ob es sich bei den Daten um natürlichsprachigen Text oder um strukturierte Datensätze in einer Datenbank handelt, und unabhängig davon, ob es um einfaches Sortieren und Filtern geht oder um komplexes automatisches Schließen. Kontrollierte Vokabulare (ob in einer einfachen Terminolgieliste oder als Taxonomien, Thesauri oder Ontologien strukturiert) sind dabei unbedingt notwendig, um Annotationen maschinell verarbeitbar zu machen; ohne terminologische Kontrolle sind Annotationen für die maschinelle Verarbeitung kaum nützlicher als an den Rand eines Buches gekrizelte Notizen. Kontrollierte Vokabulare abstrahieren von natürlichsprachlichen Ambiguitäten und Konnotationen; sie sind daher entscheidend für die semantische Verarbeitung von Forschungsdaten. Um projektübergreifende Zusammenarbeit und den semantischen Austausch von Daten zu ermöglichen, müssen Vokabulare nicht nur kontrolliert, sondern auch formell oder informell standardisiert sein. Standardisierte kontrollierte Vokabulare ermöglichen den Austausch, die Kombination und die gemeinsame Analyse annotierter Daten aus verschiedenen Quellen sowie die Implementierung generischer Werkzeuge für die semantische Verarbeitung. Erstellung und Wartung standardisierter kontrollierter Vokabulare sind jedoch zeitaufwändig und damit teuer. Zu den größten Herausforderungen zählen, dass alle beteiligten Parteien zu einem gemeinsamen Verständnis der Begriffe kommen, und dass die richtige Balance zwischen möglichst breiter Anwendbarkeit einerseits und möglichst präziser Analyse andereseits gefunden werden. Diese Ziele sind insbesondere in den Geisteswissenschaften schwierig zu erreichen: nicht nur sind die Forschungsfragen, die potentiell an einen gegebenen Datensatz gerichtet werden können, extrem weit gefächert, sondern die Kategorisierung der Daten ist häufig ein essenzieller Teil des Forschungsprozesses selbst. Es gibt daher einen eklatanten Mangel an standardisierten kontrollierten Vokabularen in den Geisteswissenschaften, der Digital-Humanities-Projekte letztlich dazu zwingt, eigene, projektspezifische Vokabulare zu definieren. Projektspezifische Vokabulare lösen können den internen Bedarf zwar kurzfristig befriedigen, sind aber nicht interoperabel und verhindern den zukünftigen Austausch und die Nachnutzung der annotierten Daten. Unser Poster stellt einen neuen konzeptuellen Ansatz zur Lösung dieser Probleme vor und beschreibt die Implementierung dieses Ansatzes in einem Softwarewerkzeug, dem Labeling System. Da es in der geisteswissenschaftlichen Forschung praktisch unmöglich ist, kontrollierte Vokabulare zu definieren, die alle denkbaren Anwendungen abdecken und generell akzeptiert sind, schlagen wir ein anderes Vorgehen vor. Bei unserem Ansatz definieren Projekte ihre eigenen Vokabulare, aber anstelle natürlichsprachlicher Definitionen werden die Terme mit einem oder mehreren Konzepten in einem Referenzthesaurus verknüpft. Der projektspezifische Term dient also quasi als »Label« für eine Menge gemeinsamer Konzepte. Dieser Ansatz ermöglicht es Projekten Vokabulare entsprechend ihrer Bedürfnisse und unter Verwendung im jeweiligen Forschungsgebiet üblichen Bezeichnungen benutzen, während gleichzeitig die Interoperabilität mit anderen Projekten über den Referenzthesaurus gewährleistet ist. Das Labeling System ist eine Webanwendung, die es Benutzern ermöglicht, SKOSVokabulare zu erstellen und auf einfache Weise deren Terme mit einem oder mehreren Konzepten in einem oder mehreren Referenzthesauri zu verknüpfen. Die Benutzeroberfläche ermöglicht die Visualisierung der definierten Vokabulare in einer hierarchischen Baumstruktur und ermöglicht den Zugriff auf Vokabulare über eine SPARQL-Schnittstelle. Das Labeling System basiert auf ausgereiften Open-SourceKomponenten und ist selbst ebenfalls frei verfügbar."@de ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Das Labeling System – ein freier Baukasten für kontrollierte Vokabulare"@de ;
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    bibo:abstract "Maschinenlesbare Annotationen sind die Voraussetzung für die semantische Verarbeitung von Daten. Diese Aussage gilt unabhängig davon, ob es sich bei den Daten um natürlichsprachigen Text oder um strukturierte Datensätze in einer Datenbank handelt, und unabhängig davon, ob es um einfaches Sortieren und Filtern geht oder um komplexes automatisches Schließen. Kontrollierte Vokabulare (ob in einer einfachen Terminolgieliste oder als Taxonomien, Thesauri oder Ontologien strukturiert) sind dabei unbedingt notwendig, um Annotationen maschinell verarbeitbar zu machen; ohne terminologische Kontrolle sind Annotationen für die maschinelle Verarbeitung kaum nützlicher als an den Rand eines Buches gekrizelte Notizen. Kontrollierte Vokabulare abstrahieren von natürlichsprachlichen Ambiguitäten und Konnotationen; sie sind daher entscheidend für die semantische Verarbeitung von Forschungsdaten. Um projektübergreifende Zusammenarbeit und den semantischen Austausch von Daten zu ermöglichen, müssen Vokabulare nicht nur kontrolliert, sondern auch formell oder informell standardisiert sein. Standardisierte kontrollierte Vokabulare ermöglichen den Austausch, die Kombination und die gemeinsame Analyse annotierter Daten aus verschiedenen Quellen sowie die Implementierung generischer Werkzeuge für die semantische Verarbeitung. Erstellung und Wartung standardisierter kontrollierter Vokabulare sind jedoch zeitaufwändig und damit teuer. Zu den größten Herausforderungen zählen, dass alle beteiligten Parteien zu einem gemeinsamen Verständnis der Begriffe kommen, und dass die richtige Balance zwischen möglichst breiter Anwendbarkeit einerseits und möglichst präziser Analyse andereseits gefunden werden. Diese Ziele sind insbesondere in den Geisteswissenschaften schwierig zu erreichen: nicht nur sind die Forschungsfragen, die potentiell an einen gegebenen Datensatz gerichtet werden können, extrem weit gefächert, sondern die Kategorisierung der Daten ist häufig ein essenzieller Teil des Forschungsprozesses selbst. Es gibt daher einen eklatanten Mangel an standardisierten kontrollierten Vokabularen in den Geisteswissenschaften, der Digital-Humanities-Projekte letztlich dazu zwingt, eigene, projektspezifische Vokabulare zu definieren. Projektspezifische Vokabulare lösen können den internen Bedarf zwar kurzfristig befriedigen, sind aber nicht interoperabel und verhindern den zukünftigen Austausch und die Nachnutzung der annotierten Daten. Unser Poster stellt einen neuen konzeptuellen Ansatz zur Lösung dieser Probleme vor und beschreibt die Implementierung dieses Ansatzes in einem Softwarewerkzeug, dem Labeling System. Da es in der geisteswissenschaftlichen Forschung praktisch unmöglich ist, kontrollierte Vokabulare zu definieren, die alle denkbaren Anwendungen abdecken und generell akzeptiert sind, schlagen wir ein anderes Vorgehen vor. Bei unserem Ansatz definieren Projekte ihre eigenen Vokabulare, aber anstelle natürlichsprachlicher Definitionen werden die Terme mit einem oder mehreren Konzepten in einem Referenzthesaurus verknüpft. Der projektspezifische Term dient also quasi als »Label« für eine Menge gemeinsamer Konzepte. Dieser Ansatz ermöglicht es Projekten Vokabulare entsprechend ihrer Bedürfnisse und unter Verwendung im jeweiligen Forschungsgebiet üblichen Bezeichnungen benutzen, während gleichzeitig die Interoperabilität mit anderen Projekten über den Referenzthesaurus gewährleistet ist. Das Labeling System ist eine Webanwendung, die es Benutzern ermöglicht, SKOSVokabulare zu erstellen und auf einfache Weise deren Terme mit einem oder mehreren Konzepten in einem oder mehreren Referenzthesauri zu verknüpfen. Die Benutzeroberfläche ermöglicht die Visualisierung der definierten Vokabulare in einer hierarchischen Baumstruktur und ermöglicht den Zugriff auf Vokabulare über eine SPARQL-Schnittstelle. Das Labeling System basiert auf ausgereiften Open-SourceKomponenten und ist selbst ebenfalls frei verfügbar."@de ;
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    dct:description "Shared controlled vocabularies are a prerequisite for collaborative annotation and semantic interchange. The creation and maintenance of such vocabularies is, however, time-consuming and expensive. The diversity of research questions in the humanities makes it virtually impossible to create shared controlled vocabularies that cover a wide range of potential applications and satisfy the needs of diverse stakeholders. In this paper we present a novel conceptual approach for mitigating these problems. We propose that projects define their own vocabularies as needed and link the vocabulary terms to one or more concepts in a reference thesaurus, so that the project-specific term effectively serves as a \"label\" for a set of shared concepts. We also describe the implementation of this approach in the Labeling System. The Labeling System is a Web application that allows users to easily import concepts or create SKOS vocabularies and link the vocabulary terms to concepts from one or more reference thesauri."@en ;
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    dct:title "The Labeling System: A Bottom-up Approach for Enriched Vocabularies in the Humanities"@en ;
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    bibo:abstract "Shared controlled vocabularies are a prerequisite for collaborative annotation and semantic interchange. The creation and maintenance of such vocabularies is, however, time-consuming and expensive. The diversity of research questions in the humanities makes it virtually impossible to create shared controlled vocabularies that cover a wide range of potential applications and satisfy the needs of diverse stakeholders. In this paper we present a novel conceptual approach for mitigating these problems. We propose that projects define their own vocabularies as needed and link the vocabulary terms to one or more concepts in a reference thesaurus, so that the project-specific term effectively serves as a \"label\" for a set of shared concepts. We also describe the implementation of this approach in the Labeling System. The Labeling System is a Web application that allows users to easily import concepts or create SKOS vocabularies and link the vocabulary terms to concepts from one or more reference thesauri."@en ;
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    dct:description "Research in samian ware advanced considerably in recent years based on a dataset collected by several scholars in the past decades. It comprises detailed and reviewed data of potters, pots and related places in the northern provinces of the Roman Empire. The spatial distribution and chronological pattern as well as the identification of kiln sites of individual potters are key research questions. Based on this data, for instance Mees proposed a reconstruction and analysis of pottery trade networks considering archaeological material. The nature of the data makes it reasonable to approach the questions utilizing the concept of Linked Open Data (LOD) to generate interoperability and potential for data dissemination. The goal of the study presented in this presentation is to migrate a subset of these data into a LOD structure and to make it accessible through URIs in a REST interface as well as via a SPARQL endpoint. The subjective semantic relations between potters, pots and places should be visualised in the manner of linking. Furthermore the resources should be linked to existing LOD projects, for instance to Pleiades for places, using the Semantic Web to get additional information for the own resource. Potter, pots and places and their attributes could be conceptually modelled using the CIDOC-CRM. However, in the study direct linking of individual objects to mature ontologies widely used in the Semantic Web was preferred, to avoid CIDOC-CRM modelling questions. This presentation shows different modelling concepts and its challenges for each resource type using (1) an own ontology, (2) existing ontologies in the web, (3) the approach of Kerameikos as well as (4) labels of the Labeling System. The semantic links between potters, pots and places could be build via an own fixed predicate ontology or using ontologies like SKOS or Open Annotation to generate loose coupled relations. The idea of LOD is to couple related resources to build up an interlinked web of data. In the context of the study it was investigated what kind of suitable web resources are available from which the samian ware resources could benefit. On the one hand, web accessible repositories for fragments and potters are missing. On the other hand, Pleiades provides a large number of places to support geographical annotations of textural sources or archaeological objects. These places are used by the Pelagios project that links data from more than 20 partners to get additional information to an abstract Pleiades place concept. In this study, find-spots and kiln sites are mapped to Pleiades places to get additional data from the Pelagios partners. The locations in the samian dataset are reviewed and show much higher precision than the Pleiades places, so this mapping challenge have to be solved, too. The results yield that research on samian ware can benefit from implementing the concept of LOD. Enhanced perspectives arise when extending the linked data to other resources like dies and coins. When the data is published it could contribute to cross-linking of archaeological information internationally. A major challenge is finding the correct modelling concept for each resource type, as well as the semantic links between each other and to relying resources in the Semantic Web. Using technologies like the Labeling System, CIDOC-CRM, SKOS and Open Annotation can help to overcome this bottleneck. The presentation presents the results of a M.Sc. thesis delivered at the Hochschule Mainz - University of Applied Sciences in cooperation with the Römisch Germanisches Zentralmuseum Mainz (RGZM) and the Institute for Spatial Information and Surveying Technology (i3mainz)."@en ;
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    dct:title "Linked Samian Ware: challenges of a LOD approach to samian ware"@en ;
    dct:type sqpt:presentation ;
    bibo:abstract "Research in samian ware advanced considerably in recent years based on a dataset collected by several scholars in the past decades. It comprises detailed and reviewed data of potters, pots and related places in the northern provinces of the Roman Empire. The spatial distribution and chronological pattern as well as the identification of kiln sites of individual potters are key research questions. Based on this data, for instance Mees proposed a reconstruction and analysis of pottery trade networks considering archaeological material. The nature of the data makes it reasonable to approach the questions utilizing the concept of Linked Open Data (LOD) to generate interoperability and potential for data dissemination. The goal of the study presented in this presentation is to migrate a subset of these data into a LOD structure and to make it accessible through URIs in a REST interface as well as via a SPARQL endpoint. The subjective semantic relations between potters, pots and places should be visualised in the manner of linking. Furthermore the resources should be linked to existing LOD projects, for instance to Pleiades for places, using the Semantic Web to get additional information for the own resource. Potter, pots and places and their attributes could be conceptually modelled using the CIDOC-CRM. However, in the study direct linking of individual objects to mature ontologies widely used in the Semantic Web was preferred, to avoid CIDOC-CRM modelling questions. This presentation shows different modelling concepts and its challenges for each resource type using (1) an own ontology, (2) existing ontologies in the web, (3) the approach of Kerameikos as well as (4) labels of the Labeling System. The semantic links between potters, pots and places could be build via an own fixed predicate ontology or using ontologies like SKOS or Open Annotation to generate loose coupled relations. The idea of LOD is to couple related resources to build up an interlinked web of data. In the context of the study it was investigated what kind of suitable web resources are available from which the samian ware resources could benefit. On the one hand, web accessible repositories for fragments and potters are missing. On the other hand, Pleiades provides a large number of places to support geographical annotations of textural sources or archaeological objects. These places are used by the Pelagios project that links data from more than 20 partners to get additional information to an abstract Pleiades place concept. In this study, find-spots and kiln sites are mapped to Pleiades places to get additional data from the Pelagios partners. The locations in the samian dataset are reviewed and show much higher precision than the Pleiades places, so this mapping challenge have to be solved, too. The results yield that research on samian ware can benefit from implementing the concept of LOD. Enhanced perspectives arise when extending the linked data to other resources like dies and coins. When the data is published it could contribute to cross-linking of archaeological information internationally. A major challenge is finding the correct modelling concept for each resource type, as well as the semantic links between each other and to relying resources in the Semantic Web. Using technologies like the Labeling System, CIDOC-CRM, SKOS and Open Annotation can help to overcome this bottleneck. The presentation presents the results of a M.Sc. thesis delivered at the Hochschule Mainz - University of Applied Sciences in cooperation with the Römisch Germanisches Zentralmuseum Mainz (RGZM) and the Institute for Spatial Information and Surveying Technology (i3mainz)."@en ;
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        fabio:Presentation,
        dcat:Dataset ;
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    dct:creator <https://w3id.org/squirrelpapers/person/0000-0002-3246-3531>,
        <https://w3id.org/squirrelpapers/person/engel-thomas> ;
    dct:description "Die heterogene Natur archäologischer Daten wird bis heute meist in relationalen Datenbanken abgebildet. Datenmodellierungen mit NoSQL-Datenbanken oder in Graphen, fristen auch im Jahr 2015 noch ein Nischendasein, obwohl die konzeptionellen wie auch die technischen Grundlagen seit langem zur Verfügung stehen und einen Zugang in das Web 3.0 und in die Linked Data Cloud ermöglichen. Das i3mainz (Institut für Raumbezogene Informations- und Messtechnik) arbeitet in verschiedenen Projekten mit interdisziplinären Kontexten an  Graphenstrukturen, insbesondere auch an der Modellierung jener mit Hilfe des W3C-standardisierten Resource Description Framework (RDF). Ein besonderes Augenmerk liegt dabei  bei der Evaluation von Methoden zur standardisierten und interoperablen Vorhaltung komplexer archäologischer Fundstellendaten. Die technische Basis bildet dabei das auf dem CIDOC Conceptual Reference Model (CIDOC CRM) und Simple Knowledge Organization System (SKOS) basierende ARCHES Heritage & Management System. Die von Kooperationspartnern innerhalb des Mainzer Zentrum für Digitalität in den Geistes- und Kulturwissenschaften»mainzed« in unterschiedlicher Form zur Verfügung gestellten archäologischen Fachdatenkönnen für eine Vielzahl von Szenarien als exemplarisch angesehen werden. Ziele der Initiative sind die standardisierte und interoperable Vorhaltung strukturiert modellierter archäologischer Datensätze. Dabei steht die Abbildung möglichst vieler Prozesse wie etwa Abfrage, Pflege, Erweiterung und Analyse im Mittelpunkt, welche auch von nicht direkt im Projekt involvierten Wissenschaftlern erbracht werden sollen. Das Paper präsentiert technische Grundlagen semantischer Datenmodellierung unter Verwendung von CIDOC CRM. Weiterhin werden prototypische Fragestellungen behandelt: Welche Voraussetzungen sollten Daten hinsichtlich Aufbau und Struktur erfüllen? Welche Schritte sind zur Datenintegration in vorhandene Graphenstrukturen nötig? Wie können diese Strukturen erweitert und auf individuelle Bedürfnisse angepasst werden? Welche prototypischen Probleme wurden dabei identifiziert? Welchen Vorteil bieten interaktive Web-Tools zur Erstellung von Thesauri (Authority Files) als Linked Open Data wie z.B. der Reference Data Manager oder das Labeling Systemgegenüber herkömmlich verwendeter, nicht HTTP-adressierbarer und nicht zitierbarer kontrollierter Vokabulare? Abschließend wird diskutiert, inwiefern CIDOC CRM modellierte Daten überhaupt geeignet sein können, eine Basis für interoperable Systeme darzustellen. Ist die CIDOC CRM inhärente Komplexität  nicht gerade deswegen als problematisch zu bezeichnen? Können auf Triple basierende Modellierungen ein gleichwertiger Ersatz für auf Tupel (relationale Datenbanken) basierende Datenstrukturen darstellen oder sogar einen Mehrwert bezüglich bislang unbeantworteter Fragestellungen bilden? Welche Vorteile für den wissenschaftlichen Diskurs sind kurz- und langfristig zu erwarten?"@de ;
    dct:identifier "https://doi.org/10.5281/zenodo.292967" ;
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    dct:title "E1-P1-Arches! Aspekte interoperabler Modellierung and Bereitstellung archäologischer Fachdaten with dem Arches Framework"@de ;
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    bibo:abstract "Die heterogene Natur archäologischer Daten wird bis heute meist in relationalen Datenbanken abgebildet. Datenmodellierungen mit NoSQL-Datenbanken oder in Graphen, fristen auch im Jahr 2015 noch ein Nischendasein, obwohl die konzeptionellen wie auch die technischen Grundlagen seit langem zur Verfügung stehen und einen Zugang in das Web 3.0 und in die Linked Data Cloud ermöglichen. Das i3mainz (Institut für Raumbezogene Informations- und Messtechnik) arbeitet in verschiedenen Projekten mit interdisziplinären Kontexten an  Graphenstrukturen, insbesondere auch an der Modellierung jener mit Hilfe des W3C-standardisierten Resource Description Framework (RDF). Ein besonderes Augenmerk liegt dabei  bei der Evaluation von Methoden zur standardisierten und interoperablen Vorhaltung komplexer archäologischer Fundstellendaten. Die technische Basis bildet dabei das auf dem CIDOC Conceptual Reference Model (CIDOC CRM) und Simple Knowledge Organization System (SKOS) basierende ARCHES Heritage & Management System. Die von Kooperationspartnern innerhalb des Mainzer Zentrum für Digitalität in den Geistes- und Kulturwissenschaften»mainzed« in unterschiedlicher Form zur Verfügung gestellten archäologischen Fachdatenkönnen für eine Vielzahl von Szenarien als exemplarisch angesehen werden. Ziele der Initiative sind die standardisierte und interoperable Vorhaltung strukturiert modellierter archäologischer Datensätze. Dabei steht die Abbildung möglichst vieler Prozesse wie etwa Abfrage, Pflege, Erweiterung und Analyse im Mittelpunkt, welche auch von nicht direkt im Projekt involvierten Wissenschaftlern erbracht werden sollen. Das Paper präsentiert technische Grundlagen semantischer Datenmodellierung unter Verwendung von CIDOC CRM. Weiterhin werden prototypische Fragestellungen behandelt: Welche Voraussetzungen sollten Daten hinsichtlich Aufbau und Struktur erfüllen? Welche Schritte sind zur Datenintegration in vorhandene Graphenstrukturen nötig? Wie können diese Strukturen erweitert und auf individuelle Bedürfnisse angepasst werden? Welche prototypischen Probleme wurden dabei identifiziert? Welchen Vorteil bieten interaktive Web-Tools zur Erstellung von Thesauri (Authority Files) als Linked Open Data wie z.B. der Reference Data Manager oder das Labeling Systemgegenüber herkömmlich verwendeter, nicht HTTP-adressierbarer und nicht zitierbarer kontrollierter Vokabulare? Abschließend wird diskutiert, inwiefern CIDOC CRM modellierte Daten überhaupt geeignet sein können, eine Basis für interoperable Systeme darzustellen. Ist die CIDOC CRM inhärente Komplexität  nicht gerade deswegen als problematisch zu bezeichnen? Können auf Triple basierende Modellierungen ein gleichwertiger Ersatz für auf Tupel (relationale Datenbanken) basierende Datenstrukturen darstellen oder sogar einen Mehrwert bezüglich bislang unbeantworteter Fragestellungen bilden? Welche Vorteile für den wissenschaftlichen Diskurs sind kurz- und langfristig zu erwarten?"@de ;
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    dct:description "In the last years several web services emerged that manage and make accessible place thesauri for the archaeologies and historical sciences. By making use of semantic technologies these applications are able to act as linked data hubs thereby making possibe the linkage of multiple datasets of varying thematic focus and of different structural properties. Another common denominator of archaeological data resources, besides geo-spatial properties, is the temporal classification of research objects. One of the applications that try to assume a role similar to that of gazetteers but for temporal concepts and cultural periods is developed in the chronOntology project. In this project, funded by the German Research Foundation, the German Archaeologial Institute (DAI) together with the i3mainz are developing a system for storing, managing, mapping and making accessible descriptions of temporal concepts. The core of this endeavor is a rich semantic modeling of various existing terminological systems for cultural periods using a data model based on the CIDOC-CRM and its extensions. The use of the rich ontological model provided by the CRM on the one hand permits representing the measurable temporal extent (with the possibility for fuzzy edges) while also making it possible to embed temporal concepts in a network of semantic relationships to other temporal concepts, connected historical regions and thematic contexts. Besides documenting the general architecture and data model of the project the paper will present possibilities of querying different heterogeneous data resources collected throughout various digitization and digging activities inside the DAI with the help of concepts defined in chronOntology. We will also point out the potential and problems of reasoning over geographically and temporally connected datasets."@en ;
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    bibo:abstract "In the last years several web services emerged that manage and make accessible place thesauri for the archaeologies and historical sciences. By making use of semantic technologies these applications are able to act as linked data hubs thereby making possibe the linkage of multiple datasets of varying thematic focus and of different structural properties. Another common denominator of archaeological data resources, besides geo-spatial properties, is the temporal classification of research objects. One of the applications that try to assume a role similar to that of gazetteers but for temporal concepts and cultural periods is developed in the chronOntology project. In this project, funded by the German Research Foundation, the German Archaeologial Institute (DAI) together with the i3mainz are developing a system for storing, managing, mapping and making accessible descriptions of temporal concepts. The core of this endeavor is a rich semantic modeling of various existing terminological systems for cultural periods using a data model based on the CIDOC-CRM and its extensions. The use of the rich ontological model provided by the CRM on the one hand permits representing the measurable temporal extent (with the possibility for fuzzy edges) while also making it possible to embed temporal concepts in a network of semantic relationships to other temporal concepts, connected historical regions and thematic contexts. Besides documenting the general architecture and data model of the project the paper will present possibilities of querying different heterogeneous data resources collected throughout various digitization and digging activities inside the DAI with the help of concepts defined in chronOntology. We will also point out the potential and problems of reasoning over geographically and temporally connected datasets."@en ;
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    dct:description "Aktuelle Entwicklungen in den digitalen Methoden der Archäologie suchen nach Lösungen für eine fachspezifisch adäquate Repräsentation chronologischer Konzepte, sowie deren persistente Adressierbarkeit. In diesem Paper wird ein konzeptioneller Vorschlag zur Lösung des archäologisch-relativchronologischen Problems mittels digitaler Methoden vorgestellt. Einerseits werden Zeitkonzepte und ihr relativchronologisches Verhältnis zueinander digital aufgearbeitet und im Semantic Web zur Verfügung gestellt. Neben Geodia, ein online-Portal für die Visualisierung von zeitlichen, geographischen und materiellen Aspekten antiker Kulturen im Mittelmeerraum und PeriodO, das als online-Gazetteer Zeitbegriffe als Linked Open Data anbietet, entwickeln das i3mainz und das DAI im Rahmen des Projekts ChronOntology eine regelbasierte Software-Architektur, die auch komplexe Zeitbegriffe in ihren Abhängigkeiten und räumlichem Kontext repräsentiert. Andererseits entstehen relativchronologische Materialzeitstufen aus einem bottom-up-Verfahren anhand datierter Fundkomplexe. Diese vom Fundmaterial heraus erarbeiteten Zeitkonzepte lassen sich in ihren relativen Bezügen quantifizieren, adressieren und digital verarbeiten. Konkret können relativchronologische Zeitangaben als Intervalle verstanden werden, die in 13 unterschiedlichen Arten semantisch verarbeitet werden können (Allen 1983; Freksa 1992). Das herausfordernde ist, dass Beschreibungen von z.B. Gefäßformen als Typologien zum Aufbau relativer Chronologien verwandt wurden, an denen wiederum Zeitkonzepte gekoppelt wurden. Dies kann mit der Keramik-Forschungsdatenbank \"Samian\" des RGZM sichtbar gemacht werden. Der digitale Umgang mit chronologischen Kreisargumentationen bildet ein methodisches Gerüst, das auch für die Mittelalterarchäologie weiterentwickelt werden kann, wenn feingranulierte digitale Datensätze mittelalterlicher Großmanufakturen von z.B. Siegburger Schnellen oder Goudaer Tonpfeifen vorliegen."@de ;
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    dct:title "Digitale Methoden zur Erarbeitung von archäologisch-relativ-chronologischen Zeitintervallen"@de ;
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    bibo:abstract "Aktuelle Entwicklungen in den digitalen Methoden der Archäologie suchen nach Lösungen für eine fachspezifisch adäquate Repräsentation chronologischer Konzepte, sowie deren persistente Adressierbarkeit. In diesem Paper wird ein konzeptioneller Vorschlag zur Lösung des archäologisch-relativchronologischen Problems mittels digitaler Methoden vorgestellt. Einerseits werden Zeitkonzepte und ihr relativchronologisches Verhältnis zueinander digital aufgearbeitet und im Semantic Web zur Verfügung gestellt. Neben Geodia, ein online-Portal für die Visualisierung von zeitlichen, geographischen und materiellen Aspekten antiker Kulturen im Mittelmeerraum und PeriodO, das als online-Gazetteer Zeitbegriffe als Linked Open Data anbietet, entwickeln das i3mainz und das DAI im Rahmen des Projekts ChronOntology eine regelbasierte Software-Architektur, die auch komplexe Zeitbegriffe in ihren Abhängigkeiten und räumlichem Kontext repräsentiert. Andererseits entstehen relativchronologische Materialzeitstufen aus einem bottom-up-Verfahren anhand datierter Fundkomplexe. Diese vom Fundmaterial heraus erarbeiteten Zeitkonzepte lassen sich in ihren relativen Bezügen quantifizieren, adressieren und digital verarbeiten. Konkret können relativchronologische Zeitangaben als Intervalle verstanden werden, die in 13 unterschiedlichen Arten semantisch verarbeitet werden können (Allen 1983; Freksa 1992). Das herausfordernde ist, dass Beschreibungen von z.B. Gefäßformen als Typologien zum Aufbau relativer Chronologien verwandt wurden, an denen wiederum Zeitkonzepte gekoppelt wurden. Dies kann mit der Keramik-Forschungsdatenbank \"Samian\" des RGZM sichtbar gemacht werden. Der digitale Umgang mit chronologischen Kreisargumentationen bildet ein methodisches Gerüst, das auch für die Mittelalterarchäologie weiterentwickelt werden kann, wenn feingranulierte digitale Datensätze mittelalterlicher Großmanufakturen von z.B. Siegburger Schnellen oder Goudaer Tonpfeifen vorliegen."@de ;
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    dct:description "In vielen archäologischen Forschungsinstituten existiert üblicherweise eine zentrale Objektdatenbank (Diathek, Objektdatenbank, zentrale Fundverwaltung) und darüber hinaus ein Bündel an einzelnen Fachdatenbanken. Diese Fachdatenbanken entstanden in der Regel im Laufe von einzelnen (oft auch aus Drittmittel finanzierten) Projekten. Charakterisiert werden sie oft durch Spezialtabellen bzw. Fachspalten, die für die jeweilige Forschungscommunity und Forschungsfrage relevant sind. Für zentrale Repositorien sind diese fachbezogenen Attribute in der Regel nicht besonders relevant, da sie sich auf nur eine ganz kleine Untergruppe von Daten bezieht. Die fachspezifischen Attribute müssen außerhalb der zentralen Repositorien von der spezifischen Forschungsgruppe gepflegt werden, da archäologische Fachtermini oft sehr detailliert sind und von globalen autoritativen Thesauri wie Heritage Data oder Getty nicht exakt erfasst werden können. Oft wissen diese verteilten Systeme auch nichts über den Inhalt eines Anderen. Dabei könnten sich nachhaltige Synergieeffekte ergeben, indem man z.B. die Grunddaten miteinander verknüpft. Linked Open Data ermöglicht es diese Datenbanken zu verknüpfen. Voraussetzung hierzu ist eine eindeutige Referenz innerhalb der Datenbank auf eine persistente externe Quelle, worauf andere Datenbanken verweisen können. Im Semantic Web wird dies durch das W3C-standardisierte Resource Description Framework (RDF) und im Web adressierbare Unique Ressource Identifier (URI) umgesetzt. Die Referenzierung kann räumlich, zeitlich oder auch über Schlagworte erfolgen. Als Beispiele solcher Referenzen können GeoNames, Pleiades oder ChronOntology gelten. Das vom Institut für Raumbezogene Informations- und Messtechnik (i3mainz) und Römisch Germanischen Zentralmuseum (RGZM) in Kooperation mit dem Mainzer Zentrum für Digitalität in den Geistes- und Kulturwissenschaften (mainzed) implementierte Labeling System ermöglicht es webbasiert über ein benutzerfreundliches GUI eigene kontrollierte Vokabulare zu erzeugen, deren Begriffe über eine Suche mit Referenzkonzepten im Web zu verlinken und Begriffe als URI zu publizieren. Für die Anfangs skizierte, in vielen Institutionen vorhandene problematische IT-Situation der nicht vernetzten lokalen Datenbanken bietet die neue Architektur des RGZM einen Lösungsansatz, welcher zurzeit implementiert wird. Die digitale Verknüpfung der verteilten Fachdatenbanken (z.B. NAVIS und Samian Research) mit der Museums-Objektdatenbank (easyDB), mit Hilfe von kontrollierten Vokabularen, wird einen neuen Zugang zu den vorhandenen digitalen Daten ermöglichen, welcher zuvor nicht erreichbar war. Mit dem Labeling System können dabei in jahrelanger europäischer Abstimmung erstellte mehrsprachige fachspezifische kontrollierte Vokabulare (z.B. Schiffsvokabulare aus den RGZM-eigenen NAVIS-Datenbanken) standardisiert als Simple Knowledge Organization System (SKOS) der Forschungscommunity zur Verfügung gestellt werden. Dabei können die Begriffe zu autoritativen Thesauri verlinkt werden, sodass sie in der Linked Data Cloud verortet sind und deren Bedeutung näher spezifiziert wird. Der Vortrag präsentiert die Grundprinzipien des Erstellungsprozesses eines kontrollierten Vokabulars mit dem Labeling System. Was ist ein kontrolliertes Vokabular? Was ist ein Begriff? Wie kann ein Begriff näher beschrieben werden? Wie stehen publizierte kontrollierte Vokabulare und deren Begriffe externen Nutzern zur Verfügung? Welche Möglichkeiten bietet die Labeling System API zur Einbindung in die eigenen Fachdatenbanken? Am Beispiel verschieden definierter Formtypen römischer Keramik wird erläutert, inwiefern ein Mapping dieser Begriffe im Labeling System erstellt und in einem zweiten Schritt in einer Fachdatenbank eingebunden werden kann. Abschließend wird diskutiert inwiefern Begriffe des Labeling System in eine Systemarchitektur, wie die des RGZM eingebunden werden können und welche Möglichkeiten dadurch generiert werden."@de ;
    dct:identifier "https://doi.org/10.5281/zenodo.292554" ;
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    dct:issued "2017-02-11"^^xsd:date ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Das Labeling System: Erstellung kontrollierter Linked Open Data Vokabulare als Metadaten-Hub für archäologische Fachdatenbanken"@de ;
    dct:type sqpt:presentation ;
    bibo:abstract "In vielen archäologischen Forschungsinstituten existiert üblicherweise eine zentrale Objektdatenbank (Diathek, Objektdatenbank, zentrale Fundverwaltung) und darüber hinaus ein Bündel an einzelnen Fachdatenbanken. Diese Fachdatenbanken entstanden in der Regel im Laufe von einzelnen (oft auch aus Drittmittel finanzierten) Projekten. Charakterisiert werden sie oft durch Spezialtabellen bzw. Fachspalten, die für die jeweilige Forschungscommunity und Forschungsfrage relevant sind. Für zentrale Repositorien sind diese fachbezogenen Attribute in der Regel nicht besonders relevant, da sie sich auf nur eine ganz kleine Untergruppe von Daten bezieht. Die fachspezifischen Attribute müssen außerhalb der zentralen Repositorien von der spezifischen Forschungsgruppe gepflegt werden, da archäologische Fachtermini oft sehr detailliert sind und von globalen autoritativen Thesauri wie Heritage Data oder Getty nicht exakt erfasst werden können. Oft wissen diese verteilten Systeme auch nichts über den Inhalt eines Anderen. Dabei könnten sich nachhaltige Synergieeffekte ergeben, indem man z.B. die Grunddaten miteinander verknüpft. Linked Open Data ermöglicht es diese Datenbanken zu verknüpfen. Voraussetzung hierzu ist eine eindeutige Referenz innerhalb der Datenbank auf eine persistente externe Quelle, worauf andere Datenbanken verweisen können. Im Semantic Web wird dies durch das W3C-standardisierte Resource Description Framework (RDF) und im Web adressierbare Unique Ressource Identifier (URI) umgesetzt. Die Referenzierung kann räumlich, zeitlich oder auch über Schlagworte erfolgen. Als Beispiele solcher Referenzen können GeoNames, Pleiades oder ChronOntology gelten. Das vom Institut für Raumbezogene Informations- und Messtechnik (i3mainz) und Römisch Germanischen Zentralmuseum (RGZM) in Kooperation mit dem Mainzer Zentrum für Digitalität in den Geistes- und Kulturwissenschaften (mainzed) implementierte Labeling System ermöglicht es webbasiert über ein benutzerfreundliches GUI eigene kontrollierte Vokabulare zu erzeugen, deren Begriffe über eine Suche mit Referenzkonzepten im Web zu verlinken und Begriffe als URI zu publizieren. Für die Anfangs skizierte, in vielen Institutionen vorhandene problematische IT-Situation der nicht vernetzten lokalen Datenbanken bietet die neue Architektur des RGZM einen Lösungsansatz, welcher zurzeit implementiert wird. Die digitale Verknüpfung der verteilten Fachdatenbanken (z.B. NAVIS und Samian Research) mit der Museums-Objektdatenbank (easyDB), mit Hilfe von kontrollierten Vokabularen, wird einen neuen Zugang zu den vorhandenen digitalen Daten ermöglichen, welcher zuvor nicht erreichbar war. Mit dem Labeling System können dabei in jahrelanger europäischer Abstimmung erstellte mehrsprachige fachspezifische kontrollierte Vokabulare (z.B. Schiffsvokabulare aus den RGZM-eigenen NAVIS-Datenbanken) standardisiert als Simple Knowledge Organization System (SKOS) der Forschungscommunity zur Verfügung gestellt werden. Dabei können die Begriffe zu autoritativen Thesauri verlinkt werden, sodass sie in der Linked Data Cloud verortet sind und deren Bedeutung näher spezifiziert wird. Der Vortrag präsentiert die Grundprinzipien des Erstellungsprozesses eines kontrollierten Vokabulars mit dem Labeling System. Was ist ein kontrolliertes Vokabular? Was ist ein Begriff? Wie kann ein Begriff näher beschrieben werden? Wie stehen publizierte kontrollierte Vokabulare und deren Begriffe externen Nutzern zur Verfügung? Welche Möglichkeiten bietet die Labeling System API zur Einbindung in die eigenen Fachdatenbanken? Am Beispiel verschieden definierter Formtypen römischer Keramik wird erläutert, inwiefern ein Mapping dieser Begriffe im Labeling System erstellt und in einem zweiten Schritt in einer Fachdatenbank eingebunden werden kann. Abschließend wird diskutiert inwiefern Begriffe des Labeling System in eine Systemarchitektur, wie die des RGZM eingebunden werden können und welche Möglichkeiten dadurch generiert werden."@de ;
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    dct:description "The mythical island Atlantis was an empire assigned to the sea god Poseidon. It was firstly elaborately described in the Critias and Timaios dialogues written by the ancient Greek philosopher Plato. Egyptians described Atlantis consisting mostly of mountains in the northern part. Poseidon fell in love with Cleito. who bore him five pairs of male twins. The forst-born, Atlas, was established as rightful king of the entire island and the ocean. One Day, there is a great concern on Atlantis. Atlas and his twin brothers gathered for the annual island party at the king's palace as they hear a sound comming from the surrounding mighty mountains. It was a avalanche! Nothing happend, puh! Since they are responsible for the well-being of their countrymen and more recently saw snowfall, they are forced to deal with the subject of \"avalanches\". Areas where avalanches occur have to be recorded, for performing arrangements to protect the population and the environment. To make this possible a GIS was established. As a result the demolition sites of avalaches will be calculated and presented to the public. This presentation focuses on faked GIS datasets and their potentatial to show exemplary methods that could be processed on any other real dataset. The main topic here, is the caculation of demolition sites of avalaches on Atlantis. In this case, the free CC BY-SA 4.0 AtlantGIS dataset by Kai-Christian Bruhn (i3mainz, Hochschule Mainz) is used. We believe that simple datasets with a narrative are most qualified to impart knowledge and skills to students. This presentation is based on a students project by Rania Taani as part of the Geoinformatics and Surveying master modul \"Interdiciplinary Applications of Spatial Information Techniques\", which focuses on applications in the Digital Humanities."@en ;
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    dct:title "Avalanches on Atlantis - Real or Fake? The \"true\" story!"@en ;
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    bibo:abstract "The mythical island Atlantis was an empire assigned to the sea god Poseidon. It was firstly elaborately described in the Critias and Timaios dialogues written by the ancient Greek philosopher Plato. Egyptians described Atlantis consisting mostly of mountains in the northern part. Poseidon fell in love with Cleito. who bore him five pairs of male twins. The forst-born, Atlas, was established as rightful king of the entire island and the ocean. One Day, there is a great concern on Atlantis. Atlas and his twin brothers gathered for the annual island party at the king's palace as they hear a sound comming from the surrounding mighty mountains. It was a avalanche! Nothing happend, puh! Since they are responsible for the well-being of their countrymen and more recently saw snowfall, they are forced to deal with the subject of \"avalanches\". Areas where avalanches occur have to be recorded, for performing arrangements to protect the population and the environment. To make this possible a GIS was established. As a result the demolition sites of avalaches will be calculated and presented to the public. This presentation focuses on faked GIS datasets and their potentatial to show exemplary methods that could be processed on any other real dataset. The main topic here, is the caculation of demolition sites of avalaches on Atlantis. In this case, the free CC BY-SA 4.0 AtlantGIS dataset by Kai-Christian Bruhn (i3mainz, Hochschule Mainz) is used. We believe that simple datasets with a narrative are most qualified to impart knowledge and skills to students. This presentation is based on a students project by Rania Taani as part of the Geoinformatics and Surveying master modul \"Interdiciplinary Applications of Spatial Information Techniques\", which focuses on applications in the Digital Humanities."@en ;
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        <https://w3id.org/squirrelpapers/person/dufner-matthias>,
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    dct:description """Als Linked Open Data wird die direkte Vernetzung von frei verfügbaren Daten im Internet bezeichnet. Aufgrund der ständig wachsenden Datenmenge ergeben sich für die Wissenschaften daraus ganz neue Erkenntnismöglichkeiten.
Dieses Wissensnetzwerk kämpft unter anderem mit dem Problem von nicht eindeutigen Begrifflichkeiten. Der natürliche Wortlaut eines Begriffs genügt datenverarbeitenden Systemen oftmals nicht, um sich dessen exakte Bedeutung zu erschließen. Eine maschinenlesbare Definition beinhaltet daher neben natürlichsprachlicher Erläuterungen auch Verknüpfungen zu anderen Datenquellen, um den korrekten Begriffskontext sicherzustellen.
Labeling System ist eine Web-App, die an dieser Stelle ansetzt und mittels einer leicht bedienbaren Nutzeroberfläche das Erstellen von Begriffen und deren maschinenlesbarer Definitionen ermöglicht.
Wissenschaftler*innen haben so die Möglichkeit, ihr Wissen als ”Linked Open Data“ zur Verfügung zu stellen und somit die Qualität der Forschungsdaten in ihrem Arbeitsgebiet zu verbessern."""@de ;
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    dct:title "labeling system"@de ;
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    bibo:abstract """Als Linked Open Data wird die direkte Vernetzung von frei verfügbaren Daten im Internet bezeichnet. Aufgrund der ständig wachsenden Datenmenge ergeben sich für die Wissenschaften daraus ganz neue Erkenntnismöglichkeiten.
Dieses Wissensnetzwerk kämpft unter anderem mit dem Problem von nicht eindeutigen Begrifflichkeiten. Der natürliche Wortlaut eines Begriffs genügt datenverarbeitenden Systemen oftmals nicht, um sich dessen exakte Bedeutung zu erschließen. Eine maschinenlesbare Definition beinhaltet daher neben natürlichsprachlicher Erläuterungen auch Verknüpfungen zu anderen Datenquellen, um den korrekten Begriffskontext sicherzustellen.
Labeling System ist eine Web-App, die an dieser Stelle ansetzt und mittels einer leicht bedienbaren Nutzeroberfläche das Erstellen von Begriffen und deren maschinenlesbarer Definitionen ermöglicht.
Wissenschaftler*innen haben so die Möglichkeit, ihr Wissen als ”Linked Open Data“ zur Verfügung zu stellen und somit die Qualität der Forschungsdaten in ihrem Arbeitsgebiet zu verbessern."""@de ;
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<https://w3id.org/squirrelpapers/v1/i7/e1> a bibo:Document,
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        dcat:Dataset ;
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    dct:creator <https://w3id.org/squirrelpapers/person/0000-0002-3246-3531>,
        <https://w3id.org/squirrelpapers/person/0000-0002-7634-5342>,
        <https://w3id.org/squirrelpapers/person/0000-0002-8155-7702>,
        <https://w3id.org/squirrelpapers/person/0000-0003-3448-5715> ;
    dct:description "Chronology in Archaeology contains many hidden assumptions. Chronological intervals resulting from data analysis are sometimes based on absolute “certain” datings (e.g. C14 or Roman emperor reign years) or derived from relative datings or combinations of these. This may even lead to circular argumentations with hidden contradictions. To produce verifiable chronological relations, a well documented data scheme containing all the relevant chronological archaeological information is required.We modeled this scheme as an RDF (Resource Description Framework) based ontology comprising Allen`s interval algebra and provided the results as Linked Open Data. This is exemplarily done by parsing CSV based datasets of two case studies, one concerning the Iron Age settlement history of the Central African rainforest, and the second focusing on a system for Roman period classification used at the RGZM in Mainz.The first case study was based on a recently finished PhD thesis and reviewed the current knowledge on the settlement history of the Central African rainforest. The available data derived from surveys and excavations between present-day Cameroon and Angola. Chronological relations between groups have been modeled within a RDF graph in order to produce a machine readable and processable semantic representation. Additionally, we used radiocarbon dates from the OpenAccess data repository Archives des datations radiocarbone d'Afrique centrale (aDRAC). For the second case study, we developed a workflow for the RGZM retrieval system “easydb” in which a wide variety of time interval descriptions is used. Apart from the historically definable Roman emperor reigning years, which can often be subdivided by the consulship years of an emperor, many descriptives such as “early Roman” or “late Roman/early Medieval” are regularly used but far from being precise. For both case studies using RDF based modeling generated a reproducible representation of the state of knowledge concerning the temporal sequences of time intervals."@en ;
    dct:identifier "https://doi.org/10.5281/zenodo.1410516" ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "RDF based modeling of relative and absolute chronological data: Examples from the central african rainforest and roman periodisation"@en ;
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    bibo:abstract "Chronology in Archaeology contains many hidden assumptions. Chronological intervals resulting from data analysis are sometimes based on absolute “certain” datings (e.g. C14 or Roman emperor reign years) or derived from relative datings or combinations of these. This may even lead to circular argumentations with hidden contradictions. To produce verifiable chronological relations, a well documented data scheme containing all the relevant chronological archaeological information is required.We modeled this scheme as an RDF (Resource Description Framework) based ontology comprising Allen`s interval algebra and provided the results as Linked Open Data. This is exemplarily done by parsing CSV based datasets of two case studies, one concerning the Iron Age settlement history of the Central African rainforest, and the second focusing on a system for Roman period classification used at the RGZM in Mainz.The first case study was based on a recently finished PhD thesis and reviewed the current knowledge on the settlement history of the Central African rainforest. The available data derived from surveys and excavations between present-day Cameroon and Angola. Chronological relations between groups have been modeled within a RDF graph in order to produce a machine readable and processable semantic representation. Additionally, we used radiocarbon dates from the OpenAccess data repository Archives des datations radiocarbone d'Afrique centrale (aDRAC). For the second case study, we developed a workflow for the RGZM retrieval system “easydb” in which a wide variety of time interval descriptions is used. Apart from the historically definable Roman emperor reigning years, which can often be subdivided by the consulship years of an emperor, many descriptives such as “early Roman” or “late Roman/early Medieval” are regularly used but far from being precise. For both case studies using RDF based modeling generated a reproducible representation of the state of knowledge concerning the temporal sequences of time intervals."@en ;
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        <https://w3id.org/squirrelpapers/person/0000-0002-7634-5342>,
        <https://w3id.org/squirrelpapers/person/heinz-guido> ;
    dct:description """Since the mid-1990s the RGZM (Römisch-Germanisches Zentralmuseum) provides distributed web-based databases containing hundreds of thousands datasets with content from different archaeological disciplines. The data storage behind it is based on various technologies and data modelling concepts like relational structures or graph (triple) structures. Data access is usually only possible via a GUI interface of the specific database. A global search among the distributed RGZM databases data is at the moment not possible. Our aim is to provide a central access point to enable a global search across the distributed databases based on Linked Data principles.
The core concept comprises a central index, the RGZM-Meta-Index (RMI), as a central Linked Data Hub for aligning the data of the distributed databases by versioned keywords or ‘index terms’. These can e.g. be presented in different languages or descriptions). In this Meta-Index, all terms which are important for describing the content of each object resource, such as a coin or a mosaic depicting a ship, are listed and provided via a RESTful API. The RMI is modelled as a RDF and linked to external resources (e.g. Pleiades, GeoNames, Getty, …) into the Linked Open Data Cloud in order to resolve the ambiguities. A user-friendly GUI will help the archaeological researchers to create the index terms and structure them hierarchically (broader, narrower), semantically (e.g. similar, related), by type (e.g. geography, person) and by label (e.g. site, material). This structure is related to the SKOS-ontology but extended to RGZM-specific needs.
Additionally, the individual databases will provide an API for returning all object resources identified by URIs which are connected to the RMI-index terms. In order to achieve this, the underlying data in the distributed databases will provide Linked Open Data resources for each object and represent a knowledge representation by using a RGZM-specific vocabulary and ontology."""@en ;
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    dct:title "RGZM-Meta-Index: A central Linked Data Hub for aligning distributed databases"@en ;
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    bibo:abstract """Since the mid-1990s the RGZM (Römisch-Germanisches Zentralmuseum) provides distributed web-based databases containing hundreds of thousands datasets with content from different archaeological disciplines. The data storage behind it is based on various technologies and data modelling concepts like relational structures or graph (triple) structures. Data access is usually only possible via a GUI interface of the specific database. A global search among the distributed RGZM databases data is at the moment not possible. Our aim is to provide a central access point to enable a global search across the distributed databases based on Linked Data principles.
The core concept comprises a central index, the RGZM-Meta-Index (RMI), as a central Linked Data Hub for aligning the data of the distributed databases by versioned keywords or ‘index terms’. These can e.g. be presented in different languages or descriptions). In this Meta-Index, all terms which are important for describing the content of each object resource, such as a coin or a mosaic depicting a ship, are listed and provided via a RESTful API. The RMI is modelled as a RDF and linked to external resources (e.g. Pleiades, GeoNames, Getty, …) into the Linked Open Data Cloud in order to resolve the ambiguities. A user-friendly GUI will help the archaeological researchers to create the index terms and structure them hierarchically (broader, narrower), semantically (e.g. similar, related), by type (e.g. geography, person) and by label (e.g. site, material). This structure is related to the SKOS-ontology but extended to RGZM-specific needs.
Additionally, the individual databases will provide an API for returning all object resources identified by URIs which are connected to the RMI-index terms. In order to achieve this, the underlying data in the distributed databases will provide Linked Open Data resources for each object and represent a knowledge representation by using a RGZM-specific vocabulary and ontology."""@en ;
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    dct:description """Since the mid-1990s the RGZM (Römisch-Germanisches Zentralmuseum Mainz) provides web-based databases containing millions of datasets with content from many different archaeological disciplines. These relational databases were constructed in interdisciplinary transnational projects and include a lot of “hidden archaeological assumptions”. The aim of our paper is to make these hidden assumptions visible and provide them as a Linked Open Data graph to establish reproducible research as a basis for Open Science.
In particular the Samian Research database [1] at the RGZM which is based on the Names on Terra Sigillata project of Leeds and Reading Universities funded by the AHRC offers nearly 250'000 identified potter stamps, which are traditionally dated in a traditional way. In Roman archaeology this is usually expressed by establishing "absolute dates" in well known “from-to” tables, whereas in reality, the situation is much more diffuse. In fact, Samian dating is quite often based upon so-called ‘dated sites’, which are themselves dated by Samian. Occurrences of South Gaulish Samian on “historically dated sites” like Hadrian’s Wall with its absolute dating or the Wetteraulimes with its less clear dating are difficult to use as dating arguments because it is not possible to say a priori whether these were “survivals” or whether they were part of a still existing South Gaulish export industry. Therefore an RDF (Resource Description Framework) based model has been developed to deal with occurrences making the Samian dating process verifiable and transparent.
This paper focuses on modelling dating arguments using a relative chronology based on Allen’s interval algebra [2] compared with absolute dates in the prototypical build Academic Meta Tool [2] to create Linked Open Data for reproducible and transparent research. This tool enables the creation of an RDF-based ontology such as for modelling dating arguments and visualising the semantic reasoning for detecting errors.
[1] http://rgzm.de/samian
[2] http://cse.unl.edu/~choueiry/Documents/Allen-CACM1983.pdf
[3] http://academic-meta-tool.xyz"""@en ;
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    dct:title "Taming the chronology of South Gaulish Samian found at Hadrian's Wall and the German Limes using Linked Open Data"@en ;
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    bibo:abstract """Since the mid-1990s the RGZM (Römisch-Germanisches Zentralmuseum Mainz) provides web-based databases containing millions of datasets with content from many different archaeological disciplines. These relational databases were constructed in interdisciplinary transnational projects and include a lot of “hidden archaeological assumptions”. The aim of our paper is to make these hidden assumptions visible and provide them as a Linked Open Data graph to establish reproducible research as a basis for Open Science.
In particular the Samian Research database [1] at the RGZM which is based on the Names on Terra Sigillata project of Leeds and Reading Universities funded by the AHRC offers nearly 250'000 identified potter stamps, which are traditionally dated in a traditional way. In Roman archaeology this is usually expressed by establishing "absolute dates" in well known “from-to” tables, whereas in reality, the situation is much more diffuse. In fact, Samian dating is quite often based upon so-called ‘dated sites’, which are themselves dated by Samian. Occurrences of South Gaulish Samian on “historically dated sites” like Hadrian’s Wall with its absolute dating or the Wetteraulimes with its less clear dating are difficult to use as dating arguments because it is not possible to say a priori whether these were “survivals” or whether they were part of a still existing South Gaulish export industry. Therefore an RDF (Resource Description Framework) based model has been developed to deal with occurrences making the Samian dating process verifiable and transparent.
This paper focuses on modelling dating arguments using a relative chronology based on Allen’s interval algebra [2] compared with absolute dates in the prototypical build Academic Meta Tool [2] to create Linked Open Data for reproducible and transparent research. This tool enables the creation of an RDF-based ontology such as for modelling dating arguments and visualising the semantic reasoning for detecting errors.
[1] http://rgzm.de/samian
[2] http://cse.unl.edu/~choueiry/Documents/Allen-CACM1983.pdf
[3] http://academic-meta-tool.xyz"""@en ;
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    dct:description """Graphdatenbanken und Triple Stores stellen bei der Modellierung von Forschungsdaten eine Alternative zu relationalen Datenbanken dar. Eine Modellierung der Daten in einem Netzwerk, anstatt in Tabellenstrukturen, eignet sie sich hervorragend um Daten interoperabel zur Verfügung zu stellen, indem man zwei Knoten aus verschiedenen Datenbanken miteinander über eine Kante verbindet. Dieser Technik bedienen sich viele Forschungsprojekte durch die Verwendung von Linked Open Data (LOD).
Sowohl gängige Graphdatenbanken als auch gebräuchliche Triple Stores bieten jedoch keine Möglichkeit Unsicherheiten oder Vagheiten zu modellieren. Um dieser Unzulänglichkeit Rechnung zu tragen, haben wir das Academic Meta Tool (AMT) entwickelt. AMT bietet die Möglichkeit sämtlichen Kanten eine Gewichtung hinzuzufügen, um dadurch die Vagheit dieser Kante auszudrücken. Eine Beziehung zwischen zwei Knoten besteht also nur zu einem gewissen Grad. Dieser Grad, d.h. das Kantengewicht, wird üblicherweise in Prozent angegeben. AMT beinhaltet zusätzlich ein Verfahren, mit dem - unter Zuhilfenahme einer vordefinierten Ontologie - aus vorhandenen graphbasierten Daten automatisch Schlussfolgerungen gezogen werden können (Reasoning).
Ein Prototyp des AMTs steht als Web-Anwendung zur Verfügung. In einer entwickelten Test-Ontologie können Personen und Interessen und deren Beziehungen in einem gewissen Grad zueinander modelliert werden. Diese Ontologie beinhaltet neben den genannten beiden Klassen fünf weitere Relationen, so dass Personen und Interessen miteinander in Beziehung gesetzt werden können. Außerdem enthält die Ontologie 11 Axiome, um automatisiert aus bestehenden Beziehungen neue Beziehungen generieren zu können.
Ein Testfall, basierend auf der zuvor beschriebenen Ontologie, bildet die Beziehungen innerhalb des mainzed (Mainzer Zentrum für Digitalität in den Geistes- und Kulturwissenschaften) Netzwerks ab. Die Mitglieder des Netzwerks stehen in fachlichen bzw. hierarchischen Beziehungen zueinander. Zudem haben die Mitglieder des heterogenen Netzwerks viele unterschiedliche Interessen, die wiederum in einer SKOS-ähnlichen Taxonomie modellierbar sind. In einer Community Aktion haben die Mitglieder jeweils ihre Sicht auf das Netzwerk dargestellt, das heißt Beziehungen und Interessen, sowie deren Vagheitsgrad angegeben. AMT kann nun dieses Netz visualisieren und weitere Schlussfolgerungen ziehen, das heißt neue Beziehungen aufzeigen, welche bei der Eingabe nicht erkennbar waren, so dass dies einen Mehrwert für das Netzwerk bildet.
Der beschriebene Testfall lässt sich auch auf andere Datengrundlagen, wie historische Personen-Netzwerke, etc. extrapolieren. Außerdem lassen sich statt der Personen-Interessen-Ontologie auch beliebige andere Ontologien verwenden, die in das AMT-Schema passen. Mit AMT präsentieren wir somit ein generisches Tool, das Unsicherheiten und Vagheiten in bestehenden Forschungsdaten darstellen und neue Folgerungen daraus schließen kann."""@de ;
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    dct:title "Academic Meta Tool – Ein Web-Tool zur Modellierung des Zweifels"@de ;
    dct:type sqpt:presentation ;
    bibo:abstract """Graphdatenbanken und Triple Stores stellen bei der Modellierung von Forschungsdaten eine Alternative zu relationalen Datenbanken dar. Eine Modellierung der Daten in einem Netzwerk, anstatt in Tabellenstrukturen, eignet sie sich hervorragend um Daten interoperabel zur Verfügung zu stellen, indem man zwei Knoten aus verschiedenen Datenbanken miteinander über eine Kante verbindet. Dieser Technik bedienen sich viele Forschungsprojekte durch die Verwendung von Linked Open Data (LOD).
Sowohl gängige Graphdatenbanken als auch gebräuchliche Triple Stores bieten jedoch keine Möglichkeit Unsicherheiten oder Vagheiten zu modellieren. Um dieser Unzulänglichkeit Rechnung zu tragen, haben wir das Academic Meta Tool (AMT) entwickelt. AMT bietet die Möglichkeit sämtlichen Kanten eine Gewichtung hinzuzufügen, um dadurch die Vagheit dieser Kante auszudrücken. Eine Beziehung zwischen zwei Knoten besteht also nur zu einem gewissen Grad. Dieser Grad, d.h. das Kantengewicht, wird üblicherweise in Prozent angegeben. AMT beinhaltet zusätzlich ein Verfahren, mit dem - unter Zuhilfenahme einer vordefinierten Ontologie - aus vorhandenen graphbasierten Daten automatisch Schlussfolgerungen gezogen werden können (Reasoning).
Ein Prototyp des AMTs steht als Web-Anwendung zur Verfügung. In einer entwickelten Test-Ontologie können Personen und Interessen und deren Beziehungen in einem gewissen Grad zueinander modelliert werden. Diese Ontologie beinhaltet neben den genannten beiden Klassen fünf weitere Relationen, so dass Personen und Interessen miteinander in Beziehung gesetzt werden können. Außerdem enthält die Ontologie 11 Axiome, um automatisiert aus bestehenden Beziehungen neue Beziehungen generieren zu können.
Ein Testfall, basierend auf der zuvor beschriebenen Ontologie, bildet die Beziehungen innerhalb des mainzed (Mainzer Zentrum für Digitalität in den Geistes- und Kulturwissenschaften) Netzwerks ab. Die Mitglieder des Netzwerks stehen in fachlichen bzw. hierarchischen Beziehungen zueinander. Zudem haben die Mitglieder des heterogenen Netzwerks viele unterschiedliche Interessen, die wiederum in einer SKOS-ähnlichen Taxonomie modellierbar sind. In einer Community Aktion haben die Mitglieder jeweils ihre Sicht auf das Netzwerk dargestellt, das heißt Beziehungen und Interessen, sowie deren Vagheitsgrad angegeben. AMT kann nun dieses Netz visualisieren und weitere Schlussfolgerungen ziehen, das heißt neue Beziehungen aufzeigen, welche bei der Eingabe nicht erkennbar waren, so dass dies einen Mehrwert für das Netzwerk bildet.
Der beschriebene Testfall lässt sich auch auf andere Datengrundlagen, wie historische Personen-Netzwerke, etc. extrapolieren. Außerdem lassen sich statt der Personen-Interessen-Ontologie auch beliebige andere Ontologien verwenden, die in das AMT-Schema passen. Mit AMT präsentieren wir somit ein generisches Tool, das Unsicherheiten und Vagheiten in bestehenden Forschungsdaten darstellen und neue Folgerungen daraus schließen kann."""@de ;
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    dct:description """In der Archäologie wurde bereits seit mehr als 120 Jahren damit begonnen, die römische Keramikgattung Terra Sigillata in Einzelformen zu klassifizieren. Jedoch spielen Unsicherheiten und Zweifel bei der Bestimmung von dieser europaweit überaus häufig vorkommenden Manufakturware eine herausragende Rolle.
Wie beim modernen Porzellan gibt es bei Terra Sigillata standardisierte "Service", d.h. verschiedene Formen (Teller, Näpfe, Schalen) gehören durch ähnlich gestaltete Ränder oder Standringe zur gleichen "Service-Familie". Da oft nur Bruchstücke von einem Rand oder einem Standring gefunden werden, kann in solchen Fällen höchstens eine Zuweisung zu einer oder mehreren Service-Familien erfolgen. Eine solche Unsicherheit in der Zuweisung gilt es zu modellieren.
Darüber hinaus geben die mit Namen versehenen Töpferstempel Auskunft darüber, wer die einzelnen Gefäße hergestellt hat. Die Bestimmung dieser Töpferstempel ist für die Chronologie dieser Ware von großer Bedeutung, da z.B. abgebrochene oder beschädigte Stempel von jüngerem Datum sein dürften als noch vollständig erhaltene Namensstempel. Da die Stempel oft von Hand und in verschiedenen Winkeln in den noch weichen Gefäßboden eingestempelt wurden, sind sie somit oft auf dem ersten Blick nicht als identisch erkennbar. Hier ist das Kriterium der Vagheit das zu modellierende Model.
Der Glücksfall, dass in einer am Römisch-Germanischen Zentralmuseum Mainz (RGZM) entwickelten Datenbank 250'000 gestempelte Gefäße des römischen Tafelgeschirrs Terra Sigillata in einem relationalen Datenmodell erfasst und über das Web verfügbar[1] sind, ermöglicht das Ausloten der Grenzen der relationalen Datenmodellierung im Kontext der „Modellierung des Zweifels“ an Hand von realen Beispielen.
Graphbasierte semantische Datenmodellierung bietet eine weitere interoperable und skalierbare Möglichkeit zur Lösung der Modellierung von Zweifel und Unsicherheiten bei der Zuweisung von Stempel und "Service-Familien".
Die Präsentation zeigt Lösungsstrategien mittels einfachem OWL-Reasoning zur Modellierung des Zweifels in der RGZM Terra Sigillata Datenbank in Realtime-Beispielen.
[1] http://rgzm.de/samian"""@de ;
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    dct:title "Putting Samian pots together – modelling ceramic service family roots – connecting figure types. Wie Graphen bei der Modellierung des Zweifels helfen können."@de ;
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    bibo:abstract """In der Archäologie wurde bereits seit mehr als 120 Jahren damit begonnen, die römische Keramikgattung Terra Sigillata in Einzelformen zu klassifizieren. Jedoch spielen Unsicherheiten und Zweifel bei der Bestimmung von dieser europaweit überaus häufig vorkommenden Manufakturware eine herausragende Rolle.
Wie beim modernen Porzellan gibt es bei Terra Sigillata standardisierte "Service", d.h. verschiedene Formen (Teller, Näpfe, Schalen) gehören durch ähnlich gestaltete Ränder oder Standringe zur gleichen "Service-Familie". Da oft nur Bruchstücke von einem Rand oder einem Standring gefunden werden, kann in solchen Fällen höchstens eine Zuweisung zu einer oder mehreren Service-Familien erfolgen. Eine solche Unsicherheit in der Zuweisung gilt es zu modellieren.
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Der Glücksfall, dass in einer am Römisch-Germanischen Zentralmuseum Mainz (RGZM) entwickelten Datenbank 250'000 gestempelte Gefäße des römischen Tafelgeschirrs Terra Sigillata in einem relationalen Datenmodell erfasst und über das Web verfügbar[1] sind, ermöglicht das Ausloten der Grenzen der relationalen Datenmodellierung im Kontext der „Modellierung des Zweifels“ an Hand von realen Beispielen.
Graphbasierte semantische Datenmodellierung bietet eine weitere interoperable und skalierbare Möglichkeit zur Lösung der Modellierung von Zweifel und Unsicherheiten bei der Zuweisung von Stempel und "Service-Familien".
Die Präsentation zeigt Lösungsstrategien mittels einfachem OWL-Reasoning zur Modellierung des Zweifels in der RGZM Terra Sigillata Datenbank in Realtime-Beispielen.
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(2) Trying to map pot fragments of Gaulish Terra Sigillata to historically defined concepts of “types” and “service families”, or aligning these typologies, ends up in modelling doubtful assumptions. Can metrics be used to define “type” definitions? How can an already existing online research community help?
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    bibo:abstract """The Linked Data Cloud is full of controlled resources, which in fact quickly run out of control. Firstly, each resource collection, e.g. a thesaurus, is cooking its own soup related to its research context. Secondly, conceptualisation of LOD assumes standardised data, but in reality, only generic concepts or real instances exist. Thirdly, archaeological items are usually related to generic instances in the LOD cloud, based on their object orientated nature. Describing these relations by modelling archaeological assumptions causes ambiguities which have to be tamed to guarantee data quality for reuse, demonstrated in three examples:
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(3) Setting up a meta-index for aligning various distributed databases at the RGZM by keywords and linking into the LOD Cloud is subject to uncertainties within the modelling of relations. As a result, this process includes dealing with the above mentioned ambiguity challenges."""@en ;
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In particular the Samian Research database at the RGZM offers nearly 250’000 identified potter stamps, which are traditionally dated in a short cutted way. In Roman archaeology this is usually expressed by establishing “absolute dates” in well known “from-to” tables, whereas in reality, the situation is much more diffuse. In fact, Limes fortress dating is often done based on circular arguments. Samian stamps, for example, are being dated by sites, which on their own are again being dated by Samian.
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    dct:description "Die Modellierung von Daten in relationalen Strukturen ist uns als Geodäten all gegenwärtig, z.B. nutzen wir PostGIS und Geoserver zum Teilen von Geodaten über das World Wide Web. In modernen Web Apps sind heute jedoch NoSQL Datenbanken weit verbreitet, welche zum Beispiel dokumentenorientiert (z.B. MongoDB) oder graphbasiert (z.B. Neo4j) sein können. Kantengewichtete Graphen sind in der Geoinformatik insbesondere im Bereich der Navigationssysteme durch den Dijkstra-Algorithmus bekannt. Zur interoperablen und semantischen Datenmodellierung sind aber auch gerichtete kantengefärbte Graphen nach den Prinzipien der Linked Data in RDF-Strukturen verbreitet (z.B. GeoSPARQL, Wikidata, LinkedGeoData). Eine besondere Herausforderung ist hierbei die Modellierung von Unsicherheit/Vagheit im Graphen und einem daraus resultierenden Reasoning. Der Vortrag gibt einen Einblick in das Academic Meta Tool, ein Web Tool zur Modellierung + Reasoning von Vagheit im Graphen am Beispiel des KonGeoS-Netzwerks."@de ;
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Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data."""@en ;
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Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.
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Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.
Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.
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fix licence in CFF file""" ;
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Celtic Inscribed Stones Project (CISP)
Ogham in 3D Project
Corpus inscriptionum insularum Celticarum (1945)
The data is divided into
Geospatial Data
Ogham Data
The data belongs to the Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe project. This project is funded by Wikimedia Deutschland and the Open Science Fellows Program.
# Change Log
fix turtle files
split geospatial turtle files"""@en ;
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Celtic Inscribed Stones Project (CISP)
Ogham in 3D Project
Corpus inscriptionum insularum Celticarum (1945)
The data is divided into
Geospatial Data
Ogham Data
The data belongs to the Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe project. This project is funded by Wikimedia Deutschland and the Open Science Fellows Program.
# Change Log
fix turtle files
split geospatial turtle files"""@en ;
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Ogham in 3D Project
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Celtic Inscribed Stones Project (CISP)
Ogham in 3D Project
Corpus inscriptionum insularum Celticarum (1945)
The data is divided into
Geospatial Data
Ogham Data
The data belongs to the Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe project. This project is funded by Wikimedia Deutschland and the Open Science Fellows Program."""@en ;
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Celtic Inscribed Stones Project (CISP)
Ogham in 3D Project
Corpus inscriptionum insularum Celticarum (1945)
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Geospatial Data
Ogham Data
The data belongs to the Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe project. This project is funded by Wikimedia Deutschland and the Open Science Fellows Program.
# Change Log
fix turtle files
split geospatial turtle files"""@en ;
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Celtic Inscribed Stones Project (CISP)
Ogham in 3D Project
Corpus inscriptionum insularum Celticarum (1945)
The data is divided into
Geospatial Data
Ogham Data
The data belongs to the Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe project. This project is funded by Wikimedia Deutschland and the Open Science Fellows Program.
# Change Log
fix turtle files
split geospatial turtle files"""@en ;
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Thiery, F., Homburg, T., Klammt, A. & Schmidt, S. C. (2022). Software Review for the Software PointSamplingTool. Archäologische Informationen 44, Early View, published online 18 Nov 2022. [PDF]""" ;
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Thiery, F., Homburg, T., Klammt, A. & Schmidt, S. C. (2022). Software Review for the Software PointSamplingTool. Archäologische Informationen 44, Early View, published online 18 Nov 2022. [PDF]""" ;
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    dct:description "Mit dem Fellow-Programm Freies  Wissen unterstützte Wikimedia Deutschland e.  V. – gemeinsam mit verschiedenen Partner*innen – Wissenschaftler*innen aus Deutschland, Österreich und der Schweiz über fünf Jahre dabei, ihre eigene Forschungsarbeit zu öffnen und damit zu Freiem Wissen beizutragen. Durch Qualifizierungsangebote, Mentoring, finanzielle Unterstützung und Vernetzung mit anderen Aktiven im Bereich Offene Wissenschaft konnten insgesamt 90 Fellows zu Praktiker*innen und Botschafter*innen für Freies Wissen ausgebildet werden. Die Fellows konnten durch die Teilnahme am Programm ihre Kenntnisse über Theorie und Praxis Offener Wissenschaft maßgeblich erweitern, ihr wissenschaftliches Umfeld dahingehend sensibilisieren und ihr  Wissen weitergeben. Aus der Praxis-Community, die sich aus und um das Programm herum gebildet hat, gingen zahlreiche Initiativen zur Förderung Freien  Wissens in der Wissenschaft hervor und es konnten einzelne Veränderungen zu Gunsten von Offener  Wissenschaft auf institutioneller Ebene erreicht werden."@de ;
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    dct:description """In historical maps, the phrase “Hic sunt dracones” (here be dragons) is used to describe areas which were unknown to the map creator. Today the WWW offers researchers the possibility of sharing their research (data) and enables the community to participate in the scientific discourse and create new knowledge. But much of this shared data is not findable or accessible, thus resulting in modern ‘unknown data dragons’. Often these ‘data dragons’ lack connections to other datasets, i. e. they are not interoperable, and in some cases also lack usability. To overcome these shortcomings, Linked Open Data (LOD) techniques can be used (Hyland et al., 2013). In 2006 Berners-Lee introduced the concept of LOD (Berners-Lee, 2006), in 2018 Sanderson instigated the “Usable” aspect at EuropeanaTech (Sanderson, 2018). Additionally, in 2016 the FAIR principles were introduced: Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016).
The Semantic Web offers a variety of vocabularies, ontologies and reference models that can be used for archaeology-related LOD modelling: CIDOC-CRM, SKOS, PROV-O, FOAF, GeoSPARQL, Wikidata, etc. The Linked Data Cloud already provides FAIR and LOUD research data repositories, data hubs and domain-specific ontologies for specific archaeological and humanities domains such as: Nomisma, Kerameikos, Pelagios, OpenContext, Portable Antiquities Scheme, ARIADNE, Linked Open Samian Ware, Linked Open ARS, Linked Open Ogham, and the Ceramic Typologies Ontology. Beyond them, many other networks for graph modelling in the digital humanities, such as the Pelagios Network, Linked Pasts, Graph Technologies / Graphs and Networks in the Humanities offer methods and resources that could be used and further developed for digital archaeological research.
The development of ever more repositories poses challenges in handling the complex facets of data quality and completeness. This is especially true for archaeological data, which are based on complex networks of concepts from different domains and linguistic backgrounds. Moreover, it is necessary to include means of assessing uncertainty in the data models to produce and publish transparent FAIR and LOUD data that can also describe specific stratigraphy or the (archaeological) context of objects.
To enable non-experts to engage with FAIR and LOUD data, research tools – little minions – were created for different purposes, such as modelling relative chronologies in RDF (e.g. Alligator), modelling and reasoning on vague edges in graph data (e.g. Academic Meta Tool), creating annotated texts and images (e.g. Recogito, Annotorius), and sparql, as well as enhancing Geo-Datasets using the SPARQLing Unicorn QGIS Plugin. In addition, community-driven knowledge bases like Wikidata not only offer data, but also provide a number of tools for using and interacting.
The positive feedback on the LOD sessions on data quality, FAIR and LOUD at CAA 2017-2021 encourages the pursuit of the debate. The goal of our online session is to bring together both experts and colleagues interested in learning about FAIR and LOUD data-driven publishing and applications, as well as to collect research application scenarios to jointly promote research domain specific solutions. We would like to discuss application-oriented and data-driven investigations into how to improve technologies for FAIR and LOUD data models as a basis for reproducible and CAREful research and exchange in the Semantic Web, as well as solutions related to one or more of the issues listed below:
• application of semantic web technologies, such as ontologies (e.g. CIDOC-CRM) or RDF, to the archaeological domain modelling of archaeological artefacts, archaeological context, including the specificity of stratigraphy, uncertainty, and vagueness
• development of research tools producing or using FAIR and LOUD data
• identifying sources and dangers of incorrect or ambiguous LOD, e. g. duplicates across different LOD sources
• keeping track of the provenance of data as a means of solving errors and identifying their source
• setting up research-question based methodologies and tools in order to label or assess datasets based on their quality
• dealing with ambiguities resulting from multiple links in the LOD cloud
• computer vision or machine learning applications built upon controlled, semantic data
• modelling comprehensible / reproducible workflows and data flows as “Linked Pipes” using RDF for documentation and reproducible research (Thiery and Homburg, 2021).
• use of Linked Open Data related tools in archaeological research, their implementation and/or enhancement
• possibilities, challenges, benefits and risks of the Wikimedia Universe in archaeological research
• implementation of reference models such as CIDOC-CRM in real-world datasets and ways to achieve LOD
• graphs of facts, beliefs, and/or assertions as a digital archaeological method
• reasoning with heterogeneous and real world archaeological data in graphs
• granularity in LOD/graphs/networks
• graph and RDF representation of specific networks of persons, objects and information relating to research questions
• interacting with graphs and graph interaction design
• LOUD techniques as a solution for information and data annotation on objects / artefacts in 2D and 3D (e.g. cuneiform tablets, ogham stones, samian ware, books, texts, …)
• semantically modelling geospatial data FAIR and LOUD
• implementation of GeoSPARQL as a geospatial standard in archaeological data
• things as a concept, such as places (e.g. Pleiades Place/Location), persons (e.g. “potters” as Actors) and events in archaeological LOD
• overcoming linguistic barriers and increasing accessibility through LOD
• implementing the CARE-principles through thoughtful LOD application
• development of educational or Open Educational resources (OERs) to increase use of LOD
We encourage presenters to derive the problems addressed from real-world datasets and to formulate proposals for solutions, preferably demonstrating (prototypes of) realised data-driven (web-) applications. Due to the thematic relevance, we target a broad and diverse audience and the challenges described should also be integrated into an archaeological context (excavation, museum, archive, etc.). Only those papers will be taken into consideration which offer the data and tools involved as FAIR data and Open Source tools in Open Science repositories (e.g. Zenodo, OSF, GitHub, GiRetLab). Exceptions to this principle (e.g. dissertation in course) should be explained.
This session is organised by the CAA SIG on Semantics and LOUD in Archaeology (SIG Data-Dragon). The core aim of this SIG is to use the SIG format to raise awareness for Linked Data in archaeology by creating a friendly and open platform to discuss and further develop semantics, and LOUD and FAIR data in archaeology.
References
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18.
Berners-Lee, T. (2006). Linked Data. URL: https://www.w3.org/DesignIssues/LinkedData.html.
Thiery, F., Homburg, T. (2021). Linked Pipes @ Linked Pasts 7: Introduction. Linked Pasts VII, Ghent, Belgium. https://doi.org/10.5281/zenodo.5781275.
Hyland, B., Atemezing, G., Pendleton, M., Srivastava, B. (2013). Linked Data Glossary, W3C Working Group Note 27 June 2013. URL: https://www.w3.org/TR/ld-glossary/.
Sanderson, R. (2018). Shout it Out: LOUD by Rob Sanderson, EuropeanaTech Conference 2018. URL: https://de.slideshare.net/Europeana/shout-it-out-loud-by-rob-sanderson-europeanatech-conference-2018-98225909."""@en ;
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    dct:title "Hic sunt dracones - Real-world data-driven knowledge modelling resulting in Semantics and FAIR-LOD based tools and workflows"@en ;
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    bibo:abstract """In historical maps, the phrase “Hic sunt dracones” (here be dragons) is used to describe areas which were unknown to the map creator. Today the WWW offers researchers the possibility of sharing their research (data) and enables the community to participate in the scientific discourse and create new knowledge. But much of this shared data is not findable or accessible, thus resulting in modern ‘unknown data dragons’. Often these ‘data dragons’ lack connections to other datasets, i. e. they are not interoperable, and in some cases also lack usability. To overcome these shortcomings, Linked Open Data (LOD) techniques can be used (Hyland et al., 2013). In 2006 Berners-Lee introduced the concept of LOD (Berners-Lee, 2006), in 2018 Sanderson instigated the “Usable” aspect at EuropeanaTech (Sanderson, 2018). Additionally, in 2016 the FAIR principles were introduced: Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016).
The Semantic Web offers a variety of vocabularies, ontologies and reference models that can be used for archaeology-related LOD modelling: CIDOC-CRM, SKOS, PROV-O, FOAF, GeoSPARQL, Wikidata, etc. The Linked Data Cloud already provides FAIR and LOUD research data repositories, data hubs and domain-specific ontologies for specific archaeological and humanities domains such as: Nomisma, Kerameikos, Pelagios, OpenContext, Portable Antiquities Scheme, ARIADNE, Linked Open Samian Ware, Linked Open ARS, Linked Open Ogham, and the Ceramic Typologies Ontology. Beyond them, many other networks for graph modelling in the digital humanities, such as the Pelagios Network, Linked Pasts, Graph Technologies / Graphs and Networks in the Humanities offer methods and resources that could be used and further developed for digital archaeological research.
The development of ever more repositories poses challenges in handling the complex facets of data quality and completeness. This is especially true for archaeological data, which are based on complex networks of concepts from different domains and linguistic backgrounds. Moreover, it is necessary to include means of assessing uncertainty in the data models to produce and publish transparent FAIR and LOUD data that can also describe specific stratigraphy or the (archaeological) context of objects.
To enable non-experts to engage with FAIR and LOUD data, research tools – little minions – were created for different purposes, such as modelling relative chronologies in RDF (e.g. Alligator), modelling and reasoning on vague edges in graph data (e.g. Academic Meta Tool), creating annotated texts and images (e.g. Recogito, Annotorius), and sparql, as well as enhancing Geo-Datasets using the SPARQLing Unicorn QGIS Plugin. In addition, community-driven knowledge bases like Wikidata not only offer data, but also provide a number of tools for using and interacting.
The positive feedback on the LOD sessions on data quality, FAIR and LOUD at CAA 2017-2021 encourages the pursuit of the debate. The goal of our online session is to bring together both experts and colleagues interested in learning about FAIR and LOUD data-driven publishing and applications, as well as to collect research application scenarios to jointly promote research domain specific solutions. We would like to discuss application-oriented and data-driven investigations into how to improve technologies for FAIR and LOUD data models as a basis for reproducible and CAREful research and exchange in the Semantic Web, as well as solutions related to one or more of the issues listed below:
• application of semantic web technologies, such as ontologies (e.g. CIDOC-CRM) or RDF, to the archaeological domain modelling of archaeological artefacts, archaeological context, including the specificity of stratigraphy, uncertainty, and vagueness
• development of research tools producing or using FAIR and LOUD data
• identifying sources and dangers of incorrect or ambiguous LOD, e. g. duplicates across different LOD sources
• keeping track of the provenance of data as a means of solving errors and identifying their source
• setting up research-question based methodologies and tools in order to label or assess datasets based on their quality
• dealing with ambiguities resulting from multiple links in the LOD cloud
• computer vision or machine learning applications built upon controlled, semantic data
• modelling comprehensible / reproducible workflows and data flows as “Linked Pipes” using RDF for documentation and reproducible research (Thiery and Homburg, 2021).
• use of Linked Open Data related tools in archaeological research, their implementation and/or enhancement
• possibilities, challenges, benefits and risks of the Wikimedia Universe in archaeological research
• implementation of reference models such as CIDOC-CRM in real-world datasets and ways to achieve LOD
• graphs of facts, beliefs, and/or assertions as a digital archaeological method
• reasoning with heterogeneous and real world archaeological data in graphs
• granularity in LOD/graphs/networks
• graph and RDF representation of specific networks of persons, objects and information relating to research questions
• interacting with graphs and graph interaction design
• LOUD techniques as a solution for information and data annotation on objects / artefacts in 2D and 3D (e.g. cuneiform tablets, ogham stones, samian ware, books, texts, …)
• semantically modelling geospatial data FAIR and LOUD
• implementation of GeoSPARQL as a geospatial standard in archaeological data
• things as a concept, such as places (e.g. Pleiades Place/Location), persons (e.g. “potters” as Actors) and events in archaeological LOD
• overcoming linguistic barriers and increasing accessibility through LOD
• implementing the CARE-principles through thoughtful LOD application
• development of educational or Open Educational resources (OERs) to increase use of LOD
We encourage presenters to derive the problems addressed from real-world datasets and to formulate proposals for solutions, preferably demonstrating (prototypes of) realised data-driven (web-) applications. Due to the thematic relevance, we target a broad and diverse audience and the challenges described should also be integrated into an archaeological context (excavation, museum, archive, etc.). Only those papers will be taken into consideration which offer the data and tools involved as FAIR data and Open Source tools in Open Science repositories (e.g. Zenodo, OSF, GitHub, GiRetLab). Exceptions to this principle (e.g. dissertation in course) should be explained.
This session is organised by the CAA SIG on Semantics and LOUD in Archaeology (SIG Data-Dragon). The core aim of this SIG is to use the SIG format to raise awareness for Linked Data in archaeology by creating a friendly and open platform to discuss and further develop semantics, and LOUD and FAIR data in archaeology.
References
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18.
Berners-Lee, T. (2006). Linked Data. URL: https://www.w3.org/DesignIssues/LinkedData.html.
Thiery, F., Homburg, T. (2021). Linked Pipes @ Linked Pasts 7: Introduction. Linked Pasts VII, Ghent, Belgium. https://doi.org/10.5281/zenodo.5781275.
Hyland, B., Atemezing, G., Pendleton, M., Srivastava, B. (2013). Linked Data Glossary, W3C Working Group Note 27 June 2013. URL: https://www.w3.org/TR/ld-glossary/.
Sanderson, R. (2018). Shout it Out: LOUD by Rob Sanderson, EuropeanaTech Conference 2018. URL: https://de.slideshare.net/Europeana/shout-it-out-loud-by-rob-sanderson-europeanatech-conference-2018-98225909."""@en ;
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    dct:description """Panel: From wiki projects to OpenStreetMap, collaborative approaches to open data creation: problems, case studies, territorial and cultural impact.
The RGZM curates the Samian Research database, a treasure-house of economic data on Roman trade and Terra Sigillata industry. Over six decades, a broad European user community of established research institutions, citizen scientists and domain-specific scientists has assembled a dataset of ~250’000 potter’s stamps from the Samian Research Database, accessible with findspots and relevant bibliography as Linked Open Samian Ware (LOSW) via a collaborative LOD hub, archaeology.link.
In 2020-2021, Samian Research began a process of integrating its data with Wikidata through the creation of a set of Samian Ware Wikidata items, including 3875 Samian Ware Discovery Sites, 103 Samian Ware Kiln Sites and 13 Kiln Regions, with accurate or approximate geospatial information and a backlink to archaeology.link.
This approach, of creating special-purpose Wikidata items, is an efficient way to map the huge geographic reach of our subject, and to call attention to many European archaeological sites and excavations that hitherto lacked a Wikidata identifier. It fell short of truly Linked Data in not offering direct access to the database API or to the bibliographic data that would allow Samian Ware sites to be merged or associated with existing Wikidata items for archaeological sites and excavations.
The site of Corinth illustrates one obvious issue to be solved. Was it correct to merge our Corinth as Samian Ware discovery site with Archaeological Site of Ancient Corinth? An API call on the database reveals 690 potters’ stamps reported from Corinth and now in the Ancient Corinth Museum, most with no specific find spot, a number from the Sanctuary of Demeter and Kore or the South Stoa. To solve the issue, the broader Wikidata community must be enlisted.
Wikidata properties and items suffice for almost the full range of Roman ceramics data, reflecting a diverse and active community of users but also diverse implementations of data models. We would like to discuss the benefits and challenges of integrating communities. Knowledge exchange must be enabled, e.g. by bidirectional links in and to LOSW / Wikidata, using properties in Wikidata (currently P2888, exact match, which causes problems with multiple assignments); a solution to this can be the creation of an archaeology.link property with the help of the Wikidata community.
Samian Research is, compared to other domain-specific database projects, very open concerning data curating possibilities. But in a specialised domain, can community-validated data entries safely generate new knowledge? We note Wikidata Community projects such as Archaeology as an umbrella for communities and initiatives, e.g. Linked Open Samian Ware, African Red Slip Ware Digital and archaeology.link to address issues of sustainability and data consistency. A common understanding of data models and the data itself can be achieved via a WikiProject Page, where essential properties and vocabularies are described."""@en ;
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    bibo:abstract """Panel: From wiki projects to OpenStreetMap, collaborative approaches to open data creation: problems, case studies, territorial and cultural impact.
The RGZM curates the Samian Research database, a treasure-house of economic data on Roman trade and Terra Sigillata industry. Over six decades, a broad European user community of established research institutions, citizen scientists and domain-specific scientists has assembled a dataset of ~250’000 potter’s stamps from the Samian Research Database, accessible with findspots and relevant bibliography as Linked Open Samian Ware (LOSW) via a collaborative LOD hub, archaeology.link.
In 2020-2021, Samian Research began a process of integrating its data with Wikidata through the creation of a set of Samian Ware Wikidata items, including 3875 Samian Ware Discovery Sites, 103 Samian Ware Kiln Sites and 13 Kiln Regions, with accurate or approximate geospatial information and a backlink to archaeology.link.
This approach, of creating special-purpose Wikidata items, is an efficient way to map the huge geographic reach of our subject, and to call attention to many European archaeological sites and excavations that hitherto lacked a Wikidata identifier. It fell short of truly Linked Data in not offering direct access to the database API or to the bibliographic data that would allow Samian Ware sites to be merged or associated with existing Wikidata items for archaeological sites and excavations.
The site of Corinth illustrates one obvious issue to be solved. Was it correct to merge our Corinth as Samian Ware discovery site with Archaeological Site of Ancient Corinth? An API call on the database reveals 690 potters’ stamps reported from Corinth and now in the Ancient Corinth Museum, most with no specific find spot, a number from the Sanctuary of Demeter and Kore or the South Stoa. To solve the issue, the broader Wikidata community must be enlisted.
Wikidata properties and items suffice for almost the full range of Roman ceramics data, reflecting a diverse and active community of users but also diverse implementations of data models. We would like to discuss the benefits and challenges of integrating communities. Knowledge exchange must be enabled, e.g. by bidirectional links in and to LOSW / Wikidata, using properties in Wikidata (currently P2888, exact match, which causes problems with multiple assignments); a solution to this can be the creation of an archaeology.link property with the help of the Wikidata community.
Samian Research is, compared to other domain-specific database projects, very open concerning data curating possibilities. But in a specialised domain, can community-validated data entries safely generate new knowledge? We note Wikidata Community projects such as Archaeology as an umbrella for communities and initiatives, e.g. Linked Open Samian Ware, African Red Slip Ware Digital and archaeology.link to address issues of sustainability and data consistency. A common understanding of data models and the data itself can be achieved via a WikiProject Page, where essential properties and vocabularies are described."""@en ;
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The NAVIS Ship Databases 1-3 at the RGZM cover ancient ships (< AD 1200). The European Union funded the NAVIS projects, and its research community consisted of scientists from more than ten countries. NAVIS I (1996-1999) comprises shipwrecks, reconstructions and models, NAVIS II (1999-2001) focuses on ancient ship depictions on objects, and NAVIS III (2002) is related to ships on Roman coins. Moreover, the NAVIS ship databases enclose terms organised in controlled vocabularies, i.e. NAVIS I: thesauri of ship components and wreck-specific thesauri; NAVIS II: thesauri of ship components and their properties, objects keywords, dating, material as well as techniques; NAVIS III: thesauri of emperors, mints, coin types and ship features.
Designing a common web app causes challenges in merging three different individual MS Access data models focussing on wrecks, models, reconstructions and depictions of archaeological objects and artefacts, especially coins, in a common data model based on PostgreSQL. Furthermore, transforming, cleaning, validating and normalising data is a highly elaborate task. These refined data enable opportunities in Semantic Web applications, LOD
and community work, e.g. NFDI4Objects TRAILs. Publishing data in the LOD hub archaeology.link connects them within the LOD Cloud, e.g. by interlinking ARS in NAVIS ONE and in ARS3D as well as iDAI.objects and entities of the Wikidata Knowledge Graph; thesauri can be published via a hub for authority files and community-driven vocabularies: DANTE; Nomisma can be used to share coins as a domain-specific hub."""@en ;
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The NAVIS Ship Databases 1-3 at the RGZM cover ancient ships (< AD 1200). The European Union funded the NAVIS projects, and its research community consisted of scientists from more than ten countries. NAVIS I (1996-1999) comprises shipwrecks, reconstructions and models, NAVIS II (1999-2001) focuses on ancient ship depictions on objects, and NAVIS III (2002) is related to ships on Roman coins. Moreover, the NAVIS ship databases enclose terms organised in controlled vocabularies, i.e. NAVIS I: thesauri of ship components and wreck-specific thesauri; NAVIS II: thesauri of ship components and their properties, objects keywords, dating, material as well as techniques; NAVIS III: thesauri of emperors, mints, coin types and ship features.
Designing a common web app causes challenges in merging three different individual MS Access data models focussing on wrecks, models, reconstructions and depictions of archaeological objects and artefacts, especially coins, in a common data model based on PostgreSQL. Furthermore, transforming, cleaning, validating and normalising data is a highly elaborate task. These refined data enable opportunities in Semantic Web applications, LOD
and community work, e.g. NFDI4Objects TRAILs. Publishing data in the LOD hub archaeology.link connects them within the LOD Cloud, e.g. by interlinking ARS in NAVIS ONE and in ARS3D as well as iDAI.objects and entities of the Wikidata Knowledge Graph; thesauri can be published via a hub for authority files and community-driven vocabularies: DANTE; Nomisma can be used to share coins as a domain-specific hub."""@en ;
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    dct:description """Der Umgang mit Typologien in der archäologischen Museumsdokumentation erfordert zunächst Methoden, die auf archäologischen Überlegungen und geometrischen Vergleichen basieren, die zur eigentlichen Typologiebildung führen. Fortgeschrittene Methoden beinhalten den Einsatz von künstlicher Intelligenz wie Machine-, bzw. Deep-Learning oder Semantic Reasoning.
Vergleiche zwischen digitalisierten Features in automatisierten Verfahren können mit regelbasierten beschriebenen archäologischen Beschreibungen kombiniert werden. Dies erfordert eine semantische Modellierung, die beide Methoden kombiniert und zu schlüssigen Typologie-Einschätzungen führt. Diese, sowie Research Software wie ein “Comparison Viewer”, ermöglichen die Anwendung von AI-Methoden wie Semantic Reasoning.
Unterschiedliche, in verschiedenen europäischen Forschungstraditionen bereits entwickelte Typologien der gleichen Fundgattung müssen in einer international orientierten Erfassung mit Hilfe von semantischen Regeln miteinander verknüpft werden. Die Ceramic Typologies Ontology (CeraTyOnt) bietet die Möglichkeit, in der Sammlungsdokumentation die Bezüge zwischen den bestehenden unterschiedlichen Typologien mit Hilfe eines Viewers zu visualisieren und im Wissensmanagement durch die Anwendung von Semantic Reasoning Objekte verschiedener Typologien miteinander in Beziehung zu setzen.
Der Vortrag zeigt Überlegungen und Implementierungen aus dem BMBF geförderten Kooperationsprojekt “ARS3D”, die am RGZM zusammen mit dem i3mainz der Hochschule Mainz entwickelt wurden, sowie aus dem Projekt “CeraTyOnt” des SFG Handlungsfeldes “Semantic Modelling & Knowledge Graphs / Artificial Intelligence” des Arbeitsbereichs Wissenschaftliche IT, Digitale Plattformen und Tools am RGZM."""@de ;
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    bibo:abstract """Der Umgang mit Typologien in der archäologischen Museumsdokumentation erfordert zunächst Methoden, die auf archäologischen Überlegungen und geometrischen Vergleichen basieren, die zur eigentlichen Typologiebildung führen. Fortgeschrittene Methoden beinhalten den Einsatz von künstlicher Intelligenz wie Machine-, bzw. Deep-Learning oder Semantic Reasoning.
Vergleiche zwischen digitalisierten Features in automatisierten Verfahren können mit regelbasierten beschriebenen archäologischen Beschreibungen kombiniert werden. Dies erfordert eine semantische Modellierung, die beide Methoden kombiniert und zu schlüssigen Typologie-Einschätzungen führt. Diese, sowie Research Software wie ein “Comparison Viewer”, ermöglichen die Anwendung von AI-Methoden wie Semantic Reasoning.
Unterschiedliche, in verschiedenen europäischen Forschungstraditionen bereits entwickelte Typologien der gleichen Fundgattung müssen in einer international orientierten Erfassung mit Hilfe von semantischen Regeln miteinander verknüpft werden. Die Ceramic Typologies Ontology (CeraTyOnt) bietet die Möglichkeit, in der Sammlungsdokumentation die Bezüge zwischen den bestehenden unterschiedlichen Typologien mit Hilfe eines Viewers zu visualisieren und im Wissensmanagement durch die Anwendung von Semantic Reasoning Objekte verschiedener Typologien miteinander in Beziehung zu setzen.
Der Vortrag zeigt Überlegungen und Implementierungen aus dem BMBF geförderten Kooperationsprojekt “ARS3D”, die am RGZM zusammen mit dem i3mainz der Hochschule Mainz entwickelt wurden, sowie aus dem Projekt “CeraTyOnt” des SFG Handlungsfeldes “Semantic Modelling & Knowledge Graphs / Artificial Intelligence” des Arbeitsbereichs Wissenschaftliche IT, Digitale Plattformen und Tools am RGZM."""@de ;
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    dct:description """Das Potential und der Nutzen moderner 3D Messverfahren zur Digitalisierung archäologischer Objekte sind unbestritten. Entsprechend werden sie inzwischen aktiv in vielfältiger Weise zur Dokumentation eingesetzt. Infolge des breiten Merkmalsspektrums von Messverfahren gibt es dabei fallbezogen einen engen Zusammenhang zwischen Methodik und Eignung zur Bedienung bestimmter fachwissenschaftlicher Fragestellungen. Geht es um eine dem Original möglichst nahe kommende Digitalisierung sind entsprechend hohe Anforderungen der Methodik zur Erfassung von Geometrie und Erscheinungsbild zu erfüllen. Derart hochwertig erfasste Objekte haben das Potential, das Original in der digitalen archäologischen Forschung inhaltlich und geometrisch zu ergänzen und durch den digitalen Charakter in unbegrenzter und offenzugänglicher Form Fachanwendern im Web zur Verfügung zu stellen. Hierzu werden webbasierte Werkzeuge zur Interaktion und Visualisierung benötigt, die Anwender*innen bei Interpretationsarbeit unterstützen. Archäologische Forschung benötigt jedoch auch fachliche Informationen (z.B. Material, Fundort, Zeitperiode, Typologie- und Ikonografieverweise), die in den Interpretationsprozess einfließen. Entsprechend sollten diese nach aktuellen Standards in den Cultural Heritage (z.B. CIDOC CRM, Thesauri und Normdaten wie Getty AAT, IconClass, Wikidata, ChronOntology) erfasst und möglichst nach FAIR[1]- und Linked Open Data Prinzipien[2] verfügbar gemacht werden. Somit beinhaltet eine offene, nachvollziehbare und transparente Bereitstellung der Daten semantische Strukturierung archäologischer Fachinformationen, sowie die Verknüpfung und Modellierung von Beobachtungen und Interpretationen. Im Sinne einer nachhaltigen und übertragbaren Vorgehensweise sind dabei existierende Standards in der Cultural Heritage- und Semantic Web Domain, wie CIDOC-CRM[3] oder PROV-O[4], und neue community-driven Vorschläge (z.B. Ontologie zu 3D Metadaten) zu berücksichtigen.
All diese Aspekte waren Gegenstand des vom BMBF geförderten Forschungsprojektes "African Red Slip Ware digital", das sich mit einer speziellen Gattung römischer Keramik - African Red Slip Ware - befasste. Dieses Paper zeigt Ergebnisse und Best-Practices aus dem ASR3D-Projekt bezüglich der 3D-Messverfahren und Metadatenmodellierung[5], sowie der semantischen Fachdatenmodellierung[6], fachlichen Analyse[7] und Datenvisualisierung[8] und Datenbereitstellung[9].
Referenzen
[1] https://doi.org/10.1038/sdata.2016.18
[2] https://www.w3.org/DesignIssues/LinkedData.html
[3] https://cidoc-crm.org
[4] https://www.w3.org/TR/prov-o/
[5] https://doi.org/10.1186/s40494-021-00561-w
[6] https://doi.org/10.5281/zenodo.5642891
[7] https://doi.org/10.5281/zenodo.5646897
[8] http://ars3d.rgzm.de
[9] https://doi.org/10.5281/zenodo.5722941"""@de ;
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    dct:title "ARS3D: Close to the Original-Erfassung archäologischer Objekte und ihre webbasierte semantisch modellierte Bereitstellung zur fachwissenschaftlichen Analyse"@de ;
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    bibo:abstract """Das Potential und der Nutzen moderner 3D Messverfahren zur Digitalisierung archäologischer Objekte sind unbestritten. Entsprechend werden sie inzwischen aktiv in vielfältiger Weise zur Dokumentation eingesetzt. Infolge des breiten Merkmalsspektrums von Messverfahren gibt es dabei fallbezogen einen engen Zusammenhang zwischen Methodik und Eignung zur Bedienung bestimmter fachwissenschaftlicher Fragestellungen. Geht es um eine dem Original möglichst nahe kommende Digitalisierung sind entsprechend hohe Anforderungen der Methodik zur Erfassung von Geometrie und Erscheinungsbild zu erfüllen. Derart hochwertig erfasste Objekte haben das Potential, das Original in der digitalen archäologischen Forschung inhaltlich und geometrisch zu ergänzen und durch den digitalen Charakter in unbegrenzter und offenzugänglicher Form Fachanwendern im Web zur Verfügung zu stellen. Hierzu werden webbasierte Werkzeuge zur Interaktion und Visualisierung benötigt, die Anwender*innen bei Interpretationsarbeit unterstützen. Archäologische Forschung benötigt jedoch auch fachliche Informationen (z.B. Material, Fundort, Zeitperiode, Typologie- und Ikonografieverweise), die in den Interpretationsprozess einfließen. Entsprechend sollten diese nach aktuellen Standards in den Cultural Heritage (z.B. CIDOC CRM, Thesauri und Normdaten wie Getty AAT, IconClass, Wikidata, ChronOntology) erfasst und möglichst nach FAIR[1]- und Linked Open Data Prinzipien[2] verfügbar gemacht werden. Somit beinhaltet eine offene, nachvollziehbare und transparente Bereitstellung der Daten semantische Strukturierung archäologischer Fachinformationen, sowie die Verknüpfung und Modellierung von Beobachtungen und Interpretationen. Im Sinne einer nachhaltigen und übertragbaren Vorgehensweise sind dabei existierende Standards in der Cultural Heritage- und Semantic Web Domain, wie CIDOC-CRM[3] oder PROV-O[4], und neue community-driven Vorschläge (z.B. Ontologie zu 3D Metadaten) zu berücksichtigen.
All diese Aspekte waren Gegenstand des vom BMBF geförderten Forschungsprojektes "African Red Slip Ware digital", das sich mit einer speziellen Gattung römischer Keramik - African Red Slip Ware - befasste. Dieses Paper zeigt Ergebnisse und Best-Practices aus dem ASR3D-Projekt bezüglich der 3D-Messverfahren und Metadatenmodellierung[5], sowie der semantischen Fachdatenmodellierung[6], fachlichen Analyse[7] und Datenvisualisierung[8] und Datenbereitstellung[9].
Referenzen
[1] https://doi.org/10.1038/sdata.2016.18
[2] https://www.w3.org/DesignIssues/LinkedData.html
[3] https://cidoc-crm.org
[4] https://www.w3.org/TR/prov-o/
[5] https://doi.org/10.1186/s40494-021-00561-w
[6] https://doi.org/10.5281/zenodo.5642891
[7] https://doi.org/10.5281/zenodo.5646897
[8] http://ars3d.rgzm.de
[9] https://doi.org/10.5281/zenodo.5722941"""@de ;
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    dct:description """Strategies for graph-based knowledge modelling as Linked Open Data (LOD) of real world archaeological data comprising vague and/or uncertain information are widely lacking. Semantically modelled information related to fuzziness and wobbliness in numismatics and ceramic studies will allow for analysis based on reasoning of archaeological research data, eventually revealing imperfect inferences. Uniform modelling of uncertainties and vagueness in research data is extremely challenging. In the context of archaeological data these aspects can appear in any phase starting from the discovery of an object through each further processing step. It also might depend on context- and meta-information, such as who performed the steps, and in the case of archaeological objects it also depends to a great extent on the preservation of the object itself and its archaeological context. It should be noted that context is a friendly notation for an endless beast!
When focusing on modelling uncertainties and vagueness in RDF, we can find different approaches, for example special properties, refined statements, attribute assignments or blank scope nodes, which have been tested in best practices for numismatics (TWW15; SM14; TM18) and ceramics (TM21), recently by using the Academic Meta Tool (AMT) (UTM19;UT18; TU18). The jury is still out on which of these approaches would be the best, since each of them has different advantages and drawbacks. At least in the domain of archaeology there is no consensus, e.g. for CIDOC CRM this matter is still an open issue (CIDOC2019) and the same is true for Nomisma.org for example. This results in either non-homogeneous modelling even within the same domains, while transformation rules between modelling concepts for vagueness and uncertainty are missing. On top of this, uncertain and vague information may simply be removed from the publicly available data or even worse, is not even captured and stored at all.
This paper discusses and evaluates modelling approaches, challenges, and limits to fuzziness and wobbliness (e.g. uncertainty, vagueness, accuracy, and precision) in research data in the exemplary contexts of numismatics and ceramic research. Both areas are similar, but both follow their own intrinsic research aspects and both underly different use wear conditions (corrosion, fragileness). By comparing the two domains, we hope to have a broader approach in the modelling possibilities and their effects. The results should serve as a basis for answering the question as to which of the modelling approaches should be preferred and how a mapping between them could be performed. Via the CAA Special Interest Group (SIG) on Semantics and LOUD in Archaeology (SIG-DataDragon) [http://datadragon.link] it then can be evaluated and scaled to other subject domains.
Bibliography
CIDOC (2019). Issue 349: Belief Values. URL: http://www.cidoc-crm.org/Issue/ID-349-belief-values.
S. Metzger (2014). Modeling Uncertainty and Beliefs using Ontologies. Bachelor-Thesis, Goethe-University Frankfurt am Main, Department of Computer Science. URL: http://www.bigdata.uni-frankfurt.de/wp-content/uploads/2021/05/Modeling-Uncertainty-and-Beliefs-using-Ontologies_Melvin_S_Metzger.pdf.
F. Thiery, A. Mees (2021). Linked Open Samian Ware - AMT Perspective. DOI: 10.5281/zenodo.5415571.
F. Thiery, M. Unold (2018). Academic Meta Tool Ontology. URL: http://academic-meta-tool.xyz/ontology.
F. Thiery, A. Mees (2018). Taming Ambiguity - Dealing with doubts in archaeological datasets using LOD, Computer Applications and Quantitative Methods in Archaeology (CAA), Tübingen, Germany, 22nd March 2018. DOI: 10.5281/zenodo.1200111.
K. Tolle, D. Wigg-Wolf. (2015).  Uncertainty Handling for Ancient Coinage. In: F. Giligny, F. Djindjian, L. Costa, P. Moscati, and S. Robert, Eds., CAA2014. 21st Century Archaeology. Concepts, Methods and Tools. Proceedings of the 42nd Annual Conference on Computer Applications and Quantitative Methods in Archaeology., 171–78. Oxford: Archaeopress.
M. Unold, F. Thiery, A. Mees (2019). Academic Meta Tool – Ein Web-Tool zur Modellierung von Vagheit. In: A. Kuczera, T. Wübbena, and T. Kollatz, Eds., Die Modellierung des Zweifels – Schlüsselideen und -konzepte zur graphbasierten Modellierung von Unsicherheiten, Zeitschrift für digitale Geisteswissenschaften / Sonderbände, 4. DOI: 10.17175/sb004_004.
M. Unold, F. Thiery (2018). Academic Meta Tool JavaScript Library. DOI: 10.5281/zenodo.3992520."""@en ;
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    dct:title "How to handle vagueness and uncertainty in graph-based LOD knowledge modelling? Dealing with archaeological numismatic and ceramological real world data."@en ;
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    bibo:abstract """Strategies for graph-based knowledge modelling as Linked Open Data (LOD) of real world archaeological data comprising vague and/or uncertain information are widely lacking. Semantically modelled information related to fuzziness and wobbliness in numismatics and ceramic studies will allow for analysis based on reasoning of archaeological research data, eventually revealing imperfect inferences. Uniform modelling of uncertainties and vagueness in research data is extremely challenging. In the context of archaeological data these aspects can appear in any phase starting from the discovery of an object through each further processing step. It also might depend on context- and meta-information, such as who performed the steps, and in the case of archaeological objects it also depends to a great extent on the preservation of the object itself and its archaeological context. It should be noted that context is a friendly notation for an endless beast!
When focusing on modelling uncertainties and vagueness in RDF, we can find different approaches, for example special properties, refined statements, attribute assignments or blank scope nodes, which have been tested in best practices for numismatics (TWW15; SM14; TM18) and ceramics (TM21), recently by using the Academic Meta Tool (AMT) (UTM19;UT18; TU18). The jury is still out on which of these approaches would be the best, since each of them has different advantages and drawbacks. At least in the domain of archaeology there is no consensus, e.g. for CIDOC CRM this matter is still an open issue (CIDOC2019) and the same is true for Nomisma.org for example. This results in either non-homogeneous modelling even within the same domains, while transformation rules between modelling concepts for vagueness and uncertainty are missing. On top of this, uncertain and vague information may simply be removed from the publicly available data or even worse, is not even captured and stored at all.
This paper discusses and evaluates modelling approaches, challenges, and limits to fuzziness and wobbliness (e.g. uncertainty, vagueness, accuracy, and precision) in research data in the exemplary contexts of numismatics and ceramic research. Both areas are similar, but both follow their own intrinsic research aspects and both underly different use wear conditions (corrosion, fragileness). By comparing the two domains, we hope to have a broader approach in the modelling possibilities and their effects. The results should serve as a basis for answering the question as to which of the modelling approaches should be preferred and how a mapping between them could be performed. Via the CAA Special Interest Group (SIG) on Semantics and LOUD in Archaeology (SIG-DataDragon) [http://datadragon.link] it then can be evaluated and scaled to other subject domains.
Bibliography
CIDOC (2019). Issue 349: Belief Values. URL: http://www.cidoc-crm.org/Issue/ID-349-belief-values.
S. Metzger (2014). Modeling Uncertainty and Beliefs using Ontologies. Bachelor-Thesis, Goethe-University Frankfurt am Main, Department of Computer Science. URL: http://www.bigdata.uni-frankfurt.de/wp-content/uploads/2021/05/Modeling-Uncertainty-and-Beliefs-using-Ontologies_Melvin_S_Metzger.pdf.
F. Thiery, A. Mees (2021). Linked Open Samian Ware - AMT Perspective. DOI: 10.5281/zenodo.5415571.
F. Thiery, M. Unold (2018). Academic Meta Tool Ontology. URL: http://academic-meta-tool.xyz/ontology.
F. Thiery, A. Mees (2018). Taming Ambiguity - Dealing with doubts in archaeological datasets using LOD, Computer Applications and Quantitative Methods in Archaeology (CAA), Tübingen, Germany, 22nd March 2018. DOI: 10.5281/zenodo.1200111.
K. Tolle, D. Wigg-Wolf. (2015).  Uncertainty Handling for Ancient Coinage. In: F. Giligny, F. Djindjian, L. Costa, P. Moscati, and S. Robert, Eds., CAA2014. 21st Century Archaeology. Concepts, Methods and Tools. Proceedings of the 42nd Annual Conference on Computer Applications and Quantitative Methods in Archaeology., 171–78. Oxford: Archaeopress.
M. Unold, F. Thiery, A. Mees (2019). Academic Meta Tool – Ein Web-Tool zur Modellierung von Vagheit. In: A. Kuczera, T. Wübbena, and T. Kollatz, Eds., Die Modellierung des Zweifels – Schlüsselideen und -konzepte zur graphbasierten Modellierung von Unsicherheiten, Zeitschrift für digitale Geisteswissenschaften / Sonderbände, 4. DOI: 10.17175/sb004_004.
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    dct:description """Challenges and opportunities from real world archaeological datasets in the community-driven LOD ecosystem
Florian Thiery, Dr. Allard W. Mees, Dr. David G. Wigg-Wolf
Introduction
Building sustainable research communities around archaeological research subjects requires domain-specific common standards. By linking data to other communities using Linked Open Data (LOD) and so-called Community Hubs, reciprocal community development can take place. In order to attain such research community dynamics, data must be semantically modelled following the FAIR principles (e.g. using CIDOC CRM) and be part of the LOD ecosystem. This leads to the research question as to which methods can be used to enrich and/or contextualise data with community work.
Methods and Materials
Real-world archaeological datasets are usually evolutionary projects. In the case of Samian Ware / Terra Sigillata, incorporating new (ceramic) material groups from other periods or regions requires adapting data schemes. The same is the case with coins: including new  (coin) material from other periods or regions requires adapting the ontology and community standards in agile processes. Methodologically, when extending the material scopes of these domain-specific projects, the participation of a much larger community is necessary in order to use LOD. An approach that is either Citizen Science driven or based on Community Hubs is able to enhance properties and classes.
This paper is based on use cases comprising ceramics (ARS3D, Samian Research) and coins (AFE-RGK, NAVISone), as well as the iconographic / epigraphic elements involved (e.g. figure types, inscriptions), that demonstrate the challenges and opportunities of their semantic modelling. Within AFE-RGK, 16,957 coins are recorded, in ARS3D 325 African Red Slip Ware vessels describing 1173 man-made features (e.g. figure types). Samian Research comprises 252,342 (sic) recorded information carriers (potter’s stamps) and 8,425 actors as a concept (potters), NAVISOne contains 613 coins and 17 ARS objects.
Results and Discussion
Whereas enhancing the domain specific research aspects themselves is expected to remain largely within the specific research community, the results of these projects confirm that potentially the more general aspects of the data (geospatial information, chronology) can - on an LOD basis - indeed successfully be outsourced into a broader user community. Community standards for the semantic modelling of ceramics and coins, as well as iconographic / epigraphic elements such as LADO (Linked Archaeological Data Ontology as a common semantic data model for archaeological artefacts, e.g. ceramics) or NUDS (Numismatic Description Schema) can be a basis for publishing data in domain-specific hubs like DANTE, a data hub for authority data and terminologies, or archaeology.link.
Using Wikidata as a Community Hub, as shown in the WikiProjects Archaeology, Linked Open Samian Ware [1], or African Red Slip Ware digital [2], in which Wikidata entries are enriched with backlinks to the specific research domain resources, leads to new opportunities, but also challenges: (i) new Wikidata properties have to be created in order to lock backlinks to the home repository. This is carried out in sometimes unpredictable active discussions within the Wikidata community, e.g. ToposText place ID (P8068), Arachne entity ID (P10510) or Nomisma ID (P2950); (ii) the often volatile discussion which data is considered relevant or not is entirely in the hands of the Wikidata community; (iii) creating an import / export strategy to identify own resources, e.g. by using the properties “part of” (P361) or “instance of” (P31) for updating the home repository with Citizen Science data; (iv) creating common data modelling schemes for entities, such as findspots, production centres or potters, e.g. using the Wikidata:Cradle tool, and (v) creating and applying strategies for modelling of different point of views, e.g. coordinates derived from Geonames or real excavations/ surveys.
The domain-specific numismatic hub nomisma.org faces very different challenges, aiming as it does to extend its coverage to such a wide chronological, geographical and (inter)national range of sub-fields (Celtic, Mediaeval and Modern, Oriental, etc.)[3]. These sub-fields can have very different descriptive conventions and standards (e.g. NUDS), so that care must be taken to ensure that there is sufficient conformity across the broader discipline to achieve full interoperability, and contradictory solutions are not adopted. Overlap between sub-fields, particularly for periods and regions that underwent constant redefinition such as mediaeval and early modern Europe, can lead to conflicts of claims of (perceived) responsibility. Here the coordinating role of the steering committee is of paramount importance. On the other hand, the pre-existence of clearly defined semantic concepts in the form of nomisma PIDs greatly enhances the ability to incorporate citizen science driven data, as is demonstrated by the recent launch of the Celtic Coin Index Digital (CCID) which holds 72126 records.
References
[1] https://www.wikidata.org/wiki/Wikidata:WikiProject_Linked_Open_Samian_Ware
[2] https://www.wikidata.org/wiki/Wikidata:WikiProject_African_Red_Slip_Ware_Digital
[3] https://nomisma.hypotheses.org/nomisma-org"""@en ;
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    bibo:abstract """Challenges and opportunities from real world archaeological datasets in the community-driven LOD ecosystem
Florian Thiery, Dr. Allard W. Mees, Dr. David G. Wigg-Wolf
Introduction
Building sustainable research communities around archaeological research subjects requires domain-specific common standards. By linking data to other communities using Linked Open Data (LOD) and so-called Community Hubs, reciprocal community development can take place. In order to attain such research community dynamics, data must be semantically modelled following the FAIR principles (e.g. using CIDOC CRM) and be part of the LOD ecosystem. This leads to the research question as to which methods can be used to enrich and/or contextualise data with community work.
Methods and Materials
Real-world archaeological datasets are usually evolutionary projects. In the case of Samian Ware / Terra Sigillata, incorporating new (ceramic) material groups from other periods or regions requires adapting data schemes. The same is the case with coins: including new  (coin) material from other periods or regions requires adapting the ontology and community standards in agile processes. Methodologically, when extending the material scopes of these domain-specific projects, the participation of a much larger community is necessary in order to use LOD. An approach that is either Citizen Science driven or based on Community Hubs is able to enhance properties and classes.
This paper is based on use cases comprising ceramics (ARS3D, Samian Research) and coins (AFE-RGK, NAVISone), as well as the iconographic / epigraphic elements involved (e.g. figure types, inscriptions), that demonstrate the challenges and opportunities of their semantic modelling. Within AFE-RGK, 16,957 coins are recorded, in ARS3D 325 African Red Slip Ware vessels describing 1173 man-made features (e.g. figure types). Samian Research comprises 252,342 (sic) recorded information carriers (potter’s stamps) and 8,425 actors as a concept (potters), NAVISOne contains 613 coins and 17 ARS objects.
Results and Discussion
Whereas enhancing the domain specific research aspects themselves is expected to remain largely within the specific research community, the results of these projects confirm that potentially the more general aspects of the data (geospatial information, chronology) can - on an LOD basis - indeed successfully be outsourced into a broader user community. Community standards for the semantic modelling of ceramics and coins, as well as iconographic / epigraphic elements such as LADO (Linked Archaeological Data Ontology as a common semantic data model for archaeological artefacts, e.g. ceramics) or NUDS (Numismatic Description Schema) can be a basis for publishing data in domain-specific hubs like DANTE, a data hub for authority data and terminologies, or archaeology.link.
Using Wikidata as a Community Hub, as shown in the WikiProjects Archaeology, Linked Open Samian Ware [1], or African Red Slip Ware digital [2], in which Wikidata entries are enriched with backlinks to the specific research domain resources, leads to new opportunities, but also challenges: (i) new Wikidata properties have to be created in order to lock backlinks to the home repository. This is carried out in sometimes unpredictable active discussions within the Wikidata community, e.g. ToposText place ID (P8068), Arachne entity ID (P10510) or Nomisma ID (P2950); (ii) the often volatile discussion which data is considered relevant or not is entirely in the hands of the Wikidata community; (iii) creating an import / export strategy to identify own resources, e.g. by using the properties “part of” (P361) or “instance of” (P31) for updating the home repository with Citizen Science data; (iv) creating common data modelling schemes for entities, such as findspots, production centres or potters, e.g. using the Wikidata:Cradle tool, and (v) creating and applying strategies for modelling of different point of views, e.g. coordinates derived from Geonames or real excavations/ surveys.
The domain-specific numismatic hub nomisma.org faces very different challenges, aiming as it does to extend its coverage to such a wide chronological, geographical and (inter)national range of sub-fields (Celtic, Mediaeval and Modern, Oriental, etc.)[3]. These sub-fields can have very different descriptive conventions and standards (e.g. NUDS), so that care must be taken to ensure that there is sufficient conformity across the broader discipline to achieve full interoperability, and contradictory solutions are not adopted. Overlap between sub-fields, particularly for periods and regions that underwent constant redefinition such as mediaeval and early modern Europe, can lead to conflicts of claims of (perceived) responsibility. Here the coordinating role of the steering committee is of paramount importance. On the other hand, the pre-existence of clearly defined semantic concepts in the form of nomisma PIDs greatly enhances the ability to incorporate citizen science driven data, as is demonstrated by the recent launch of the Celtic Coin Index Digital (CCID) which holds 72126 records.
References
[1] https://www.wikidata.org/wiki/Wikidata:WikiProject_Linked_Open_Samian_Ware
[2] https://www.wikidata.org/wiki/Wikidata:WikiProject_African_Red_Slip_Ware_Digital
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    dct:description """Linked Open Time: Reproducible LOD-driven workflows and research tools for validating Roman Limes and Hadrian’s Wall relative time intervals based on Samian (Terra Sigillata)
Dr. Allard W. Mees, Florian Thiery, Sophie C. Schmidt
Introduction
The handling of relative time intervals, including vague and uncertain relations between them, is a major challenge in modelling chronologies in archaeology. Underlying dating mechanisms such as circular argumentations or dependencies on open end time intervals require tools for verification and visualisation of the underlying reasoning process. We are working with algorithms to combine relative time intervals with absolute dates by using quantitative parameters, resulting in a machine readable representation and a pipeline of tools using Semantic Web technologies to enable reproducible research. The consecutive phases of the Roman Limes have always been thought to be a corner-stone in establishing chronologies in archaeology. However, since there are only very few absolute dates attached to the Limes sections, many Limes parts are - if at all - only describable as relatively vague time intervals.
Methods and Materials
In order to tackle the issues mentioned above, we developed two methods which are built on one another: the Alligator, and the Academic Meta Tool (AMT) approaches. As their starting point, an *.agt file is used which includes the values name, xyz-coordinates, start / end dates (if available), as well as uncertainty information (e.g. a “fixed” absolute dates or floating endings). This file is generated from the correspondence analysis (ca) output (based on a CSV comprising Limes intervals, potters, number of occurrences) in which the quantitative relations of the euclidean distances between finds and proposed time intervals are calculated. Following the “horseshoe paradigm”, the ca result may provide a measure of (possible chronological) overlap between the Limes intervals. It is important to note that a ca does not start with any pre-known dating information. Therefore, the *.agt file contains dating metadata (e.g. start / end dates, uncertainty information) from a separate file. The Alligator approach starts with (i) a 3D distance calculation using the coordinates of the first 3 ca dimensions (xyz) in the *.agt file. Then the “Alligator Algorithm” will be applied by (ii) calculating undated wobbly floating periods (e.g. terminus post quem points, derived from a historical sources, or terminus ante quem points derived from dendrochronological dates) by finding the next 3D ca neighbour. Following this step (iii) the new virtual time intervals are calculated and the resulting “virtual fuzzy years” are generated based on Allen’s interval algebra. Any resulting “virtual fuzzy year” is then (iiii) transformed into relative intervals and stored in RDF format [1]. The AMT approach is applied by using the AMT library. It uses a specific AMT ontology for relative time intervals [2], in which temporal reasoning can be applied.
Each relation contains a degree of connection that is based on the rules of vagueness. In our use case, the estimated Allen intervals and the potters’ overlap of the Limes parts is (j) described with the (normalised) Pearson correlation coefficient. In the case of the AMT approach, we (jj) have to transform the intervals into the AMT format and include the degree of connection as a (normalised) Pearson correlation coefficient. This RDF file serves as input for the AMT. With AMT (jjj) temporal reasoning according to the specific-ontology can be calculated and afterwards (jjjj) exported and visualised. As a use case, this Linked Open Time workflow validates Roman Limes and Hadrian’s Wall relative time intervals, based on the Samian (Terra Sigillata) found on these Limes sections. The data stems from the online database “Samian Research”. Several Limes time intervals can be defined convincingly (e.g. Elisabethenstraße: AD 74-104, Wetteraulimes: AD 110-260), others are less certain. Although there is only a vague possible starting date known during Hadrian’s reign (after AD 122), we neither know the precise starting date nor its end date. As for the North Sea foundations, we assume that Hadrian founded them AD 120 (emperor Hadrian gave his name to the newly founded town Forum Hadriani), but we do not know what their end date was.
Results and Discussion
The approaches are resulting in research tools: the *.agt file format, an AMT ontology for temporal reasoning, the R-based research tool “Alligator CA” (in cooperation with the CAA SIG SSLA), the JAVA-based “Alligator”, the Python-based “Alligator-AMT-Transformator”, and the JavaScript-based “AMT time” have been implemented. In combination as a Linked Pipe [3], they are able to base the mechanisms of Limes-dating on specific material find-categories more comprehensible. As an example, it is possible to analyse and visualise the dependencies within the dating arguments of individual Limes sections when using the Terra Sigillata found on their Limes sites. “Virtual timelines” show the position of the “Elisabethenstraße” and the “Wetterau Limes” in “virtual fuzzy years” as a fundament for a relative Limes chronology. The results of this use case seem to confirm the widespread assumption that Hadrian’s Wall, the North Sea Coast foundations and the Wetterau Limes were founded in the same decades. Based on very similar Samian consumption profiles, they are all located on the right (“late”) side. However, the process of nearest neighbour findings also suggests a somewhat earlier starting date of Hadrian’s Wall, similar to Wetterau Limes. There may have been a number of reasons for this phenomenon: a) the quantities involved are causing volatility, b) the amount of Samian Ware available on the British/Scottish or German markets were different, causing supply differences c) although it is possible with Samian as a find category to establish relative chronologies (especially when there are no absolute dates available), it has no reliable capability for yielding absolute dates. This method of dealing with uncertain time intervals opens possibilities for comparisons with other timeline oriented methods used to date Limes sites, such as series of coin dates. When applying the workflow, there are some issues which still need to be discussed and implemented: a) including weights from dating metadata, e.g. dating method, b) refine the algorithm to add wobbliness to the calculated virtual fuzzy years, c) weighing of factor loads in different ca dimensions, d) taking quality values of individual ca units and types into account.
References
[1] DOI: 10.5281/zenodo.1436351
[2] DOIs: 10.5281/zenodo.1157985 and 10.5281/zenodo.1160350 / https://github.com/mainzed/academicmetatool-viewer/blob/master/time/ontology_time.ttl
[3] DOI: 10.5281/zenodo.6412435"""@en ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Linked Open Time: Reproducible LOD-driven workflows and research tools for validating Roman Limes and Hadrian's Wall relative time intervals based on Samian (Terra Sigillata)"@en ;
    dct:type sqpt:presentation ;
    bibo:abstract """Linked Open Time: Reproducible LOD-driven workflows and research tools for validating Roman Limes and Hadrian’s Wall relative time intervals based on Samian (Terra Sigillata)
Dr. Allard W. Mees, Florian Thiery, Sophie C. Schmidt
Introduction
The handling of relative time intervals, including vague and uncertain relations between them, is a major challenge in modelling chronologies in archaeology. Underlying dating mechanisms such as circular argumentations or dependencies on open end time intervals require tools for verification and visualisation of the underlying reasoning process. We are working with algorithms to combine relative time intervals with absolute dates by using quantitative parameters, resulting in a machine readable representation and a pipeline of tools using Semantic Web technologies to enable reproducible research. The consecutive phases of the Roman Limes have always been thought to be a corner-stone in establishing chronologies in archaeology. However, since there are only very few absolute dates attached to the Limes sections, many Limes parts are - if at all - only describable as relatively vague time intervals.
Methods and Materials
In order to tackle the issues mentioned above, we developed two methods which are built on one another: the Alligator, and the Academic Meta Tool (AMT) approaches. As their starting point, an *.agt file is used which includes the values name, xyz-coordinates, start / end dates (if available), as well as uncertainty information (e.g. a “fixed” absolute dates or floating endings). This file is generated from the correspondence analysis (ca) output (based on a CSV comprising Limes intervals, potters, number of occurrences) in which the quantitative relations of the euclidean distances between finds and proposed time intervals are calculated. Following the “horseshoe paradigm”, the ca result may provide a measure of (possible chronological) overlap between the Limes intervals. It is important to note that a ca does not start with any pre-known dating information. Therefore, the *.agt file contains dating metadata (e.g. start / end dates, uncertainty information) from a separate file. The Alligator approach starts with (i) a 3D distance calculation using the coordinates of the first 3 ca dimensions (xyz) in the *.agt file. Then the “Alligator Algorithm” will be applied by (ii) calculating undated wobbly floating periods (e.g. terminus post quem points, derived from a historical sources, or terminus ante quem points derived from dendrochronological dates) by finding the next 3D ca neighbour. Following this step (iii) the new virtual time intervals are calculated and the resulting “virtual fuzzy years” are generated based on Allen’s interval algebra. Any resulting “virtual fuzzy year” is then (iiii) transformed into relative intervals and stored in RDF format [1]. The AMT approach is applied by using the AMT library. It uses a specific AMT ontology for relative time intervals [2], in which temporal reasoning can be applied.
Each relation contains a degree of connection that is based on the rules of vagueness. In our use case, the estimated Allen intervals and the potters’ overlap of the Limes parts is (j) described with the (normalised) Pearson correlation coefficient. In the case of the AMT approach, we (jj) have to transform the intervals into the AMT format and include the degree of connection as a (normalised) Pearson correlation coefficient. This RDF file serves as input for the AMT. With AMT (jjj) temporal reasoning according to the specific-ontology can be calculated and afterwards (jjjj) exported and visualised. As a use case, this Linked Open Time workflow validates Roman Limes and Hadrian’s Wall relative time intervals, based on the Samian (Terra Sigillata) found on these Limes sections. The data stems from the online database “Samian Research”. Several Limes time intervals can be defined convincingly (e.g. Elisabethenstraße: AD 74-104, Wetteraulimes: AD 110-260), others are less certain. Although there is only a vague possible starting date known during Hadrian’s reign (after AD 122), we neither know the precise starting date nor its end date. As for the North Sea foundations, we assume that Hadrian founded them AD 120 (emperor Hadrian gave his name to the newly founded town Forum Hadriani), but we do not know what their end date was.
Results and Discussion
The approaches are resulting in research tools: the *.agt file format, an AMT ontology for temporal reasoning, the R-based research tool “Alligator CA” (in cooperation with the CAA SIG SSLA), the JAVA-based “Alligator”, the Python-based “Alligator-AMT-Transformator”, and the JavaScript-based “AMT time” have been implemented. In combination as a Linked Pipe [3], they are able to base the mechanisms of Limes-dating on specific material find-categories more comprehensible. As an example, it is possible to analyse and visualise the dependencies within the dating arguments of individual Limes sections when using the Terra Sigillata found on their Limes sites. “Virtual timelines” show the position of the “Elisabethenstraße” and the “Wetterau Limes” in “virtual fuzzy years” as a fundament for a relative Limes chronology. The results of this use case seem to confirm the widespread assumption that Hadrian’s Wall, the North Sea Coast foundations and the Wetterau Limes were founded in the same decades. Based on very similar Samian consumption profiles, they are all located on the right (“late”) side. However, the process of nearest neighbour findings also suggests a somewhat earlier starting date of Hadrian’s Wall, similar to Wetterau Limes. There may have been a number of reasons for this phenomenon: a) the quantities involved are causing volatility, b) the amount of Samian Ware available on the British/Scottish or German markets were different, causing supply differences c) although it is possible with Samian as a find category to establish relative chronologies (especially when there are no absolute dates available), it has no reliable capability for yielding absolute dates. This method of dealing with uncertain time intervals opens possibilities for comparisons with other timeline oriented methods used to date Limes sites, such as series of coin dates. When applying the workflow, there are some issues which still need to be discussed and implemented: a) including weights from dating metadata, e.g. dating method, b) refine the algorithm to add wobbliness to the calculated virtual fuzzy years, c) weighing of factor loads in different ca dimensions, d) taking quality values of individual ca units and types into account.
References
[1] DOI: 10.5281/zenodo.1436351
[2] DOIs: 10.5281/zenodo.1157985 and 10.5281/zenodo.1160350 / https://github.com/mainzed/academicmetatool-viewer/blob/master/time/ontology_time.ttl
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    dct:description """Linked Pipes: A little minion for reproducible research
Florian Thiery, RGZM; Timo Homburg, University of Applied Sciences Mainz
At Linked Pasts IV in Mainz 2018, the Linked Pasts Network had a session on "Linked Data Tools and Pipelines": As a result, the Linked Pipes Working Group was born. The goal: creating so-called Linked Pipes as semantically modelled pipelines for (not only) LOD related workflows using the help of the WWW, and existing research software solutions such as JavaScript based little minions. Currently Linked Pipes are developed by the Research Squirrel Engineers Network (Timo Homburg and Florian Thiery), and is part of the Pelagios Network Annotation Activity as well as the Linked Pasts Network.
Digital Workflows (as known as Linked Pipes) exist in mind, but researchers should visualise and semantically model them using well-known ontologies such as the Provenance Ontology (PROV-O) and the Dataflow Ontology (DFD) to create a part for reproducible research. Additionally, research workflows are usually not published in interoperable formats, such as RDF. It would be an improvement for archaeology if researchers could document Liked Pipes e.g. for a research publication and share the workflow with colleagues. Linked Pipes consists of tools, which can be easily part of the so-called Linked Open Data Cloud and therefore interlinked with other tools by using Wikidata [1] where you can create or edit existing items (research tools) with the property “used by” (P1535) “Linked Pipes” (Q73897190), e.g. SPARQLing Unicorn QGIS Plugin (Q74005133), Recogito (Q74692524), Annotoriuos (Q110585388). Linked Pipes should be reusable once defined in Linked Data. Creating a “Linked Pipe Registry” will help to share Linked Pipes in a central repository. But how? a) a registry file in a git repository which links to Pipe RDF files published elsewhere save, e.g. Zenodo -> DOI or Solid Pod -> access control, and b) visualisation of these pipes on demand in JavaScript in a Linked Pipe Viewer [2].
This minion talk focuses on the Linked Pipe Viewer published on Github [3]. The viewer is based on JavScript and the flowchart.js library. Currently this framework does not support the “Yourdon / DeMarco” notation, as DFD visualising backbone. We currently adopted the syntax, to model digital data(-stores) (subroutine|data|blue), not digital resources (inputoutput|toolwd|purple), tool in Wikidata (subroutine|data|yellow) and local (maybe unpublished) tools (inputoutput|tool|yellow) and started to model use cases:
• Digital Text -> Recogito Geo Annotation -> QGIS Map
• Book Text -> Recogito Townland Annotation -> QGIS Map
• Ogham CIIC Book -> R based Density Map
• Samian Research Data -> Samian Ware in Wikidata
• 3D Scan of clay tablet -> Machine Learning Data
References
[1] https://www.wikidata.org/wiki/Wikidata:WikiProject_LinkedPipes
[2] http://viewer.linkedpipes.xyz
[3] https://github.com/Research-Squirrel-Engineers/LinkedPipes"""@en ;
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Florian Thiery, RGZM; Timo Homburg, University of Applied Sciences Mainz
At Linked Pasts IV in Mainz 2018, the Linked Pasts Network had a session on "Linked Data Tools and Pipelines": As a result, the Linked Pipes Working Group was born. The goal: creating so-called Linked Pipes as semantically modelled pipelines for (not only) LOD related workflows using the help of the WWW, and existing research software solutions such as JavaScript based little minions. Currently Linked Pipes are developed by the Research Squirrel Engineers Network (Timo Homburg and Florian Thiery), and is part of the Pelagios Network Annotation Activity as well as the Linked Pasts Network.
Digital Workflows (as known as Linked Pipes) exist in mind, but researchers should visualise and semantically model them using well-known ontologies such as the Provenance Ontology (PROV-O) and the Dataflow Ontology (DFD) to create a part for reproducible research. Additionally, research workflows are usually not published in interoperable formats, such as RDF. It would be an improvement for archaeology if researchers could document Liked Pipes e.g. for a research publication and share the workflow with colleagues. Linked Pipes consists of tools, which can be easily part of the so-called Linked Open Data Cloud and therefore interlinked with other tools by using Wikidata [1] where you can create or edit existing items (research tools) with the property “used by” (P1535) “Linked Pipes” (Q73897190), e.g. SPARQLing Unicorn QGIS Plugin (Q74005133), Recogito (Q74692524), Annotoriuos (Q110585388). Linked Pipes should be reusable once defined in Linked Data. Creating a “Linked Pipe Registry” will help to share Linked Pipes in a central repository. But how? a) a registry file in a git repository which links to Pipe RDF files published elsewhere save, e.g. Zenodo -> DOI or Solid Pod -> access control, and b) visualisation of these pipes on demand in JavaScript in a Linked Pipe Viewer [2].
This minion talk focuses on the Linked Pipe Viewer published on Github [3]. The viewer is based on JavScript and the flowchart.js library. Currently this framework does not support the “Yourdon / DeMarco” notation, as DFD visualising backbone. We currently adopted the syntax, to model digital data(-stores) (subroutine|data|blue), not digital resources (inputoutput|toolwd|purple), tool in Wikidata (subroutine|data|yellow) and local (maybe unpublished) tools (inputoutput|tool|yellow) and started to model use cases:
• Digital Text -> Recogito Geo Annotation -> QGIS Map
• Book Text -> Recogito Townland Annotation -> QGIS Map
• Ogham CIIC Book -> R based Density Map
• Samian Research Data -> Samian Ware in Wikidata
• 3D Scan of clay tablet -> Machine Learning Data
References
[1] https://www.wikidata.org/wiki/Wikidata:WikiProject_LinkedPipes
[2] http://viewer.linkedpipes.xyz
[3] https://github.com/Research-Squirrel-Engineers/LinkedPipes"""@en ;
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The African Red Slip Ware (ARS) is a central archaeological object type for the understanding of late antique world of ideas, their exchange and socio-economic history. The 3-year project ARS3D, funded by the German Federal Ministry of Education and Research (BMBF), started in February 2018 and aims to document, digitise and publish the relief-decorated ARS, located in the Römisch-Germanischen Zentralmuseum (RGZM). The ARS3D project will publish the facts and archaeological interpretations of these ARS objects. In this process the ARSs are 3D scanned and processed to create their 3D surrogates. These 3D models support archaeological interpretations which should be documented. Here we present our ontology that forms the structural fundament for the documentation and the underlying triplestore as data repository. The axioms are primarily based on CIDOC-CRM with few additions to fulfil our requirements. In addition, we included axioms to provide structure for storing meta- and para- information generated during the creation of 3D models. We have also included axioms from PROV-O, the W3C compliance ontology for structuring the provenance information of the 3D generation process. An interactive web application provides the graphical user interface of this ARS data repository. In this paper we consider data quality aspects for: 1) Provenance quality that affects the archaeological interpretations. 2) Archaeological factual quality on ARSs such as iconographical descriptions by bibliographic references and external resources, e.g. Icon Class to access the LOD cloud. 3) Identifying the provenance and storing chronological information."""@en ;
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    dct:title "Semantic modelling and publishing African Red Slip Ware as Linked Data"@en ;
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The African Red Slip Ware (ARS) is a central archaeological object type for the understanding of late antique world of ideas, their exchange and socio-economic history. The 3-year project ARS3D, funded by the German Federal Ministry of Education and Research (BMBF), started in February 2018 and aims to document, digitise and publish the relief-decorated ARS, located in the Römisch-Germanischen Zentralmuseum (RGZM). The ARS3D project will publish the facts and archaeological interpretations of these ARS objects. In this process the ARSs are 3D scanned and processed to create their 3D surrogates. These 3D models support archaeological interpretations which should be documented. Here we present our ontology that forms the structural fundament for the documentation and the underlying triplestore as data repository. The axioms are primarily based on CIDOC-CRM with few additions to fulfil our requirements. In addition, we included axioms to provide structure for storing meta- and para- information generated during the creation of 3D models. We have also included axioms from PROV-O, the W3C compliance ontology for structuring the provenance information of the 3D generation process. An interactive web application provides the graphical user interface of this ARS data repository. In this paper we consider data quality aspects for: 1) Provenance quality that affects the archaeological interpretations. 2) Archaeological factual quality on ARSs such as iconographical descriptions by bibliographic references and external resources, e.g. Icon Class to access the LOD cloud. 3) Identifying the provenance and storing chronological information."""@en ;
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The Linked Data Cloud is full of controlled resources, which quality is in fact difficult to handle. Firstly, each resource collection, e.g. a thesaurus, is cooking its own soup related to its research context. Secondly, conceptualisation of Linked Open Data (LOD) assumes standardised data, but in reality, either generic concepts or real instances exist. Thirdly, archaeological items are usually related to generic instances in the LOD cloud, based on their object oriented nature. Describing these relations by modelling archaeological assumptions causes ambiguities which have to be tamed in order to guarantee data quality which can be reused. In this paper we will demonstrate this in three examples from the databases of the Römisch-Germanisches Zentralmuseum Mainz and are proposing ways to solve the handling of ambiguities with a software framework called Academic Meta Tool (AMT)."""@en ;
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    dct:description """Abstract for the talk given at the Digital Archaeology Bern 2023 conference, 01st February 2023:
Wikidata (https://www.wikidata.org) is an open knowledge base, which a growing number of volunteers and researchers use to add content to the Linked Open Data Cloud. Linked Open Data (LOD) aims at interlinking openly available datasets and thereby facilitating the combination and joint processing of existing data from various digital sources. Wikidata functions as the central storage for structured data of Wikimedia projects such as Wikipedia and Wikimedia Commons and does not only record statements, but also their sources, acting as a secondary database. It can be edited by anyone, the data is multilingual and available under the Public Domain (CC0). Archaeological data has already been integrated using i. a. subject specific classes, e.g. ‘Bronze Age’ (Q11761) as an “archaeological age” (Q15401699) and properties, such as the ‘Art & Architecture Thesaurus ID’ (P1014) to link concepts with the Getty AAT. Examples include data on Greek Vase painters, Irish Ogham Stones, Samian Ware and African Red Slip Ware (Schmidt et al. 2022).
In this talk I would like to showcase a project to create a reference collection of Neolithic ceramics for volunteer field walkers in Wikidata. This is part of a small ongoing project, which was originally funded by Wikimedia Deutschland e.V. within the Open Science Fellows Program in 2020/21. It employs a Cradle tool form for easy and coherent adding of information by field walkers, including an image uploaded to Wikimedia. The Wikidata Query Tool enables searching for data within Wikidata without any knowledge of the underlying query language SPARQL, thereby facilitating the use by non IT specialists. OpenRefine and QuickStatements enable batch uploads and edits to Wikidata, which will be used by the authors to contribute to the reference collection.
By this example, we will discuss concepts of “Open Data” in the archaeological scientific context. Who decides, which data can or should be available and to whom? How are information flows mediated? The (currently) ‘extremely’ open, community-based approach of Wikidata, where scientists, as any other member of the community, are not able to control information statements made, may discourage crowdsourced knowledge sceptics. Studies have shown that some vandalism occurs (0,004% cf Heindorf et al 2016). To mitigate the spread of misinformation, Wikidata encourages the use of source attribution as references into the statements, similarly to Wikipedia. Statements are also connected to a version control system and discussion pages where different views can be debated.
A main problem the volunteer field walkers identified for their work was access to scientific publication and reference material. Wikidata can be used to improve data sharing practices between them and the scientific community and thus facilitate their contribution to archaeological knowledge production.
Heindorf et al 2016: Heindorf, S.; Potthast, M.; Stein, B.; Engels, G Towards Vandalism Detection in Knowledge Bases: Corpus Construction and Analysis, October 2016. Conference: 25th International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis (USA), DOI: 10.1145/2983323.2983740
Schmidt et al 2022: Schmidt, S.C.; Thiery, F.; Trognitz, M. Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata. Digital 2022, 2, 333–364. https://doi.org/10.3390/digital2030019"""@en ;
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    dct:title "Wikidata as a Knowledge Base and Research Tool for Open Science in Archaeology"@en ;
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    bibo:abstract """Abstract for the talk given at the Digital Archaeology Bern 2023 conference, 01st February 2023:
Wikidata (https://www.wikidata.org) is an open knowledge base, which a growing number of volunteers and researchers use to add content to the Linked Open Data Cloud. Linked Open Data (LOD) aims at interlinking openly available datasets and thereby facilitating the combination and joint processing of existing data from various digital sources. Wikidata functions as the central storage for structured data of Wikimedia projects such as Wikipedia and Wikimedia Commons and does not only record statements, but also their sources, acting as a secondary database. It can be edited by anyone, the data is multilingual and available under the Public Domain (CC0). Archaeological data has already been integrated using i. a. subject specific classes, e.g. ‘Bronze Age’ (Q11761) as an “archaeological age” (Q15401699) and properties, such as the ‘Art & Architecture Thesaurus ID’ (P1014) to link concepts with the Getty AAT. Examples include data on Greek Vase painters, Irish Ogham Stones, Samian Ware and African Red Slip Ware (Schmidt et al. 2022).
In this talk I would like to showcase a project to create a reference collection of Neolithic ceramics for volunteer field walkers in Wikidata. This is part of a small ongoing project, which was originally funded by Wikimedia Deutschland e.V. within the Open Science Fellows Program in 2020/21. It employs a Cradle tool form for easy and coherent adding of information by field walkers, including an image uploaded to Wikimedia. The Wikidata Query Tool enables searching for data within Wikidata without any knowledge of the underlying query language SPARQL, thereby facilitating the use by non IT specialists. OpenRefine and QuickStatements enable batch uploads and edits to Wikidata, which will be used by the authors to contribute to the reference collection.
By this example, we will discuss concepts of “Open Data” in the archaeological scientific context. Who decides, which data can or should be available and to whom? How are information flows mediated? The (currently) ‘extremely’ open, community-based approach of Wikidata, where scientists, as any other member of the community, are not able to control information statements made, may discourage crowdsourced knowledge sceptics. Studies have shown that some vandalism occurs (0,004% cf Heindorf et al 2016). To mitigate the spread of misinformation, Wikidata encourages the use of source attribution as references into the statements, similarly to Wikipedia. Statements are also connected to a version control system and discussion pages where different views can be debated.
A main problem the volunteer field walkers identified for their work was access to scientific publication and reference material. Wikidata can be used to improve data sharing practices between them and the scientific community and thus facilitate their contribution to archaeological knowledge production.
Heindorf et al 2016: Heindorf, S.; Potthast, M.; Stein, B.; Engels, G Towards Vandalism Detection in Knowledge Bases: Corpus Construction and Analysis, October 2016. Conference: 25th International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis (USA), DOI: 10.1145/2983323.2983740
Schmidt et al 2022: Schmidt, S.C.; Thiery, F.; Trognitz, M. Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata. Digital 2022, 2, 333–364. https://doi.org/10.3390/digital2030019"""@en ;
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    dct:description """Das LEIZA stellt seine Forschungsdaten nicht nur der wissenschaftlichen Fachwelt als Linked Open Data zur Verfügung. Als modernes strategisches Element lässt es auch die Bürgerwissenschaften / Citizen Science an den Forschungsdaten mitarbeiten und partizipieren. Die unterschiedlichen Möglichkeiten zur Beteiligung von internationalen Citizen Scientists an LEIZA-Forschungsprozessen werden am Beispiel des Wikimedia Universums und der Organisation von Terra Sigillata Datenkuratierungs-Workshops dargestellt.
Ein Vortrag von Florian Thiery M.Sc. und Dr. Allard Mees in der Reihe »Wintervorträge« über Forschungsdaten, Wikdata, Citizen Science und Partizipation von Ehrenamtlichen."""@de ;
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    dct:title "LEIZA-Forschungsdaten in der großen weiten Welt der Citizen Science"@de ;
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    bibo:abstract """Das LEIZA stellt seine Forschungsdaten nicht nur der wissenschaftlichen Fachwelt als Linked Open Data zur Verfügung. Als modernes strategisches Element lässt es auch die Bürgerwissenschaften / Citizen Science an den Forschungsdaten mitarbeiten und partizipieren. Die unterschiedlichen Möglichkeiten zur Beteiligung von internationalen Citizen Scientists an LEIZA-Forschungsprozessen werden am Beispiel des Wikimedia Universums und der Organisation von Terra Sigillata Datenkuratierungs-Workshops dargestellt.
Ein Vortrag von Florian Thiery M.Sc. und Dr. Allard Mees in der Reihe »Wintervorträge« über Forschungsdaten, Wikdata, Citizen Science und Partizipation von Ehrenamtlichen."""@de ;
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    dct:description "Vorlesungsfolien im Zusammenhang mit einer Gastvorlesung in 07.012.897 P Kleinfunddokumentation und computergestützte Fotografie am Beispiel römischer Tonlampen von PD Dr. Matthias Grawehr im Wintersemester 2022/2023, Johannes Gutenberg Universität Mainz Fachbereich 07 | Klassische Archäologie."@de ;
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    dct:title "Kleinfund-Dokumentation mit der Hilfe von Semantik"@de ;
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    bibo:abstract "Vorlesungsfolien im Zusammenhang mit einer Gastvorlesung in 07.012.897 P Kleinfunddokumentation und computergestützte Fotografie am Beispiel römischer Tonlampen von PD Dr. Matthias Grawehr im Wintersemester 2022/2023, Johannes Gutenberg Universität Mainz Fachbereich 07 | Klassische Archäologie."@de ;
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    dct:description """Iᚑᚌᚆᚐᚋ (Ogham) stones are monoliths with the early medieval Primitive Irish Ogham script, placed on the island of Ireland and in the western part of Britain between the 4th and 9th centuries. They are an essential source for Archaic or Proto-Irish language and society. The letters of the Ogham alphabet consist of strokes written at different lengths and angles along (often natural) edges of the stones from the bottom left to the top and back down to the right. Names on the stones appear to be dedicated to a person. It remains unclear, however, whether the stones were grave markers, for example, or denoted land ownership. The stones are mostly no longer at the original site, which is, on the one hand, essential for cartographic recording (do you link the original site if known?) and, on the other hand, makes it more difficult to deduce their original function.
Documentation on Ogham stones is available in many analogue sources and partly open online databases or repositories. However, as there is no common identification standard (e.g. stones, sites), these data cannot be analysed together; they need to be interoperable. The ogham.link project of the Research Squirrel Engineers Network creates a semantically modelled data basis as Linked Open Data (LOD) using community hubs such as Open Street Map and Wikidata. A Fellowship from Wikimedia Deutschland has already funded it in the Free Knowledge Fellow Programme as 'Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe'. The data and geodata are extracted through a workflow, modelled and made available via various hubs. In the Fellow project, this was done primarily as native RDF data and as Quick Statements for integration into Wikidata. In Wikidata, sites can be linked to Irish townlands, for which there is a platform townlands.ie based on OSM data if these cannot be determined more precisely on site.
Various archaeological items have been tagged in OpenStreetMap by "b-unicycling", including historic=ogham_stone. This makes Ogham data available in OSM with the tag ogham_stone and lends itself to linking the data mentioned in Wikidata, along with images on Wikimedia Commons and Flickr, to create an Ogham Knowledge Graph.
This talk gives insights into the documentation, modelling and publication of selected Ogham stones from Ireland in OSM (user: b-unicycling, fthierygeo) and Wikidata (user: fthierygeo, Sophie_C._Schmidt). It shows how to create a Knowledge Graph with meta- and paradata and images. On top of that, the talks show how to integrate OSM data and Wikidata in QGIS using several Plugins, such as the "SPARQLing Unicon QGIS Plugin" or "QuickOSM" to perform further analysis. It also gives an overview of townland modelling and the publication of heritage sites using Web GIS systems and open geodata.
Version 2023/07/01: modified slide 19"""@en ;
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    bibo:abstract """Iᚑᚌᚆᚐᚋ (Ogham) stones are monoliths with the early medieval Primitive Irish Ogham script, placed on the island of Ireland and in the western part of Britain between the 4th and 9th centuries. They are an essential source for Archaic or Proto-Irish language and society. The letters of the Ogham alphabet consist of strokes written at different lengths and angles along (often natural) edges of the stones from the bottom left to the top and back down to the right. Names on the stones appear to be dedicated to a person. It remains unclear, however, whether the stones were grave markers, for example, or denoted land ownership. The stones are mostly no longer at the original site, which is, on the one hand, essential for cartographic recording (do you link the original site if known?) and, on the other hand, makes it more difficult to deduce their original function.
Documentation on Ogham stones is available in many analogue sources and partly open online databases or repositories. However, as there is no common identification standard (e.g. stones, sites), these data cannot be analysed together; they need to be interoperable. The ogham.link project of the Research Squirrel Engineers Network creates a semantically modelled data basis as Linked Open Data (LOD) using community hubs such as Open Street Map and Wikidata. A Fellowship from Wikimedia Deutschland has already funded it in the Free Knowledge Fellow Programme as 'Irish ᚑᚌᚆᚐᚋ Stones in the Wikimedia Universe'. The data and geodata are extracted through a workflow, modelled and made available via various hubs. In the Fellow project, this was done primarily as native RDF data and as Quick Statements for integration into Wikidata. In Wikidata, sites can be linked to Irish townlands, for which there is a platform townlands.ie based on OSM data if these cannot be determined more precisely on site.
Various archaeological items have been tagged in OpenStreetMap by "b-unicycling", including historic=ogham_stone. This makes Ogham data available in OSM with the tag ogham_stone and lends itself to linking the data mentioned in Wikidata, along with images on Wikimedia Commons and Flickr, to create an Ogham Knowledge Graph.
This talk gives insights into the documentation, modelling and publication of selected Ogham stones from Ireland in OSM (user: b-unicycling, fthierygeo) and Wikidata (user: fthierygeo, Sophie_C._Schmidt). It shows how to create a Knowledge Graph with meta- and paradata and images. On top of that, the talks show how to integrate OSM data and Wikidata in QGIS using several Plugins, such as the "SPARQLing Unicon QGIS Plugin" or "QuickOSM" to perform further analysis. It also gives an overview of townland modelling and the publication of heritage sites using Web GIS systems and open geodata.
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    dct:description """Background
“Dated sites” in archaeology are usually not “dated sites”. In the first century AD e.g., there are only two verifiable “absolute dated sites”: Pompei [1] and Inchtuthil [2], of which ancient authors indicate their dates. Only parts of the “dated sites” can be set in relative relation to each other, e.g. if they belong to a group of sites within a newly occupied area or if they are part of a string of military sites related to a military campaign etc. For this purpose, we can use e.g. Allen's interval algebra [3;4], such as “after” or “during” [5-8]. However, most of the temporality of “dated sites” is usually derived from a series of historical assumptions and interpolations. Yet, the chronology of these materials is often based on material coming from the manifold of other assumed “dated sites”, thus potentially resulting in a circulus vitiosus. On top of that, “dated sites” are frequently given dates like “from - to”, suggesting a dating security which is actually only a probability. A methodological way out of this dilemma is possible when we enhance the assumed external dating evidence and go back to the factual occurrences and overlaps in the data from the suspected “dated sites”, since only these data are free of any temporal interpretations.
This results in four different research questions:
(1) How can any resulting time order of “dated sites” be comprehensively encoded and semantically modelled, e.g. by using Allen’s interval algebra and graph technologies [5;6]?
(2) How can a reproducible algorithm deal with incomplete or vague dates to fix missing start or endpoints by creating “virtual fuzzy and wobbly dates”? Are graph-based digital tools like Alligator [9] or the Academic Meta Tool (AMT) [10] usable for this purpose?
(3) How can results be visualised for researchers using graphs, timelines or maps [8, chapter 9]?
(4) Do we need a different concept of temporalities for such “dated sites”?
Methods and data
As a method, the chronological assumptions used in defining “dated sites” based on archaeological find materials in the 1st century AD must be deconstructed and restricted to the occurrences and overlaps within and between “find materials” from suspected “dated sites” only, to arrive at a relative chronology. This can be achieved by using correspondence analysis and visualising the resulting dimension values on maps recalculated in Hue values. By applying the “Alligator Method” [11], including the Alligator and AMT tools, we can fix missing or enhance existing start- or endpoints by adding information about the vagueness/uncertainties of the hitherto “from-to” information. The resulting relative chronology can be transferred and refined by Allen's interval algebra and visualised in graph structures such as the Resource Description Framework (RDF).
To cover as many as possible assumed “dated sites”, the Samian (Terra Sigillata) curated in the Open Data accessible research infrastructure Samian Research (https://www.rgzm.de/samian) with its 250’000 potter’s stamps from all over Europe is used. The paper is focused on data related to the historical southern German Limes developments and the enrollment of the Roman occupation of Britain as a cross-check for the statistically achieved relative chronologies.
Findings
It turns out that it is possible to visualise historical development on maps, timelines and graph structures without using historical assumptions about individual “dated sites”. Simultaneously, this paper demonstrates that dating individual sites with the help of Samian cannot result in precise datings as usually expected in archaeology but requires the application of a different concept of temporality. The development of the Samian consumption patterns in the progressing Southern German Limes and the enrollment of the occupation of Britain appears to be a gradual development only. Hence the precise dating ability of the “find category” Samian has been overcharged since the beginning of Samian studies in the 19th century.
Applying the before-mentioned methods, based on semantic modelling and W3C standards such as RDF [12-14] and inspired by LOD techniques, enables the FAIRification of these data related to temporality, especially by creating interoperability and reproducibility.
References
[1] Plinius the Younger, Letters (chapter LXV).
[2] Tacitus, Agricola (chapter XXIX-XXXVII).
[3] Allen, JF. 1983 Maintaining knowledge about temporal intervals. Communications of the ACM 26(11): 832–843. DOI: 10.1145/182.358434.
[4] Freksa, C. 1991 Conceptual Neighborhood and its role in temporal and spatial reasoning. In:. Singh, MG and Travé-Massuyés, L (eds.). Proceedings of the IMACS Workshop on Decision Support Systems and Qualitative Reasoning. 1991. Amsterdam: North-Holland. pp. 181–187.
[5] Cox, S, Little C 2022 Time Ontology in OWL. https://www.w3.org/TR/owl-time/#topology
[6] Cox, S, Hobbs J, and Pan, F 2016. OWL-Time. https://www.w3.org/2006/time
[7] Shaw R 202. Modeling and Reasoning about Incomplete, Uncertain, and Approximate Historical Dates. Extended Abstracts of Graphs and Networks in the Humanities 2022:
Technologies, Models, Analyses, and Visualizations, 11.2. https://t1p.de/5m3ow
[8] Thiery, F 2013 Semantic Web und Linked Data: Generierung von Interoperabilität in archäologischen Fachdaten am Beispiel römischer Töpferstempel. Squirrel Papers 4(1): No. 3. DOI: 10.5281/zenodo.774861
[9] Thiery, F, Mees AW. 2022 alligator - Allen Transformer. Squirrel Papers 4(2): No. 5. DOI: 10.5281/zenodo.2540709
[10] Unold, M, Thiery, F and Mees, A. 2019 Academic Meta Tool. Ein Web-Tool zur Modellierung von Vagheit. ZfdG Sonderband 4: No. 6. DOI: 10.17175/SB004_004.
[11] Thiery, F, Mees AW. 2023 Alligator Method. Squirrel Papers 5(1): No. 2. DOI: 10.5281/zenodo.7624871
[12] Thiery, F 2018 Alligator Vocabulary. Vättern Edition. https://rgzm.github.io/alligator/vocab/
[13] Thiery, F, Unold, M 2018 Academic Meta Tool Ontology. Leonard Edition. https://academic-meta-tool.xyz/ontology/
[14] Thiery, F, Unold, M 2018 Academic Meta Tool Vocabulary. Penny Edition. https://academic-meta-tool.xyz/vocab/"""@en ;
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    dct:title "Dating Dated Sites - Using Correspondence Analysis to handle Chronologies as Graphs."@en ;
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    bibo:abstract """Background
“Dated sites” in archaeology are usually not “dated sites”. In the first century AD e.g., there are only two verifiable “absolute dated sites”: Pompei [1] and Inchtuthil [2], of which ancient authors indicate their dates. Only parts of the “dated sites” can be set in relative relation to each other, e.g. if they belong to a group of sites within a newly occupied area or if they are part of a string of military sites related to a military campaign etc. For this purpose, we can use e.g. Allen's interval algebra [3;4], such as “after” or “during” [5-8]. However, most of the temporality of “dated sites” is usually derived from a series of historical assumptions and interpolations. Yet, the chronology of these materials is often based on material coming from the manifold of other assumed “dated sites”, thus potentially resulting in a circulus vitiosus. On top of that, “dated sites” are frequently given dates like “from - to”, suggesting a dating security which is actually only a probability. A methodological way out of this dilemma is possible when we enhance the assumed external dating evidence and go back to the factual occurrences and overlaps in the data from the suspected “dated sites”, since only these data are free of any temporal interpretations.
This results in four different research questions:
(1) How can any resulting time order of “dated sites” be comprehensively encoded and semantically modelled, e.g. by using Allen’s interval algebra and graph technologies [5;6]?
(2) How can a reproducible algorithm deal with incomplete or vague dates to fix missing start or endpoints by creating “virtual fuzzy and wobbly dates”? Are graph-based digital tools like Alligator [9] or the Academic Meta Tool (AMT) [10] usable for this purpose?
(3) How can results be visualised for researchers using graphs, timelines or maps [8, chapter 9]?
(4) Do we need a different concept of temporalities for such “dated sites”?
Methods and data
As a method, the chronological assumptions used in defining “dated sites” based on archaeological find materials in the 1st century AD must be deconstructed and restricted to the occurrences and overlaps within and between “find materials” from suspected “dated sites” only, to arrive at a relative chronology. This can be achieved by using correspondence analysis and visualising the resulting dimension values on maps recalculated in Hue values. By applying the “Alligator Method” [11], including the Alligator and AMT tools, we can fix missing or enhance existing start- or endpoints by adding information about the vagueness/uncertainties of the hitherto “from-to” information. The resulting relative chronology can be transferred and refined by Allen's interval algebra and visualised in graph structures such as the Resource Description Framework (RDF).
To cover as many as possible assumed “dated sites”, the Samian (Terra Sigillata) curated in the Open Data accessible research infrastructure Samian Research (https://www.rgzm.de/samian) with its 250’000 potter’s stamps from all over Europe is used. The paper is focused on data related to the historical southern German Limes developments and the enrollment of the Roman occupation of Britain as a cross-check for the statistically achieved relative chronologies.
Findings
It turns out that it is possible to visualise historical development on maps, timelines and graph structures without using historical assumptions about individual “dated sites”. Simultaneously, this paper demonstrates that dating individual sites with the help of Samian cannot result in precise datings as usually expected in archaeology but requires the application of a different concept of temporality. The development of the Samian consumption patterns in the progressing Southern German Limes and the enrollment of the occupation of Britain appears to be a gradual development only. Hence the precise dating ability of the “find category” Samian has been overcharged since the beginning of Samian studies in the 19th century.
Applying the before-mentioned methods, based on semantic modelling and W3C standards such as RDF [12-14] and inspired by LOD techniques, enables the FAIRification of these data related to temporality, especially by creating interoperability and reproducibility.
References
[1] Plinius the Younger, Letters (chapter LXV).
[2] Tacitus, Agricola (chapter XXIX-XXXVII).
[3] Allen, JF. 1983 Maintaining knowledge about temporal intervals. Communications of the ACM 26(11): 832–843. DOI: 10.1145/182.358434.
[4] Freksa, C. 1991 Conceptual Neighborhood and its role in temporal and spatial reasoning. In:. Singh, MG and Travé-Massuyés, L (eds.). Proceedings of the IMACS Workshop on Decision Support Systems and Qualitative Reasoning. 1991. Amsterdam: North-Holland. pp. 181–187.
[5] Cox, S, Little C 2022 Time Ontology in OWL. https://www.w3.org/TR/owl-time/#topology
[6] Cox, S, Hobbs J, and Pan, F 2016. OWL-Time. https://www.w3.org/2006/time
[7] Shaw R 202. Modeling and Reasoning about Incomplete, Uncertain, and Approximate Historical Dates. Extended Abstracts of Graphs and Networks in the Humanities 2022:
Technologies, Models, Analyses, and Visualizations, 11.2. https://t1p.de/5m3ow
[8] Thiery, F 2013 Semantic Web und Linked Data: Generierung von Interoperabilität in archäologischen Fachdaten am Beispiel römischer Töpferstempel. Squirrel Papers 4(1): No. 3. DOI: 10.5281/zenodo.774861
[9] Thiery, F, Mees AW. 2022 alligator - Allen Transformer. Squirrel Papers 4(2): No. 5. DOI: 10.5281/zenodo.2540709
[10] Unold, M, Thiery, F and Mees, A. 2019 Academic Meta Tool. Ein Web-Tool zur Modellierung von Vagheit. ZfdG Sonderband 4: No. 6. DOI: 10.17175/SB004_004.
[11] Thiery, F, Mees AW. 2023 Alligator Method. Squirrel Papers 5(1): No. 2. DOI: 10.5281/zenodo.7624871
[12] Thiery, F 2018 Alligator Vocabulary. Vättern Edition. https://rgzm.github.io/alligator/vocab/
[13] Thiery, F, Unold, M 2018 Academic Meta Tool Ontology. Leonard Edition. https://academic-meta-tool.xyz/ontology/
[14] Thiery, F, Unold, M 2018 Academic Meta Tool Vocabulary. Penny Edition. https://academic-meta-tool.xyz/vocab/"""@en ;
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    dct:description """Background
ᚑᚌᚆᚐᚋ (Ogham) stones, e.g. the Ogham stone CIIC 178, are monoliths with the early medieval Primitive Irish Ogham script, mostly erected on the island of Ireland and in the western part of Britain between the 4th and 9th centuries. They are an important source for Archaic or Proto-Irish language and society. Names on the stones seem to be dedicated to a person. It remains unclear, however, whether the stones were grave markers, for example, or designated land ownership [1;2;3;4]. The inscriptions consist of names and relations (formula words) such as MAQI (ᚋᚐᚊᚔ; engl. son) to feature kinship or tribal relations [2].
Ogham stones have been part of research for decades. Over time, several books, online databases and repositories comprising different numbering systems and readings of ogham inscriptions were produced. So the critical question is, “How to model research on Ogham (e.g. the readings of inscriptions) over time and different media using different sources and platforms with the help of graphs and Semantic Web technologies?”.
Methods and data
This paper describes a use case from the “Linked Open Ogham Data Project” [5;6], which was set up in 2019 by the Research Squirrel Engineers Network and supported by the Wikimedia Germany Open Science Fellows Program. It uses “analogue” publications/catalogues on Ogham [1;2;7;8;9], online databases/repositories such as “Ogham in 3D” or “Celtic Inscribed Stones Project” (CISP) to transform and model the data with the help of Semantic Web and graph technologies within a hybrid Ogham LOD workflow [10], based on the idea of Open Science and the FAIR Principles as Linked Open Data (LOD) [11], in Wikidata and Open Street Map (OSM). Semantic modelling and LOD techniques such as RDF and Wikidata are used to describe, e.g. the different readings of inscriptions over time [12].
Discussion
This paper shows that using semantic modelling and LOD techniques to create a network of stones, data and individual views, e.g. readings of inscriptions, helps to model temporalities in research. For this purpose, digitalisation and LODification of books, LODification of “old fashioned” 1990s databases such as “CISP” or “TITUS ogam” as well as import data in community hubs like Wikidata or OSM is needed. Volunteers and Citizen Scientits can also help improve the data within this system.
As an example: other identifiers of CIIC 178 [1,p.170]: CISP~COUME/1, Cuppage 268 [7], Wikidata Q106680850, OSM node 5145413640 and as LOD http://lod.ogham.link/data/Y50000178. Macalister [1] read “ERC MAQI MAQI-ERCIAS MU DOVINIA”, Cuppage et al. [7] “ERC MAQI MAQI-ERCIAS [MU] DOVINI[A]” and McManus [2] “ERC MAQI MAQI-ERCIA[S MU] DOVI[N]IA”. The readings are slightly similar but contain a lot of uncertainties over time. Still, nevertheless, the mentioned names (Goidelic/male) “ERC”, “MAQI-ERCIAS”, and “DOVINIA” and relations like “MAQI” (engl. son of) are still the same. LOD and Wikidata help to model and visualise these opinions and relations as temporalities in research.
References
[1] Macalister, RAS. 1945. Corpus inscriptionum insularum Celticarum. Vol. I. Dublin: Stationery Office.
[2] MacManus, D. 1997. A guide to Ogam. Maynooth monographs 4. Maynooth: An Sagart.
[3] Schmidt, SC and Thiery, F. 2022 SPARQLing Ogham Stones: New Options for Analyzing Analog Editions by Digitization in Wikidata. CEUR Workshop Proceedings 3110(Graph Technologies in the Humanities 2020): 211–244. DOI: 10.5281/zenodo.6380914.
[4] Bogdani, J, Montalbano, R and Rosati, P. 2021. ArcheoFOSS XIV 2020: Open Software, Hardware, Processes, Data and Formats in Archaeological Research. Oxford: Archaeopress Publishing Ltd, pp.119-127.
[5] https://t1p.de/wd-ogham
[6] https://t1p.de/wd-looi
[7] Cuppage, J and Bennett, I. 1986. Archaeological survey of the Dingle Peninsula: a description of the field antiquities of the Barony of Corca Dhuibhne from the Mesolithic period to the 17th century A.D. = Suirbhé seandálaíochta Chorca Dhuibhne. Ballyferriter: Oidhreacht Chorca Dhuibhne.
[8] O’Sullivan, A and Sheehan, J. 1996. The Iveragh Peninsula. An Archaeological Survey of South Kerry. Cork: Cork University Press.
[9] Ziegler, S. 1994. Die Sprache der altirischen Ogam-Inschriften. Göttingen: Vandenhoeck and Ruprecht.
[10] Thiery, F, Homburg, T, Schmidt, SC, Voß, J and Trognitz, M. 2021 SPARQLing Geodesy for Cultural Heritage – New Opportunities for Publishing and Analysing Volunteered Linked (geo-)data. In:. FIG Peer Review Journal. 21 June 2021. Kopenhagen: FIG. DOI: 10.5281/zenodo.5639381.
[11] Thiery, F. 2022. SPARQLing ᚑᚌᚆᚐᚋ – Irische Ogham Steine als Linked Open Data. Zeitschrift für Digitale Geisteswissenschaften Fabrikation von Erkenntnis. Experimente in den Digital Humanities (5): 11. DOI: 10.17175/sb005_010
[12] Schmidt, SC, Thiery, F and Trognitz, M. 2022 Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata. Digital 2(3): 333–364. DOI: 10.3390/digital2030019."""@en ;
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    dct:title "Temporalities in research: How to deal with ᚑᚌᚆᚐᚋ publishing methods over time?"@en ;
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    bibo:abstract """Background
ᚑᚌᚆᚐᚋ (Ogham) stones, e.g. the Ogham stone CIIC 178, are monoliths with the early medieval Primitive Irish Ogham script, mostly erected on the island of Ireland and in the western part of Britain between the 4th and 9th centuries. They are an important source for Archaic or Proto-Irish language and society. Names on the stones seem to be dedicated to a person. It remains unclear, however, whether the stones were grave markers, for example, or designated land ownership [1;2;3;4]. The inscriptions consist of names and relations (formula words) such as MAQI (ᚋᚐᚊᚔ; engl. son) to feature kinship or tribal relations [2].
Ogham stones have been part of research for decades. Over time, several books, online databases and repositories comprising different numbering systems and readings of ogham inscriptions were produced. So the critical question is, “How to model research on Ogham (e.g. the readings of inscriptions) over time and different media using different sources and platforms with the help of graphs and Semantic Web technologies?”.
Methods and data
This paper describes a use case from the “Linked Open Ogham Data Project” [5;6], which was set up in 2019 by the Research Squirrel Engineers Network and supported by the Wikimedia Germany Open Science Fellows Program. It uses “analogue” publications/catalogues on Ogham [1;2;7;8;9], online databases/repositories such as “Ogham in 3D” or “Celtic Inscribed Stones Project” (CISP) to transform and model the data with the help of Semantic Web and graph technologies within a hybrid Ogham LOD workflow [10], based on the idea of Open Science and the FAIR Principles as Linked Open Data (LOD) [11], in Wikidata and Open Street Map (OSM). Semantic modelling and LOD techniques such as RDF and Wikidata are used to describe, e.g. the different readings of inscriptions over time [12].
Discussion
This paper shows that using semantic modelling and LOD techniques to create a network of stones, data and individual views, e.g. readings of inscriptions, helps to model temporalities in research. For this purpose, digitalisation and LODification of books, LODification of “old fashioned” 1990s databases such as “CISP” or “TITUS ogam” as well as import data in community hubs like Wikidata or OSM is needed. Volunteers and Citizen Scientits can also help improve the data within this system.
As an example: other identifiers of CIIC 178 [1,p.170]: CISP~COUME/1, Cuppage 268 [7], Wikidata Q106680850, OSM node 5145413640 and as LOD http://lod.ogham.link/data/Y50000178. Macalister [1] read “ERC MAQI MAQI-ERCIAS MU DOVINIA”, Cuppage et al. [7] “ERC MAQI MAQI-ERCIAS [MU] DOVINI[A]” and McManus [2] “ERC MAQI MAQI-ERCIA[S MU] DOVI[N]IA”. The readings are slightly similar but contain a lot of uncertainties over time. Still, nevertheless, the mentioned names (Goidelic/male) “ERC”, “MAQI-ERCIAS”, and “DOVINIA” and relations like “MAQI” (engl. son of) are still the same. LOD and Wikidata help to model and visualise these opinions and relations as temporalities in research.
References
[1] Macalister, RAS. 1945. Corpus inscriptionum insularum Celticarum. Vol. I. Dublin: Stationery Office.
[2] MacManus, D. 1997. A guide to Ogam. Maynooth monographs 4. Maynooth: An Sagart.
[3] Schmidt, SC and Thiery, F. 2022 SPARQLing Ogham Stones: New Options for Analyzing Analog Editions by Digitization in Wikidata. CEUR Workshop Proceedings 3110(Graph Technologies in the Humanities 2020): 211–244. DOI: 10.5281/zenodo.6380914.
[4] Bogdani, J, Montalbano, R and Rosati, P. 2021. ArcheoFOSS XIV 2020: Open Software, Hardware, Processes, Data and Formats in Archaeological Research. Oxford: Archaeopress Publishing Ltd, pp.119-127.
[5] https://t1p.de/wd-ogham
[6] https://t1p.de/wd-looi
[7] Cuppage, J and Bennett, I. 1986. Archaeological survey of the Dingle Peninsula: a description of the field antiquities of the Barony of Corca Dhuibhne from the Mesolithic period to the 17th century A.D. = Suirbhé seandálaíochta Chorca Dhuibhne. Ballyferriter: Oidhreacht Chorca Dhuibhne.
[8] O’Sullivan, A and Sheehan, J. 1996. The Iveragh Peninsula. An Archaeological Survey of South Kerry. Cork: Cork University Press.
[9] Ziegler, S. 1994. Die Sprache der altirischen Ogam-Inschriften. Göttingen: Vandenhoeck and Ruprecht.
[10] Thiery, F, Homburg, T, Schmidt, SC, Voß, J and Trognitz, M. 2021 SPARQLing Geodesy for Cultural Heritage – New Opportunities for Publishing and Analysing Volunteered Linked (geo-)data. In:. FIG Peer Review Journal. 21 June 2021. Kopenhagen: FIG. DOI: 10.5281/zenodo.5639381.
[11] Thiery, F. 2022. SPARQLing ᚑᚌᚆᚐᚋ – Irische Ogham Steine als Linked Open Data. Zeitschrift für Digitale Geisteswissenschaften Fabrikation von Erkenntnis. Experimente in den Digital Humanities (5): 11. DOI: 10.17175/sb005_010
[12] Schmidt, SC, Thiery, F and Trognitz, M. 2022 Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata. Digital 2(3): 333–364. DOI: 10.3390/digital2030019."""@en ;
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Static API Generation: CKAN and vCard
Detection of Ontolex-Lemon vocabulary
VOID File Generation
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    dct:description """Forschungsdatenmanagement (FDM) nimmt insbesondere auch in den Geistes- und Kulturwissenschaften eine immer größere Wichtigkeit ein. Insbesondere in diesen oft interdisziplinären Projekten spielen die Dokumentation, Verarbeitung und Annotation von 3D-Daten eine große Rolle. Ob in der Archäologie, Architektur und Bauforschung, Kunstgeschichte oder Denkmalpflege, überall müssen (3D-)Daten mit Metadaten versorgt und verknüpft werden, interoperable für Menschen sowie Maschinen verständliche community-basierte Standards geschaffen werden und Research Tools entwickelt werden um die FAIRifizierung dieser Daten gerecht zu werden. Innerhalb der deutschen Forschungslandschaft geschieht dieser Schritt über die von der DFG geförderte Nationale Forschungsdateninfrastruktur (NFDI). Innerhalb der NFDI haben sich einzelne Fachkonsortien gebildet die verschiedenste Themenfelder abdecken, so z.B. für das Ingenieurswesen und die Geodäsie NFDI4Earth und NFDI4Ing, sowie für die Humanities insbesonder NFDI4Objects (N4O) und NFDI4Culture (N4C). Beide Konsortien beschäftigen sich mit Objekten (materielle Hinterlassenschaften) zu Kulturgütern der Menschheitsgeschichte. In einzelnen Arbeitsbereichen (Task Areas) spielen insbesondere die 3D-Dokumentation (jeweils TA1), die qualifizierte Verarbeitung mit Hilfe von Standards (jeweils TA2) und die Bereitstellung von FAIRification Tools (N4C: TA3; N4O: TA5) eine entscheidende Rolle.
In diesem Vortrag werden zwei unterschiedliche Tools zur Annotation von 3D-Daten mit Hilfe von Linked Open Data (LOD) und des Semantic Webs vorgestellt. Das aus N4O stammende Beispiel entstammt dem BMBF-gefördereten Africian Red Slip Ware digital Projekt, kurz ARS3D (DOI: 10.52825/cordi.v1i.326) das auf offenen Standards (CIDOC CRM) und Open Source Tools (z.B. RDF4J und 3DHOP) aufsetzt. Beispiele aus N4C werden mit Hilfe des Open Source Tools “Semantic Kompakkt” (https://semantic-kompakkt.de; z.B. https://semantic-kompakkt.de/entity/6548f15d004b905661dc013b) vorgestellt, welches auf der Software “Kompakkt” (https://kompakkt.de; https://kompakkt.de/entity/5d6435522e8bee556eedf0c0) und einer Wikibase-Instanz (https://wikiba.se) besteht, vgl. SemanticKompakkt_CoRDI_slides2023."""@de ;
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    dct:title "3D and Research Data Management (RDM): Examples for semantic 3D annotation/modelling in the data qualification process in the NFDI consortia NFDI4Objects (N4O) and NFDI4Culture (N4C)"@de ;
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    bibo:abstract """Forschungsdatenmanagement (FDM) nimmt insbesondere auch in den Geistes- und Kulturwissenschaften eine immer größere Wichtigkeit ein. Insbesondere in diesen oft interdisziplinären Projekten spielen die Dokumentation, Verarbeitung und Annotation von 3D-Daten eine große Rolle. Ob in der Archäologie, Architektur und Bauforschung, Kunstgeschichte oder Denkmalpflege, überall müssen (3D-)Daten mit Metadaten versorgt und verknüpft werden, interoperable für Menschen sowie Maschinen verständliche community-basierte Standards geschaffen werden und Research Tools entwickelt werden um die FAIRifizierung dieser Daten gerecht zu werden. Innerhalb der deutschen Forschungslandschaft geschieht dieser Schritt über die von der DFG geförderte Nationale Forschungsdateninfrastruktur (NFDI). Innerhalb der NFDI haben sich einzelne Fachkonsortien gebildet die verschiedenste Themenfelder abdecken, so z.B. für das Ingenieurswesen und die Geodäsie NFDI4Earth und NFDI4Ing, sowie für die Humanities insbesonder NFDI4Objects (N4O) und NFDI4Culture (N4C). Beide Konsortien beschäftigen sich mit Objekten (materielle Hinterlassenschaften) zu Kulturgütern der Menschheitsgeschichte. In einzelnen Arbeitsbereichen (Task Areas) spielen insbesondere die 3D-Dokumentation (jeweils TA1), die qualifizierte Verarbeitung mit Hilfe von Standards (jeweils TA2) und die Bereitstellung von FAIRification Tools (N4C: TA3; N4O: TA5) eine entscheidende Rolle.
In diesem Vortrag werden zwei unterschiedliche Tools zur Annotation von 3D-Daten mit Hilfe von Linked Open Data (LOD) und des Semantic Webs vorgestellt. Das aus N4O stammende Beispiel entstammt dem BMBF-gefördereten Africian Red Slip Ware digital Projekt, kurz ARS3D (DOI: 10.52825/cordi.v1i.326) das auf offenen Standards (CIDOC CRM) und Open Source Tools (z.B. RDF4J und 3DHOP) aufsetzt. Beispiele aus N4C werden mit Hilfe des Open Source Tools “Semantic Kompakkt” (https://semantic-kompakkt.de; z.B. https://semantic-kompakkt.de/entity/6548f15d004b905661dc013b) vorgestellt, welches auf der Software “Kompakkt” (https://kompakkt.de; https://kompakkt.de/entity/5d6435522e8bee556eedf0c0) und einer Wikibase-Instanz (https://wikiba.se) besteht, vgl. SemanticKompakkt_CoRDI_slides2023."""@de ;
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    dct:description """Die nachvollziehbare/kolloberative Erfassung und FAIRifizierung von Forschungsdaten wird in der Citizen Science Community immer wichtiger, um so ein Teil eines archäologischen Wissensgraphen zu werden und das bereits vernetzte Datennetzwerk durch qualifizierte Daten anzureichern. Denn nur so können diese Daten auch mit anderen Daten verknüpft werden und in internationale Initiativen (z.B. NFDI4Objects) und Community-Hubs (z.B. Wikidata, OpenStreetMap) aktiv eingebunden werden. Dabei sind Open Source (FOSS) Research und FAIRification Tools leider oft nicht verfügbar. Diese, im Zusammenwirken mit Linked Open Data Projekten als Demonstratoren, können von Community- und freiwilligen Initiativen wie z.B. dem Research Squirrel Engineers Network jedoch erstellt und kuratiert werden.
Dieses Paper stellt die Research Squirrel Engineers Network Initiative, drei Research und FAIRification Tools und drei Projekte der Research Squirrels vor. Diese können als digitale Services zum digitalen Datenmanagement der Archäologie dienen. Das Paper stellt daher auch die Ziele, den Nutzen und die Umsetzung der Squirrel Tools vor.
Das Research Squirrel Engineers Network (gegründet 2019 zur Implementierung des SPARQL Unicorns) ist ein loser Zusammenschluss von Linked Open Data/Wikidata-Enthusias*Innen, Research Software Engineers und Citizen Scientists mit den Schwerpunkten Archäoinformatik, Digital Humanities und Geoinformatik. Die Mitglieder entwickeln und maintainen zusammen Research und FAIRification Tools und setzen diese in konkreten Projekten um.
Ein FAIRification Tool für das digitale Datenmanagement ist das SPARQL Unicorn und dessen Implementierung für QGIS. Das SPARQLing Unicorn QGIS Plugin (Abb. 1, rechts) ermöglicht es, Linked-Data-Anfragen in (Geo)SPARQL an Triple-Stores zu senden (Abb. 1, links) und bereitet die Ergebnisse für die Geocommunity in QGIS auf. Es bietet derzeit drei Hauptfunktionen: (A) Vereinfachte Abfrage von Semantic Web Datenquellen (B) Anreicherung von Geodaten und (C) Transformation von QGIS-Vektorebenen nach RDF. Darüber hinaus ermöglicht das SPARQL Unicorn Ontology Documentation Tool z.B. per GitHub Action die automatisierte Erstellung von HTML Seiten von Linked Open Data Veröffentlichungen. Ein Beispiel ist die in gezeigte Abfrage zu irischen Ogham Sites auf der Dingle-Halbinsel, bzw. Daten aus dem Dissertationsprojekt von Sophie C. Schmidt zu “Brandenburg 5.000 v. Chr.”  indem ein CIDOC CRM Datenmodell in Linked Open Data überführt und mit Hilfe des SPARQL Unicorns als HTML visualisiert wurde. Ein weiterer Service ist die “Fuzzy Spatial Locations Ontology”, in der Vagheiten, Unsicherheiten und Mehrdeutigkeiten in der Georeferenzierung mit Hilfe von Semantik und einer Ontology (basierend auf PROV-O) FAIR und nachvollziehbar gemacht werden. Ein Beispiel hierfür sind Modellierungen von Fundorten der Erruption des Campanian Iginimbrite in den Phlegräischen Feldern (39,940 yr b2k ± 150 years) die oft mit archäologischen Fundorten korrespondieren, z.B. die Toplitsa Höhle in Bulgarien. Die “Squirrel Papers” komplementieren die Services um eine Plattform zu schaffen, auf der Working Papers, Daten, Software, Präsentationsfolien und Poster zur Zitation veröffentlich werden können.
Begleitet werden diese Services von Linked Open Data / Wikidata / Open Street Map und Wikimedia Commons Projekten, wie z.B. Linked Open Ogham, Holy Wells in Irland oder Linked Reindeers."""@de ;
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    dct:title "Das Research Squirrel Engineers Network - Digitale Services zum digitalen Datenmanagement aus der Archäoinformatik- und Citizen Science-Community"@de ;
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    bibo:abstract """Die nachvollziehbare/kolloberative Erfassung und FAIRifizierung von Forschungsdaten wird in der Citizen Science Community immer wichtiger, um so ein Teil eines archäologischen Wissensgraphen zu werden und das bereits vernetzte Datennetzwerk durch qualifizierte Daten anzureichern. Denn nur so können diese Daten auch mit anderen Daten verknüpft werden und in internationale Initiativen (z.B. NFDI4Objects) und Community-Hubs (z.B. Wikidata, OpenStreetMap) aktiv eingebunden werden. Dabei sind Open Source (FOSS) Research und FAIRification Tools leider oft nicht verfügbar. Diese, im Zusammenwirken mit Linked Open Data Projekten als Demonstratoren, können von Community- und freiwilligen Initiativen wie z.B. dem Research Squirrel Engineers Network jedoch erstellt und kuratiert werden.
Dieses Paper stellt die Research Squirrel Engineers Network Initiative, drei Research und FAIRification Tools und drei Projekte der Research Squirrels vor. Diese können als digitale Services zum digitalen Datenmanagement der Archäologie dienen. Das Paper stellt daher auch die Ziele, den Nutzen und die Umsetzung der Squirrel Tools vor.
Das Research Squirrel Engineers Network (gegründet 2019 zur Implementierung des SPARQL Unicorns) ist ein loser Zusammenschluss von Linked Open Data/Wikidata-Enthusias*Innen, Research Software Engineers und Citizen Scientists mit den Schwerpunkten Archäoinformatik, Digital Humanities und Geoinformatik. Die Mitglieder entwickeln und maintainen zusammen Research und FAIRification Tools und setzen diese in konkreten Projekten um.
Ein FAIRification Tool für das digitale Datenmanagement ist das SPARQL Unicorn und dessen Implementierung für QGIS. Das SPARQLing Unicorn QGIS Plugin (Abb. 1, rechts) ermöglicht es, Linked-Data-Anfragen in (Geo)SPARQL an Triple-Stores zu senden (Abb. 1, links) und bereitet die Ergebnisse für die Geocommunity in QGIS auf. Es bietet derzeit drei Hauptfunktionen: (A) Vereinfachte Abfrage von Semantic Web Datenquellen (B) Anreicherung von Geodaten und (C) Transformation von QGIS-Vektorebenen nach RDF. Darüber hinaus ermöglicht das SPARQL Unicorn Ontology Documentation Tool z.B. per GitHub Action die automatisierte Erstellung von HTML Seiten von Linked Open Data Veröffentlichungen. Ein Beispiel ist die in gezeigte Abfrage zu irischen Ogham Sites auf der Dingle-Halbinsel, bzw. Daten aus dem Dissertationsprojekt von Sophie C. Schmidt zu “Brandenburg 5.000 v. Chr.”  indem ein CIDOC CRM Datenmodell in Linked Open Data überführt und mit Hilfe des SPARQL Unicorns als HTML visualisiert wurde. Ein weiterer Service ist die “Fuzzy Spatial Locations Ontology”, in der Vagheiten, Unsicherheiten und Mehrdeutigkeiten in der Georeferenzierung mit Hilfe von Semantik und einer Ontology (basierend auf PROV-O) FAIR und nachvollziehbar gemacht werden. Ein Beispiel hierfür sind Modellierungen von Fundorten der Erruption des Campanian Iginimbrite in den Phlegräischen Feldern (39,940 yr b2k ± 150 years) die oft mit archäologischen Fundorten korrespondieren, z.B. die Toplitsa Höhle in Bulgarien. Die “Squirrel Papers” komplementieren die Services um eine Plattform zu schaffen, auf der Working Papers, Daten, Software, Präsentationsfolien und Poster zur Zitation veröffentlich werden können.
Begleitet werden diese Services von Linked Open Data / Wikidata / Open Street Map und Wikimedia Commons Projekten, wie z.B. Linked Open Ogham, Holy Wells in Irland oder Linked Reindeers."""@de ;
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    dct:description """Paper for Panel 1: Towards a Common Infrastructural Framework: Managing and Exploiting Big Data in Archaeological Research through Open Data Systems - Remote Talk
Title: Infrastructural Frameworks: Managing and publishing distributed Archaeological Research Data through the Wikibase Ecosystem
Author: Florian Thiery
Affiliation: Research Squrrel Engineers Network (Mainz, Germany)
Keywords: Wikibase, Wikidata, Linked Open Data, Research Data Management, Archaeological Infrastructure, distributed systems
Author Biography: Florian Thiery is a Research Software Engineer (RSE), Ontology Engineer and Data Scientist trained in geodesy and geoinformatics. He has been working for more than 10 years in LOD-related DH projects such as iDAI.chronontology and African Red Slip Ware digital (ARS3D). Currently, Florian is part of the staff at NFDI4Objects, which develops a Research Data Management Structure for (among other things) archaeological data together with German institutions and international partners. He is also founder of the Research Squirrel Engineers Network, wich codes the SPARQLing Unicorn QGIS Plugin and is responsible for the Linked Open Ogham Data, as well as the fuzzy-sl Wikibase.
Author Bibliography
Schmidt, Sophie C., Florian Thiery, and Martina Trognitz. ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’. Digital 2, no. 3 (22 June 2022): 333–64. https://doi.org/10.3390/digital2030019.
Thiery, Florian, Fiona Schenk, Stefanie Baars, Karsten Tolle, and Peter Thiery. ‘Modellierung von Fuzzyness / Wobbliness in Geodaten - Am Beispiel Archäologischer Und Geowissenschaftlicher Fundortreferenzen’. Tagungsband FOSSGIS-Konferenz 2024 2024 (18 March 2024): 64–73. https://doi.org/10.5281/zenodo.10571858.
Thiery, Florian, and Peter Thiery. ‘Linked Open Ogham. How to Publish and Interlink Various Ogham Data?’ Archeologia e Calcolatori 34, no. 1 (2023): 105–14. https://doi.org/10.19282/ac.34.1.2023.12.
Abstract
The creation of Linked Open Data (LOD) within frameworks like Wikibase has led to an increasing volume of "archaeological big data" stored in distributed systems across the LOD Cloud. This growth necessitates strategies to enable federated SPARQL queries, facilitating the realisation of Linked Open Usable Data. While the Arches Management System offers a robust platform for organising and visualising archaeological data, the Wikibase Ecosystem, with its integrated LOD Knowledge Graph infrastructures, presents an alternative.
This paper explores the role of Wikibase within broader Knowledge Graph frameworks, such as the NFDI4Objects Knowledge Graph, in the context of archaeological big data. Case studies will highlight successful applications, challenges in integrating diverse open data sources, and the use of LOD technologies for data analysis and visualisation. Focusing on the Wikibase data model and its relation to the CIDOC CRM, the paper will examine strategies for interconnecting multiple Wikibase instances with distinct classes and properties. The example of Irish Ogham Stones will illustrate this approach, exploring instances such as FactGrid, fuzzy-sl, Semantic Kompakkt, and Wikidata, alongside external resources like CISP and Ogham in 3D in the Linked Open Ogham resource. The paper will demonstrate how federated queries can be performed, addressing the ongoing challenges in this field."""@en ;
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    bibo:abstract """Paper for Panel 1: Towards a Common Infrastructural Framework: Managing and Exploiting Big Data in Archaeological Research through Open Data Systems - Remote Talk
Title: Infrastructural Frameworks: Managing and publishing distributed Archaeological Research Data through the Wikibase Ecosystem
Author: Florian Thiery
Affiliation: Research Squrrel Engineers Network (Mainz, Germany)
Keywords: Wikibase, Wikidata, Linked Open Data, Research Data Management, Archaeological Infrastructure, distributed systems
Author Biography: Florian Thiery is a Research Software Engineer (RSE), Ontology Engineer and Data Scientist trained in geodesy and geoinformatics. He has been working for more than 10 years in LOD-related DH projects such as iDAI.chronontology and African Red Slip Ware digital (ARS3D). Currently, Florian is part of the staff at NFDI4Objects, which develops a Research Data Management Structure for (among other things) archaeological data together with German institutions and international partners. He is also founder of the Research Squirrel Engineers Network, wich codes the SPARQLing Unicorn QGIS Plugin and is responsible for the Linked Open Ogham Data, as well as the fuzzy-sl Wikibase.
Author Bibliography
Schmidt, Sophie C., Florian Thiery, and Martina Trognitz. ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’. Digital 2, no. 3 (22 June 2022): 333–64. https://doi.org/10.3390/digital2030019.
Thiery, Florian, Fiona Schenk, Stefanie Baars, Karsten Tolle, and Peter Thiery. ‘Modellierung von Fuzzyness / Wobbliness in Geodaten - Am Beispiel Archäologischer Und Geowissenschaftlicher Fundortreferenzen’. Tagungsband FOSSGIS-Konferenz 2024 2024 (18 March 2024): 64–73. https://doi.org/10.5281/zenodo.10571858.
Thiery, Florian, and Peter Thiery. ‘Linked Open Ogham. How to Publish and Interlink Various Ogham Data?’ Archeologia e Calcolatori 34, no. 1 (2023): 105–14. https://doi.org/10.19282/ac.34.1.2023.12.
Abstract
The creation of Linked Open Data (LOD) within frameworks like Wikibase has led to an increasing volume of "archaeological big data" stored in distributed systems across the LOD Cloud. This growth necessitates strategies to enable federated SPARQL queries, facilitating the realisation of Linked Open Usable Data. While the Arches Management System offers a robust platform for organising and visualising archaeological data, the Wikibase Ecosystem, with its integrated LOD Knowledge Graph infrastructures, presents an alternative.
This paper explores the role of Wikibase within broader Knowledge Graph frameworks, such as the NFDI4Objects Knowledge Graph, in the context of archaeological big data. Case studies will highlight successful applications, challenges in integrating diverse open data sources, and the use of LOD technologies for data analysis and visualisation. Focusing on the Wikibase data model and its relation to the CIDOC CRM, the paper will examine strategies for interconnecting multiple Wikibase instances with distinct classes and properties. The example of Irish Ogham Stones will illustrate this approach, exploring instances such as FactGrid, fuzzy-sl, Semantic Kompakkt, and Wikidata, alongside external resources like CISP and Ogham in 3D in the Linked Open Ogham resource. The paper will demonstrate how federated queries can be performed, addressing the ongoing challenges in this field."""@en ;
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    dct:description """Paper for Panel 6: Open Data for Urban Archaeological Potential Assessment
Title: Jupyter Python Minion: Empowering Open Data for Urban Archaeological Insights
Author: Florian Thiery
Affiliation: Research Squrrel Engineers Network (Mainz, Germany)
Keywords: Wikibase, Wikidata, Linked Open Data, Research Software Engineering, Archaeological Infrastructure, Open Data, Jupyter Notebooks, Python
Author Biography: Florian Thiery is a Research Software Engineer (RSE), Ontology Engineer and Data Scientist trained in geodesy and geoinformatics. He has been working for more than 10 years in LOD-related DH projects such as iDAI.chronontology and African Red Slip Ware digital (ARS3D). Currently, Florian is part of the staff at NFDI4Objects, which develops a Research Data Management Structure for (among other things) archaeological data together with German institutions and international partners. He is also founder of the Research Squirrel Engineers Network, wich codes the SPARQLing Unicorn QGIS Plugin and is responsible for the Linked Open Ogham Data, as well as the fuzzy-sl Wikibase.
Author Bibliography
Schmidt, Sophie C., Florian Thiery, and Martina Trognitz. ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’. Digital 2, no. 3 (22 June 2022): 333–64. https://doi.org/10.3390/digital2030019.
Thiery, Florian, Fiona Schenk, Stefanie Baars, Karsten Tolle, and Peter Thiery. ‘Modellierung von Fuzzyness / Wobbliness in Geodaten - Am Beispiel Archäologischer Und Geowissenschaftlicher Fundortreferenzen’. Tagungsband FOSSGIS-Konferenz 2024 2024 (18 March 2024): 64–73. https://doi.org/10.5281/zenodo.10571858.
Thiery, Florian, and Peter Thiery. ‘Linked Open Ogham. How to Publish and Interlink Various Ogham Data?’ Archeologia e Calcolatori 34, no. 1 (2023): 105–14. https://doi.org/10.19282/ac.34.1.2023.12.
Abstract
Publishing and analysing open archaeological data is crucial for sustainable urban planning and cultural heritage management. The Jupyter Python Minion offers an innovative, open-source approach to streamline Linked Open Data (LOD) workflows, enabling reproducible and accessible research. By combining Python's powerful libraries (pyplot, geopandas, pandas) with Jupyter Notebooks, this tool supports integrating archaeological data from sources like Wikidata, other Wikibase instances, and LOD knowledge graphs.
This presentation highlights how the Minion simplifies complex tasks like SPARQL querying and data visualisation. Case studies include mapping Ogham stone distributions, analysing Irish holy wells, and exploring geoscientific findspots from the Campanian Ignimbrite eruption alongside archaeological cave sites. Additionally, the tool integrates data on Roman Samian ware (terra sigillata) production sites, showcasing the potential to visualise regional trends and spatial patterns.
By making these workflows shareable and reproducible, the Minion aligns with open science principles and bridges the gap between computational methods and domain expertise. The tool’s geospatial visualisations and linked data integration support urban archaeological potential assessments, offering urban planners, archaeologists, and researchers a transparent framework to assess and safeguard buried heritage in cities like Rome."""@en ;
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    dct:title "Jupyter Python Minion: Empowering Open Data for Urban Archaeological Insights"@en ;
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    bibo:abstract """Paper for Panel 6: Open Data for Urban Archaeological Potential Assessment
Title: Jupyter Python Minion: Empowering Open Data for Urban Archaeological Insights
Author: Florian Thiery
Affiliation: Research Squrrel Engineers Network (Mainz, Germany)
Keywords: Wikibase, Wikidata, Linked Open Data, Research Software Engineering, Archaeological Infrastructure, Open Data, Jupyter Notebooks, Python
Author Biography: Florian Thiery is a Research Software Engineer (RSE), Ontology Engineer and Data Scientist trained in geodesy and geoinformatics. He has been working for more than 10 years in LOD-related DH projects such as iDAI.chronontology and African Red Slip Ware digital (ARS3D). Currently, Florian is part of the staff at NFDI4Objects, which develops a Research Data Management Structure for (among other things) archaeological data together with German institutions and international partners. He is also founder of the Research Squirrel Engineers Network, wich codes the SPARQLing Unicorn QGIS Plugin and is responsible for the Linked Open Ogham Data, as well as the fuzzy-sl Wikibase.
Author Bibliography
Schmidt, Sophie C., Florian Thiery, and Martina Trognitz. ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’. Digital 2, no. 3 (22 June 2022): 333–64. https://doi.org/10.3390/digital2030019.
Thiery, Florian, Fiona Schenk, Stefanie Baars, Karsten Tolle, and Peter Thiery. ‘Modellierung von Fuzzyness / Wobbliness in Geodaten - Am Beispiel Archäologischer Und Geowissenschaftlicher Fundortreferenzen’. Tagungsband FOSSGIS-Konferenz 2024 2024 (18 March 2024): 64–73. https://doi.org/10.5281/zenodo.10571858.
Thiery, Florian, and Peter Thiery. ‘Linked Open Ogham. How to Publish and Interlink Various Ogham Data?’ Archeologia e Calcolatori 34, no. 1 (2023): 105–14. https://doi.org/10.19282/ac.34.1.2023.12.
Abstract
Publishing and analysing open archaeological data is crucial for sustainable urban planning and cultural heritage management. The Jupyter Python Minion offers an innovative, open-source approach to streamline Linked Open Data (LOD) workflows, enabling reproducible and accessible research. By combining Python's powerful libraries (pyplot, geopandas, pandas) with Jupyter Notebooks, this tool supports integrating archaeological data from sources like Wikidata, other Wikibase instances, and LOD knowledge graphs.
This presentation highlights how the Minion simplifies complex tasks like SPARQL querying and data visualisation. Case studies include mapping Ogham stone distributions, analysing Irish holy wells, and exploring geoscientific findspots from the Campanian Ignimbrite eruption alongside archaeological cave sites. Additionally, the tool integrates data on Roman Samian ware (terra sigillata) production sites, showcasing the potential to visualise regional trends and spatial patterns.
By making these workflows shareable and reproducible, the Minion aligns with open science principles and bridges the gap between computational methods and domain expertise. The tool’s geospatial visualisations and linked data integration support urban archaeological potential assessments, offering urban planners, archaeologists, and researchers a transparent framework to assess and safeguard buried heritage in cities like Rome."""@en ;
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Within the NFDI e.V., several "sections" are dealing with overarching topics, e.g., the "Sektion Common Infrastructures" with its working groups on "Data Integration (DI)", "Data Management Planning (DMP)", "Data Science and Artificial Intelligence (DSAI)", "Electronic Lab Notebooks (ELN)", "Persistent Identifiers (PID)" and "Research Software Engineering (RSE)".
The RSE working group connects the NFDI consortia in software-related aspects. It focuses on three areas: Research software, software communities and software infrastructure at NFDI. In an advisory and supportive capacity, the working group operates a central forum and establishes the necessary software ecosystem within NFDI for the professional development of software infrastructure components, which in their entirety represent an integral part of the NFDI. In addition, the working group serves as an interface for the NFDI to comparable European and international initiatives in order to promote the connectivity of the NFDI with other infrastructures.
This meet-up would like to bring RSEs already working or interested in the NFDI together to create an active network and encourage RSEs to be part of the RSE working group in the NFDI Section Infra."""@en ;
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    dct:title "Research Software Engineering within the NFDI (INFRA-WG-RSE)"@en ;
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    bibo:abstract """Research Software Engineering is a fundamental part of the German National Research Data Infrastructure (NFDI). In accordance with that, a "deRSE Arbeitskreis NFDI" will serve as a connection point for RSEs in the NFDI inside deRSE e.V.
Within the NFDI e.V., several "sections" are dealing with overarching topics, e.g., the "Sektion Common Infrastructures" with its working groups on "Data Integration (DI)", "Data Management Planning (DMP)", "Data Science and Artificial Intelligence (DSAI)", "Electronic Lab Notebooks (ELN)", "Persistent Identifiers (PID)" and "Research Software Engineering (RSE)".
The RSE working group connects the NFDI consortia in software-related aspects. It focuses on three areas: Research software, software communities and software infrastructure at NFDI. In an advisory and supportive capacity, the working group operates a central forum and establishes the necessary software ecosystem within NFDI for the professional development of software infrastructure components, which in their entirety represent an integral part of the NFDI. In addition, the working group serves as an interface for the NFDI to comparable European and international initiatives in order to promote the connectivity of the NFDI with other infrastructures.
This meet-up would like to bring RSEs already working or interested in the NFDI together to create an active network and encourage RSEs to be part of the RSE working group in the NFDI Section Infra."""@en ;
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    dct:description """Introduction
Publishing sustainable research data and providing appropriate access for many research communities challenges many players: Researchers, RSEs, standardisation organizations and data repositories. With national research data infrastructures (NFDI) being set up in Germany, the latter could be solved in the mid- to long-term for specific datasets. In the meantime, researchers often produce datasets of data in research projects which are provided as services, e.g. from a web page, but may, due to a lack of funding, disappear in that form after the research project has ended. To circumvent this, open research data is hosted long-term on public platforms like university libraries, Zenodo or Github. However, this hosted data is not necessarily easily discoverable by different research communities. On top of that, research data is rarely published in isolation, but with links to related datasets, leading to the creation of link-preserving, FAIR linked open data (LOD) as RDF dumps, modelling data interoperably in common vocabularies. LOD in RDF preserves links, but is not necessarily Linked Open Usable Data (LOUD), i.e. it does not provide data in ways different research communities expect. We would like to address this problem of missing LOUD data while removing requirements on the backend such as hardware and software to a minimum.
Documentation-Tool
We believe that a solution to this data provision problem is publishing research data as static webpages and using standardised static APIs to serve data in ways different research communities expect.
We developed a documentation extension to our SPARQLing Unicorn QGIS Plugin, allowing to publish RDF data dumps as HTML page and RDF serialization per data instance, similar to what frontends to triple stores such as Pubby provide.
It is published as a QGIS Plugin, a standalone script on Github and a Github Action.
The resulting data dump is hostable on static webspaces e.g. Github pages and allows navigating the contents of the LOD data in HTML including a class tree. It may include:
* Further data formats: Graph Data (GraphML, GEXF), General Purpose (CSV)
* SPARQL querying in JavaScript using the data dump
* Generation of static APIs, e.g. JSON documents mimicking standardized APIs, for
* OGC API Features: Access to FeatureCollections from e.g. QGIS
* IIIF Presentation API 3.0: IIIF Manifest Files for images/media in the knowledge graph including typed collections
* CKAN API: Datasets in the DCAT vocabulary or data collections
Static APIs further the accessibility of LOD data for different research communities and increase the chances of data reusage and exposure in different research fields, while at the same time not depending on additional infrastructures for data provision.
Limitations and Future Work
Our talk shows the feasibility of using publicly available examples for geodata and CKAN (SPP Dataset, AncientPorts Dataset, CIGS Datatset) and the ARS-LOD dataset for static IIIF-data.
We discuss requirements and limitations of this kind of publishing in a RDM publishing workflow, in relation to NFDI plans and how to extend this approach to only partially open data using a Solid pod publishing workflow."""@en ;
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    dct:title "The SPARQL Unicorn: A Research Tool for Linked Open Data in QGIS and git-action-based ontology documentation"@en ;
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    bibo:abstract """Introduction
Publishing sustainable research data and providing appropriate access for many research communities challenges many players: Researchers, RSEs, standardisation organizations and data repositories. With national research data infrastructures (NFDI) being set up in Germany, the latter could be solved in the mid- to long-term for specific datasets. In the meantime, researchers often produce datasets of data in research projects which are provided as services, e.g. from a web page, but may, due to a lack of funding, disappear in that form after the research project has ended. To circumvent this, open research data is hosted long-term on public platforms like university libraries, Zenodo or Github. However, this hosted data is not necessarily easily discoverable by different research communities. On top of that, research data is rarely published in isolation, but with links to related datasets, leading to the creation of link-preserving, FAIR linked open data (LOD) as RDF dumps, modelling data interoperably in common vocabularies. LOD in RDF preserves links, but is not necessarily Linked Open Usable Data (LOUD), i.e. it does not provide data in ways different research communities expect. We would like to address this problem of missing LOUD data while removing requirements on the backend such as hardware and software to a minimum.
Documentation-Tool
We believe that a solution to this data provision problem is publishing research data as static webpages and using standardised static APIs to serve data in ways different research communities expect.
We developed a documentation extension to our SPARQLing Unicorn QGIS Plugin, allowing to publish RDF data dumps as HTML page and RDF serialization per data instance, similar to what frontends to triple stores such as Pubby provide.
It is published as a QGIS Plugin, a standalone script on Github and a Github Action.
The resulting data dump is hostable on static webspaces e.g. Github pages and allows navigating the contents of the LOD data in HTML including a class tree. It may include:
* Further data formats: Graph Data (GraphML, GEXF), General Purpose (CSV)
* SPARQL querying in JavaScript using the data dump
* Generation of static APIs, e.g. JSON documents mimicking standardized APIs, for
* OGC API Features: Access to FeatureCollections from e.g. QGIS
* IIIF Presentation API 3.0: IIIF Manifest Files for images/media in the knowledge graph including typed collections
* CKAN API: Datasets in the DCAT vocabulary or data collections
Static APIs further the accessibility of LOD data for different research communities and increase the chances of data reusage and exposure in different research fields, while at the same time not depending on additional infrastructures for data provision.
Limitations and Future Work
Our talk shows the feasibility of using publicly available examples for geodata and CKAN (SPP Dataset, AncientPorts Dataset, CIGS Datatset) and the ARS-LOD dataset for static IIIF-data.
We discuss requirements and limitations of this kind of publishing in a RDM publishing workflow, in relation to NFDI plans and how to extend this approach to only partially open data using a Solid pod publishing workflow."""@en ;
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    dct:title "Modellierung von Fuzzyness & Wobbliness in Geodaten am Beispiel archäologischer und geowissenschaftlicher Fundorte"@de ;
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    bibo:abstract "Insbesondere bei der Bereitstellung von Open Data nach den FAIR-Prinzipien zur bestmöglichen Offenheit und Transparenz ist die Angabe von Unsicherheiten und Zweifeln für den Nachnutzenden von enormer Bedeutung. Bei interdisziplinärer Zusammenarbeit ist dieser Aspekt umso wichtiger. In diesem Paper werden fünf data-driven interdisziplinäre Use-Cases für den Umgang mit und die Modellierung von vagen und unsicheren Georeferenzen aus dem Bereich der Archäologie und Geowissenschaften vorgestellt."@de ;
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    dct:description "We introduce the ontology documentation feature of the SPARQLing Unicorn QGIS Plugin, allowing the conversion of RDF data dumps to static HTML deployments with static versions of well-known APIs such as OGC API Features. Contents of RDF data are deployed interoperably accessible for many research communities. Our talk shows the tool's motivation, conversion process with test datasets, discusses limitations, standardization issues and future developments of this approach in general."@de ;
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    dct:title "The SPARQL Unicorn Ontology documentation: Exposing RDF geodata using static GeoAPIs"@de ;
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        <https://w3id.org/squirrelpapers/person/distel-anne-karoline>,
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    dct:description """Nachhaltige, nachvollziehbare, kolloberative Erfassung und FAIRifizierung wird in der Citizen Science Community immer wichtiger. Nur so können diese Daten mit anderen Daten vernetzt und in internationale Initiativen (z.B. NFDI4Objects) und Community-Hubs (z.B. Wikidata, OpenStreetMap, Portable Antiquities Scheme) aktiv eingebunden werden.  Das WWW stellt dazu kolloberative Plattformen wie das Wikiversum, Open Street Map (OSM), Sketchfab zur Verfügung. Diese können von Initiativen wie z.B. dem Research Squirrel Engineers Network oder einer Wikimedia Community of Practive zu “Offenen KulturDaten”, genutzt werden um so Teil eines archäologischen Wissensgraphen zu werden und den bereits bestehenden durch qualifizierte Daten anzureichern.
Zur Erfassung und Bereitstellung von Medien, gibt es z.B. Wikimedia Commons. Auch die digitale low-cost Erfassung mit Structure form Motion (SfM) Technologien ist mittlerweile mit dem Smartphone (z.B. KIRIengine) auch für nicht Geodäten möglich. Das Teilen der Daten kann über z.B  Sketchfab bzw. Open Source 3DHOP-Instanzen (z.B. archaeosquirrels.cloud), inkl. Dokumentation über z.B. GitHub mit dem SPARQL Unicorn Ontology Documentation Tool erfolgen. Die (semantische) Bereitstellung von ist auch für die Citizen Science Community in Wikidata oder in Wikibase-Instanzen, z.B. auf wikibase.cloud, oder OSM möglich.
Dieser Vortrag zeigt die Anwendung, Chancen und Herausforderungen dieser Technologien und Plattformen für Citizen Scientists anhand von vier Use Cases zu Ogham Steinen und Holy Wells in Irland und UK.
Use Cases
Im Rahmen eines Events am University College Cork (UCC) wurde ein Vorhaben des Research Squirrel Engineers Networks zur Dokumentation und FAIRen Bereitstellung irischer Ogham Steinen in Cork (Cork Public Museum, UCC) umgesetzt (Abb. 1): Ogham Steine CHUIS/1, GAERS/1 und UCC 4/CIIC 81. Diese wurden mit Fotos dokumentiert und in Wikimedia Commons integriert. Mit Hilfe von KIRIengine konnten 3D-Meshes erzeugt (Abb. 2) und über Sketchfab und 3DHOP zur Verfügung gestellt werden. Eine einheitliche Wikidata- und OSM-Modellierung von Ogham Steinen hilft zudem dabei die Daten in die Linked Open Data Cloud zu integrieren.
Das Ogham in 3D und das OG(H)AM Projekt digitalisiert u.A. irische und britische Ogham Steine mit photogrammetrischen Methoden (Abb. 3) und stellt diese Modelle und TEI EPIDOC unter freien Lizenzen zur Verfügung. Beispiele sind die Ogham Steine Ballinrannig V/VI.
Citizen Scientits im Vereinigten Königreich (UK), wie Douglas Ledingham erzeugen 3D-Meshes ehrenamtlich und teilen diese über Plattformen wie Sketchfab oder archaeosquirrels.cloud. Ein weiteres Beispiel ist der Fund eines Ogham Stones durch Citizen Scientist Graham Senior in Coventry (England). Dieser Fund ist nach fachwissenschaftler Begutachtung auf den Websiten des Portable Antiquities Scheme (PAS) veröffentlicht worden und wird mit freien Bildern (Abb. 4), 3D-Modellen (z.B. zur Nachnutzung als 3D-Print, vgl. Abb. 4) flankiert und ist in Wikidata und OSM integriert.
Im Citizen Science Projekt HolyWells werden Heilige Quellen im irischen County Kilkenny in Open Street Map gemappt, in Wikidata integriert, mit Wikimedia Commons angereichert und mit Hilfe von KIRIengine 3D meshes erzeugt und auf Sketchfab veröffentlicht. Ein Beispiel hierfür ist die “Freshford: St Lachtain's Well” (Abb. 5), beschrieben mit Q121840779, publiziert in Sketchfab und mit 3DHOP. Ein weiteres Beispiel “Callan St Augustine's Well”, beschrieben mit Q122189562, in Wikipedia, publiziert in Sketchfab und mit 3DHOP."""@de ;
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    dct:title "3D-Erfassung und FAIRe Publikation von Cultural Heritage Artefakten, insbesondere Ogham Steine und Holy Wells in Zusammenarbeit mit Citizen Scientists"@de ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """Nachhaltige, nachvollziehbare, kolloberative Erfassung und FAIRifizierung wird in der Citizen Science Community immer wichtiger. Nur so können diese Daten mit anderen Daten vernetzt und in internationale Initiativen (z.B. NFDI4Objects) und Community-Hubs (z.B. Wikidata, OpenStreetMap, Portable Antiquities Scheme) aktiv eingebunden werden.  Das WWW stellt dazu kolloberative Plattformen wie das Wikiversum, Open Street Map (OSM), Sketchfab zur Verfügung. Diese können von Initiativen wie z.B. dem Research Squirrel Engineers Network oder einer Wikimedia Community of Practive zu “Offenen KulturDaten”, genutzt werden um so Teil eines archäologischen Wissensgraphen zu werden und den bereits bestehenden durch qualifizierte Daten anzureichern.
Zur Erfassung und Bereitstellung von Medien, gibt es z.B. Wikimedia Commons. Auch die digitale low-cost Erfassung mit Structure form Motion (SfM) Technologien ist mittlerweile mit dem Smartphone (z.B. KIRIengine) auch für nicht Geodäten möglich. Das Teilen der Daten kann über z.B  Sketchfab bzw. Open Source 3DHOP-Instanzen (z.B. archaeosquirrels.cloud), inkl. Dokumentation über z.B. GitHub mit dem SPARQL Unicorn Ontology Documentation Tool erfolgen. Die (semantische) Bereitstellung von ist auch für die Citizen Science Community in Wikidata oder in Wikibase-Instanzen, z.B. auf wikibase.cloud, oder OSM möglich.
Dieser Vortrag zeigt die Anwendung, Chancen und Herausforderungen dieser Technologien und Plattformen für Citizen Scientists anhand von vier Use Cases zu Ogham Steinen und Holy Wells in Irland und UK.
Use Cases
Im Rahmen eines Events am University College Cork (UCC) wurde ein Vorhaben des Research Squirrel Engineers Networks zur Dokumentation und FAIRen Bereitstellung irischer Ogham Steinen in Cork (Cork Public Museum, UCC) umgesetzt (Abb. 1): Ogham Steine CHUIS/1, GAERS/1 und UCC 4/CIIC 81. Diese wurden mit Fotos dokumentiert und in Wikimedia Commons integriert. Mit Hilfe von KIRIengine konnten 3D-Meshes erzeugt (Abb. 2) und über Sketchfab und 3DHOP zur Verfügung gestellt werden. Eine einheitliche Wikidata- und OSM-Modellierung von Ogham Steinen hilft zudem dabei die Daten in die Linked Open Data Cloud zu integrieren.
Das Ogham in 3D und das OG(H)AM Projekt digitalisiert u.A. irische und britische Ogham Steine mit photogrammetrischen Methoden (Abb. 3) und stellt diese Modelle und TEI EPIDOC unter freien Lizenzen zur Verfügung. Beispiele sind die Ogham Steine Ballinrannig V/VI.
Citizen Scientits im Vereinigten Königreich (UK), wie Douglas Ledingham erzeugen 3D-Meshes ehrenamtlich und teilen diese über Plattformen wie Sketchfab oder archaeosquirrels.cloud. Ein weiteres Beispiel ist der Fund eines Ogham Stones durch Citizen Scientist Graham Senior in Coventry (England). Dieser Fund ist nach fachwissenschaftler Begutachtung auf den Websiten des Portable Antiquities Scheme (PAS) veröffentlicht worden und wird mit freien Bildern (Abb. 4), 3D-Modellen (z.B. zur Nachnutzung als 3D-Print, vgl. Abb. 4) flankiert und ist in Wikidata und OSM integriert.
Im Citizen Science Projekt HolyWells werden Heilige Quellen im irischen County Kilkenny in Open Street Map gemappt, in Wikidata integriert, mit Wikimedia Commons angereichert und mit Hilfe von KIRIengine 3D meshes erzeugt und auf Sketchfab veröffentlicht. Ein Beispiel hierfür ist die “Freshford: St Lachtain's Well” (Abb. 5), beschrieben mit Q121840779, publiziert in Sketchfab und mit 3DHOP. Ein weiteres Beispiel “Callan St Augustine's Well”, beschrieben mit Q122189562, in Wikipedia, publiziert in Sketchfab und mit 3DHOP."""@de ;
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    dct:description """Data modelling in archaeological research must handle uncertainty and ambiguities, especially in georeferencing. This enables re-using research data using the FAIR principles (Findable, Accessible, Interoperable, Reusable) and Open Science while guaranteeing data quality. Furthermore, for linking data, modelling vagueness, georeferencing methods and events, and FAIRification, graph-based modelling as Linked Open Data (LOD) proposed by Berners-Lee is the method and technique of choice. However, due to the enormous variety of research domains, an interdisciplinary, commonly understandable modelling of uncertainties and vagueness in research data is highly challenging. We will present two data-driven interdisciplinary use cases for dealing with and modelling vague and uncertain georeferenced findspots as LOD from the archaeological and geosciences domains.
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This poster presents exemplary modelling approaches for archaeology and geosciences, demonstrating the importance of dealing with doubts in the georeferencing process."""@en ;
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    dct:title "Taming Doubts - How to semantically model fuzzy and wobbly georeferences in archaeology and in the geosciences"@en ;
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    bibo:abstract """Data modelling in archaeological research must handle uncertainty and ambiguities, especially in georeferencing. This enables re-using research data using the FAIR principles (Findable, Accessible, Interoperable, Reusable) and Open Science while guaranteeing data quality. Furthermore, for linking data, modelling vagueness, georeferencing methods and events, and FAIRification, graph-based modelling as Linked Open Data (LOD) proposed by Berners-Lee is the method and technique of choice. However, due to the enormous variety of research domains, an interdisciplinary, commonly understandable modelling of uncertainties and vagueness in research data is highly challenging. We will present two data-driven interdisciplinary use cases for dealing with and modelling vague and uncertain georeferenced findspots as LOD from the archaeological and geosciences domains.
Graph-based data modelling uses three technologies: (I) Wikidata, (II) ontologies and Linked Open data using the Fuzzy Spatial Locations Ontology (FSLO), and (III) Wikibase. The main modelling idea is to publish and model the following georeferencing information as Linked Open Data: (1) describe where the geoinformation comes from, (2) describe the method of how the coordinate was created, (3) describe the uncertainty issue(s), and (4) use references into the Semantic Web. Methods I-III can be applied to at least use cases from the archaeological and geosciences domain: Ogham Stones in Ireland and the Eruption of the Campanian Ignimbrite.
This poster presents exemplary modelling approaches for archaeology and geosciences, demonstrating the importance of dealing with doubts in the georeferencing process."""@en ;
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Description and Objectives of the Community Cluster "Semantic Modelling & Linked Open Data" approved as part of NDFI4Objects in April 2023
Beschreibung und Zielsetzung des im Rahmen von NDFI4Objects im April 2023 beschlossenen Community Clusters "Semantic Modelling & Linked Open Data"
Communication
Current information about and collaboration in this Community Cluster via the following RocketChat channel: https://n4o-chat.nfdi.de/channel/CC_SemanticsLOD
Aktuelle Informationen zu und Mitarbeit in diesem Community Cluster über folgenden RocketChat-Kanal: https://n4o-chat.nfdi.de/channel/CC_SemanticsLOD
Kostituierende Sitzung / Inaugural Meeting
24.01.2024, 10.00 AM CET, Zoom Link will be announced in the RocketChat channel"""@en ;
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Description and Objectives of the Community Cluster "Semantic Modelling & Linked Open Data" approved as part of NDFI4Objects in April 2023
Beschreibung und Zielsetzung des im Rahmen von NDFI4Objects im April 2023 beschlossenen Community Clusters "Semantic Modelling & Linked Open Data"
Communication
Current information about and collaboration in this Community Cluster via the following RocketChat channel: https://n4o-chat.nfdi.de/channel/CC_SemanticsLOD
Aktuelle Informationen zu und Mitarbeit in diesem Community Cluster über folgenden RocketChat-Kanal: https://n4o-chat.nfdi.de/channel/CC_SemanticsLOD
Kostituierende Sitzung / Inaugural Meeting
24.01.2024, 10.00 AM CET, Zoom Link will be announced in the RocketChat channel"""@en ;
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    dct:description """Forschungsdaten in der Archäologie sind sehr heterogen und immer im domänenbezogenen Kontext und interdisziplinär zu betrachten. Um diese Daten untereinander austauschbar, interoperabel und kompatibel zu machen, sind ein gemeinsames Verständnis und eine technische Implementierung nötig. Nur so kann eine übergreifende Abfrage des resultierenden interdisziplinären Knowledge Graphen erfolgen.
Um diese Herausforderung anzunehmen, möchte die Nationale Forschungsdateninfrastruktur (NFDI) beitragen und hat sich in mehrere fachbezogene Konsortien strukturiert. Das der Archäologie nahe Konsortium NFDI4Objects (N4O) ist eine Forschungsdateninfrastruktur für die materiellen Hinterlassenschaften der Menschheitsgeschichte und richtet sich an Forscher*innen und Praktiker*innen, deren Arbeitsschwerpunkte auf dem materiellen Erbe von rund drei Millionen Jahren Menschheits- und Umweltgeschichte liegen.
Eine Strategie, um eine Datenhomogenisierung und inhaltliche Vernetzung innerhalb von NFDI4Objects zu implementieren und eine Anschlussfähigkeit an die anderen konsortialen Knowledge Graphen zu gewährleisten, ist die Erstellung eines gemeinsamen Minimal-Objekt-Metadatensets (N4O MMDS) und einer dazugehörigen Objekt-Ontologie (N4O OO). Diese wird in NFDI4Objects in einer Temporary Working Group (TWG) erfolgen. In den Bereichen der Sammlungen sind Austauschformate wie LIDO und die Minimaldatensatz-Empfehlung für Museen und Sammlungen der DDB bereits implementiert und müssen für die heterogenen Kontexte und Daten von NFDI4Objects noch angepasst werden. Die N4O OO ist eine RDF-Repräsentation (Basis für Linked Open Data) des Metadatensets und basiert u.A. auf CIDOC-CRM,  schema.org, PROV-O. Hier kann die LADO-Ontologie des LEIZA eine Basis bieten. Der N4O MMDS und die N4O OO bilden den Objektzyklus, der sukzessive durch die NFDI4Objects Task Areas mit den Aufgaben documenting, collecting, analysing und protecting mit Informationen angereichert wird, ab. Diese Strukturen werden genutzt, da der Datenfluss in den Knowledge Graphen des Konsortiums LIDO/XML und RDF vorsieht (Abb. 1). Kleine FAIRification Tools wie das SPARQLing Unicorn Plugin, und das SPARQL Unicorn Ontology Documentation Tool können zudem dabei helfen, die für den Knowledge Graphen benötigten RDF-Daten zu visualisieren und für den Menschen lesbar bereitzustellen.
Dieser Vortrag nimmt existierende Beispiele aus der Archäologie (z.B. Irish Ogham Stones, Samian Research, ARS3D, NAVISone, Brandenburg 5000 BC, …), den Geowissenschaften (z.B. Campanian Ignimbrite), sowie aus dem Sammlungskontext (z.B. Teilbestände aus Goethes heterogenen Sammlungen an der Klassik Stiftung Weimar).  Anhand dieser soll aufgezeigt werden, wo die Gemeinsamkeiten und Unterschiede der Daten, aber auch wo die Herausforderungen bei der Erstellung einer übergreifenden, generischen Struktur zum Datenaustausch und Aufbau eines Knowledge Graphen liegen.
Die LOD der Irish Ogham Stones basieren auf der Ogham Ontology, die u.A. CIDOC-CRM, SKOS, FOAF, GeoSPARQL und PROV-O als Grundlage nimmt. Die RDF-Modellierungen von Samian Research, ARS3D, NAVISone und Brandenburg 5000 BC bilden eine Teilmenge der LADO Ontologie. Diese Ontologie entwickelte sich aus der “Ogham Ontology” und basiert daher auf den gleichen Grundsätzen und Basis-Ontologien. Die Campanian Ignimbrite Daten basieren auf der “Fuzzy Spatial Locations Ontology”, welche wiederum auf u.A. auf CIDOC-CRM und schema.org basiert. Die Daten der Klassik Stiftung Weimar werden hingegen in einer relationalen Museumsdatenbank erfasst, die beim Datenexport u. a. das Metadatenschema LIDO ausgibt.
Zudem wird aufgezeigt, wie man sich aktiv an der TWG zur Entwicklung einer N4O Objects Ontology (N4O OO) und eines Minimal Metadaten-Set (N4O MMDS) beteiligen kann."""@de ;
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    bibo:abstract """Forschungsdaten in der Archäologie sind sehr heterogen und immer im domänenbezogenen Kontext und interdisziplinär zu betrachten. Um diese Daten untereinander austauschbar, interoperabel und kompatibel zu machen, sind ein gemeinsames Verständnis und eine technische Implementierung nötig. Nur so kann eine übergreifende Abfrage des resultierenden interdisziplinären Knowledge Graphen erfolgen.
Um diese Herausforderung anzunehmen, möchte die Nationale Forschungsdateninfrastruktur (NFDI) beitragen und hat sich in mehrere fachbezogene Konsortien strukturiert. Das der Archäologie nahe Konsortium NFDI4Objects (N4O) ist eine Forschungsdateninfrastruktur für die materiellen Hinterlassenschaften der Menschheitsgeschichte und richtet sich an Forscher*innen und Praktiker*innen, deren Arbeitsschwerpunkte auf dem materiellen Erbe von rund drei Millionen Jahren Menschheits- und Umweltgeschichte liegen.
Eine Strategie, um eine Datenhomogenisierung und inhaltliche Vernetzung innerhalb von NFDI4Objects zu implementieren und eine Anschlussfähigkeit an die anderen konsortialen Knowledge Graphen zu gewährleisten, ist die Erstellung eines gemeinsamen Minimal-Objekt-Metadatensets (N4O MMDS) und einer dazugehörigen Objekt-Ontologie (N4O OO). Diese wird in NFDI4Objects in einer Temporary Working Group (TWG) erfolgen. In den Bereichen der Sammlungen sind Austauschformate wie LIDO und die Minimaldatensatz-Empfehlung für Museen und Sammlungen der DDB bereits implementiert und müssen für die heterogenen Kontexte und Daten von NFDI4Objects noch angepasst werden. Die N4O OO ist eine RDF-Repräsentation (Basis für Linked Open Data) des Metadatensets und basiert u.A. auf CIDOC-CRM,  schema.org, PROV-O. Hier kann die LADO-Ontologie des LEIZA eine Basis bieten. Der N4O MMDS und die N4O OO bilden den Objektzyklus, der sukzessive durch die NFDI4Objects Task Areas mit den Aufgaben documenting, collecting, analysing und protecting mit Informationen angereichert wird, ab. Diese Strukturen werden genutzt, da der Datenfluss in den Knowledge Graphen des Konsortiums LIDO/XML und RDF vorsieht (Abb. 1). Kleine FAIRification Tools wie das SPARQLing Unicorn Plugin, und das SPARQL Unicorn Ontology Documentation Tool können zudem dabei helfen, die für den Knowledge Graphen benötigten RDF-Daten zu visualisieren und für den Menschen lesbar bereitzustellen.
Dieser Vortrag nimmt existierende Beispiele aus der Archäologie (z.B. Irish Ogham Stones, Samian Research, ARS3D, NAVISone, Brandenburg 5000 BC, …), den Geowissenschaften (z.B. Campanian Ignimbrite), sowie aus dem Sammlungskontext (z.B. Teilbestände aus Goethes heterogenen Sammlungen an der Klassik Stiftung Weimar).  Anhand dieser soll aufgezeigt werden, wo die Gemeinsamkeiten und Unterschiede der Daten, aber auch wo die Herausforderungen bei der Erstellung einer übergreifenden, generischen Struktur zum Datenaustausch und Aufbau eines Knowledge Graphen liegen.
Die LOD der Irish Ogham Stones basieren auf der Ogham Ontology, die u.A. CIDOC-CRM, SKOS, FOAF, GeoSPARQL und PROV-O als Grundlage nimmt. Die RDF-Modellierungen von Samian Research, ARS3D, NAVISone und Brandenburg 5000 BC bilden eine Teilmenge der LADO Ontologie. Diese Ontologie entwickelte sich aus der “Ogham Ontology” und basiert daher auf den gleichen Grundsätzen und Basis-Ontologien. Die Campanian Ignimbrite Daten basieren auf der “Fuzzy Spatial Locations Ontology”, welche wiederum auf u.A. auf CIDOC-CRM und schema.org basiert. Die Daten der Klassik Stiftung Weimar werden hingegen in einer relationalen Museumsdatenbank erfasst, die beim Datenexport u. a. das Metadatenschema LIDO ausgibt.
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    dct:description """Im Konsortium NFDI4Objects stehen die physischen Hinterlassenschaften der Menschheits- und Umweltgeschichte im Mittelpunkt. Diese sind immer in Zusammenhang mit Akteuren, Ereignissen, Zeiträumen und Orten zu betrachten: Wer hat was, wann, wo, mit einem bestimmten Objekt getan. Durch Ereignisse werden Objekte in einen bestimmten Kontext gesetzt, der nähere Aussagen zu ihnen liefert und Zusammenhänge zwischen ihnen, aber auch mit ihnen verknüpften Personen, Orten und Zeitschichten aufzeigt. Die digitale Objektbiografie [1] umfasst die ereignisbasierte, forschungsgetriebene Datenaufbereitung, bei der Objekte mit allen verfügbaren Informationen zu ihren einzelnen Stationen sowie den Quellen, aus denen die Informationen stammen, verknüpft werden.
Primäres Ziel dieser Arbeitsgruppe ist der Aufbau einer von der NFDI4Objects getragenen N4O Anwendungsontologie (N4O Object Ontology; N4O OO), um die multidisziplinären, heterogenen Daten des Konsortiums optimal für eine Integration in den Wissengraphen des Konsortiums [2] aufbereiten und zur Verfügung stellen zu können. Die Ontologie enthält dabei Elemente einer Objektbiografie, welche u. a. auf dem CIDOC CRM Referenzmodell [3] basiert, baut aber auf einem Minimal-Metadatensatz (Minimal Metadata-Set; N4O MMDS) auf. Da das N4O-Derivat der NFDIcore Ontology (N4O Core) bei einer digitalen Ressource (nfdi:Resource [4]) selbst endet, z.B. nfdi:Dataset oder nfdi:SemanticExpressivity, ist eine Ontologie für die Zusammenführung der Forschungsdaten notwendig. Die Entwicklung wird in einer gemeinsamen Diskussion mit den Task Areas, entsprechenden TWGs und Community Clustern realisiert.
Der N4O MMDS wird vor allem durch das fachliche Arbeitsprogramm - dem Objektzyklus [5] - der Task Areas 1-4 “Documentation”, “Collecting”, “Analysis & Experiments” sowie “Protecting” bestimmt und berührt dabei verschiedene Themengebiete, wie Ausgrabung, (digitale) Rekonstruktion und Annotation, Sammlung, Kunstgeschichte, Restaurierung/Konservierung, naturwissenschaftliche Analysen, Denkmalpflege, Provenienzforschung. Dieser Ansatz geht vom konkreten Gegenstand der Forschung - dem Objekt - aus, die Datenstrukturen zu entwickeln.
In dieser TWG wird definiert, welche Elemente/Attribute der N4O MMDS [6] enthalten muss, d. h., welche Informationen zur Beschreibung von Objektdaten auf dem Weg durch den Objektzyklus notwendig sind. Dies beinhaltet die digitalen Schritte: capture, qualify, analyse, share and interlink [7] Der Er wird in einer technischen Beschreibung (z.B. JSON) zur Verfügung gestellt und muss für den individuellen Datenaustausch der heterogenen Systeme in diverse “Dialekte” transformiert werden, z.B. LIDO/XML. Der N4O MMDS und die N4O OO als dessen RDF-Repräsentation (basierend auf CIDOC CRM, schema.org, PROV-O, SKOS, BFO) bilden den Objektzyklus, der sukzessive durch die Task Areas 1-4 mit mit Informationen angereichert wird, ab. Diese Strukturen werden genutzt, da der Datenfluss in den Knowledge Graph zum einen LIDO/XML und RDF vorsieht [8].
Diese Ontologie wird gemeinsam mit den im Konsortium tätigen Wissenschaftler*Innen und Stakeholdern anhand von Use Cases in der TWG “Development of a common N4O Objects Ontology (N4O OO) and a Minimal Metadata-Set (N4O MMDS)” erarbeitet. Eine inhaltliche Abstimmung mit den anderen geistes- und kulturwissenschaftlichen Konsortien der sogenannten Memorandum-of-understanding-Gruppe [9], bestehend aus NFDI4Culture, NFDI4Memory, NFDI4Objects und Text+, zur Herstellung der Interoperabilität ist durch die Zusammenarbeit in verschiedenen Arbeitsgruppen sichergestellt. Weitere benachbarte Konsortien, z.B. NFDI4Earth, NFDI4Biodiversity, MaRDI etc., werden ebenfalls in die Entwicklungsprozesse einbezogen, um die naturwissenschaftlichen Aspekte abzudecken.""" ;
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    bibo:abstract """Im Konsortium NFDI4Objects stehen die physischen Hinterlassenschaften der Menschheits- und Umweltgeschichte im Mittelpunkt. Diese sind immer in Zusammenhang mit Akteuren, Ereignissen, Zeiträumen und Orten zu betrachten: Wer hat was, wann, wo, mit einem bestimmten Objekt getan. Durch Ereignisse werden Objekte in einen bestimmten Kontext gesetzt, der nähere Aussagen zu ihnen liefert und Zusammenhänge zwischen ihnen, aber auch mit ihnen verknüpften Personen, Orten und Zeitschichten aufzeigt. Die digitale Objektbiografie [1] umfasst die ereignisbasierte, forschungsgetriebene Datenaufbereitung, bei der Objekte mit allen verfügbaren Informationen zu ihren einzelnen Stationen sowie den Quellen, aus denen die Informationen stammen, verknüpft werden.
Primäres Ziel dieser Arbeitsgruppe ist der Aufbau einer von der NFDI4Objects getragenen N4O Anwendungsontologie (N4O Object Ontology; N4O OO), um die multidisziplinären, heterogenen Daten des Konsortiums optimal für eine Integration in den Wissengraphen des Konsortiums [2] aufbereiten und zur Verfügung stellen zu können. Die Ontologie enthält dabei Elemente einer Objektbiografie, welche u. a. auf dem CIDOC CRM Referenzmodell [3] basiert, baut aber auf einem Minimal-Metadatensatz (Minimal Metadata-Set; N4O MMDS) auf. Da das N4O-Derivat der NFDIcore Ontology (N4O Core) bei einer digitalen Ressource (nfdi:Resource [4]) selbst endet, z.B. nfdi:Dataset oder nfdi:SemanticExpressivity, ist eine Ontologie für die Zusammenführung der Forschungsdaten notwendig. Die Entwicklung wird in einer gemeinsamen Diskussion mit den Task Areas, entsprechenden TWGs und Community Clustern realisiert.
Der N4O MMDS wird vor allem durch das fachliche Arbeitsprogramm - dem Objektzyklus [5] - der Task Areas 1-4 “Documentation”, “Collecting”, “Analysis & Experiments” sowie “Protecting” bestimmt und berührt dabei verschiedene Themengebiete, wie Ausgrabung, (digitale) Rekonstruktion und Annotation, Sammlung, Kunstgeschichte, Restaurierung/Konservierung, naturwissenschaftliche Analysen, Denkmalpflege, Provenienzforschung. Dieser Ansatz geht vom konkreten Gegenstand der Forschung - dem Objekt - aus, die Datenstrukturen zu entwickeln.
In dieser TWG wird definiert, welche Elemente/Attribute der N4O MMDS [6] enthalten muss, d. h., welche Informationen zur Beschreibung von Objektdaten auf dem Weg durch den Objektzyklus notwendig sind. Dies beinhaltet die digitalen Schritte: capture, qualify, analyse, share and interlink [7] Der Er wird in einer technischen Beschreibung (z.B. JSON) zur Verfügung gestellt und muss für den individuellen Datenaustausch der heterogenen Systeme in diverse “Dialekte” transformiert werden, z.B. LIDO/XML. Der N4O MMDS und die N4O OO als dessen RDF-Repräsentation (basierend auf CIDOC CRM, schema.org, PROV-O, SKOS, BFO) bilden den Objektzyklus, der sukzessive durch die Task Areas 1-4 mit mit Informationen angereichert wird, ab. Diese Strukturen werden genutzt, da der Datenfluss in den Knowledge Graph zum einen LIDO/XML und RDF vorsieht [8].
Diese Ontologie wird gemeinsam mit den im Konsortium tätigen Wissenschaftler*Innen und Stakeholdern anhand von Use Cases in der TWG “Development of a common N4O Objects Ontology (N4O OO) and a Minimal Metadata-Set (N4O MMDS)” erarbeitet. Eine inhaltliche Abstimmung mit den anderen geistes- und kulturwissenschaftlichen Konsortien der sogenannten Memorandum-of-understanding-Gruppe [9], bestehend aus NFDI4Culture, NFDI4Memory, NFDI4Objects und Text+, zur Herstellung der Interoperabilität ist durch die Zusammenarbeit in verschiedenen Arbeitsgruppen sichergestellt. Weitere benachbarte Konsortien, z.B. NFDI4Earth, NFDI4Biodiversity, MaRDI etc., werden ebenfalls in die Entwicklungsprozesse einbezogen, um die naturwissenschaftlichen Aspekte abzudecken.""" ;
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    dct:description """Das Konsortium NFDI4Objects der Nationalen Forschungsdateninfrastruktur (NFDI) beschäftigt sich mit dem Forschungsdatenmanagement des materiellen Erbes von rund drei Millionen Jahren Menschheits- und Umweltgeschichte und entwickelt digitale Services für die interdisziplinäre archäologische Forschung. Die Arbeitsbereiche (Task Areas; TA) in NFDI4Objects hangeln sich entlang des Konzept der Objektbiografie, in dem ein Objekt dokumentiert (TA1: Documentation), gesammelt (TA2: Collecting), analysiert (TA3: Analysis & Experiments), geschützt (TA4: Protecting), gespeichert und geteilt (TA5: Storage, Access & Dissemination) wird. Gleiches gilt für digitale (Meta-)Daten über das Objekt, die in einem Forschungsdatenlebenszyklus die Stationen “plan & design”, “collect & capture”, “enrich & interpret”, “archive & preserve”, “share, publish and discover”, sowie “resuse & cite” durchlaufen. Begleitet wird dies durch FAIRification Services, welche zu data -management, -capturing, -enrichment, -preservation, -discovery und -connecting in NFDI4Objects entwickelt und bereitgestellt werden.
Task Area 2 “Collecting” entwickelt eine vernetzte Forschungsdateninfrastruktur im Bereich der Sammlungen und Museen, begleitet mit qualitätsorientierten Datenmanagementprozessen, die die komplexen Anforderungen des wissenschaftlichen Sammelns von Objekten, der sammlungsbezogenen Forschung und Provenienzforschung unterstützen. Task Area 4 “Protecting” befasst sich mit Fragen des Datenmanagements und institutionsübergreifenden Datenaustauschs in den Bereichen Cultural Heritage, Denkmalschutz – insbesondere aus Sicht der föderalen behördlichen Struktur und deren gesetzlichen Vorgaben und Richtlinien – sowie Konservierung und Restaurierung von Sammlungsobjekten oder Denkmälern.
Dieses Paper zeigt, welche digitalen Dienste – insbesondere in und aus den den Task Areas 2 “Collecting” und 4 “Protecting” – für die Archäologien in Deutschland und darüber hinaus in und mit Hilfe von NFDI4Objects (weiter-)entwickelt werden, bzw. bereits vorhanden oder geplant sind. Es wird aufgezeigt, welcher Nutzen durch digitale Dienste für den Datenlebenszyklus in der Archäologie entstehen um Anwendungen zwischen Grabungsalltag, musealer Arbeit und wissenschaftlicher Arbeit zu verknüpfen. In diesem Prozess der FAIRification hin zu Open Science spielen Open Source Software (FOSS), Open Data – auch Linked Open Data – und Open Access unter Einhaltung internationaler Vorgaben (z.B. DSGVO) eine entscheidente Rolle.
Wir stellen in diesem Paper in Zusammenarbeit mit den Institutionen und Partnern aus NFDI4Objects folgende FAIRification Services vor:
bereits vorhanden
Academic Meta Tool – API for RDF modelling of vagueness in research data
re3dragon – lookup and resolve tool for LOD resources
Data and Research Tool Framework archaeology.link
Alligator – An Allen Transformator
LEIZA Conservation Thesaurus
in Entwicklung (in Zusammenarbeit mit der Community)
Provenienzgazetteers zur FAIRen Bereitstellung in einer Wikibase
Knowledge Management Tool for Conservation and Restoration Processes
Recommendations/Good Practices and Research Toolkits for Knowledge Modelling
Standards and interfaces for interdisciplinary knowledge modelling and data exchange of heterogeneous systems
Tools for managing knowledge modelling, including fuzzy and wobbly semantics
Wikibase und Semantic Media Wiki Infrastructures
Recommendations/Good Practices zur Integration in das Wikiversum"""@de ;
    dct:identifier "https://doi.org/10.5281/zenodo.13897159" ;
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    dct:issued "2024-10-07"^^xsd:date ;
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    dct:license <https://creativecommons.org/licenses/by/4.0/> ;
    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Interdisziplinäre Knowledge Graphen? Wieso man eine gemeinsame Object-Ontologie und ein Minimal-Metadatenset benötigt, um FDM in einem Knowledge Graphen zum Leben zu erwecken"@de ;
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    bibo:abstract """Das Konsortium NFDI4Objects der Nationalen Forschungsdateninfrastruktur (NFDI) beschäftigt sich mit dem Forschungsdatenmanagement des materiellen Erbes von rund drei Millionen Jahren Menschheits- und Umweltgeschichte und entwickelt digitale Services für die interdisziplinäre archäologische Forschung. Die Arbeitsbereiche (Task Areas; TA) in NFDI4Objects hangeln sich entlang des Konzept der Objektbiografie, in dem ein Objekt dokumentiert (TA1: Documentation), gesammelt (TA2: Collecting), analysiert (TA3: Analysis & Experiments), geschützt (TA4: Protecting), gespeichert und geteilt (TA5: Storage, Access & Dissemination) wird. Gleiches gilt für digitale (Meta-)Daten über das Objekt, die in einem Forschungsdatenlebenszyklus die Stationen “plan & design”, “collect & capture”, “enrich & interpret”, “archive & preserve”, “share, publish and discover”, sowie “resuse & cite” durchlaufen. Begleitet wird dies durch FAIRification Services, welche zu data -management, -capturing, -enrichment, -preservation, -discovery und -connecting in NFDI4Objects entwickelt und bereitgestellt werden.
Task Area 2 “Collecting” entwickelt eine vernetzte Forschungsdateninfrastruktur im Bereich der Sammlungen und Museen, begleitet mit qualitätsorientierten Datenmanagementprozessen, die die komplexen Anforderungen des wissenschaftlichen Sammelns von Objekten, der sammlungsbezogenen Forschung und Provenienzforschung unterstützen. Task Area 4 “Protecting” befasst sich mit Fragen des Datenmanagements und institutionsübergreifenden Datenaustauschs in den Bereichen Cultural Heritage, Denkmalschutz – insbesondere aus Sicht der föderalen behördlichen Struktur und deren gesetzlichen Vorgaben und Richtlinien – sowie Konservierung und Restaurierung von Sammlungsobjekten oder Denkmälern.
Dieses Paper zeigt, welche digitalen Dienste – insbesondere in und aus den den Task Areas 2 “Collecting” und 4 “Protecting” – für die Archäologien in Deutschland und darüber hinaus in und mit Hilfe von NFDI4Objects (weiter-)entwickelt werden, bzw. bereits vorhanden oder geplant sind. Es wird aufgezeigt, welcher Nutzen durch digitale Dienste für den Datenlebenszyklus in der Archäologie entstehen um Anwendungen zwischen Grabungsalltag, musealer Arbeit und wissenschaftlicher Arbeit zu verknüpfen. In diesem Prozess der FAIRification hin zu Open Science spielen Open Source Software (FOSS), Open Data – auch Linked Open Data – und Open Access unter Einhaltung internationaler Vorgaben (z.B. DSGVO) eine entscheidente Rolle.
Wir stellen in diesem Paper in Zusammenarbeit mit den Institutionen und Partnern aus NFDI4Objects folgende FAIRification Services vor:
bereits vorhanden
Academic Meta Tool – API for RDF modelling of vagueness in research data
re3dragon – lookup and resolve tool for LOD resources
Data and Research Tool Framework archaeology.link
Alligator – An Allen Transformator
LEIZA Conservation Thesaurus
in Entwicklung (in Zusammenarbeit mit der Community)
Provenienzgazetteers zur FAIRen Bereitstellung in einer Wikibase
Knowledge Management Tool for Conservation and Restoration Processes
Recommendations/Good Practices and Research Toolkits for Knowledge Modelling
Standards and interfaces for interdisciplinary knowledge modelling and data exchange of heterogeneous systems
Tools for managing knowledge modelling, including fuzzy and wobbly semantics
Wikibase und Semantic Media Wiki Infrastructures
Recommendations/Good Practices zur Integration in das Wikiversum"""@de ;
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    dct:description "Den Teilnehmenden soll die aktive Arbeit mit Linked Open Data nähergebracht werden. Ihnen werden digitale Werkzeuge (hier QGIS, Wikidata und Wikibase) vorgestellt und eine Einführung darin gegeben. Im Mittelpunkt stehen die Modellierung von Daten in Wikibase-Instanzen und das dynamische Abfragen mit Hilfe von Open Source Tools, wie das SPARQLing Unicorn QGIS Plugin, bzw. online unter: http://nomisma.org/sparql/. Einführung in die einfache Erzeugung von RDF-Daten aus CSV-Dateien (z.B. durch die Nutzung von RDFier)."@de ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Workshop III: Linked Open Data am Beispiel von RDF-Daten, Wikidata und Wikibase"@de ;
    dct:type sqpt:workshop ;
    bibo:abstract "Den Teilnehmenden soll die aktive Arbeit mit Linked Open Data nähergebracht werden. Ihnen werden digitale Werkzeuge (hier QGIS, Wikidata und Wikibase) vorgestellt und eine Einführung darin gegeben. Im Mittelpunkt stehen die Modellierung von Daten in Wikibase-Instanzen und das dynamische Abfragen mit Hilfe von Open Source Tools, wie das SPARQLing Unicorn QGIS Plugin, bzw. online unter: http://nomisma.org/sparql/. Einführung in die einfache Erzeugung von RDF-Daten aus CSV-Dateien (z.B. durch die Nutzung von RDFier)."@de ;
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        <https://w3id.org/squirrelpapers/person/0000-0003-3223-2079> ;
    dct:description "Den Teilnehmenden sollen das N4O Minimal-Metadatensatz/Minimal Metadata-Set (N4O MMDS) als “Minimaldatensatz” und Grundlage für Austauschformate und die N4O Objects Ontology (N4O OO) als semantische Struktur im Knowledge Graph nähergebracht werden."@de ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Workshop VI: Harmonisierung und Integration von Konsortialdiensten durch Verknüpfung dezentraler Systeme in NFDI4Objects"@de ;
    dct:type sqpt:workshop ;
    bibo:abstract "Den Teilnehmenden sollen das N4O Minimal-Metadatensatz/Minimal Metadata-Set (N4O MMDS) als “Minimaldatensatz” und Grundlage für Austauschformate und die N4O Objects Ontology (N4O OO) als semantische Struktur im Knowledge Graph nähergebracht werden."@de ;
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    dct:description """General Information
Description and Objectives of the Community Cluster "Research Software Engineering (RSE)" approved as part of NDFI4Objects in April 2023
Beschreibung und Zielsetzung des im Rahmen von NDFI4Objects im April 2023 beschlossenen Community Clusters "Research Software Engineering (RSE)"
Communication
Current information about and collaboration in this Community Cluster via the following RocketChat channel: https://n4o-chat.nfdi.de/channel/CC_ResearchSoftwareEngineering
Aktuelle Informationen zu und Mitarbeit in diesem Community Cluster über folgenden RocketChat-Kanal: https://n4o-chat.nfdi.de/channel/CC_ResearchSoftwareEngineering
Konstituierende Sitzung / Inaugural Meeting
24.01.2024, 14.00 AM CET, Zoom Link will be announced in the RocketChat channel""" ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "NFDI4Objects - Community Cluster \"Research Software Engineering (RSE)\"" ;
    dct:type sqpt:proposal ;
    bibo:abstract """General Information
Description and Objectives of the Community Cluster "Research Software Engineering (RSE)" approved as part of NDFI4Objects in April 2023
Beschreibung und Zielsetzung des im Rahmen von NDFI4Objects im April 2023 beschlossenen Community Clusters "Research Software Engineering (RSE)"
Communication
Current information about and collaboration in this Community Cluster via the following RocketChat channel: https://n4o-chat.nfdi.de/channel/CC_ResearchSoftwareEngineering
Aktuelle Informationen zu und Mitarbeit in diesem Community Cluster über folgenden RocketChat-Kanal: https://n4o-chat.nfdi.de/channel/CC_ResearchSoftwareEngineering
Konstituierende Sitzung / Inaugural Meeting
24.01.2024, 14.00 AM CET, Zoom Link will be announced in the RocketChat channel""" ;
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        fabio:Expression,
        dcat:Dataset ;
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    dct:creator <https://w3id.org/squirrelpapers/person/0000-0002-3246-3531>,
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    dct:description """Linked Open Data (LOD) is an essential component of modern RDM initiatives for the FAIRification and comprehensible modelling of research data in the spirit of Open Science. Especially in the NFDI – and its various consortia – ontologies and the resulting LOD play a decisive role in linking heterogeneous and interdisciplinary research data. The W3C standard used for this purpose – the Resource Description Framework (RDF) – already offers a diverse toolkit to enable data to interact with each other in an interoperable manner. Geodata in particular is an entity that brings different data together.
In addition to its established standards from the OGC service family (WMS, WFS, OGC API features, ...), the Open Geospatial Consortium (OGC) has also implemented a standard for geodata in RDF: GeoSPARQL.
However, the creation, publication and processing of (geo-)LOD in daily work, e.g., GIS, is not trivial and requires some expertise. In addition, community hubs such as Wikidata (Wikimedia) offer great opportunities for the participation of citizen scientists and the integration of data from Citizen Science. The SPARQL Unicorn was developed as a QGIS plugin [https://t1p.de/sparqlunicorn-qgis], which is capable of importing RDF data into the local GIS via SPARQL queries, to integrate this into daily working practice. It also offers options for transforming geodata into linked data and for generating HTML documentation of ontologies and research data [https://t1p.de/sparqlunicorn-ontdoc].
This workshop provides basic knowledge of geodata as Linked Open Data, the SPARQL query language and Wikidata. In addition, FAIRification tools such as SPARQL Unicorn will be introduced to integrate geodata modelled as LOD in daily work, e.g. with GIS. The workshop relies on data from the humanities and cultural sciences, in particular from archaeology."""@en ;
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    bibo:abstract """Linked Open Data (LOD) is an essential component of modern RDM initiatives for the FAIRification and comprehensible modelling of research data in the spirit of Open Science. Especially in the NFDI – and its various consortia – ontologies and the resulting LOD play a decisive role in linking heterogeneous and interdisciplinary research data. The W3C standard used for this purpose – the Resource Description Framework (RDF) – already offers a diverse toolkit to enable data to interact with each other in an interoperable manner. Geodata in particular is an entity that brings different data together.
In addition to its established standards from the OGC service family (WMS, WFS, OGC API features, ...), the Open Geospatial Consortium (OGC) has also implemented a standard for geodata in RDF: GeoSPARQL.
However, the creation, publication and processing of (geo-)LOD in daily work, e.g., GIS, is not trivial and requires some expertise. In addition, community hubs such as Wikidata (Wikimedia) offer great opportunities for the participation of citizen scientists and the integration of data from Citizen Science. The SPARQL Unicorn was developed as a QGIS plugin [https://t1p.de/sparqlunicorn-qgis], which is capable of importing RDF data into the local GIS via SPARQL queries, to integrate this into daily working practice. It also offers options for transforming geodata into linked data and for generating HTML documentation of ontologies and research data [https://t1p.de/sparqlunicorn-ontdoc].
This workshop provides basic knowledge of geodata as Linked Open Data, the SPARQL query language and Wikidata. In addition, FAIRification tools such as SPARQL Unicorn will be introduced to integrate geodata modelled as LOD in daily work, e.g. with GIS. The workshop relies on data from the humanities and cultural sciences, in particular from archaeology."""@en ;
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    dct:description """NFDI4Objects (N4O) represents a broad community dealing with material remains of human history from around 3 million years and involves numerous disciplines from the humanities, cultural studies and natural sciences with an archaeological and historical focus [1]. The objects examined include serially produced objects such as coins and organic remains such as wood, bones, or pollen. The objects and their relations are constantly changing, causing individual biographies. Therefore, the N4O central tasks include (a) comprehending representations of physical objects as research data, (b) relating them to individual contexts, (c) transforming them adequately into the digital space, and (d) curating them according to domain-specific requirements. Consequently, the NFDI4Objects Research Data Lifecycle goes along with the Object Biography. This leads to a paradigm derived from the FAIRification workflow: (i) capturing, (ii) semantic (meta) data modelling and qualifying, (iii) applying (scientific) analysis, (iv) sharing the data by visualisation and FAIRified publication, (v) interlinking and integrating data into knowledge graphs [2].
Within the NFDI infrastructure and to apply the FAIR principles, research (meta)data must be shared, interlinked, and modelled in a common and community-accepted way. To implement this, N4O Fundamental Services (N4O FS) and N4O Community Services (N4O CS) will be provided. N4O FS are, e.g., DANTE, cocoda [3], BARTOC, the N4O Knowledge Graph [4], the FOSS N4O Management Hub [5], and the N4O Commons Inkubator [6]. N4O CS are FAIRification Tools and Services such as Alligator, Academic Meta Tool, re3dragon, SPARQLing Unicorn QGIS Plugin [7], archaeology.link, and Wikibase / Semantic Media Wiki / Triplestore infrastructures.
N4O contains many terminologies [8], such as ontologies, authority files, and community-driven vocabularies, that must be published and indexed (via DANTE and BARTOC) and semantically aligned (via Cocoda [3]) to contribute to TS4NFDI. Building a Knowledge Graph Infrastructure within NFDI requires the establishment of consortia-driven Knowledge Graphs that make data interchangeable within the consortium itself. This is done in N4O by a N4O Minimal Metadata-Set (N4O MMDS) and its derived N4O Objects Ontology (N4O OO). The N4O MMDS comprises information that is absolutely necessary to describe object data on its way through the object life cycle (capture, qualify, analyse, share and interlink). Its content will be developed in close cooperation with the (humanities and cultural studies) MoU group in order to establish interoperability within these domains. The N4O minimal metadata set will be provided in a technical description (e.g. JSON). It will be transformed into various ‘dialects’, e.g. LIDO/XML, for the individual data exchange of heterogeneous systems. The N4O MMDS and the N4O OO as its RDF representation (based on CIDOC-CRM, schema.org, PROV-O, SKOS, BFO) map the object cycle, which is successively enriched with information through the task areas with the tasks of documenting, collecting, analysing and protecting. These structures are used because the data flow in the Knowledge Graph provides for LIDO/XML and RDF.
This paper will showcase a selection of N4O Fundamental Services and N4O Community Services and explain how NFDI4Objects can contribute to TS4NFDI and KGI4NFDI.
References
[1] https://zenodo.org/doi/10.5281/zenodo.10409227
[2] https://doi.org/10.52825/cordi.v1i.326
[3] https://zenodo.org/doi/10.5281/zenodo.10820783
[4] https://nfdi4objects.github.io/n4o-property-graph/
[5] https://zenodo.org/doi/10.5281/zenodo.10756823
[6] https://zenodo.org/doi/10.5281/zenodo.10973050
[7] https://zenodo.org/doi/10.5281/zenodo.10774878
[8] https://zenodo.org/doi/10.5281/zenodo.10906911"""@en ;
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    dct:title "NFDI4Objects and the Base4NFDI Services: An Overview"@en ;
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    bibo:abstract """NFDI4Objects (N4O) represents a broad community dealing with material remains of human history from around 3 million years and involves numerous disciplines from the humanities, cultural studies and natural sciences with an archaeological and historical focus [1]. The objects examined include serially produced objects such as coins and organic remains such as wood, bones, or pollen. The objects and their relations are constantly changing, causing individual biographies. Therefore, the N4O central tasks include (a) comprehending representations of physical objects as research data, (b) relating them to individual contexts, (c) transforming them adequately into the digital space, and (d) curating them according to domain-specific requirements. Consequently, the NFDI4Objects Research Data Lifecycle goes along with the Object Biography. This leads to a paradigm derived from the FAIRification workflow: (i) capturing, (ii) semantic (meta) data modelling and qualifying, (iii) applying (scientific) analysis, (iv) sharing the data by visualisation and FAIRified publication, (v) interlinking and integrating data into knowledge graphs [2].
Within the NFDI infrastructure and to apply the FAIR principles, research (meta)data must be shared, interlinked, and modelled in a common and community-accepted way. To implement this, N4O Fundamental Services (N4O FS) and N4O Community Services (N4O CS) will be provided. N4O FS are, e.g., DANTE, cocoda [3], BARTOC, the N4O Knowledge Graph [4], the FOSS N4O Management Hub [5], and the N4O Commons Inkubator [6]. N4O CS are FAIRification Tools and Services such as Alligator, Academic Meta Tool, re3dragon, SPARQLing Unicorn QGIS Plugin [7], archaeology.link, and Wikibase / Semantic Media Wiki / Triplestore infrastructures.
N4O contains many terminologies [8], such as ontologies, authority files, and community-driven vocabularies, that must be published and indexed (via DANTE and BARTOC) and semantically aligned (via Cocoda [3]) to contribute to TS4NFDI. Building a Knowledge Graph Infrastructure within NFDI requires the establishment of consortia-driven Knowledge Graphs that make data interchangeable within the consortium itself. This is done in N4O by a N4O Minimal Metadata-Set (N4O MMDS) and its derived N4O Objects Ontology (N4O OO). The N4O MMDS comprises information that is absolutely necessary to describe object data on its way through the object life cycle (capture, qualify, analyse, share and interlink). Its content will be developed in close cooperation with the (humanities and cultural studies) MoU group in order to establish interoperability within these domains. The N4O minimal metadata set will be provided in a technical description (e.g. JSON). It will be transformed into various ‘dialects’, e.g. LIDO/XML, for the individual data exchange of heterogeneous systems. The N4O MMDS and the N4O OO as its RDF representation (based on CIDOC-CRM, schema.org, PROV-O, SKOS, BFO) map the object cycle, which is successively enriched with information through the task areas with the tasks of documenting, collecting, analysing and protecting. These structures are used because the data flow in the Knowledge Graph provides for LIDO/XML and RDF.
This paper will showcase a selection of N4O Fundamental Services and N4O Community Services and explain how NFDI4Objects can contribute to TS4NFDI and KGI4NFDI.
References
[1] https://zenodo.org/doi/10.5281/zenodo.10409227
[2] https://doi.org/10.52825/cordi.v1i.326
[3] https://zenodo.org/doi/10.5281/zenodo.10820783
[4] https://nfdi4objects.github.io/n4o-property-graph/
[5] https://zenodo.org/doi/10.5281/zenodo.10756823
[6] https://zenodo.org/doi/10.5281/zenodo.10973050
[7] https://zenodo.org/doi/10.5281/zenodo.10774878
[8] https://zenodo.org/doi/10.5281/zenodo.10906911"""@en ;
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    dct:description """Introduction
The current era of Archaeology 4.0 (Thiery, 2019), also known as the Knowledge Era or Era of Computing, comprises three paradigms: (I) traditional scripts, (II) Artificial Intelligence (AI) techniques, and (III) Knowledge Graphs using technologies of I and II. To reach this era, archaeology has to go through several steps: research data stored in books (Analogue Era), applying digitisation processes to publish research data online (Digital Era), applying semantic modelling and publishing Linked Open Data (LOD) (Berners-Lee, 2006) in cooperation with community hubs (Semantic Era), and applying AI technologies including semantic reasoning to generate new knowledge (Knowledge Era) (Thiery, Veller, et al., 2023). Similar paradigms are present in other fields concerning material and immaterial Cultural Heritage (CH), including art history, architecture, and performing arts, among others (Padilla et al., 2024; Gruß, 2023). To address these paradigms, the CH community requires open-source, accessible tools to manage data according to the FAIR[1] and CARE[2] principles.
This paper examines the use cases for adopting one specific approach and software to model community-driven data within the LOD Cloud, namely Wikibase. Wikibase is the software behind Wikidata (Vrandecic, 2013), the knowledge database within the Wikiverse, developed and maintained by Wikimedia Germany. It is a free and open-source software that can be used for external databases and Linked Open Data projects to share semantically structured data that both humans and machines can further reuse. We look to Wikibase instances developed and supported across several humanities related NFDI (German National Research Data Infrastructure) consortia to show the potential for knowledge modelling using Wikibase in the CH domain.
Material & Data
To create the foundation for the Semantic and Knowledge Era in the humanities, NFDI consortia (Brünger-Weilandt et al., 2020; Altenhöner et al., 2020) and Wikimedia work closely together to publish media and data as part of the Wikiverse (Fig. 1).
NFDI4Objects (Thiery, Mees, et al., 2023) uses Wikibase instances to model a provenance gazetteer for persons and corporate bodies[3], conservation science[4], and fuzziness and wobbliness in archaeological and geological[5] findspots. It also contributes to Wikidata with the Linked Open Samian Ware[6], the Linked Open Ogham[7], and African Red Slip Ware digital[8] datasets.
NFDI4Culture provides Wikibase instances as a core service[9] for the community serving a variety of use cases, ranging from backend data solution for Semantic Kompakkt[10], a 3D viewing and annotation environment with a strong grounding in the art history and architecture communities (Rossenova et al., 2023), to individual research projects focusing on specific cultural collections. An NFDI4Culture satellite project develops a private Wikibase instance focused on documenting and preserving endangered cultural heritage in Ukraine (TIB, 2023). Additionally, the NFDI4Culture Knowledge Graph service connects with Wikidata for federated querying and data enrichment (Sack et al., 2023).
NFDI4Memory uses the famous Wikibase instance FactGrid, a database for historians, to store and provide their data.
Methodology
The above-mentioned projects adopt open science methods, including the FAIR (and CARE) data principles, through the LOD capabilities of Wikibase. The data model (Fig. 2) of Wikibase (and also Wikidata) structures data as semantic triples and makes these accessible to end users via a user-friendly graphical interface. It consists of entities that include items, labels or identifiers to describe them and semantic statements that attribute properties with specific values to the item. These values may be other items within the database or textual information (Bacchi and Bergamin, 2018). It is also possible to further define the primary triples with secondary statements about additional qualifications of the primary values and source references.
This approach fits closely to the way humanities researchers are used to represent knowledge in their domains, too – as statements consisting of a subject, a predicate and an object, with the possibility to add additional argumentation to each statement (in the form of qualifiers) and to add relevant references, too – making Wikibase and Wikidata suitable environments to represent data from the CH domain (Schmidt, Thiery, and Trognitz, 2022; Rossenova, Duchesne, and Blümel, 2022).
Results & Conclusions
Flexibility in how data can be structured, and accessible user interfaces provide excellent ground for wide Wikibase adoption in the CH domain. Still, issues around how best to harmonise and map data across this proliferating field to fully exploit the semantic capabilities for interconnection and federation remain. Theoretically, Wikibase instances can easily federate across each other and/or with Wikidata with the help of property mappings or Construct queries via the SPARQL endpoint (Rossenova, Duchesne, and Blümel, 2022). In practice, however, much of the Wikibase ecosystem remains relatively siloed due to the great need for more community coordination across the varied humanities disciplines and the need for ontology harmonisation through community agreements with the citizen science contributors to Wikidata. On the plus side, Working Groups within individual consortia[11], the cross-cutting NFDI Sections[12], and the open-source developer and user communities around Wikidata and Wikibase are well aware of these challenges. They are working towards concrete solutions (Anders et al., 2022).
Discussion
Using Open Science methods, LOD and Wikibase software, all the material and data described above provide a wealth of semantically structured and openly accessible data ready for further reuse. The applied Wikibase approach helps to publish data that can meet the requirements of the Semantic and Knowledge Era paradigms and enable further AI-technology applications. The next step in achieving the full potential of this wealth of openly available CH data remains closer collaboration across individual research communities and the open-source tool communities to agree upon common, harmonised ontological standards or supervise the application of AI-supported workflows towards broader harmonisation.
References
Altenhöner, R., I. Blümel, F. Boehm, J. Bove, K. Bicher, C. Bracht, et al. (2020), ‘NFDI4Culture - Consortium for research data on material and immaterial cultural heritage’, Research Ideas and Outcomes, 6, e57036, https://doi.org/10.3897/rio.6.e57036
Anders, I., T. Arera-Rütenik, S. Arndt, R. Baum, N. Betancort, I. Blümel, et al. (2022), ‘Ontology Harmonization and Mapping - Working Group Charter (NFDI section-metadata)’, https://doi.org/10.5281/ZENODO.6726519
Bacchi, C., and G. Bergamin (2018), ‘New ways of creating and sharing bibliographic information : an experiment of using the Wikibase Data Model for UNIMARC data’, JLIS, 3, https://doi.org/10.4403/jlis.it-12458
Berners-Lee, T. (2006), ‘Linked Data’, https://www.w3.org/DesignIssues/LinkedData.html [accessed 31 May 2024]
Brünger-Weilandt, S., K.-C. Bruhn, A. W. Busch, E. Hinrichs, G. Maier, J. Paulmann, et al. (2020), ‘Memorandum of Understanding by NFDI Initiatives from the Humanities and Cultural Studies’, https://doi.org/10.5281/ZENODO.3265762
Gruß, M. (2023), ‘Collection meets Research IV: Collection data as research data?’, https://nfdi4culture.de/id/E5204 [accessed 31 May 2024]
Padilla, T., L. Allen, S. Varner, S. Potvin, H. Frost, and E. Russey Roke (2024), ‘Always Already Computational: Collections as Data’, https://collectionsasdata.github.io/ [accessed 31 May 2024]
Rossenova, L., P. Duchesne, and I. Blümel (2022), ‘Wikidata and Wikibase as complementary research data management services for cultural heritage data’, Proceedings of the 3rd Wikidata Workshop 2022 Co-Located with the 21st International Semantic Web Conference (ISWC2022), https://ceur-ws.org/Vol-3262/paper15.pdf
Rossenova, L., L. Sohmen, P. Duchesne, L. Günther, Z. Schubert, and I. Bluemel (2023), ‘Towards a common data model for semantic annotation of digital media: A new FOSS toolchain’, https://doi.org/10.5281/ZENODO.8228588
Sack, H., T. Schrade, O. Bruns, E. Posthumus, T. Tietz, E. Norouzi, et al. (2023), ‘Knowledge Graph Based RDM Solutions: NFDI4Culture - NFDI-MatWerk - NFDI4DataScience’, Proceedings of the Conference on Research Data Infrastructure, 1, https://doi.org/10.52825/cordi.v1i.371
Schmidt, S. C., F. Thiery, and M. Trognitz (2022), ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’, Digital, 2:3, 333–64, https://doi.org/10.3390/digital2030019
Thiery, F. (2019), ‘Archaeology 4.0: Archaeology in the Third Era of Computing’, Squirrel Papers, 1:1, #2, https://doi.org/10.5281/zenodo.2629595
Thiery, F., A. W. Mees, B. Weisser, F. F. Schäfer, S. Baars, S. Nolte, et al. (2023), ‘Object-Related Research Data Workflows Within NFDI4Objects and Beyond’, Proceedings of the Conference on Research Data Infrastructure, 1, https://doi.org/10.52825/cordi.v1i.326
Thiery, F., J. Veller, L. Raddatz, L. Rokohl, F. Boochs, and A. W. Mees (2023), ‘A Semi-Automatic Semantic-Model-Based Comparison Workflow for Archaeological Features on Roman Ceramics’, ISPRS International Journal of Geo-Information, 12:4, 167, https://doi.org/10.3390/ijgi12040167
TIB (2023), ‘In the media: How TIB and DDK are helping to save Ukraine’s cultural heritage’, https://www.tib.eu/en/tib/news-and-events/news/details/in-the-media-how-tib-and-ddk-are-helping-to-save-ukraines-cultural-heritage [accessed 31 May 2024]
Vrandecic, D. (2013), ‘The Rise of Wikidata’, IEEE Intelligent Systems, 28:4, 90–95, https://doi.org/10.1109/MIS.2013.119
[1] cf. https://www.go-fair.org/fair-principles/
[2] cf. https://www.gida-global.org/care
[3] cf. https://n4o-prov.wikibase.cloud/wiki/Item:Q20 and https://n4o-prov.wikibase.cloud/wiki/Item:Q23
[4] cf. https://n4o-ta4-dev.wikibase.cloud/wiki/Item:Q12
[5] cf. https://fuzzy-sl.wikibase.cloud/wiki/Item:Q70
[6] cf. https://www.wikidata.org/wiki/Wikidata:WikiProject_Linked_Open_Samian_Ware
[7] cf. https://www.wikidata.org/wiki/Wikidata:WikiProject_Irish_Ogham_Stones
[8] cf. https://www.wikidata.org/wiki/Wikidata:WikiProject_African_Red_Slip_Ware_Digital
[9] cf. https://nfdi4culture.de/services/details/wikibase4research.html
[10] cf. https://nfdi4culture.de/services/details/semantic-kompakkt.html
[11] cf. https://nfdi4culture.de/services/details/linked-open-data-working-group.html
[12] cf. https://www.nfdi.de/section-metadata/"""@en ;
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    bibo:abstract """Introduction
The current era of Archaeology 4.0 (Thiery, 2019), also known as the Knowledge Era or Era of Computing, comprises three paradigms: (I) traditional scripts, (II) Artificial Intelligence (AI) techniques, and (III) Knowledge Graphs using technologies of I and II. To reach this era, archaeology has to go through several steps: research data stored in books (Analogue Era), applying digitisation processes to publish research data online (Digital Era), applying semantic modelling and publishing Linked Open Data (LOD) (Berners-Lee, 2006) in cooperation with community hubs (Semantic Era), and applying AI technologies including semantic reasoning to generate new knowledge (Knowledge Era) (Thiery, Veller, et al., 2023). Similar paradigms are present in other fields concerning material and immaterial Cultural Heritage (CH), including art history, architecture, and performing arts, among others (Padilla et al., 2024; Gruß, 2023). To address these paradigms, the CH community requires open-source, accessible tools to manage data according to the FAIR[1] and CARE[2] principles.
This paper examines the use cases for adopting one specific approach and software to model community-driven data within the LOD Cloud, namely Wikibase. Wikibase is the software behind Wikidata (Vrandecic, 2013), the knowledge database within the Wikiverse, developed and maintained by Wikimedia Germany. It is a free and open-source software that can be used for external databases and Linked Open Data projects to share semantically structured data that both humans and machines can further reuse. We look to Wikibase instances developed and supported across several humanities related NFDI (German National Research Data Infrastructure) consortia to show the potential for knowledge modelling using Wikibase in the CH domain.
Material & Data
To create the foundation for the Semantic and Knowledge Era in the humanities, NFDI consortia (Brünger-Weilandt et al., 2020; Altenhöner et al., 2020) and Wikimedia work closely together to publish media and data as part of the Wikiverse (Fig. 1).
NFDI4Objects (Thiery, Mees, et al., 2023) uses Wikibase instances to model a provenance gazetteer for persons and corporate bodies[3], conservation science[4], and fuzziness and wobbliness in archaeological and geological[5] findspots. It also contributes to Wikidata with the Linked Open Samian Ware[6], the Linked Open Ogham[7], and African Red Slip Ware digital[8] datasets.
NFDI4Culture provides Wikibase instances as a core service[9] for the community serving a variety of use cases, ranging from backend data solution for Semantic Kompakkt[10], a 3D viewing and annotation environment with a strong grounding in the art history and architecture communities (Rossenova et al., 2023), to individual research projects focusing on specific cultural collections. An NFDI4Culture satellite project develops a private Wikibase instance focused on documenting and preserving endangered cultural heritage in Ukraine (TIB, 2023). Additionally, the NFDI4Culture Knowledge Graph service connects with Wikidata for federated querying and data enrichment (Sack et al., 2023).
NFDI4Memory uses the famous Wikibase instance FactGrid, a database for historians, to store and provide their data.
Methodology
The above-mentioned projects adopt open science methods, including the FAIR (and CARE) data principles, through the LOD capabilities of Wikibase. The data model (Fig. 2) of Wikibase (and also Wikidata) structures data as semantic triples and makes these accessible to end users via a user-friendly graphical interface. It consists of entities that include items, labels or identifiers to describe them and semantic statements that attribute properties with specific values to the item. These values may be other items within the database or textual information (Bacchi and Bergamin, 2018). It is also possible to further define the primary triples with secondary statements about additional qualifications of the primary values and source references.
This approach fits closely to the way humanities researchers are used to represent knowledge in their domains, too – as statements consisting of a subject, a predicate and an object, with the possibility to add additional argumentation to each statement (in the form of qualifiers) and to add relevant references, too – making Wikibase and Wikidata suitable environments to represent data from the CH domain (Schmidt, Thiery, and Trognitz, 2022; Rossenova, Duchesne, and Blümel, 2022).
Results & Conclusions
Flexibility in how data can be structured, and accessible user interfaces provide excellent ground for wide Wikibase adoption in the CH domain. Still, issues around how best to harmonise and map data across this proliferating field to fully exploit the semantic capabilities for interconnection and federation remain. Theoretically, Wikibase instances can easily federate across each other and/or with Wikidata with the help of property mappings or Construct queries via the SPARQL endpoint (Rossenova, Duchesne, and Blümel, 2022). In practice, however, much of the Wikibase ecosystem remains relatively siloed due to the great need for more community coordination across the varied humanities disciplines and the need for ontology harmonisation through community agreements with the citizen science contributors to Wikidata. On the plus side, Working Groups within individual consortia[11], the cross-cutting NFDI Sections[12], and the open-source developer and user communities around Wikidata and Wikibase are well aware of these challenges. They are working towards concrete solutions (Anders et al., 2022).
Discussion
Using Open Science methods, LOD and Wikibase software, all the material and data described above provide a wealth of semantically structured and openly accessible data ready for further reuse. The applied Wikibase approach helps to publish data that can meet the requirements of the Semantic and Knowledge Era paradigms and enable further AI-technology applications. The next step in achieving the full potential of this wealth of openly available CH data remains closer collaboration across individual research communities and the open-source tool communities to agree upon common, harmonised ontological standards or supervise the application of AI-supported workflows towards broader harmonisation.
References
Altenhöner, R., I. Blümel, F. Boehm, J. Bove, K. Bicher, C. Bracht, et al. (2020), ‘NFDI4Culture - Consortium for research data on material and immaterial cultural heritage’, Research Ideas and Outcomes, 6, e57036, https://doi.org/10.3897/rio.6.e57036
Anders, I., T. Arera-Rütenik, S. Arndt, R. Baum, N. Betancort, I. Blümel, et al. (2022), ‘Ontology Harmonization and Mapping - Working Group Charter (NFDI section-metadata)’, https://doi.org/10.5281/ZENODO.6726519
Bacchi, C., and G. Bergamin (2018), ‘New ways of creating and sharing bibliographic information : an experiment of using the Wikibase Data Model for UNIMARC data’, JLIS, 3, https://doi.org/10.4403/jlis.it-12458
Berners-Lee, T. (2006), ‘Linked Data’, https://www.w3.org/DesignIssues/LinkedData.html [accessed 31 May 2024]
Brünger-Weilandt, S., K.-C. Bruhn, A. W. Busch, E. Hinrichs, G. Maier, J. Paulmann, et al. (2020), ‘Memorandum of Understanding by NFDI Initiatives from the Humanities and Cultural Studies’, https://doi.org/10.5281/ZENODO.3265762
Gruß, M. (2023), ‘Collection meets Research IV: Collection data as research data?’, https://nfdi4culture.de/id/E5204 [accessed 31 May 2024]
Padilla, T., L. Allen, S. Varner, S. Potvin, H. Frost, and E. Russey Roke (2024), ‘Always Already Computational: Collections as Data’, https://collectionsasdata.github.io/ [accessed 31 May 2024]
Rossenova, L., P. Duchesne, and I. Blümel (2022), ‘Wikidata and Wikibase as complementary research data management services for cultural heritage data’, Proceedings of the 3rd Wikidata Workshop 2022 Co-Located with the 21st International Semantic Web Conference (ISWC2022), https://ceur-ws.org/Vol-3262/paper15.pdf
Rossenova, L., L. Sohmen, P. Duchesne, L. Günther, Z. Schubert, and I. Bluemel (2023), ‘Towards a common data model for semantic annotation of digital media: A new FOSS toolchain’, https://doi.org/10.5281/ZENODO.8228588
Sack, H., T. Schrade, O. Bruns, E. Posthumus, T. Tietz, E. Norouzi, et al. (2023), ‘Knowledge Graph Based RDM Solutions: NFDI4Culture - NFDI-MatWerk - NFDI4DataScience’, Proceedings of the Conference on Research Data Infrastructure, 1, https://doi.org/10.52825/cordi.v1i.371
Schmidt, S. C., F. Thiery, and M. Trognitz (2022), ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’, Digital, 2:3, 333–64, https://doi.org/10.3390/digital2030019
Thiery, F. (2019), ‘Archaeology 4.0: Archaeology in the Third Era of Computing’, Squirrel Papers, 1:1, #2, https://doi.org/10.5281/zenodo.2629595
Thiery, F., A. W. Mees, B. Weisser, F. F. Schäfer, S. Baars, S. Nolte, et al. (2023), ‘Object-Related Research Data Workflows Within NFDI4Objects and Beyond’, Proceedings of the Conference on Research Data Infrastructure, 1, https://doi.org/10.52825/cordi.v1i.326
Thiery, F., J. Veller, L. Raddatz, L. Rokohl, F. Boochs, and A. W. Mees (2023), ‘A Semi-Automatic Semantic-Model-Based Comparison Workflow for Archaeological Features on Roman Ceramics’, ISPRS International Journal of Geo-Information, 12:4, 167, https://doi.org/10.3390/ijgi12040167
TIB (2023), ‘In the media: How TIB and DDK are helping to save Ukraine’s cultural heritage’, https://www.tib.eu/en/tib/news-and-events/news/details/in-the-media-how-tib-and-ddk-are-helping-to-save-ukraines-cultural-heritage [accessed 31 May 2024]
Vrandecic, D. (2013), ‘The Rise of Wikidata’, IEEE Intelligent Systems, 28:4, 90–95, https://doi.org/10.1109/MIS.2013.119
[1] cf. https://www.go-fair.org/fair-principles/
[2] cf. https://www.gida-global.org/care
[3] cf. https://n4o-prov.wikibase.cloud/wiki/Item:Q20 and https://n4o-prov.wikibase.cloud/wiki/Item:Q23
[4] cf. https://n4o-ta4-dev.wikibase.cloud/wiki/Item:Q12
[5] cf. https://fuzzy-sl.wikibase.cloud/wiki/Item:Q70
[6] cf. https://www.wikidata.org/wiki/Wikidata:WikiProject_Linked_Open_Samian_Ware
[7] cf. https://www.wikidata.org/wiki/Wikidata:WikiProject_Irish_Ogham_Stones
[8] cf. https://www.wikidata.org/wiki/Wikidata:WikiProject_African_Red_Slip_Ware_Digital
[9] cf. https://nfdi4culture.de/services/details/wikibase4research.html
[10] cf. https://nfdi4culture.de/services/details/semantic-kompakkt.html
[11] cf. https://nfdi4culture.de/services/details/linked-open-data-working-group.html
[12] cf. https://www.nfdi.de/section-metadata/"""@en ;
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Within the digital transformation from an analogue world to the Knowledge Era in the Third Era of Computing (Thiery, 2019), computational archaeology and Research Software Engineering methods play a huge role (Thiery, Veller, et al., 2023). These methods are also crucial for data FAIRification (Wilkinson et al., 2016) – making data Findable, Accessible, Interoperable, and Reusable – addressed in the German National Research Data Infrastructure, called NFDI (Hartl, Wössner, and Sure-Vetter, 2021) to apply Open Science and FAIRify research data. Within Cultural heritage data networks, so-called knowledge graphs must be interdisciplinary and interoperable. This causes challenges such as heterogeneity and islands of data. The digital transformation is how we build bridges via Semantic Web technologies and Citizen Science Hubs such as Wikidata and Open Street Map (OSM).
The NFDI4Objects consortium (Bibby et al., 2023) represents a broad community dealing with material remains of human history from around 3 million years. It involves numerous disciplines, including the humanities, cultural studies, and natural sciences, with an archaeological and historical focus. The objects include potsherds of common ware, serially produced objects such as coins, organic remains such as wood, bones or pollen, inscribed clay tablets, papyri, and stones. The objects and relations constantly change, causing individual biographies: documenting, collecting, analysing, protecting, storing, and sharing. Therefore, the NFDI4Objects central tasks include (a) creating representations of physical objects as research data, (b) relating them to their contexts, (c) transforming them adequately into the digital space, and (d) curating them according to domain-specific requirements (Thiery, Mees, et al., 2023). Consequently, the NFDI4Objects Research Data Life Cycle goes along with the Object Biography.
Material & Data
This paper uses research data from several domains and sources: from archaeology, such as Irish Ogham stones (Macalister, 1945; MacManus, 1997; Schmidt and Thiery, 2022) and the silver coinage of Croton (Rutter, 1997; 2001; Stazio, 1984; Garraffo, 1987), provenance research (Hopp, 2018; Hopp and von dem Bussche, 2023; 2022a; 2022b), and geosciences (Schenk et al., 2024; Thiery and Schenk, 2023a; 2023c).
Ogham stones are monoliths bearing inscriptions in the early medieval Gaelic "primitive Irish" Ogham script, erected mainly on the island of Ireland and in the western part of Great Britain between the 4th and 9th century AD. Examples are CIIC 178 at Coumeenole North / Dunmore Head (Fig. 2; most west point of Ireland), OSM-Node 5145413640, coordinates: 52.1103°N, -10.4732°E) or CIIC 81 in front of the Visitor Centre at University College Cork (UCC), OSM-Node 11071361392, coordinates: 51.8938°N, -8.4921°E.
Hoard analyses of the silver coinage of Croton (6th to 3rd century BC), an Achaean colony in southern Italy, include spatial uncertainties. The sites identified are mainly derived from the literature and have varying degrees of precision about their geographical location.
About 40,000 yr b2k[1], the largest eruption of the Campanian Ignimbrite (CI) took place in the Phlegraean Fields (Barberi et al., 1978; De Vivo et al., 2001; Schenk et al., 2024). Evidence of the ash fall from this Late Pleistocene volcanic event is found in Central Europe, often in isolated watersheds and valleys. These sites are recorded in publications, e.g. by precise coordinates or references to cities, regions, caves, and archaeological sites (Thiery and Schenk, 2023a).
In a project by the Numismatic Collection (Berlin State Museums), all acquisitions from 1933 to 1945 were examined, and their previous owners and sellers, i.e. people or corporate bodies, were identified (Weisser, 2022a; 2022b). Identifiers were created in the coin cabinet's own authority file data portal (NDP) and stored with standard data from other portals such as GND or VIAF.
Methodology
To create interdisciplinary and interoperable Knowledge Graphs, Semantic Web technologies such as the Resource Description Framework (RDF) (Klyne, Carroll, and McBride, 2014) and Linked Open Data (Berners-Lee, 2006; Schmidt, Thiery, and Trognitz, 2022) are used. Combined with handling doubts – in the context of NFDI4Objects project often named using the umbrella terms “fuzziness and wobbliness” (Thiery et al., 2021) – such as vagueness, uncertainty, and ambiguities in data modelling, this makes research data comprehensible; especially using the “Fuzzy Spatial Locations Ontology” (Thiery, 2023b), based on “PROV-O”, “SKOS” and “GeoSPARQL” (Thiery, 2023a: 3–5). On top of that, the use of the FOSS software Wikibase and its instances, such as Wikidata, leads this method to Open Science.
Results & Conclusions
The methodology used leads to modelling strategies to FAIRify data. The resulting LOD from the Campanian Ignimbrite project (Thiery and Schenk, 2023b) or the Croton project (Thiery and Baars, 2023) as RDF can be converted into human-readable HTML files using the “SPARQL Unicorn Ontology Documentation Research Tool” (Homburg and Thiery, 2024) based on the SPARQL Unicorn (Thiery et al., 2020; Thiery and Homburg, 2024). The SPARQL Unicorn also enables QGIS users to integrate these data into GIS analysis.
One more challenging example is the hoard: «San Giorgio Ionico 1949, San Giorgio Ionico (near Taranto), on the property of E. De Finis» (Squirrel-ID no. 3001), which includes i. a. coinage of Croton and poses the question: “Where was the property of E. De Finis located?”. With the help of Lo Porto (1990), Siciliano (2002) and OSM Node 68530185, the coordinate POINT(17.3787 40.4579) can be determined as shown in crotonsite_3001.
The Wikidata Workflow creates entries in Wikidata, e.g., Q106680733, CIIC 81 as “Squirrel Stone” as part of the Ogham stones collection (can be queried via https://w.wiki/9jQv).
Semantically modelled entries in a Wikibase instance (here: https://n4o-prov.wikibase.cloud) enable to open the knowledge about coin dealers (e.g., Zakaris Bezdikian; Item:Q20) or corporate bodies (e.g., Ignaz Storek steel foundry and machine factory in Brünn; Item:Q23).
Discussion
This paper shows exemplary processes for creating interdisciplinary knowledge graphs using semantic modelling techniques like Linked Open Data, Wikibase, and Wikidata. These digital approaches and applied methods in computational archaeology create a shared understanding and the possibility of common standards using the NFDI4Objects community.
References
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Bibby, D., K.-C. Bruhn, A. Busch, F. Dührkohp, and et al. (2023), ‘NFDI4Objects - Proposal’, NFDI4Objects Zenodo Community, https://doi.org/10.5281/zenodo.10409227.
De Vivo, B., G. Rolandi, P. B. Gans, A. Calvert, W. A. Bohrson, F. J. Spera, et al. (2001), ‘New constraints on the pyroclastic eruptive history of the Campanian volcanic Plain (Italy)’, Mineralogy and Petrology, 73:1–3, 47–65, https://doi.org/10.1007/s007100170010.
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Schmidt, S. C., and F. Thiery (2022), ‘SPARQLing Ogham Stones: New Options for Analyzing Analog Editions by Digitization in Wikidata’, CEUR Workshop Proceedings, 3110:Graph Technologies in the Humanities 2020, 211–44, https://doi.org/10.5281/zenodo.6380914.
Schmidt, S. C., F. Thiery, and M. Trognitz (2022), ‘Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata’, Digital, 2:3, 333–64, https://doi.org/10.3390/digital2030019.
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——— (2023a), ‘Dealing with Doubts: Modelling Approaches in site georeferencing’, Squirrel Papers, 5(1):1, #7, https://doi.org/10.5281/zenodo.10403509.
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Thiery, F., and S. Baars (2023), ‘Croton Site Instances Collection’, Squirrel Papers, Research Squirrel Engineers, via @croton-geo, https://research-squirrel-engineers.github.io/croton-geo/Site_collection/index.html [accessed 17 April 2024].
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Thiery, F., A. Mees, K. Tolle, and D. Wigg-Wolf (2021), ‘TRAIL 2.2: Evaluation of fuzziness and wobbliness in numismatics and ceramology’, NFDI4Objects TRAILS, 2021, No. 2.2, https://doi.org/10.5281/zenodo.5654897.
Thiery, F., A. W. Mees, B. Weisser, F. F. Schäfer, S. Baars, S. Nolte, et al. (2023), ‘Object-Related Research Data Workflows Within NFDI4Objects and Beyond’, Proceedings of the Conference on Research Data Infrastructure, 1, https://doi.org/10.52825/cordi.v1i.326.
Thiery, F., and F. Schenk (2023a), ‘Campanian Ignimbrite Geo Locations’, Squirrel Papers, 5(2):2, #2, https://doi.org/10.5281/zenodo.10361309.
——— (2023b), ‘CI Site Instances Collection’, Squirrel Papers, Research Squirrel Engineers, via @campanian-ignimbrite-geo, https://research-squirrel-engineers.github.io/campanian-ignimbrite-geo/Site_collection/index.html [accessed 17 April 2024].
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Thiery, F., S. C. Schmidt, T. Homburg, and M. Trognitz (2020), ‘The SPARQL Unicorn: An introduction’, Squirrel Papers, 2:1, #1, https://doi.org/10.5281/zenodo.3742185.
Thiery, F., J. Veller, L. Raddatz, L. Rokohl, F. Boochs, and A. W. Mees (2023), ‘A Semi-Automatic Semantic-Model-Based Comparison Workflow for Archaeological Features on Roman Ceramics’, ISPRS International Journal of Geo-Information, 12:4, 167, https://doi.org/10.3390/ijgi12040167.
Weisser, B. (2022a), ‘Digitale Provenienzforschung am Münzkabinett der Staatlichen Museen zu Berlin. Erwerbungen zwischen 1933 und 1945’, Sonderheft Der Geldgeschichtlichen Nachrichten, 57, 260–67.
——— (ed.) (2022b), ‘Münzsammlungen in Deutschland zwischen 1933 und 1945. Erwerbungen und Normdaten.’, Sonderheft Der Geldgeschichtlichen Nachrichten, 57, 257–356.
Wilkinson, M. D., M. Dumontier, Ij. J. Aalbersberg, G. Appleton, and et al. (2016), ‘The FAIR Guiding Principles for scientific data management and stewardship’, Scientific Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18.
[1] yr b2k: The geosciences use their own systems to describe years. For example, “Before Present” (BP) is used, which means "before 1950 AD". The term "b2k" means "before the year 2000 AD", i.e. the year 2000 is used as a reference point. 40,000 yr b2k therefore means approx. 40,000 years before the year 2000 AD, i.e. approx. 38,000 BC."""@en ;
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    dct:title "Interdisciplinary Knowledge Graphs: Semantic Modelling using Linked Open Data, Wikibase and the Wikiverse"@en ;
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Within the digital transformation from an analogue world to the Knowledge Era in the Third Era of Computing (Thiery, 2019), computational archaeology and Research Software Engineering methods play a huge role (Thiery, Veller, et al., 2023). These methods are also crucial for data FAIRification (Wilkinson et al., 2016) – making data Findable, Accessible, Interoperable, and Reusable – addressed in the German National Research Data Infrastructure, called NFDI (Hartl, Wössner, and Sure-Vetter, 2021) to apply Open Science and FAIRify research data. Within Cultural heritage data networks, so-called knowledge graphs must be interdisciplinary and interoperable. This causes challenges such as heterogeneity and islands of data. The digital transformation is how we build bridges via Semantic Web technologies and Citizen Science Hubs such as Wikidata and Open Street Map (OSM).
The NFDI4Objects consortium (Bibby et al., 2023) represents a broad community dealing with material remains of human history from around 3 million years. It involves numerous disciplines, including the humanities, cultural studies, and natural sciences, with an archaeological and historical focus. The objects include potsherds of common ware, serially produced objects such as coins, organic remains such as wood, bones or pollen, inscribed clay tablets, papyri, and stones. The objects and relations constantly change, causing individual biographies: documenting, collecting, analysing, protecting, storing, and sharing. Therefore, the NFDI4Objects central tasks include (a) creating representations of physical objects as research data, (b) relating them to their contexts, (c) transforming them adequately into the digital space, and (d) curating them according to domain-specific requirements (Thiery, Mees, et al., 2023). Consequently, the NFDI4Objects Research Data Life Cycle goes along with the Object Biography.
Material & Data
This paper uses research data from several domains and sources: from archaeology, such as Irish Ogham stones (Macalister, 1945; MacManus, 1997; Schmidt and Thiery, 2022) and the silver coinage of Croton (Rutter, 1997; 2001; Stazio, 1984; Garraffo, 1987), provenance research (Hopp, 2018; Hopp and von dem Bussche, 2023; 2022a; 2022b), and geosciences (Schenk et al., 2024; Thiery and Schenk, 2023a; 2023c).
Ogham stones are monoliths bearing inscriptions in the early medieval Gaelic "primitive Irish" Ogham script, erected mainly on the island of Ireland and in the western part of Great Britain between the 4th and 9th century AD. Examples are CIIC 178 at Coumeenole North / Dunmore Head (Fig. 2; most west point of Ireland), OSM-Node 5145413640, coordinates: 52.1103°N, -10.4732°E) or CIIC 81 in front of the Visitor Centre at University College Cork (UCC), OSM-Node 11071361392, coordinates: 51.8938°N, -8.4921°E.
Hoard analyses of the silver coinage of Croton (6th to 3rd century BC), an Achaean colony in southern Italy, include spatial uncertainties. The sites identified are mainly derived from the literature and have varying degrees of precision about their geographical location.
About 40,000 yr b2k[1], the largest eruption of the Campanian Ignimbrite (CI) took place in the Phlegraean Fields (Barberi et al., 1978; De Vivo et al., 2001; Schenk et al., 2024). Evidence of the ash fall from this Late Pleistocene volcanic event is found in Central Europe, often in isolated watersheds and valleys. These sites are recorded in publications, e.g. by precise coordinates or references to cities, regions, caves, and archaeological sites (Thiery and Schenk, 2023a).
In a project by the Numismatic Collection (Berlin State Museums), all acquisitions from 1933 to 1945 were examined, and their previous owners and sellers, i.e. people or corporate bodies, were identified (Weisser, 2022a; 2022b). Identifiers were created in the coin cabinet's own authority file data portal (NDP) and stored with standard data from other portals such as GND or VIAF.
Methodology
To create interdisciplinary and interoperable Knowledge Graphs, Semantic Web technologies such as the Resource Description Framework (RDF) (Klyne, Carroll, and McBride, 2014) and Linked Open Data (Berners-Lee, 2006; Schmidt, Thiery, and Trognitz, 2022) are used. Combined with handling doubts – in the context of NFDI4Objects project often named using the umbrella terms “fuzziness and wobbliness” (Thiery et al., 2021) – such as vagueness, uncertainty, and ambiguities in data modelling, this makes research data comprehensible; especially using the “Fuzzy Spatial Locations Ontology” (Thiery, 2023b), based on “PROV-O”, “SKOS” and “GeoSPARQL” (Thiery, 2023a: 3–5). On top of that, the use of the FOSS software Wikibase and its instances, such as Wikidata, leads this method to Open Science.
Results & Conclusions
The methodology used leads to modelling strategies to FAIRify data. The resulting LOD from the Campanian Ignimbrite project (Thiery and Schenk, 2023b) or the Croton project (Thiery and Baars, 2023) as RDF can be converted into human-readable HTML files using the “SPARQL Unicorn Ontology Documentation Research Tool” (Homburg and Thiery, 2024) based on the SPARQL Unicorn (Thiery et al., 2020; Thiery and Homburg, 2024). The SPARQL Unicorn also enables QGIS users to integrate these data into GIS analysis.
One more challenging example is the hoard: «San Giorgio Ionico 1949, San Giorgio Ionico (near Taranto), on the property of E. De Finis» (Squirrel-ID no. 3001), which includes i. a. coinage of Croton and poses the question: “Where was the property of E. De Finis located?”. With the help of Lo Porto (1990), Siciliano (2002) and OSM Node 68530185, the coordinate POINT(17.3787 40.4579) can be determined as shown in crotonsite_3001.
The Wikidata Workflow creates entries in Wikidata, e.g., Q106680733, CIIC 81 as “Squirrel Stone” as part of the Ogham stones collection (can be queried via https://w.wiki/9jQv).
Semantically modelled entries in a Wikibase instance (here: https://n4o-prov.wikibase.cloud) enable to open the knowledge about coin dealers (e.g., Zakaris Bezdikian; Item:Q20) or corporate bodies (e.g., Ignaz Storek steel foundry and machine factory in Brünn; Item:Q23).
Discussion
This paper shows exemplary processes for creating interdisciplinary knowledge graphs using semantic modelling techniques like Linked Open Data, Wikibase, and Wikidata. These digital approaches and applied methods in computational archaeology create a shared understanding and the possibility of common standards using the NFDI4Objects community.
References
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——— (2023b), ‘CI Site Instances Collection’, Squirrel Papers, Research Squirrel Engineers, via @campanian-ignimbrite-geo, https://research-squirrel-engineers.github.io/campanian-ignimbrite-geo/Site_collection/index.html [accessed 17 April 2024].
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——— (ed.) (2022b), ‘Münzsammlungen in Deutschland zwischen 1933 und 1945. Erwerbungen und Normdaten.’, Sonderheft Der Geldgeschichtlichen Nachrichten, 57, 257–356.
Wilkinson, M. D., M. Dumontier, Ij. J. Aalbersberg, G. Appleton, and et al. (2016), ‘The FAIR Guiding Principles for scientific data management and stewardship’, Scientific Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18.
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    bibo:abstract "Die 3D-Erfassung und die FAIRe Bereitstellung von 3D-Daten aus den Cultural Heritage, wie der Archäologie, nimmt immer wichtigere Züge an. Auch das Thema Semantik Web und Linked Open Data wird immer wichtiger, um so Annotationen zu teilen und sich über kontrollierte Vokabulare und Ontologien mit anderen Daten zu vernetzten. Der Vortrag gibt einen Einblick in das ARS3D-Projekt, sowie die Digitalisierung von Ogham Steinen und irischen Holy Wells."@de ;
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The WG RSE comprises several tasks that are also related to Base4NFDI:
Task Jupyter Services: bring together the JupyterHub providers in Germany; collect and list available hubs at a common website; evaluate the possibility of an NFDI-wide Jupyter service. This is addressed in the Jupyter4NFDI Basic Service (in its initialisation phase. Jupyter4NFDI aims to address the fragmented deployment of Jupyter Notebooks across NFDI consortia by offering a centralised service. This service will simplify access, improve user experience, and extend Jupyter's reach to a wider audience within and beyond the NFDI.
Task Software Marketplace: Allow access to the portfolio of research software in the NFDI; develop a concept for the Marketplace; consider requirements from the consortia and available solutions. This has been substantiated in a proposal to Base4NFDI.
Task Training Materials: Create recommendations to spread good practices using available content.
Task Mission Statement: Develop a common mission statement, "Software Engineering in the NFDI“.
Task Status Quo Survey and Report: Apply an RSE survey for a "Landscape Analysis" within the NFDI.
Task Overarching Initiatives: Ensure connectivity and compatibility with European and international efforts.
Task Software Ecosystem: Develop a draft strategy for establishing a software ecosystem within the NFDI.
Task Use Cases: Identify use cases for a cross-consortium, prototypical development of the software ecosystem.
Task Quality Criteria for Research Software: Identify and develop suitable, easy-to-record indicators.
Task Criteria for NFDI Components: Ensure reliability, compatibility, and security; provide checklists/test suite.
This contribution presents the WG RSE activities and the possibilities for participation (small subgroups work on individual tasks, monthly WG-wide coordination meetings) in the working group discussion rounds that bring the NFDI consortia together in all software-related aspects."""@en ;
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    dct:title "Research Software Engineering within the NFDI (INFRA-WG-RSE)"@en ;
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    bibo:abstract """Research Software Engineering (RSE) is fundamental to the German National Research Data Infrastructure (NFDI). Within the NFDI e.V., several sections deal with overarching topics, including RSE technologies and knowledge, especially the "Section Common Infrastructures" with its working groups on "Data Integration (DI)", "Data Management Planning (DMP)", "Data Science and Artificial Intelligence (DSAI)", "Electronic Lab Notebooks (ELN)", "Persistent Identifiers (PID)" and "Research Software Engineering (RSE)".
The RSE working group connects the NFDI consortia in software-related aspects. It focuses on Research software, software communities, and software infrastructure at NFDI. The working group operates a central forum in an advisory and supportive capacity. It establishes the necessary software ecosystem within NFDI for the professional development of software infrastructure components, which represent an integral part of the NFDI. In addition, the working group serves as an interface for the NFDI to compare European and international initiatives to promote the connectivity of the NFDI with other infrastructures.
The WG RSE comprises several tasks that are also related to Base4NFDI:
Task Jupyter Services: bring together the JupyterHub providers in Germany; collect and list available hubs at a common website; evaluate the possibility of an NFDI-wide Jupyter service. This is addressed in the Jupyter4NFDI Basic Service (in its initialisation phase. Jupyter4NFDI aims to address the fragmented deployment of Jupyter Notebooks across NFDI consortia by offering a centralised service. This service will simplify access, improve user experience, and extend Jupyter's reach to a wider audience within and beyond the NFDI.
Task Software Marketplace: Allow access to the portfolio of research software in the NFDI; develop a concept for the Marketplace; consider requirements from the consortia and available solutions. This has been substantiated in a proposal to Base4NFDI.
Task Training Materials: Create recommendations to spread good practices using available content.
Task Mission Statement: Develop a common mission statement, "Software Engineering in the NFDI“.
Task Status Quo Survey and Report: Apply an RSE survey for a "Landscape Analysis" within the NFDI.
Task Overarching Initiatives: Ensure connectivity and compatibility with European and international efforts.
Task Software Ecosystem: Develop a draft strategy for establishing a software ecosystem within the NFDI.
Task Use Cases: Identify use cases for a cross-consortium, prototypical development of the software ecosystem.
Task Quality Criteria for Research Software: Identify and develop suitable, easy-to-record indicators.
Task Criteria for NFDI Components: Ensure reliability, compatibility, and security; provide checklists/test suite.
This contribution presents the WG RSE activities and the possibilities for participation (small subgroups work on individual tasks, monthly WG-wide coordination meetings) in the working group discussion rounds that bring the NFDI consortia together in all software-related aspects."""@en ;
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        <https://w3id.org/squirrelpapers/person/0000-0002-3246-3531>,
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        <https://w3id.org/squirrelpapers/person/0009-0008-0586-9350>,
        <https://w3id.org/squirrelpapers/person/allison-jim> ;
    dct:description """Nowadays computer applications as well as statistical and computational approaches constitute a big part of the toolbox of every archaeologist, as they open tremendous possibilities for all research. These can be ready-to-use (proprietary) software applications but also “Little Minions” (self-scripted tools) or research software (e.g. implementation of statistic algorithms in R, Python), which are written by researchers. Both, research software and research data are part of Computational Archaeology and play an important role in up-to-date archaeological research. Optimally research data (and software) is FAIR(4RS) – Findable, Accessible, Interoperable, Re-usable (for Research Software) – and reproducible of which other users can benefit from or even develop further. The increasing number of topics and papers at the international and national chapters of the CAA show manifold applications but also implications. Working Groups like the SIG SSLA (https://sslarch.github.io) or the “Little Minions” (https://littleminions.link) also deal with Computational Archaeology and are building a community.
Several initiatives, such as the German National Research Data Infrastructure (NFDI) – especially NFDI4Objects – or the European Open Science Cloud (EOSC), engage with this topic to strengthen the position of Computational Archaeologists and Research Software Engineers, highlight the scientific merit of their work, and ensure researchers receive credit for computational approaches, software development, as well as for writing papers.
To support this, this session invites contributions dealing with various aspects of Computational Archaeology, but not limited to:
- treating source code/software as research data
- presenting a “Little Minion”
- discussing challenges in Computational Archaeology
- advancing new algorithms and statistical/computational analysis methods
- (critical) use of AI, discussing pitfalls and complications
- addressing the divide between FAIR(4RS) principles and practices
- incorporating FAIR(4RS) principles into the teaching curriculum
- discussing approaches concerning problems and solutions to legacy data and software
- making complex statistical and computational methods accessible to main-stream archaeology"""@en ;
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    dct:title "Applying Archaeological Research Software Engineering as Little Minions: Statistical & Computational Approaches to Daily Archaeological Tasks\""@en ;
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    bibo:abstract """Nowadays computer applications as well as statistical and computational approaches constitute a big part of the toolbox of every archaeologist, as they open tremendous possibilities for all research. These can be ready-to-use (proprietary) software applications but also “Little Minions” (self-scripted tools) or research software (e.g. implementation of statistic algorithms in R, Python), which are written by researchers. Both, research software and research data are part of Computational Archaeology and play an important role in up-to-date archaeological research. Optimally research data (and software) is FAIR(4RS) – Findable, Accessible, Interoperable, Re-usable (for Research Software) – and reproducible of which other users can benefit from or even develop further. The increasing number of topics and papers at the international and national chapters of the CAA show manifold applications but also implications. Working Groups like the SIG SSLA (https://sslarch.github.io) or the “Little Minions” (https://littleminions.link) also deal with Computational Archaeology and are building a community.
Several initiatives, such as the German National Research Data Infrastructure (NFDI) – especially NFDI4Objects – or the European Open Science Cloud (EOSC), engage with this topic to strengthen the position of Computational Archaeologists and Research Software Engineers, highlight the scientific merit of their work, and ensure researchers receive credit for computational approaches, software development, as well as for writing papers.
To support this, this session invites contributions dealing with various aspects of Computational Archaeology, but not limited to:
- treating source code/software as research data
- presenting a “Little Minion”
- discussing challenges in Computational Archaeology
- advancing new algorithms and statistical/computational analysis methods
- (critical) use of AI, discussing pitfalls and complications
- addressing the divide between FAIR(4RS) principles and practices
- incorporating FAIR(4RS) principles into the teaching curriculum
- discussing approaches concerning problems and solutions to legacy data and software
- making complex statistical and computational methods accessible to main-stream archaeology"""@en ;
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        <https://w3id.org/squirrelpapers/person/quintana-miguel-angel-dilena>,
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    dct:description """Over the last 15 years, digital methods have been well-established in all documentary stages of archaeological research. As is known, each excavation develops integrally in a three-dimensional setting, hence the relevance of digitally gathering each component of the whole volumetric information. This includes the 3D capturing and modelling of excavation sites and recovered artefacts or structures that emerge via different approaches like Light Detection and Ranging (LiDaR), 3D (laser), programmatically combined procedures, scanning or photogrammetrical techniques, e.g. Structure from Motion (SfM).
However, much of that spatial data is lost through the misapplication of documentation practices, the informatic guidelines and a missing Research Data Management (RDM). Crucial discussions around consistency and reproducibility in diverse software environments regarding these 3D methodologies, or their respective capacity for long-term storage and findability, accessibility, interoperability, and reusability (FAIR) faculties are ignored or left aside. In addition, the metadata modelling for these processes is still in its very early stages. Therefore, these discrepancies can impede different teams from comparing and disseminating the computerised results.
For this purpose, collaborative efforts, both in creating common documentation standards, free open source software (FOSS) and FAIRification tools and developing future-proof archiving instances have been made by various worldwide initiatives. However, they require a decidedly global perspective and acceptance.
We aim to bring together archaeologists and researchers concerned with the above-stated issues and work with all archaeological periods and digital technologies. We encourage them to present their views on this matter to debate about standardised methodologies capable of capturing, generating, archiving and disseminating archaeological 3D digital information."""@en ;
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    dct:title "Best Practices on Archaeological 3D Data Management: Methods and Approaches to Capture, Generate, and Disseminate 3D Information in Archaeology\""@en ;
    dct:type sqpt:conference-session ;
    bibo:abstract """Over the last 15 years, digital methods have been well-established in all documentary stages of archaeological research. As is known, each excavation develops integrally in a three-dimensional setting, hence the relevance of digitally gathering each component of the whole volumetric information. This includes the 3D capturing and modelling of excavation sites and recovered artefacts or structures that emerge via different approaches like Light Detection and Ranging (LiDaR), 3D (laser), programmatically combined procedures, scanning or photogrammetrical techniques, e.g. Structure from Motion (SfM).
However, much of that spatial data is lost through the misapplication of documentation practices, the informatic guidelines and a missing Research Data Management (RDM). Crucial discussions around consistency and reproducibility in diverse software environments regarding these 3D methodologies, or their respective capacity for long-term storage and findability, accessibility, interoperability, and reusability (FAIR) faculties are ignored or left aside. In addition, the metadata modelling for these processes is still in its very early stages. Therefore, these discrepancies can impede different teams from comparing and disseminating the computerised results.
For this purpose, collaborative efforts, both in creating common documentation standards, free open source software (FOSS) and FAIRification tools and developing future-proof archiving instances have been made by various worldwide initiatives. However, they require a decidedly global perspective and acceptance.
We aim to bring together archaeologists and researchers concerned with the above-stated issues and work with all archaeological periods and digital technologies. We encourage them to present their views on this matter to debate about standardised methodologies capable of capturing, generating, archiving and disseminating archaeological 3D digital information."""@en ;
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        <https://w3id.org/squirrelpapers/person/0009-0005-1140-3352>,
        <https://w3id.org/squirrelpapers/person/alpino-nadine> ;
    dct:description """Infrastructures for Research Data Management (RDM) and applying the FAIR (Findable, Accessible, Interoperable, Re-usable) and Open Science Principles are key features in modern (digital) Cultural Heritage. Nowadays, computer applications, as well as statistical and computational approaches, constitute a big part of the toolbox of every CH researcher, as they open tremendous possibilities for all research. Several initiatives, such as the German National Research Data Infrastructure (NFDI) – especially NFDI4Objects – or the European Open Science Cloud (EOSC), use this topic to strengthen new technologies along the individual object biography. This includes (a) comprehending representations of physical objects as research data, (b) relating them to individual contexts, (c) transforming them adequately into the digital space, and (d) curating them according to domain-specific requirements. This leads to a paradigm derived from the FAIRification workflow: (i) capturing, (ii) semantic (meta) data modelling and qualifying, (iii) applying (scientific) analysis, (iv) sharing the data by visualisation and FAIRified publication, (v) interlinking and integrating data into knowledge graphs.
To support the activities within the before mentioned paradigm, this session invites contributions dealing with various aspects of RDM, FAIR and Open Science, but not limited to:
applying new documentation methods for data capturing in the CH domain
applying (semantic) data modelling (e.g. CIDOC CRM) for data qualification (e.g. RDF, LPG, etc.)
applying new scientific analysis in Cultural Heritage, not limited to natural sciences, geosciences, geodesy, conservation sciences
FAIRification Tools and Research Software (incl., e.g. small R/Python scripts, “CAA Little Minions”) and statistical/computational approaches
concepts to share research (meta) data in free and open available comprehensible FAIRified representations
methods and concepts for integrating and interlinking research data into domain-focused and overarching knowledge graphs
working together with Citizen Science and their knowledge bases/repositories (e.g. Wikiverse, Open Street Map, Portable Antiquities Scheme, …)
dealing with legacy data, data sustainability and long-term archiving
presenting experiences as DOs (Good Practices) and DON’Ts (Worst Practices)
applying AI methods and tools for CH"""@en ;
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    dct:title "Research Data Management in Cultural Heritage goes Digital – New Technologies along the object biography\""@en ;
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    bibo:abstract """Infrastructures for Research Data Management (RDM) and applying the FAIR (Findable, Accessible, Interoperable, Re-usable) and Open Science Principles are key features in modern (digital) Cultural Heritage. Nowadays, computer applications, as well as statistical and computational approaches, constitute a big part of the toolbox of every CH researcher, as they open tremendous possibilities for all research. Several initiatives, such as the German National Research Data Infrastructure (NFDI) – especially NFDI4Objects – or the European Open Science Cloud (EOSC), use this topic to strengthen new technologies along the individual object biography. This includes (a) comprehending representations of physical objects as research data, (b) relating them to individual contexts, (c) transforming them adequately into the digital space, and (d) curating them according to domain-specific requirements. This leads to a paradigm derived from the FAIRification workflow: (i) capturing, (ii) semantic (meta) data modelling and qualifying, (iii) applying (scientific) analysis, (iv) sharing the data by visualisation and FAIRified publication, (v) interlinking and integrating data into knowledge graphs.
To support the activities within the before mentioned paradigm, this session invites contributions dealing with various aspects of RDM, FAIR and Open Science, but not limited to:
applying new documentation methods for data capturing in the CH domain
applying (semantic) data modelling (e.g. CIDOC CRM) for data qualification (e.g. RDF, LPG, etc.)
applying new scientific analysis in Cultural Heritage, not limited to natural sciences, geosciences, geodesy, conservation sciences
FAIRification Tools and Research Software (incl., e.g. small R/Python scripts, “CAA Little Minions”) and statistical/computational approaches
concepts to share research (meta) data in free and open available comprehensible FAIRified representations
methods and concepts for integrating and interlinking research data into domain-focused and overarching knowledge graphs
working together with Citizen Science and their knowledge bases/repositories (e.g. Wikiverse, Open Street Map, Portable Antiquities Scheme, …)
dealing with legacy data, data sustainability and long-term archiving
presenting experiences as DOs (Good Practices) and DON’Ts (Worst Practices)
applying AI methods and tools for CH"""@en ;
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    dct:creator <https://w3id.org/squirrelpapers/person/0000-0002-1854-6760>,
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    dct:description """Nowadays computer applications as well as statistical and computational approaches constitute a big part of the toolbox of every archaeologist, as they open tremendous possibilities for all research. These can be ready-to-use (proprietary) software applications but also “Little Minions” (self-scripted tools) or research software (e.g. implementation of statistic algorithms in R, Python), which are written by researchers. Both, research software and research data are part of Computational Archaeology and play an important role in up-to-date archaeological research. Optimally research data (and software) is FAIR(4RS) – Findable, Accessible, Interoperable, Re-usable (for Research Software) – and reproducible of which other users can benefit from or even develop further. The increasing number of topics and papers at the international and national chapters of the CAA show manifold applications but also implications. Working Groups like the SIG SSLA (https://sslarch.github.io) or the “Little Minions” (https://littleminions.link) also deal with Computational Archaeology and are building a community.
Several initiatives, such as the German National Research Data Infrastructure (NFDI) – especially NFDI4Objects – or the European Open Science Cloud (EOSC), engage with this topic to strengthen the position of Computational Archaeologists and Research Software Engineers, highlight the scientific merit of their work, and ensure researchers receive credit for computational approaches, software development, as well as for writing papers."""@en ;
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    dct:title "Applying Archaeological Research Software Engineering as Little Minions: Statistical & Computational Approaches to Daily Archaeological Tasks"@en ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """Nowadays computer applications as well as statistical and computational approaches constitute a big part of the toolbox of every archaeologist, as they open tremendous possibilities for all research. These can be ready-to-use (proprietary) software applications but also “Little Minions” (self-scripted tools) or research software (e.g. implementation of statistic algorithms in R, Python), which are written by researchers. Both, research software and research data are part of Computational Archaeology and play an important role in up-to-date archaeological research. Optimally research data (and software) is FAIR(4RS) – Findable, Accessible, Interoperable, Re-usable (for Research Software) – and reproducible of which other users can benefit from or even develop further. The increasing number of topics and papers at the international and national chapters of the CAA show manifold applications but also implications. Working Groups like the SIG SSLA (https://sslarch.github.io) or the “Little Minions” (https://littleminions.link) also deal with Computational Archaeology and are building a community.
Several initiatives, such as the German National Research Data Infrastructure (NFDI) – especially NFDI4Objects – or the European Open Science Cloud (EOSC), engage with this topic to strengthen the position of Computational Archaeologists and Research Software Engineers, highlight the scientific merit of their work, and ensure researchers receive credit for computational approaches, software development, as well as for writing papers."""@en ;
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    dct:description """“Dated sites” in archaeology are usually not “dated sites”. Most of the temporality of “dated sites” is usually derived from historical assumptions. However, the chronology of these materials is often based on material coming from the manifold of other assumed “dated sites”, thus potentially resulting in a circulus vitiosus. A methodological way out of this dilemma is possible when we enhance the assumed external dating evidence and go back to the factual occurrences and overlaps in the data from the suspected “dated sites” since only these data are free of any temporal interpretations.
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To cover as many assumed “dated sites” as possible, the Samian Research database with its 250,000 potter’s stamps from all over Europe is used.
It turns out that it is possible to visualise historical development on maps, timelines and graph structures without using historical-chronological assumptions about individual “dated sites”. Simultaneously, this paper demonstrates the application of a different concept of temporality. The development of the Samian consumption patterns in the progressing Southern German Limes and the enrollment of the occupation of Britain appears to be gradual only. Applying the methods above, based on Linked Open Data techniques, enables the FAIRification of these data related to temporality, especially by creating interoperability and reproducibility."""@en ;
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    dct:title "Taming Time: Using Correspondence Analysis to handle relative chronologies as graphs"@en ;
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    bibo:abstract """“Dated sites” in archaeology are usually not “dated sites”. Most of the temporality of “dated sites” is usually derived from historical assumptions. However, the chronology of these materials is often based on material coming from the manifold of other assumed “dated sites”, thus potentially resulting in a circulus vitiosus. A methodological way out of this dilemma is possible when we enhance the assumed external dating evidence and go back to the factual occurrences and overlaps in the data from the suspected “dated sites” since only these data are free of any temporal interpretations.
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To cover as many assumed “dated sites” as possible, the Samian Research database with its 250,000 potter’s stamps from all over Europe is used.
It turns out that it is possible to visualise historical development on maps, timelines and graph structures without using historical-chronological assumptions about individual “dated sites”. Simultaneously, this paper demonstrates the application of a different concept of temporality. The development of the Samian consumption patterns in the progressing Southern German Limes and the enrollment of the occupation of Britain appears to be gradual only. Applying the methods above, based on Linked Open Data techniques, enables the FAIRification of these data related to temporality, especially by creating interoperability and reproducibility."""@en ;
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In der archäologischen Forschung stellen Unsicherheiten und Mehrdeutigkeiten in Geodaten eine große Herausforderung dar. Historische Orte wie „Mainz“ können je nach Kontext unterschiedliche Bedeutungen haben – sei es das römische Mainz, das mittelalterliche Mainz oder die Stadt im 20. Jahrhundert. Um solche Unsicherheiten transparent zu dokumentieren und für die Nachnutzung zugänglich zu machen, ist der Einsatz von Linked Open Data (LOD) und Ontologien ein vielversprechender Ansatz. Das vorliegende Paper stellt die Fuzzy Spatial Locations Ontology (fuzzy-sl) vor, eine Ontologie, die auf PROV-O, SKOS und GeoSPARQL basiert und dabei hilft, unscharfe geographische Informationen nachvollziehbar zu modellieren und zu zitieren. Diese Ontologie ermöglicht es, vagueness, Unsicherheiten und verschiedene Interpretationen von Orten konsistent in einer Wikibase-Umgebung zu erfassen. Die Modellierung erfolgt durch eine Kombination von Konzepten wie coordinate metadata (CM) und geolocation metadata (GLM), die es ermöglichen, unterschiedliche Genauigkeiten und Quellenangaben der Daten klar zu differenzieren. Ein zentrales Ziel der Studie ist die FAIRifizierung von Daten gemäß den FAIR-Prinzipien (Findable, Accessible, Interoperable, Reusable). Dies wird durch die Integration von fuzzy-sl in bestehende Linked Open Data-Ökosysteme wie Wikidata, Semantic Kompakkt und FactGrid erreicht. Dadurch wird ein interoperables Netzwerk geschaffen, das unterschiedliche Datensätze miteinander verknüpft und so die Provenienz von Daten transparent macht. Neben der archäologischen Anwendung demonstriert das Paper auch die Integration von fuzzy-sl in weitere Disziplinen wie die Numismatik und die Geowissenschaften. Fallstudien, darunter Fundorte römischer Terra Sigillata in London und mittelalterliche Ogham-Steine, verdeutlichen, wie Unsicherheiten bei Fundorten und historischen Daten effektiv behandelt werden können. Abschließend hebt die Arbeit hervor, wie fuzzy-sl dazu beitragen kann, unscharfe geographische Daten nicht nur zu dokumentieren, sondern auch in modernen Linked-Data-Umgebungen zitierbar zu machen. Dies eröffnet neue Möglichkeiten für die wissenschaftliche Zusammenarbeit und den Datenaustausch in der Archäologie, Geschichtsforschung und darüber hinaus. Alle erstellten Daten und Modelle stehen unter einer offenen Creative Commons Lizenz (CC BY 4.0) zur Verfügung, um die Nachnutzung und Weiterentwicklung durch die Forschungsgemeinschaft zu fördern.
== English ==
In archaeological research, uncertainties and ambiguities in geodata present significant challenges. Historical locations such as “Mainz” can have multiple interpretations depending on context—whether it refers to Roman Mainz, medieval Mainz, or the city in the 20th century. To document such uncertainties transparently and make them available for reuse, the use of Linked Open Data (LOD) and ontologies is a promising approach. This paper introduces the Fuzzy Spatial Locations Ontology (fuzzy-sl), an ontology based on PROV-O, SKOS, and GeoSPARQL, which aids in modelling and citing uncertain geographic information in a comprehensible manner. The ontology facilitates the documentation of vagueness, uncertainties, and different interpretations of locations consistently within a Wikibase environment. Modelling is achieved through a combination of concepts such as coordinate metadata (CM) and geolocation metadata (GLM), enabling the differentiation of varying accuracies and data sources. A key objective of the study is the FAIRification of data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable). This is accomplished by integrating fuzzy-sl into existing Linked Open Data ecosystems such as Wikidata, Semantic Kompakkt, and FactGrid. This creates an interoperable network that connects various datasets and enhances the transparency of data provenance. In addition to its application in archaeology, the paper also demonstrates the integration of fuzzy-sl in other disciplines such as numismatics and geosciences. Case studies, including Roman Terra Sigillata findspots in London and medieval Ogham stones, illustrate how uncertainties in findspots and historical data can be effectively addressed. Finally, the paper emphasises how fuzzy-sl can contribute not only to documenting vague geographic data but also to making it citable in modern Linked Data environments. This opens up new opportunities for scientific collaboration and data exchange across archaeology, historical research, and beyond. All created data and models are available under an open Creative Commons Licence (CC BY 4.0) to encourage reuse and further development by the research community."""@de ;
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== English ==
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This record contains a dynamic HTML website with figures that are mentioned in the paper.
Abstract
This article discusses digital methods to model and handle the ordering of so-called dated sites in archaeology. This findspot attribute is usually elaborated by indirect evidence based on the chronology of find materials, which are often based on material derived from other assumed dated sites, thus potentially resulting in a circulus vitiosus. To avoid self-fulfilling dating methods, comparative tools such as correspondence analysis or algorithms like Allen’s interval algebra can be applied to stamped Roman terra sigillata (Samian) pottery vessels and their occurrences in Roman sites. The TiGeR method and the Alligator method demonstrate ways to establish and visualize the chronological order of dated sites. With the TiGeR method, a sequential order of chronologically sensitive material can be plotted on a map, where the coloring of the dots is realized by calculating hue color values from the normalized values of the first component of the correspondence analysis. The resulting maps and graphs chronologically display the historical conquering of the Roman provinces of Britannia and Germania. Based on Allen’s interval algebra, the Alligator method is able to chronologically sort groups of dated sites, such as limes sections, when only a few chronological indications are known beforehand and other limes sections can only be judged by the overlap of material which they have in common. The Academic Meta Tool offers a decision procedure (reasoning) to calculate the chronological relative order of limes sections. This tool is also capable of visualizing the results as graph structures.""" ;
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This record contains a dynamic HTML website with figures that are mentioned in the paper.
Abstract
This article discusses digital methods to model and handle the ordering of so-called dated sites in archaeology. This findspot attribute is usually elaborated by indirect evidence based on the chronology of find materials, which are often based on material derived from other assumed dated sites, thus potentially resulting in a circulus vitiosus. To avoid self-fulfilling dating methods, comparative tools such as correspondence analysis or algorithms like Allen’s interval algebra can be applied to stamped Roman terra sigillata (Samian) pottery vessels and their occurrences in Roman sites. The TiGeR method and the Alligator method demonstrate ways to establish and visualize the chronological order of dated sites. With the TiGeR method, a sequential order of chronologically sensitive material can be plotted on a map, where the coloring of the dots is realized by calculating hue color values from the normalized values of the first component of the correspondence analysis. The resulting maps and graphs chronologically display the historical conquering of the Roman provinces of Britannia and Germania. Based on Allen’s interval algebra, the Alligator method is able to chronologically sort groups of dated sites, such as limes sections, when only a few chronological indications are known beforehand and other limes sections can only be judged by the overlap of material which they have in common. The Academic Meta Tool offers a decision procedure (reasoning) to calculate the chronological relative order of limes sections. This tool is also capable of visualizing the results as graph structures.""" ;
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    dct:title "FAIRes Kulturerbe mit dem Smartphone und SfM? Wie man das durch Smartphone Apps, Open Source Tools und semantische Annotation im Wikiversum und OSM erreichen kann"@de ;
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    dct:description """Integrating diverse datasets into a unified, interoperable representation is a key challenge in interdisciplinary research. Heterogeneous information networks provide a graph-based framework for linking datasets while preserving their semantic structure. This work explores the use of ontologies to ensure semantic interoperability in cultural heritage knowledge graphs.
Using the CIDOC Conceptual Reference Model (CIDOC-CRM) alongside other commonly used or domain-specific ontologies, this study develops a hybrid ontology for archaeo-natural research data (ArNO) based on the experience gained in NFDI4Objects Task Area 3. The integration follows Linked Open Data principles, ensuring schema-level consistency through ontology-based modelling and enforcing data-level consistency with terminologies. By establishing schema- and data-level standards, this work contributes to FAIR-compliant research infrastructures, enhancing data reuse across humanities and natural sciences.
The approach is demonstrated through the integration of ancient DNA datasets from the Poseidon framework. This enables cross-dataset analysis by linking biological, archaeological, and contextual data. The study also addresses challenges in CIDOC-CRM-based modelling, e.g., its event-centric nature."""@en ;
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    dct:title "Data-Driven Community Standards for Interdisciplinary Heterogeneous Information Networks"@en ;
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    bibo:abstract """Integrating diverse datasets into a unified, interoperable representation is a key challenge in interdisciplinary research. Heterogeneous information networks provide a graph-based framework for linking datasets while preserving their semantic structure. This work explores the use of ontologies to ensure semantic interoperability in cultural heritage knowledge graphs.
Using the CIDOC Conceptual Reference Model (CIDOC-CRM) alongside other commonly used or domain-specific ontologies, this study develops a hybrid ontology for archaeo-natural research data (ArNO) based on the experience gained in NFDI4Objects Task Area 3. The integration follows Linked Open Data principles, ensuring schema-level consistency through ontology-based modelling and enforcing data-level consistency with terminologies. By establishing schema- and data-level standards, this work contributes to FAIR-compliant research infrastructures, enhancing data reuse across humanities and natural sciences.
The approach is demonstrated through the integration of ancient DNA datasets from the Poseidon framework. This enables cross-dataset analysis by linking biological, archaeological, and contextual data. The study also addresses challenges in CIDOC-CRM-based modelling, e.g., its event-centric nature."""@en ;
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    dct:description """Open Science principles provide a framework to ensure cultural heritage data is Findable, Accessible, Interoperable, and Reusable (FAIR). This panel, showing collaboration across disciplines and regions, demonstrates how FAIR methods advance the understanding and dissemination of Irish and UK cultural heritage through three key approaches:
3D/2D Capturing and Publication: High-resolution 3D and 2D capturing techniques document and preserve cultural artefacts like Ogham stones and Holy Wells, enabling detailed analysis and online global access. Projects like Ogham in 3D and OG(H)AM exemplify this by offering rich, multi-dimensional datasets for researchers and public engagement.
Linked Open Data (LOD): Encoding heritage data in formats like TEI EPIDOC and integrating it into LOD networks ensures interoperability and connects distributed datasets. Initiatives such as ogham.link demonstrate how LOD fosters collaboration, enriches cultural narratives, and enhances accessibility.
Volunteered Knowledge Hubs: Platforms like Wikidata and OpenStreetMap (OSM) facilitate community-driven heritage preservation and knowledge sharing. These hubs expand beyond academic contributions to include diverse perspectives, as seen in projects focusing on Ogham inscriptions and Holy Wells.
This panel, which focuses on Ogham and Holy Wells, draws on active projects in which the panel chairs are directly involved. These elements provide a robust foundation for discussing how Open Science and FAIR methodologies intersect with digital humanities, archaeology, and community engagement.
Ogham (ᚑᚌᚆᚐᚋ) - a unique writing system across space and time - is a distinctive writing system created for an early form of the Irish language, primarily used during the 1st Millennium AD on stone pillars to inscribe personal and kin-group names. These inscriptions, found mainly in Ireland and parts of Britain, hold immense value for linguistic, archaeological, and historical studies. Since the 1990s, projects like the Celtic Inscribed Stones Project (CISP) and Ogham in 3D have applied Digital Humanities tools to document and analyse these inscriptions. By employing 3D models, TEI XML encodings, and citizen science contributions via platforms like Wikidata and OSM, FAIR methodologies have transformed the accessibility and study of this unique script.
Holy Wells - sites of spiritual and community significance - located throughout Ireland, the UK and elsewhere, embody a rich cultural, historical, and spiritual heritage that is in danger of getting lost due to a shift in spiritual and amenity needs as well as farming practices. As a water source linked to early Christian and pre-Christian traditions, they are often associated with saints, healing practices, possibly baptisms and local folklore. Documenting these sacred sites poses challenges, as their histories blend oral traditions with physical features in remote locations. FAIR methods have enabled systematic cataloguing through geospatial mapping, photogrammetry, community-driven data collection, and digital storytelling. Platforms like OpenStreetMap and the Wikiverse integrate localised knowledge into broader datasets, ensuring the digital preservation of Holy Wells for future generations of pilgrims and researchers alike.
This panel highlights how Ogham and Holy Wells exemplify the potential of Open Science to bridge local heritage and global scholarship through FAIR principles, advancing collaborative and inclusive cultural preservation practices.
A. Beyond Digital Boundaries -  Interaction between dimensions (2/2.5/3D)
This presentation will discuss the boundaries imposed on visualisations of ogham-inscribed objects by the dimensionality of the data, and how the OG(H)AM and OPal+ (Ogham Palaeography+) initiatives aim to cross these boundaries using different imaging methods and software packages. Specifically, this will discuss the different processes available to use 3D models to generate 2D images, and how the images generated can be used for other types of research, including palaeography. The ogham script is unusual in the way it wraps around the edges of inscribed objects (mainly stone pillars) rather than being laid out on a flat surface. Of course, this makes 3d visualisation especially useful for ogham-inscribed objects generally. However, this script aspect can be challenging for the palaeographic study of ogham. Therefore, unwrapping 3d models for 2D visualisation is a method being utilised in the OPal+ and EMISoS (Early Medieval Irish Scripts on Stone) projects.
B. Beyond Digital Boundaries - Collaboration across (Linked) Open Data formats
This presentation will highlight different approaches to transforming multidisciplinary Ogham Data into Linked Open Data (LOD). LOD helps to make these different Ogham Stone Concepts, with several provenances, modifications, corrections, etc. citable (via a URI) and create a network of improving information over time. The ogham data has been encoded by the OG(H)AM project in EpiDoc format. EpiDoc is an international, collaborative effort that provides guidelines and tools for encoding scholarly and educational editions of ancient documents. It uses a subset of the TEI (Text Encoding Initiative) standard for the representation of texts in digital form and was developed initially for the publication of digital editions of ancient inscriptions. Crucially, EpiDoc addresses not only the transcription and editorial treatment of texts themselves, but also the history, provenance and materiality of the objects on which the texts appear. This xml data now serves as a basis for transforming via semi-automatic scripts into LOD to make it even FAIRer.
C. Beyond Digital Boundaries - Working with Wikidata and OpenStreetMap
This presentation explores the integration of Og(h)am and Holy Wells data within community-driven initiatives and citizen science platforms, such as OpenStreetMap (OSM), the Wikiverse, and 3D modelling using photogrammetric methods via Structure from Motion (SfM). In OSM, the tag historic=ogham_stone allows citizen scientists to document attributes of Og(h)am stones, including inscriptions, material, dimensions, accessibility, and links to Wikidata and Wikimedia Commons. Additionally, OSM provides geospatial data on townlands and baronies (primarily via townlands.ie), aiding in the geolocation of findspots referenced in literature and excavation reports. Related initiatives, such as documenting holy wells using the OSM tag place_of_worship=holy_well and Wikidata’s WikiProject Holy Wells, offer valuable frameworks for structuring cultural heritage data. The Wikiverse, comprising Wikidata, Wikimedia Commons, and Wikipedia, is a collaborative hub for sharing Og(h)am data through structured entries, visual media, and descriptive text. Additionally, user-friendly smartphone applications facilitate the creation of 3D models via photogrammetric techniques, which can be linked to OSM and Wikidata, enhancing accessibility and preservation efforts."""@en ;
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    dct:title "Beyond Digital Boundaries - Linked Open Data and Wikidata with OG(H)AM and Holy Wells"@en ;
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    bibo:abstract """Open Science principles provide a framework to ensure cultural heritage data is Findable, Accessible, Interoperable, and Reusable (FAIR). This panel, showing collaboration across disciplines and regions, demonstrates how FAIR methods advance the understanding and dissemination of Irish and UK cultural heritage through three key approaches:
3D/2D Capturing and Publication: High-resolution 3D and 2D capturing techniques document and preserve cultural artefacts like Ogham stones and Holy Wells, enabling detailed analysis and online global access. Projects like Ogham in 3D and OG(H)AM exemplify this by offering rich, multi-dimensional datasets for researchers and public engagement.
Linked Open Data (LOD): Encoding heritage data in formats like TEI EPIDOC and integrating it into LOD networks ensures interoperability and connects distributed datasets. Initiatives such as ogham.link demonstrate how LOD fosters collaboration, enriches cultural narratives, and enhances accessibility.
Volunteered Knowledge Hubs: Platforms like Wikidata and OpenStreetMap (OSM) facilitate community-driven heritage preservation and knowledge sharing. These hubs expand beyond academic contributions to include diverse perspectives, as seen in projects focusing on Ogham inscriptions and Holy Wells.
This panel, which focuses on Ogham and Holy Wells, draws on active projects in which the panel chairs are directly involved. These elements provide a robust foundation for discussing how Open Science and FAIR methodologies intersect with digital humanities, archaeology, and community engagement.
Ogham (ᚑᚌᚆᚐᚋ) - a unique writing system across space and time - is a distinctive writing system created for an early form of the Irish language, primarily used during the 1st Millennium AD on stone pillars to inscribe personal and kin-group names. These inscriptions, found mainly in Ireland and parts of Britain, hold immense value for linguistic, archaeological, and historical studies. Since the 1990s, projects like the Celtic Inscribed Stones Project (CISP) and Ogham in 3D have applied Digital Humanities tools to document and analyse these inscriptions. By employing 3D models, TEI XML encodings, and citizen science contributions via platforms like Wikidata and OSM, FAIR methodologies have transformed the accessibility and study of this unique script.
Holy Wells - sites of spiritual and community significance - located throughout Ireland, the UK and elsewhere, embody a rich cultural, historical, and spiritual heritage that is in danger of getting lost due to a shift in spiritual and amenity needs as well as farming practices. As a water source linked to early Christian and pre-Christian traditions, they are often associated with saints, healing practices, possibly baptisms and local folklore. Documenting these sacred sites poses challenges, as their histories blend oral traditions with physical features in remote locations. FAIR methods have enabled systematic cataloguing through geospatial mapping, photogrammetry, community-driven data collection, and digital storytelling. Platforms like OpenStreetMap and the Wikiverse integrate localised knowledge into broader datasets, ensuring the digital preservation of Holy Wells for future generations of pilgrims and researchers alike.
This panel highlights how Ogham and Holy Wells exemplify the potential of Open Science to bridge local heritage and global scholarship through FAIR principles, advancing collaborative and inclusive cultural preservation practices.
A. Beyond Digital Boundaries -  Interaction between dimensions (2/2.5/3D)
This presentation will discuss the boundaries imposed on visualisations of ogham-inscribed objects by the dimensionality of the data, and how the OG(H)AM and OPal+ (Ogham Palaeography+) initiatives aim to cross these boundaries using different imaging methods and software packages. Specifically, this will discuss the different processes available to use 3D models to generate 2D images, and how the images generated can be used for other types of research, including palaeography. The ogham script is unusual in the way it wraps around the edges of inscribed objects (mainly stone pillars) rather than being laid out on a flat surface. Of course, this makes 3d visualisation especially useful for ogham-inscribed objects generally. However, this script aspect can be challenging for the palaeographic study of ogham. Therefore, unwrapping 3d models for 2D visualisation is a method being utilised in the OPal+ and EMISoS (Early Medieval Irish Scripts on Stone) projects.
B. Beyond Digital Boundaries - Collaboration across (Linked) Open Data formats
This presentation will highlight different approaches to transforming multidisciplinary Ogham Data into Linked Open Data (LOD). LOD helps to make these different Ogham Stone Concepts, with several provenances, modifications, corrections, etc. citable (via a URI) and create a network of improving information over time. The ogham data has been encoded by the OG(H)AM project in EpiDoc format. EpiDoc is an international, collaborative effort that provides guidelines and tools for encoding scholarly and educational editions of ancient documents. It uses a subset of the TEI (Text Encoding Initiative) standard for the representation of texts in digital form and was developed initially for the publication of digital editions of ancient inscriptions. Crucially, EpiDoc addresses not only the transcription and editorial treatment of texts themselves, but also the history, provenance and materiality of the objects on which the texts appear. This xml data now serves as a basis for transforming via semi-automatic scripts into LOD to make it even FAIRer.
C. Beyond Digital Boundaries - Working with Wikidata and OpenStreetMap
This presentation explores the integration of Og(h)am and Holy Wells data within community-driven initiatives and citizen science platforms, such as OpenStreetMap (OSM), the Wikiverse, and 3D modelling using photogrammetric methods via Structure from Motion (SfM). In OSM, the tag historic=ogham_stone allows citizen scientists to document attributes of Og(h)am stones, including inscriptions, material, dimensions, accessibility, and links to Wikidata and Wikimedia Commons. Additionally, OSM provides geospatial data on townlands and baronies (primarily via townlands.ie), aiding in the geolocation of findspots referenced in literature and excavation reports. Related initiatives, such as documenting holy wells using the OSM tag place_of_worship=holy_well and Wikidata’s WikiProject Holy Wells, offer valuable frameworks for structuring cultural heritage data. The Wikiverse, comprising Wikidata, Wikimedia Commons, and Wikipedia, is a collaborative hub for sharing Og(h)am data through structured entries, visual media, and descriptive text. Additionally, user-friendly smartphone applications facilitate the creation of 3D models via photogrammetric techniques, which can be linked to OSM and Wikidata, enhancing accessibility and preservation efforts."""@en ;
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    dct:description """Open Science principles provide a framework to ensure cultural heritage data is Findable, Accessible, Interoperable, and Reusable (FAIR). This panel, showing collaboration across disciplines and regions, demonstrates how FAIR methods advance the understanding and dissemination of Irish and UK cultural heritage through three key approaches:
3D/2D Capturing and Publication: High-resolution 3D and 2D capturing techniques document and preserve cultural artefacts like Ogham stones and Holy Wells, enabling detailed analysis and online global access. Projects like Ogham in 3D and OG(H)AM exemplify this by offering rich, multi-dimensional datasets for researchers and public engagement.
Linked Open Data (LOD): Encoding heritage data in formats like TEI EPIDOC and integrating it into LOD networks ensures interoperability and connects distributed datasets. Initiatives such as ogham.link demonstrate how LOD fosters collaboration, enriches cultural narratives, and enhances accessibility.
Volunteered Knowledge Hubs: Platforms like Wikidata and OpenStreetMap (OSM) facilitate community-driven heritage preservation and knowledge sharing. These hubs expand beyond academic contributions to include diverse perspectives, as seen in projects focusing on Ogham inscriptions and Holy Wells.
This panel, which focuses on Ogham and Holy Wells, draws on active projects in which the panel chairs are directly involved. These elements provide a robust foundation for discussing how Open Science and FAIR methodologies intersect with digital humanities, archaeology, and community engagement.
Ogham (ᚑᚌᚆᚐᚋ) - a unique writing system across space and time - is a distinctive writing system created for an early form of the Irish language, primarily used during the 1st Millennium AD on stone pillars to inscribe personal and kin-group names. These inscriptions, found mainly in Ireland and parts of Britain, hold immense value for linguistic, archaeological, and historical studies. Since the 1990s, projects like the Celtic Inscribed Stones Project (CISP) and Ogham in 3D have applied Digital Humanities tools to document and analyse these inscriptions. By employing 3D models, TEI XML encodings, and citizen science contributions via platforms like Wikidata and OSM, FAIR methodologies have transformed the accessibility and study of this unique script.
Holy Wells - sites of spiritual and community significance - located throughout Ireland, the UK and elsewhere, embody a rich cultural, historical, and spiritual heritage that is in danger of getting lost due to a shift in spiritual and amenity needs as well as farming practices. As a water source linked to early Christian and pre-Christian traditions, they are often associated with saints, healing practices, possibly baptisms and local folklore. Documenting these sacred sites poses challenges, as their histories blend oral traditions with physical features in remote locations. FAIR methods have enabled systematic cataloguing through geospatial mapping, photogrammetry, community-driven data collection, and digital storytelling. Platforms like OpenStreetMap and the Wikiverse integrate localised knowledge into broader datasets, ensuring the digital preservation of Holy Wells for future generations of pilgrims and researchers alike.
This panel highlights how Ogham and Holy Wells exemplify the potential of Open Science to bridge local heritage and global scholarship through FAIR principles, advancing collaborative and inclusive cultural preservation practices.
A. Beyond Digital Boundaries -  Interaction between dimensions (2/2.5/3D)
This presentation will discuss the boundaries imposed on visualisations of ogham-inscribed objects by the dimensionality of the data, and how the OG(H)AM and OPal+ (Ogham Palaeography+) initiatives aim to cross these boundaries using different imaging methods and software packages. Specifically, this will discuss the different processes available to use 3D models to generate 2D images, and how the images generated can be used for other types of research, including palaeography. The ogham script is unusual in the way it wraps around the edges of inscribed objects (mainly stone pillars) rather than being laid out on a flat surface. Of course, this makes 3d visualisation especially useful for ogham-inscribed objects generally. However, this script aspect can be challenging for the palaeographic study of ogham. Therefore, unwrapping 3d models for 2D visualisation is a method being utilised in the OPal+ and EMISoS (Early Medieval Irish Scripts on Stone) projects.
B. Beyond Digital Boundaries - Collaboration across (Linked) Open Data formats
This presentation will highlight different approaches to transforming multidisciplinary Ogham Data into Linked Open Data (LOD). LOD helps to make these different Ogham Stone Concepts, with several provenances, modifications, corrections, etc. citable (via a URI) and create a network of improving information over time. The ogham data has been encoded by the OG(H)AM project in EpiDoc format. EpiDoc is an international, collaborative effort that provides guidelines and tools for encoding scholarly and educational editions of ancient documents. It uses a subset of the TEI (Text Encoding Initiative) standard for the representation of texts in digital form and was developed initially for the publication of digital editions of ancient inscriptions. Crucially, EpiDoc addresses not only the transcription and editorial treatment of texts themselves, but also the history, provenance and materiality of the objects on which the texts appear. This xml data now serves as a basis for transforming via semi-automatic scripts into LOD to make it even FAIRer.
C. Beyond Digital Boundaries - Working with Wikidata and OpenStreetMap
This presentation explores the integration of Og(h)am and Holy Wells data within community-driven initiatives and citizen science platforms, such as OpenStreetMap (OSM), the Wikiverse, and 3D modelling using photogrammetric methods via Structure from Motion (SfM). In OSM, the tag historic=ogham_stone allows citizen scientists to document attributes of Og(h)am stones, including inscriptions, material, dimensions, accessibility, and links to Wikidata and Wikimedia Commons. Additionally, OSM provides geospatial data on townlands and baronies (primarily via townlands.ie), aiding in the geolocation of findspots referenced in literature and excavation reports. Related initiatives, such as documenting holy wells using the OSM tag place_of_worship=holy_well and Wikidata’s WikiProject Holy Wells, offer valuable frameworks for structuring cultural heritage data. The Wikiverse, comprising Wikidata, Wikimedia Commons, and Wikipedia, is a collaborative hub for sharing Og(h)am data through structured entries, visual media, and descriptive text. Additionally, user-friendly smartphone applications facilitate the creation of 3D models via photogrammetric techniques, which can be linked to OSM and Wikidata, enhancing accessibility and preservation efforts."""@en ;
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    bibo:abstract """Open Science principles provide a framework to ensure cultural heritage data is Findable, Accessible, Interoperable, and Reusable (FAIR). This panel, showing collaboration across disciplines and regions, demonstrates how FAIR methods advance the understanding and dissemination of Irish and UK cultural heritage through three key approaches:
3D/2D Capturing and Publication: High-resolution 3D and 2D capturing techniques document and preserve cultural artefacts like Ogham stones and Holy Wells, enabling detailed analysis and online global access. Projects like Ogham in 3D and OG(H)AM exemplify this by offering rich, multi-dimensional datasets for researchers and public engagement.
Linked Open Data (LOD): Encoding heritage data in formats like TEI EPIDOC and integrating it into LOD networks ensures interoperability and connects distributed datasets. Initiatives such as ogham.link demonstrate how LOD fosters collaboration, enriches cultural narratives, and enhances accessibility.
Volunteered Knowledge Hubs: Platforms like Wikidata and OpenStreetMap (OSM) facilitate community-driven heritage preservation and knowledge sharing. These hubs expand beyond academic contributions to include diverse perspectives, as seen in projects focusing on Ogham inscriptions and Holy Wells.
This panel, which focuses on Ogham and Holy Wells, draws on active projects in which the panel chairs are directly involved. These elements provide a robust foundation for discussing how Open Science and FAIR methodologies intersect with digital humanities, archaeology, and community engagement.
Ogham (ᚑᚌᚆᚐᚋ) - a unique writing system across space and time - is a distinctive writing system created for an early form of the Irish language, primarily used during the 1st Millennium AD on stone pillars to inscribe personal and kin-group names. These inscriptions, found mainly in Ireland and parts of Britain, hold immense value for linguistic, archaeological, and historical studies. Since the 1990s, projects like the Celtic Inscribed Stones Project (CISP) and Ogham in 3D have applied Digital Humanities tools to document and analyse these inscriptions. By employing 3D models, TEI XML encodings, and citizen science contributions via platforms like Wikidata and OSM, FAIR methodologies have transformed the accessibility and study of this unique script.
Holy Wells - sites of spiritual and community significance - located throughout Ireland, the UK and elsewhere, embody a rich cultural, historical, and spiritual heritage that is in danger of getting lost due to a shift in spiritual and amenity needs as well as farming practices. As a water source linked to early Christian and pre-Christian traditions, they are often associated with saints, healing practices, possibly baptisms and local folklore. Documenting these sacred sites poses challenges, as their histories blend oral traditions with physical features in remote locations. FAIR methods have enabled systematic cataloguing through geospatial mapping, photogrammetry, community-driven data collection, and digital storytelling. Platforms like OpenStreetMap and the Wikiverse integrate localised knowledge into broader datasets, ensuring the digital preservation of Holy Wells for future generations of pilgrims and researchers alike.
This panel highlights how Ogham and Holy Wells exemplify the potential of Open Science to bridge local heritage and global scholarship through FAIR principles, advancing collaborative and inclusive cultural preservation practices.
A. Beyond Digital Boundaries -  Interaction between dimensions (2/2.5/3D)
This presentation will discuss the boundaries imposed on visualisations of ogham-inscribed objects by the dimensionality of the data, and how the OG(H)AM and OPal+ (Ogham Palaeography+) initiatives aim to cross these boundaries using different imaging methods and software packages. Specifically, this will discuss the different processes available to use 3D models to generate 2D images, and how the images generated can be used for other types of research, including palaeography. The ogham script is unusual in the way it wraps around the edges of inscribed objects (mainly stone pillars) rather than being laid out on a flat surface. Of course, this makes 3d visualisation especially useful for ogham-inscribed objects generally. However, this script aspect can be challenging for the palaeographic study of ogham. Therefore, unwrapping 3d models for 2D visualisation is a method being utilised in the OPal+ and EMISoS (Early Medieval Irish Scripts on Stone) projects.
B. Beyond Digital Boundaries - Collaboration across (Linked) Open Data formats
This presentation will highlight different approaches to transforming multidisciplinary Ogham Data into Linked Open Data (LOD). LOD helps to make these different Ogham Stone Concepts, with several provenances, modifications, corrections, etc. citable (via a URI) and create a network of improving information over time. The ogham data has been encoded by the OG(H)AM project in EpiDoc format. EpiDoc is an international, collaborative effort that provides guidelines and tools for encoding scholarly and educational editions of ancient documents. It uses a subset of the TEI (Text Encoding Initiative) standard for the representation of texts in digital form and was developed initially for the publication of digital editions of ancient inscriptions. Crucially, EpiDoc addresses not only the transcription and editorial treatment of texts themselves, but also the history, provenance and materiality of the objects on which the texts appear. This xml data now serves as a basis for transforming via semi-automatic scripts into LOD to make it even FAIRer.
C. Beyond Digital Boundaries - Working with Wikidata and OpenStreetMap
This presentation explores the integration of Og(h)am and Holy Wells data within community-driven initiatives and citizen science platforms, such as OpenStreetMap (OSM), the Wikiverse, and 3D modelling using photogrammetric methods via Structure from Motion (SfM). In OSM, the tag historic=ogham_stone allows citizen scientists to document attributes of Og(h)am stones, including inscriptions, material, dimensions, accessibility, and links to Wikidata and Wikimedia Commons. Additionally, OSM provides geospatial data on townlands and baronies (primarily via townlands.ie), aiding in the geolocation of findspots referenced in literature and excavation reports. Related initiatives, such as documenting holy wells using the OSM tag place_of_worship=holy_well and Wikidata’s WikiProject Holy Wells, offer valuable frameworks for structuring cultural heritage data. The Wikiverse, comprising Wikidata, Wikimedia Commons, and Wikipedia, is a collaborative hub for sharing Og(h)am data through structured entries, visual media, and descriptive text. Additionally, user-friendly smartphone applications facilitate the creation of 3D models via photogrammetric techniques, which can be linked to OSM and Wikidata, enhancing accessibility and preservation efforts."""@en ;
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    dct:description """Open Science principles provide a framework to ensure cultural heritage data is Findable, Accessible, Interoperable, and Reusable (FAIR). This panel, showing collaboration across disciplines and regions, demonstrates how FAIR methods advance the understanding and dissemination of Irish and UK cultural heritage through three key approaches:
3D/2D Capturing and Publication: High-resolution 3D and 2D capturing techniques document and preserve cultural artefacts like Ogham stones and Holy Wells, enabling detailed analysis and online global access. Projects like Ogham in 3D and OG(H)AM exemplify this by offering rich, multi-dimensional datasets for researchers and public engagement.
Linked Open Data (LOD): Encoding heritage data in formats like TEI EPIDOC and integrating it into LOD networks ensures interoperability and connects distributed datasets. Initiatives such as ogham.link demonstrate how LOD fosters collaboration, enriches cultural narratives, and enhances accessibility.
Volunteered Knowledge Hubs: Platforms like Wikidata and OpenStreetMap (OSM) facilitate community-driven heritage preservation and knowledge sharing. These hubs expand beyond academic contributions to include diverse perspectives, as seen in projects focusing on Ogham inscriptions and Holy Wells.
This panel, which focuses on Ogham and Holy Wells, draws on active projects in which the panel chairs are directly involved. These elements provide a robust foundation for discussing how Open Science and FAIR methodologies intersect with digital humanities, archaeology, and community engagement.
Ogham (ᚑᚌᚆᚐᚋ) - a unique writing system across space and time - is a distinctive writing system created for an early form of the Irish language, primarily used during the 1st Millennium AD on stone pillars to inscribe personal and kin-group names. These inscriptions, found mainly in Ireland and parts of Britain, hold immense value for linguistic, archaeological, and historical studies. Since the 1990s, projects like the Celtic Inscribed Stones Project (CISP) and Ogham in 3D have applied Digital Humanities tools to document and analyse these inscriptions. By employing 3D models, TEI XML encodings, and citizen science contributions via platforms like Wikidata and OSM, FAIR methodologies have transformed the accessibility and study of this unique script.
Holy Wells - sites of spiritual and community significance - located throughout Ireland, the UK and elsewhere, embody a rich cultural, historical, and spiritual heritage that is in danger of getting lost due to a shift in spiritual and amenity needs as well as farming practices. As a water source linked to early Christian and pre-Christian traditions, they are often associated with saints, healing practices, possibly baptisms and local folklore. Documenting these sacred sites poses challenges, as their histories blend oral traditions with physical features in remote locations. FAIR methods have enabled systematic cataloguing through geospatial mapping, photogrammetry, community-driven data collection, and digital storytelling. Platforms like OpenStreetMap and the Wikiverse integrate localised knowledge into broader datasets, ensuring the digital preservation of Holy Wells for future generations of pilgrims and researchers alike.
This panel highlights how Ogham and Holy Wells exemplify the potential of Open Science to bridge local heritage and global scholarship through FAIR principles, advancing collaborative and inclusive cultural preservation practices.
A. Beyond Digital Boundaries -  Interaction between dimensions (2/2.5/3D)
This presentation will discuss the boundaries imposed on visualisations of ogham-inscribed objects by the dimensionality of the data, and how the OG(H)AM and OPal+ (Ogham Palaeography+) initiatives aim to cross these boundaries using different imaging methods and software packages. Specifically, this will discuss the different processes available to use 3D models to generate 2D images, and how the images generated can be used for other types of research, including palaeography. The ogham script is unusual in the way it wraps around the edges of inscribed objects (mainly stone pillars) rather than being laid out on a flat surface. Of course, this makes 3d visualisation especially useful for ogham-inscribed objects generally. However, this script aspect can be challenging for the palaeographic study of ogham. Therefore, unwrapping 3d models for 2D visualisation is a method being utilised in the OPal+ and EMISoS (Early Medieval Irish Scripts on Stone) projects.
B. Beyond Digital Boundaries - Collaboration across (Linked) Open Data formats
This presentation will highlight different approaches to transforming multidisciplinary Ogham Data into Linked Open Data (LOD). LOD helps to make these different Ogham Stone Concepts, with several provenances, modifications, corrections, etc. citable (via a URI) and create a network of improving information over time. The ogham data has been encoded by the OG(H)AM project in EpiDoc format. EpiDoc is an international, collaborative effort that provides guidelines and tools for encoding scholarly and educational editions of ancient documents. It uses a subset of the TEI (Text Encoding Initiative) standard for the representation of texts in digital form and was developed initially for the publication of digital editions of ancient inscriptions. Crucially, EpiDoc addresses not only the transcription and editorial treatment of texts themselves, but also the history, provenance and materiality of the objects on which the texts appear. This xml data now serves as a basis for transforming via semi-automatic scripts into LOD to make it even FAIRer.
C. Beyond Digital Boundaries - Working with Wikidata and OpenStreetMap
This presentation explores the integration of Og(h)am and Holy Wells data within community-driven initiatives and citizen science platforms, such as OpenStreetMap (OSM), the Wikiverse, and 3D modelling using photogrammetric methods via Structure from Motion (SfM). In OSM, the tag historic=ogham_stone allows citizen scientists to document attributes of Og(h)am stones, including inscriptions, material, dimensions, accessibility, and links to Wikidata and Wikimedia Commons. Additionally, OSM provides geospatial data on townlands and baronies (primarily via townlands.ie), aiding in the geolocation of findspots referenced in literature and excavation reports. Related initiatives, such as documenting holy wells using the OSM tag place_of_worship=holy_well and Wikidata’s WikiProject Holy Wells, offer valuable frameworks for structuring cultural heritage data. The Wikiverse, comprising Wikidata, Wikimedia Commons, and Wikipedia, is a collaborative hub for sharing Og(h)am data through structured entries, visual media, and descriptive text. Additionally, user-friendly smartphone applications facilitate the creation of 3D models via photogrammetric techniques, which can be linked to OSM and Wikidata, enhancing accessibility and preservation efforts."""@en ;
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    bibo:abstract """Open Science principles provide a framework to ensure cultural heritage data is Findable, Accessible, Interoperable, and Reusable (FAIR). This panel, showing collaboration across disciplines and regions, demonstrates how FAIR methods advance the understanding and dissemination of Irish and UK cultural heritage through three key approaches:
3D/2D Capturing and Publication: High-resolution 3D and 2D capturing techniques document and preserve cultural artefacts like Ogham stones and Holy Wells, enabling detailed analysis and online global access. Projects like Ogham in 3D and OG(H)AM exemplify this by offering rich, multi-dimensional datasets for researchers and public engagement.
Linked Open Data (LOD): Encoding heritage data in formats like TEI EPIDOC and integrating it into LOD networks ensures interoperability and connects distributed datasets. Initiatives such as ogham.link demonstrate how LOD fosters collaboration, enriches cultural narratives, and enhances accessibility.
Volunteered Knowledge Hubs: Platforms like Wikidata and OpenStreetMap (OSM) facilitate community-driven heritage preservation and knowledge sharing. These hubs expand beyond academic contributions to include diverse perspectives, as seen in projects focusing on Ogham inscriptions and Holy Wells.
This panel, which focuses on Ogham and Holy Wells, draws on active projects in which the panel chairs are directly involved. These elements provide a robust foundation for discussing how Open Science and FAIR methodologies intersect with digital humanities, archaeology, and community engagement.
Ogham (ᚑᚌᚆᚐᚋ) - a unique writing system across space and time - is a distinctive writing system created for an early form of the Irish language, primarily used during the 1st Millennium AD on stone pillars to inscribe personal and kin-group names. These inscriptions, found mainly in Ireland and parts of Britain, hold immense value for linguistic, archaeological, and historical studies. Since the 1990s, projects like the Celtic Inscribed Stones Project (CISP) and Ogham in 3D have applied Digital Humanities tools to document and analyse these inscriptions. By employing 3D models, TEI XML encodings, and citizen science contributions via platforms like Wikidata and OSM, FAIR methodologies have transformed the accessibility and study of this unique script.
Holy Wells - sites of spiritual and community significance - located throughout Ireland, the UK and elsewhere, embody a rich cultural, historical, and spiritual heritage that is in danger of getting lost due to a shift in spiritual and amenity needs as well as farming practices. As a water source linked to early Christian and pre-Christian traditions, they are often associated with saints, healing practices, possibly baptisms and local folklore. Documenting these sacred sites poses challenges, as their histories blend oral traditions with physical features in remote locations. FAIR methods have enabled systematic cataloguing through geospatial mapping, photogrammetry, community-driven data collection, and digital storytelling. Platforms like OpenStreetMap and the Wikiverse integrate localised knowledge into broader datasets, ensuring the digital preservation of Holy Wells for future generations of pilgrims and researchers alike.
This panel highlights how Ogham and Holy Wells exemplify the potential of Open Science to bridge local heritage and global scholarship through FAIR principles, advancing collaborative and inclusive cultural preservation practices.
A. Beyond Digital Boundaries -  Interaction between dimensions (2/2.5/3D)
This presentation will discuss the boundaries imposed on visualisations of ogham-inscribed objects by the dimensionality of the data, and how the OG(H)AM and OPal+ (Ogham Palaeography+) initiatives aim to cross these boundaries using different imaging methods and software packages. Specifically, this will discuss the different processes available to use 3D models to generate 2D images, and how the images generated can be used for other types of research, including palaeography. The ogham script is unusual in the way it wraps around the edges of inscribed objects (mainly stone pillars) rather than being laid out on a flat surface. Of course, this makes 3d visualisation especially useful for ogham-inscribed objects generally. However, this script aspect can be challenging for the palaeographic study of ogham. Therefore, unwrapping 3d models for 2D visualisation is a method being utilised in the OPal+ and EMISoS (Early Medieval Irish Scripts on Stone) projects.
B. Beyond Digital Boundaries - Collaboration across (Linked) Open Data formats
This presentation will highlight different approaches to transforming multidisciplinary Ogham Data into Linked Open Data (LOD). LOD helps to make these different Ogham Stone Concepts, with several provenances, modifications, corrections, etc. citable (via a URI) and create a network of improving information over time. The ogham data has been encoded by the OG(H)AM project in EpiDoc format. EpiDoc is an international, collaborative effort that provides guidelines and tools for encoding scholarly and educational editions of ancient documents. It uses a subset of the TEI (Text Encoding Initiative) standard for the representation of texts in digital form and was developed initially for the publication of digital editions of ancient inscriptions. Crucially, EpiDoc addresses not only the transcription and editorial treatment of texts themselves, but also the history, provenance and materiality of the objects on which the texts appear. This xml data now serves as a basis for transforming via semi-automatic scripts into LOD to make it even FAIRer.
C. Beyond Digital Boundaries - Working with Wikidata and OpenStreetMap
This presentation explores the integration of Og(h)am and Holy Wells data within community-driven initiatives and citizen science platforms, such as OpenStreetMap (OSM), the Wikiverse, and 3D modelling using photogrammetric methods via Structure from Motion (SfM). In OSM, the tag historic=ogham_stone allows citizen scientists to document attributes of Og(h)am stones, including inscriptions, material, dimensions, accessibility, and links to Wikidata and Wikimedia Commons. Additionally, OSM provides geospatial data on townlands and baronies (primarily via townlands.ie), aiding in the geolocation of findspots referenced in literature and excavation reports. Related initiatives, such as documenting holy wells using the OSM tag place_of_worship=holy_well and Wikidata’s WikiProject Holy Wells, offer valuable frameworks for structuring cultural heritage data. The Wikiverse, comprising Wikidata, Wikimedia Commons, and Wikipedia, is a collaborative hub for sharing Og(h)am data through structured entries, visual media, and descriptive text. Additionally, user-friendly smartphone applications facilitate the creation of 3D models via photogrammetric techniques, which can be linked to OSM and Wikidata, enhancing accessibility and preservation efforts."""@en ;
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    dct:description "Die digitale Transformation archäologischer Kulturgüter ermöglicht neue Formen der wissenschaftlichen Vernetzung – und zeigt zugleich, wie historische Artefakte Einfluss auf heutige Gemeinschaften nehmen. Der Ogham-Stein CIIC 5061 (GIGHA/1) auf der Isle of Gigha steht exemplarisch dafür: Seine frühmittelalterliche Inschrift “VICULA MAQ COMGINI” – ein klassisches Beispiel der sogenannten MUCOI-Formeln (Abstammungsformeln, die die Zugehörigkeit zu Clans ausdrücken) – wurde nicht nur wissenschaftlich dokumentiert, sondern prägt heute sichtbar die lokale Identität. Die Mikrobrauerei Gigha Brewing nutzt Ogham-Symbole kreativ in ihrem Branding – zum Beispiel bei einem Bier nach Kölsch’scher Brauart – und verbindet damit kulturelles Erbe mit zeitgenössischem Leben. Im OG(H)AM-Projekt werden Daten zu solchen Inschriften im TEI/EpiDoc-Format kodiert und nun im Rahmen von NFDI4Objects vom Research Squirrel Engineers in Linked Open Data (LOD) und FAIR Digital Objects (FDOs) transformiert. Dabei entstehen Entitäten, die mittels Ontologien (z. B. CIDOC CRM) referenziert und z.B. in den N$O Knowledge Graph integriert werden. Anhand der “Ogham of the Month”-Initiativen (z.B. Januar 2025 – Gigha Brewing) und speziell der MUCOI-Inschriften wird gezeigt, wie sich semantische Muster wie Personennetzwerke und regionale Verteilungen modellieren lassen. Der Workflow umfasst die Extraktion von Entitäten aus TEI-Daten, deren Transformation in RDF-basierte Wissensstrukturen sowie die Anreicherung mit externen offenen community-driven Ressourcen (u.a. Wikidata, OpenStreetMap, Wikimedia Commons). Im Mittelpunkt steht die Frage, wie digitale Infrastrukturen wie das federated NFDI Knowledge Graph Ecosystem dazu beitragen, historische Zeugnisse wie den Ogham-Stein von Gigha nachhaltig sichtbar, interoperabel und nachnutzbar zu machen – und wie Geschichte, offene Daten und moderne Communitys sich auf kreative Weise gegenseitig bereichern."@de ;
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    dct:description """This talk explores how Wikibase functions as a Semantic Knowledge Base within GLAM and computational archaeology, embedded in a broader interdisciplinary knowledge ecosystem. Three illustrative clusters are presented:
(a) Community‑based Wikibase Instances – using community-driven platforms such as Wikidata, FactGrid, and Semantic Kompakkt to engage Citizen Scientists through federated queries. We will share a case study involving Irish Ogham inscriptions and Irish Holy Wells, enriched with SfM 3D models.
(b) Research‑focused instances on wikibase.cloud – demonstrating the modelling of uncertainty using the fuzzy‑sl Wikibase, and the integration of 3D object Wikibases, e.g. via SquirrelBase.
(c) Community‑driven reference hubs for FAIRification tools and software catalogues – showcasing the implementation of FAIRification tools like re3dragon (the “dragon lair”) and upcoming software registries such as chublets.software – based on open‑archaeo – contributing to NFDI4Objects and the NFDI software ecosystem via nfdi.software.
Together, these examples illustrate how Wikibase supports interoperability, citizen science, and the FAIR (and FAIR4RS) principles, forming a sustainable ecosystem of knowledge bases across domains. The talk will share insights on data modelling, tool integration, federated access, and sustainability challenges of interdisciplinary infrastructures."""@en ;
    dct:identifier "https://doi.org/10.5281/zenodo.17475014" ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "How to FAIRify Citizen Science? Using the Wikibase Ecosystem for Volunteered Cultural Heritage Information Systems"@en ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """This talk explores how Wikibase functions as a Semantic Knowledge Base within GLAM and computational archaeology, embedded in a broader interdisciplinary knowledge ecosystem. Three illustrative clusters are presented:
(a) Community‑based Wikibase Instances – using community-driven platforms such as Wikidata, FactGrid, and Semantic Kompakkt to engage Citizen Scientists through federated queries. We will share a case study involving Irish Ogham inscriptions and Irish Holy Wells, enriched with SfM 3D models.
(b) Research‑focused instances on wikibase.cloud – demonstrating the modelling of uncertainty using the fuzzy‑sl Wikibase, and the integration of 3D object Wikibases, e.g. via SquirrelBase.
(c) Community‑driven reference hubs for FAIRification tools and software catalogues – showcasing the implementation of FAIRification tools like re3dragon (the “dragon lair”) and upcoming software registries such as chublets.software – based on open‑archaeo – contributing to NFDI4Objects and the NFDI software ecosystem via nfdi.software.
Together, these examples illustrate how Wikibase supports interoperability, citizen science, and the FAIR (and FAIR4RS) principles, forming a sustainable ecosystem of knowledge bases across domains. The talk will share insights on data modelling, tool integration, federated access, and sustainability challenges of interdisciplinary infrastructures."""@en ;
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    dct:description """Squirrels, LOD and RFAIRT
The comprehensible and collaborative collection and FAIRification of research data is becoming increasingly important in the Citizen Science community to become part of, for example, an archaeological or geoscientific Linked Open Data (LOD) Knowledge Graph and to enrich the already connected data network with qualified data (Schmidt et al. 2022). Only in this way can these data be linked with other data and actively integrated into international initiatives such as NFDI4Objects and community hubs (e.g., Wikidata, OpenStreetMap). Unfortunately, open-source (FOSS) research and Research FAIRification Tools (RFAIRT) are often not available. However, these can be created and curated by community and volunteer initiatives such as the Research Squirrel Engineers Network (Thiery et al. 2025), in collaboration with Linked Open Data projects as demonstrators. One example is the SPARQL Unicorn Research Toolkit (Thiery et al. 2025) which contains the SPARQLing Unicorn QGIS Plugin, the SPARQL Unicorn Ontology Documentation Tool and SPARQLing Jupyter Python Minions.
Research Squirrel Engineers
The Research Squirrel Engineers Network (founded in 2019 to implement the SPARQL Unicorn Research Toolkit) is an informal association of Linked Open Data/Wikidata enthusiasts, research software engineers, and citizen scientists with a focus on computational archaeology, digital humanities, and geoinformatics. The members collaboratively develop and maintain research and Research FAIRification Tools and implement them in interoperable data-driven projects.
SPARQL Unicorn Research Toolit
One FAIRification tool for digital data management is within the SPARQL Unicorn Research Toolkit is an implementation for the FLOSS GIS software QGIS. The SPARQLing Unicorn QGIS Plugin (Thiery & Homburg 2020) allows sending Linked Data queries in (Geo)SPARQL to triple stores and prepares the results for the geocommunity in QGIS. It currently offers three main functions: (A) simplified querying of semantic web data sources, (B) transformation of QGIS vector layers to RDF, and (C) documentation of LOD as an HTML page. Additionally, the SPARQL Unicorn Ontology Documentation Tool (Homburg & Thiery 2024) enables, for example, the automated creation of HTML pages from Linked Open Data publications in other applications via GitHub Action. The example (Fig. 1-4) shows findspots of the Campanian Ignimbrite eruption (Schenk et al. 2022) as LOD in QGIS and GitHub.
Jupyter Python Minions
Another key tool within the SPARQL Unicorn Research Toolkit are SPARQLing Python Minions, which enables users to send SPARQL queries from Jupyter Notebooks and Python Scripts to Wikidata, Wikibases, triple stores, and Solid Pods and visualise the results. This allows for direct integration of Semantic Web data into Python-based data analysis workflows. By leveraging Jupyter Notebooks, users can efficiently process, analyse, and visualise the retrieved data, further expanding the usability of SPARQL Unicorn tools for researchers across various domains. The example shows queries on Wikidata (Fig. 5-6)."""@en ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Research Squirrel Engineering Community-driven grassroots Research FAIRification Tools (RFAIRT) coded from Humanities and Geosciences"@en ;
    dct:type sqpt:conference-paper,
        sqpt:poster ;
    bibo:abstract """Squirrels, LOD and RFAIRT
The comprehensible and collaborative collection and FAIRification of research data is becoming increasingly important in the Citizen Science community to become part of, for example, an archaeological or geoscientific Linked Open Data (LOD) Knowledge Graph and to enrich the already connected data network with qualified data (Schmidt et al. 2022). Only in this way can these data be linked with other data and actively integrated into international initiatives such as NFDI4Objects and community hubs (e.g., Wikidata, OpenStreetMap). Unfortunately, open-source (FOSS) research and Research FAIRification Tools (RFAIRT) are often not available. However, these can be created and curated by community and volunteer initiatives such as the Research Squirrel Engineers Network (Thiery et al. 2025), in collaboration with Linked Open Data projects as demonstrators. One example is the SPARQL Unicorn Research Toolkit (Thiery et al. 2025) which contains the SPARQLing Unicorn QGIS Plugin, the SPARQL Unicorn Ontology Documentation Tool and SPARQLing Jupyter Python Minions.
Research Squirrel Engineers
The Research Squirrel Engineers Network (founded in 2019 to implement the SPARQL Unicorn Research Toolkit) is an informal association of Linked Open Data/Wikidata enthusiasts, research software engineers, and citizen scientists with a focus on computational archaeology, digital humanities, and geoinformatics. The members collaboratively develop and maintain research and Research FAIRification Tools and implement them in interoperable data-driven projects.
SPARQL Unicorn Research Toolit
One FAIRification tool for digital data management is within the SPARQL Unicorn Research Toolkit is an implementation for the FLOSS GIS software QGIS. The SPARQLing Unicorn QGIS Plugin (Thiery & Homburg 2020) allows sending Linked Data queries in (Geo)SPARQL to triple stores and prepares the results for the geocommunity in QGIS. It currently offers three main functions: (A) simplified querying of semantic web data sources, (B) transformation of QGIS vector layers to RDF, and (C) documentation of LOD as an HTML page. Additionally, the SPARQL Unicorn Ontology Documentation Tool (Homburg & Thiery 2024) enables, for example, the automated creation of HTML pages from Linked Open Data publications in other applications via GitHub Action. The example (Fig. 1-4) shows findspots of the Campanian Ignimbrite eruption (Schenk et al. 2022) as LOD in QGIS and GitHub.
Jupyter Python Minions
Another key tool within the SPARQL Unicorn Research Toolkit are SPARQLing Python Minions, which enables users to send SPARQL queries from Jupyter Notebooks and Python Scripts to Wikidata, Wikibases, triple stores, and Solid Pods and visualise the results. This allows for direct integration of Semantic Web data into Python-based data analysis workflows. By leveraging Jupyter Notebooks, users can efficiently process, analyse, and visualise the retrieved data, further expanding the usability of SPARQL Unicorn tools for researchers across various domains. The example shows queries on Wikidata (Fig. 5-6)."""@en ;
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        <https://w3id.org/squirrelpapers/person/0000-0003-4889-9353> ;
    dct:description """The relevance of Open Science and Open Data is becoming increasingly obvious in modern day publications. Frequently, scientists write their own analysis code, as the complexity of analysis increases and the combination of methods become more relevant – from code conversion, to measuring and comparing. These functions and methods are not stable, are subject to change, are constrained to the use case and data used etc.
Opening and maintaining data already poses a substantial number of issues, including versioning, provenance, format, and generator specific constraints, such as precision, resolution etc. These problems intensify as we regard code that generates such data. In this presentation we will talk about the problems associated with maintaining research code.
As such, for example, code executability is even more difficult to maintain as data readability. This is mostly due to the strong dependencies of code to libraries, drivers, hardware and operating systems. All of these dependencies are subject to frequent changes, which may cause the code to not execute properly anymore or – in the worst case – still execute but deliver different results. Maintaining the code is effort-intensive and therefore basically impossible in the context of a research publication. Obviously, if the code is adapted, we need to maintain all previous versions and refer to the right versions used in the publication to ensure that in principle the same results can be reproduced should a divergence arise as a consequence of maintenance.
Due to the specificity of the code, i.e. it being originally developed for a very specific use case and data format, it requires even more effort to adapt that given code to another context, if e.g. the data format or resolution changes, even if the type of analysis and the research question remains the same. Where possible, therefore, the algorithm behind the analysis code may be of more importance than the code itself, given that it is numerically correct. As an implication, implementations may diverge from the numerical results due to the platform accuracy – ideally only minimally, however. As noted, though, this is not appropriate for too complex code where the algorithm would be too difficult to represent and explain, or for AI based and related methods that depend additional data, aka learning context. Implicitly, such methods would have to be treated differently.
With respect to ensuring that data is not only FAIR, but also reproducible under any circumstances, we follow the suggestion that code must be treated in the same fashion, by making sure that all algorithmic processes published are
- Reproducible in the sense that the results can be achieved again with the same process and context
- Executable at any point in time (though not necessarily on any machine)
- Attributable to the data and author at the stage of publication and
- Literal in so far as that the algorithm is a sound and correct representation of the mathematical methods to be applied."""@en ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Keeping it REAL: Sustainability Challenges for Research Software in Computational Archaeology"@en ;
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    bibo:abstract """The relevance of Open Science and Open Data is becoming increasingly obvious in modern day publications. Frequently, scientists write their own analysis code, as the complexity of analysis increases and the combination of methods become more relevant – from code conversion, to measuring and comparing. These functions and methods are not stable, are subject to change, are constrained to the use case and data used etc.
Opening and maintaining data already poses a substantial number of issues, including versioning, provenance, format, and generator specific constraints, such as precision, resolution etc. These problems intensify as we regard code that generates such data. In this presentation we will talk about the problems associated with maintaining research code.
As such, for example, code executability is even more difficult to maintain as data readability. This is mostly due to the strong dependencies of code to libraries, drivers, hardware and operating systems. All of these dependencies are subject to frequent changes, which may cause the code to not execute properly anymore or – in the worst case – still execute but deliver different results. Maintaining the code is effort-intensive and therefore basically impossible in the context of a research publication. Obviously, if the code is adapted, we need to maintain all previous versions and refer to the right versions used in the publication to ensure that in principle the same results can be reproduced should a divergence arise as a consequence of maintenance.
Due to the specificity of the code, i.e. it being originally developed for a very specific use case and data format, it requires even more effort to adapt that given code to another context, if e.g. the data format or resolution changes, even if the type of analysis and the research question remains the same. Where possible, therefore, the algorithm behind the analysis code may be of more importance than the code itself, given that it is numerically correct. As an implication, implementations may diverge from the numerical results due to the platform accuracy – ideally only minimally, however. As noted, though, this is not appropriate for too complex code where the algorithm would be too difficult to represent and explain, or for AI based and related methods that depend additional data, aka learning context. Implicitly, such methods would have to be treated differently.
With respect to ensuring that data is not only FAIR, but also reproducible under any circumstances, we follow the suggestion that code must be treated in the same fashion, by making sure that all algorithmic processes published are
- Reproducible in the sense that the results can be achieved again with the same process and context
- Executable at any point in time (though not necessarily on any machine)
- Attributable to the data and author at the stage of publication and
- Literal in so far as that the algorithm is a sound and correct representation of the mathematical methods to be applied."""@en ;
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        <https://w3id.org/squirrelpapers/person/0000-0002-3246-3531> ;
    dct:description """Research Software Engineering is fundamental to the German National Research Data Infrastructure (NFDI). Following that, a "deRSE Arbeitskreis NFDI" serves as a connection point for RSEs in the NFDI inside deRSE e.V.
Within the NFDI e.V., several "sections" are dealing with overarching topics, e.g., the "Sektion Common Infrastructures" with its working groups on "Data Integration (DI)", "Data Management Planning (DMP)", "Data Science and Artificial Intelligence (DSAI)", "Electronic Lab Notebooks (ELN)", "Persistent Identifiers (PID)" and "Research Software Engineering (RSE)".
The RSE working group connects the NFDI consortia in software-related aspects. It focuses on three areas: Research software, software communities and software infrastructure at NFDI. The working group operates a central forum in an advisory and supportive capacity. It establishes the necessary software ecosystem within NFDI for the professional development of software infrastructure components, which represent an integral part of the NFDI. In addition, the working group serves as an interface for the NFDI to compare European and international initiatives to promote the connectivity of the NFDI with other infrastructures.
This poster, as a meet-up place, would like to bring RSEs already working or interested in the NFDI together to create an active network and encourage them to join the RSE working group in the NFDI Section Infra."""@en ;
    dct:identifier "https://doi.org/10.5281/zenodo.14898391" ;
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    dct:title "Research Software Engineering in the NFDI (INFRA-WG-RSE)"@en ;
    dct:type sqpt:conference-paper,
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    bibo:abstract """Research Software Engineering is fundamental to the German National Research Data Infrastructure (NFDI). Following that, a "deRSE Arbeitskreis NFDI" serves as a connection point for RSEs in the NFDI inside deRSE e.V.
Within the NFDI e.V., several "sections" are dealing with overarching topics, e.g., the "Sektion Common Infrastructures" with its working groups on "Data Integration (DI)", "Data Management Planning (DMP)", "Data Science and Artificial Intelligence (DSAI)", "Electronic Lab Notebooks (ELN)", "Persistent Identifiers (PID)" and "Research Software Engineering (RSE)".
The RSE working group connects the NFDI consortia in software-related aspects. It focuses on three areas: Research software, software communities and software infrastructure at NFDI. The working group operates a central forum in an advisory and supportive capacity. It establishes the necessary software ecosystem within NFDI for the professional development of software infrastructure components, which represent an integral part of the NFDI. In addition, the working group serves as an interface for the NFDI to compare European and international initiatives to promote the connectivity of the NFDI with other infrastructures.
This poster, as a meet-up place, would like to bring RSEs already working or interested in the NFDI together to create an active network and encourage them to join the RSE working group in the NFDI Section Infra."""@en ;
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    dct:description """This conferene paper was funded by the Forschungszentrum Jülich (FZJ) and JuRSE with a Travel Grant
The comprehensible/collaborative creation and FAIRification of research data is becoming increasingly important in the Citizen Science community to become part of an interdisciplinary knowledge graph and enrich the already interconnected data network with qualified data. Only in this way can this data be linked to other data and actively integrated into international initiatives (e.g. NFDI) and community hubs (e.g. Wikidata, FactGrid, Semantic Kompakkt, OpenStreetMap). Unfortunately, open-source (FOSS) research and FAIRification tools are often unavailable. However, these, in combination with Linked Open Data projects as demonstrators, can be created and curated by community and voluntary initiatives such as the Research Squirrel Engineers Network.
This paper presents the Research Squirrel Engineers Network initiative, three research and FAIRification tools, and three Research Squirrels projects, as well as how Research Software Engineering may help make it even more helpful for the NFDI. These can serve as digital services for digital data management in archaeology and so be part of substantial interdisciplinary initiatives such as the NFDI. The paper, therefore, also presents the aims, benefits and implementation of the squirrel tools.
The Research Squirrel Engineers Network (founded in 2019 to implement the SPARQL Unicorn) is a loose association of Linked Open Data/Wikidata enthusiasts, Research Software Engineers and Citizen Scientists focusing on computational archaeology, digital humanities and geoinformatics. The members develop and maintain research and FAIRification tools and implement them in concrete projects.
A FAIRification tool for digital data management is the SPARQL Unicorn and its implementation for QGIS. The "SPARQLing Unicorn QGIS Plugin" allows sending linked data queries in (Geo)SPARQL to triple stores and prepares the results for the geo-community in QGIS. It currently offers three main functions: (A) Simplified querying of Semantic Web data sources, (B) Enrichment of geodata, and (C) Transformation of QGIS vector layers to RDF. In addition, the SPARQL Unicorn Ontology Documentation Tool enables the automated creation of HTML pages of Linked Open Data publications, e.g. via GitHub Action. One example are Irish Ogham sites on the Dingle Peninsula or data from Sophie C. Schmidt's dissertation project on "Brandenburg 5,000 BC" by converting a CIDOC CRM data model into Linked Open Data and visualising it as HTML with the help of SPARQL Unicorn.
Another service is the "Fuzzy Spatial Locations Ontology", in which georeferencing's vagueness, uncertainties and ambiguities are made FAIR and comprehensible with the help of semantics and an ontology (based on PROV-O). An example of this is the modelling of sites of the eruption of the Campanian Ignimbrite in the Phlegraean Fields (39,940 yr b2k ± 150 years), which often correspond to archaeological sites, e.g. the Toplitsa Cave in Bulgaria. The "Squirrel Papers" complement the services to create a platform for publishing working papers, data, software, presentation slides and posters for citation.
These services are accompanied by LOD / Wikidata / Open Street Map and Wikimedia Commons projects, such as Linked Open Ogham, Holy Wells in Ireland, or Linked Reindeers, where Scripts (primarily written in Python) help transform the tabular data into RDF or Quick Statements."""@en ;
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    dct:title "Research Squirrel Engineers How an independent RSE-driven network may help the NFDI"@en ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """This conferene paper was funded by the Forschungszentrum Jülich (FZJ) and JuRSE with a Travel Grant
The comprehensible/collaborative creation and FAIRification of research data is becoming increasingly important in the Citizen Science community to become part of an interdisciplinary knowledge graph and enrich the already interconnected data network with qualified data. Only in this way can this data be linked to other data and actively integrated into international initiatives (e.g. NFDI) and community hubs (e.g. Wikidata, FactGrid, Semantic Kompakkt, OpenStreetMap). Unfortunately, open-source (FOSS) research and FAIRification tools are often unavailable. However, these, in combination with Linked Open Data projects as demonstrators, can be created and curated by community and voluntary initiatives such as the Research Squirrel Engineers Network.
This paper presents the Research Squirrel Engineers Network initiative, three research and FAIRification tools, and three Research Squirrels projects, as well as how Research Software Engineering may help make it even more helpful for the NFDI. These can serve as digital services for digital data management in archaeology and so be part of substantial interdisciplinary initiatives such as the NFDI. The paper, therefore, also presents the aims, benefits and implementation of the squirrel tools.
The Research Squirrel Engineers Network (founded in 2019 to implement the SPARQL Unicorn) is a loose association of Linked Open Data/Wikidata enthusiasts, Research Software Engineers and Citizen Scientists focusing on computational archaeology, digital humanities and geoinformatics. The members develop and maintain research and FAIRification tools and implement them in concrete projects.
A FAIRification tool for digital data management is the SPARQL Unicorn and its implementation for QGIS. The "SPARQLing Unicorn QGIS Plugin" allows sending linked data queries in (Geo)SPARQL to triple stores and prepares the results for the geo-community in QGIS. It currently offers three main functions: (A) Simplified querying of Semantic Web data sources, (B) Enrichment of geodata, and (C) Transformation of QGIS vector layers to RDF. In addition, the SPARQL Unicorn Ontology Documentation Tool enables the automated creation of HTML pages of Linked Open Data publications, e.g. via GitHub Action. One example are Irish Ogham sites on the Dingle Peninsula or data from Sophie C. Schmidt's dissertation project on "Brandenburg 5,000 BC" by converting a CIDOC CRM data model into Linked Open Data and visualising it as HTML with the help of SPARQL Unicorn.
Another service is the "Fuzzy Spatial Locations Ontology", in which georeferencing's vagueness, uncertainties and ambiguities are made FAIR and comprehensible with the help of semantics and an ontology (based on PROV-O). An example of this is the modelling of sites of the eruption of the Campanian Ignimbrite in the Phlegraean Fields (39,940 yr b2k ± 150 years), which often correspond to archaeological sites, e.g. the Toplitsa Cave in Bulgaria. The "Squirrel Papers" complement the services to create a platform for publishing working papers, data, software, presentation slides and posters for citation.
These services are accompanied by LOD / Wikidata / Open Street Map and Wikimedia Commons projects, such as Linked Open Ogham, Holy Wells in Ireland, or Linked Reindeers, where Scripts (primarily written in Python) help transform the tabular data into RDF or Quick Statements."""@en ;
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    dct:description """This conferene paper was funded by the Forschungszentrum Jülich (FZJ) and JuRSE with a Travel Grant
The Semantic Web is a treasure trove of interconnected knowledge graphs, providing access to datasets that are invaluable for research in cultural heritage and archaeology. Resources such as triplestores (e.g., the NFDI4Objects Knowledge Graph), Wikibase instances (e.g., Wikidata and FactGrid), and Solid Pods housing geoscientific data open new avenues for interdisciplinary exploration. However, researchers face significant challenges in utilising these resources effectively. Writing SPARQL queries requires a steep learning curve, and the data often returns in formats like sparql-results+xml or sparql-results+json, which are not user-friendly for immediate analysis or visualisation.
Python has become a critical tool in addressing these challenges. As a versatile scripting language, Python enables researchers to automate workflows, ensure reproducibility, and integrate datasets seamlessly. However, many researchers lack the technical skills or frameworks needed to implement Python solutions in their work. This is where Jupyter Notebooks provide a critical advantage. Combining an intuitive, shareable environment with the computational power of Python, Jupyter Notebooks make it easy to share not just results but the entire workflow. This transparency enhances reproducibility, facilitates collaboration, and aligns with FAIR principles.
The Jupyter Python Minion builds on this framework, offering an open-source solution to simplify SPARQL querying and data visualisation. By integrating widely used Python libraries such as pyplot, wordcloud, geopandas, and contextily, it transforms raw Linked Open Data (LOD) into actionable insights. Researchers can produce bar charts, pie charts, maps, and word clouds with minimal effort, bridging the gap between technical expertise and domain-specific knowledge. Importantly, the tool enables researchers to document their computational workflows within Jupyter Notebooks, creating reusable resources for the broader research community.
The need for such tools in archaeological research software engineering is pressing. Computational archaeology increasingly relies on integrating diverse datasets—geospatial, semantic, and cultural—but the technical barriers often hinder broader adoption. By lowering these barriers, the Jupyter Python Minion empowers researchers to harness the power of Python scripts for reproducible and shareable analysis.
In this talk, I will demonstrate the utility of the Jupyter Python Minion through five use cases:
1. Playful exploration of Pokémon properties, demonstrating SPARQL queries and visualisation techniques like scatterplots and bar charts.
2. Mapping the distribution of Samian ware kiln sites, showcasing the regional breakdown of archaeological production centres.
3. Exploring Irish Holy Wells, revealing etymological patterns through word clouds and pie charts.
4. Geospatial analysis of Irish Ogham Stones, including density maps and OpenStreetMap-based visualisations to highlight regional clusters.
5. Integration of geoscientific findspots from Solid Pods, using SPARQL queries to categorise archaeological and geological locations affected by the Campanian Ignimbrite eruption.
These examples highlight the transformative potential of integrating Python scripting with Jupyter Notebooks for reproducible research. The tool’s shareability fosters collaboration across disciplines, from archaeology to geosciences, and promotes a culture of openness and accessibility in research software engineering.
This talk will contribute to key themes in RSE, including computational workflows, open-source tools, and software usability, while providing attendees with actionable insights to adopt and adapt these methods in their own research."""@en ;
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    dct:title "Jupyter Python Minions - Simplifying SPARQL Queries and Visualisations for Archaeological Data"@en ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """This conferene paper was funded by the Forschungszentrum Jülich (FZJ) and JuRSE with a Travel Grant
The Semantic Web is a treasure trove of interconnected knowledge graphs, providing access to datasets that are invaluable for research in cultural heritage and archaeology. Resources such as triplestores (e.g., the NFDI4Objects Knowledge Graph), Wikibase instances (e.g., Wikidata and FactGrid), and Solid Pods housing geoscientific data open new avenues for interdisciplinary exploration. However, researchers face significant challenges in utilising these resources effectively. Writing SPARQL queries requires a steep learning curve, and the data often returns in formats like sparql-results+xml or sparql-results+json, which are not user-friendly for immediate analysis or visualisation.
Python has become a critical tool in addressing these challenges. As a versatile scripting language, Python enables researchers to automate workflows, ensure reproducibility, and integrate datasets seamlessly. However, many researchers lack the technical skills or frameworks needed to implement Python solutions in their work. This is where Jupyter Notebooks provide a critical advantage. Combining an intuitive, shareable environment with the computational power of Python, Jupyter Notebooks make it easy to share not just results but the entire workflow. This transparency enhances reproducibility, facilitates collaboration, and aligns with FAIR principles.
The Jupyter Python Minion builds on this framework, offering an open-source solution to simplify SPARQL querying and data visualisation. By integrating widely used Python libraries such as pyplot, wordcloud, geopandas, and contextily, it transforms raw Linked Open Data (LOD) into actionable insights. Researchers can produce bar charts, pie charts, maps, and word clouds with minimal effort, bridging the gap between technical expertise and domain-specific knowledge. Importantly, the tool enables researchers to document their computational workflows within Jupyter Notebooks, creating reusable resources for the broader research community.
The need for such tools in archaeological research software engineering is pressing. Computational archaeology increasingly relies on integrating diverse datasets—geospatial, semantic, and cultural—but the technical barriers often hinder broader adoption. By lowering these barriers, the Jupyter Python Minion empowers researchers to harness the power of Python scripts for reproducible and shareable analysis.
In this talk, I will demonstrate the utility of the Jupyter Python Minion through five use cases:
1. Playful exploration of Pokémon properties, demonstrating SPARQL queries and visualisation techniques like scatterplots and bar charts.
2. Mapping the distribution of Samian ware kiln sites, showcasing the regional breakdown of archaeological production centres.
3. Exploring Irish Holy Wells, revealing etymological patterns through word clouds and pie charts.
4. Geospatial analysis of Irish Ogham Stones, including density maps and OpenStreetMap-based visualisations to highlight regional clusters.
5. Integration of geoscientific findspots from Solid Pods, using SPARQL queries to categorise archaeological and geological locations affected by the Campanian Ignimbrite eruption.
These examples highlight the transformative potential of integrating Python scripting with Jupyter Notebooks for reproducible research. The tool’s shareability fosters collaboration across disciplines, from archaeology to geosciences, and promotes a culture of openness and accessibility in research software engineering.
This talk will contribute to key themes in RSE, including computational workflows, open-source tools, and software usability, while providing attendees with actionable insights to adopt and adapt these methods in their own research."""@en ;
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Within this session, we as the WG RSE in the Section Common Infrastructures, will give an impulsive talk on research data and software management in domain-specific NFDI consortia, base4NFDI, and other sections.
Following that, we invite NFDI-involved RSEs to join a “Stand-Up-Science” part to briefly present their RSE challenges and solutions in spontaneous or not-so-spontaneous lightning talks (max 7-10 minutes) or short insights into research software such as code, architecture, etc.
Questions to answer could be:
Name / last academic degree / Affiliation / current task
What are your connections to the NFDI? (existing or possible)
What was the last thing I coded/worked on
Which research data did I last reuse?
Which research software did I last reuse?
Favourite git command
Give a reason why you were told that the software/data could not be published
What advice (best/good practice) would you give to a new RSE?
If you want to present something, please use the survey at https://umfragenup.uni-potsdam.de/nfdi-rse/.
All the aspects mentioned will be part of a joint paper on the “RSE in NFDI” topic."""@en ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "How to improve the visibility and added value of RSE(s) in NFDI"@en ;
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Within this session, we as the WG RSE in the Section Common Infrastructures, will give an impulsive talk on research data and software management in domain-specific NFDI consortia, base4NFDI, and other sections.
Following that, we invite NFDI-involved RSEs to join a “Stand-Up-Science” part to briefly present their RSE challenges and solutions in spontaneous or not-so-spontaneous lightning talks (max 7-10 minutes) or short insights into research software such as code, architecture, etc.
Questions to answer could be:
Name / last academic degree / Affiliation / current task
What are your connections to the NFDI? (existing or possible)
What was the last thing I coded/worked on
Which research data did I last reuse?
Which research software did I last reuse?
Favourite git command
Give a reason why you were told that the software/data could not be published
What advice (best/good practice) would you give to a new RSE?
If you want to present something, please use the survey at https://umfragenup.uni-potsdam.de/nfdi-rse/.
All the aspects mentioned will be part of a joint paper on the “RSE in NFDI” topic."""@en ;
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    dct:description "Bei der Erforschung von Objekten spielen sowohl exakte als auch interpretierende Aussagen eine zentrale Rolle. Letztere können durch “hidden assumptions” der Autor*innen, unvollständige Objekterhaltung oder ungenaue technische Konzepte stark beeinflusst werden. Die einheitliche Modellierung der damit verbundenen “fuzziness and wobbliness” (z. B. uncertainty, vagueness, accuracy and precision) in Forschungsdaten ist eine Herausforderung, da in den Forschungsdomänen unterschiedliche Ansätze entwickelt oder implementiert werden. Wichtig dabei ist zuerst die Definition von “fuzziness and wobbliness” (Whitepaper #1). Ziel der TWG ist es, bestehende graphbasierte (RDF-)Modellierungsansätze (Whitepaper #2), Software und Skripte (Whitepaper #3) für “fuzziness and wobbliness” in Forschungsdaten zu sammeln, zu bewerten und zu erweitern. Auf diese Weise sollen gemeinsame Schnittstellen identifiziert werden, die den Übergang von einem domainspezischen/forschungsdisziplinspezifischen Modellierungsansatz zu einem anderen ermöglichen. Neben der Sammlung mathematischer, informationstechnischer und anderer relevanter Implikationen bestehender Modellierungsansätze wird diese TWG prototypische Modellierungsansätze in Use Cases implementieren, um Wissenschaftler*innen und Studierenden die Möglichkeit zu geben, die Vor- und Nachteile nachprüfbar zu bewerten."@en ;
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    dct:title "NFDI4Objects TWG - Community-Standards for modelling fuzziness & wobbliness in research data using Semantic Web technologies and formalisms (FuzzyWobblySW)\""@en ;
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    bibo:abstract "Bei der Erforschung von Objekten spielen sowohl exakte als auch interpretierende Aussagen eine zentrale Rolle. Letztere können durch “hidden assumptions” der Autor*innen, unvollständige Objekterhaltung oder ungenaue technische Konzepte stark beeinflusst werden. Die einheitliche Modellierung der damit verbundenen “fuzziness and wobbliness” (z. B. uncertainty, vagueness, accuracy and precision) in Forschungsdaten ist eine Herausforderung, da in den Forschungsdomänen unterschiedliche Ansätze entwickelt oder implementiert werden. Wichtig dabei ist zuerst die Definition von “fuzziness and wobbliness” (Whitepaper #1). Ziel der TWG ist es, bestehende graphbasierte (RDF-)Modellierungsansätze (Whitepaper #2), Software und Skripte (Whitepaper #3) für “fuzziness and wobbliness” in Forschungsdaten zu sammeln, zu bewerten und zu erweitern. Auf diese Weise sollen gemeinsame Schnittstellen identifiziert werden, die den Übergang von einem domainspezischen/forschungsdisziplinspezifischen Modellierungsansatz zu einem anderen ermöglichen. Neben der Sammlung mathematischer, informationstechnischer und anderer relevanter Implikationen bestehender Modellierungsansätze wird diese TWG prototypische Modellierungsansätze in Use Cases implementieren, um Wissenschaftler*innen und Studierenden die Möglichkeit zu geben, die Vor- und Nachteile nachprüfbar zu bewerten."@en ;
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    dct:description """Die TWG N4O ObjectMetaDataSet and ObjectOntology wird in dieser Iteration den aktuellen Bedürfnissen (NFDI Core Metadata Profile, etc.) inhaltlich agil angepasst. Diese TWG entwickelt kein Datenmodell für die Erstellung von Objektbiografien und definiert ebenfalls keine Anwendungsontologie für CIDOC CRM. Zudem werden keine LIDO-Anwendungsprofile entwickelt. Sie dient vor allem zur Entwicklung von technischen Referenzmodellen für Metadaten und publizierten Forschungsdaten. Die Themengebiete erzeugen fachliche Profile als Module der OCMDP sowie fachspezifische Profil-Ontologien, die jeweils auf CIDOC CRM Klassen referenzieren, um die Kompatibilität mit dem N4O KG herzustellen und die Validierung positiv zu gestalten; die TWG entwickelt ein abgeglichenes gemeinsames harmonisiertes Metadaten- und Ontologieprofil, keine fachspezifischen Lösungen.
Primäres Ziel ist der Aufbau eines von der NFDI4Objects-Community getragenes Object Core Metadata Profile (OCMDP). Dieses Kerndatenschema stellt die Kompatibilität mit dem sich in Aufbau befindlichen NFDI Core Meta Data Profile (NFDI Sektionen) und dem NFDI Core (FIZ Karlsruhe) sicher, so dass Metadaten in NFDI4Objects innerhalb der NFDI-Konsortien interoperabel zur Verfügung stehen. Dazu müssen abgestimmte N4O eigene Elemente/Terms (properties) und Entitäten (classes) erzeugt und durch einen Cross-Walk mit anderen Schemata (u. a. schema.org, DCAT, DataCite, DublinCore) verknüpft werden. Der OCMDP ist die Basis für ein RDF-basiertes Austauschformat (Objects Metadata Junction Ontology - OMJO), welche Daten in einen N4O Metadata Knowledge Graph (N4O MKG) einspielt und diese FAIR zur Verfügung stellt. Zur FAIRifizierung von Forschungsdaten und Herstellung der Kompatibilität mit internationalen Cultural Heritage Daten auf Basis des CIDOC CRM und seiner Extensions wird eine Material Cultural Heritage Crosswalk Ontology (MaCHeCO) entwickelt, um hier gemeinsam mit der N4O Community Basis-Entitäten (classes)  in individuellen Ontologien durch einen hierarchischen CrossWalk zu CIDOC CRM als Crosswalk-Ontology im N4O Knowledge Graph (N4O KG) festzulegen. Zur Interaktion zwischen MaCHeCO und OMJO werden Anknüpfungsklassen zur föderierten Abfrage der beiden Knowledge Graphen (KG und MKG) definiert.
Das OCMDP und die MaCHeCO werden vor allem durch das fachliche Arbeitsprogramm - und damit dem Objektzyklus - der Task Areas 1-4 “Documentation”, “Collecting”, “Analysis & Experiments” sowie “Protecting” bestimmt und berühren dabei die Themengebiete Ausgrabung, (digitale) Rekonstruktion und Annotation, Sammlung, Kunstgeschichte, Restaurierung/Konservierung, naturwissenschaftliche Analysen, Denkmalpflege, Provenienzforschung, etc. Die Themengebiete erzeugen dabei fachliche Profile als Module der OCMDP, sowie fachspezifische Profil-Ontologien, die jeweils auf CIDOC CRM Klassen referenzieren (per cross-walk) um die Kompatibilität mit dem N4O KG herzustellen und die Validierung positiv zu gestalten.
Daraus ergeben sich als Aufgaben für die TWG u. a.:
Definition der Begrifflichkeiten zu einem Objects Core (N4O Proposal G6.5, p. 93; M6.3, p.95)
Festlegung von gemeinsamen Basis-Entitäten (classes) in individuellen Ontologien und hierarchischer CrossWalk u.A. auf CIDOC CRM 7.1.3 für MaCHeCO als Crosswalk-Ontologie im N4O Knowledge Graph
Entwicklung eines Object Core Metadata Profile (OCMDP) und Verknüpfung zu MaCHeCO und dem Knowledge Graph
Aufbau eines gemeinsamen Thesaurus zur Definition der Elemente (properties) und Entitäten (classes, zum Ontologieaustausch) im Object Core Metadata Profile (OCMDP)
OCMDP-Super-Element-CrossWalk zu NFDI Core Metadata Profile (schema.org / DataCite / DCAT / NFDI Core / CodeMeta) sowie DublinCore und Wikidata
Definition eines Austauschformats (CSV; XML; JSON)
Definition einer OCMDP Ontology (RDF Repräsentation des OCMDP) und der Object Metadata Junction Ontology (OMJO)
Elemente (properties) im Object Core Metadata Profile (OCMDP)
Gemeinsame Anknüpfungsentitäten (classes) zwischen OMJO und MaCHeO zur föderierten Abfrage der beiden Knowledge Graphen""" ;
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    dct:license <https://creativecommons.org/licenses/by/4.0/> ;
    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "NFDI4Objects TWG - N4O ObjectMetaDataSet (N4O OMDS) & N4O ObjectOntology (N4O OO)" ;
    dct:type sqpt:proposal ;
    bibo:abstract """Die TWG N4O ObjectMetaDataSet and ObjectOntology wird in dieser Iteration den aktuellen Bedürfnissen (NFDI Core Metadata Profile, etc.) inhaltlich agil angepasst. Diese TWG entwickelt kein Datenmodell für die Erstellung von Objektbiografien und definiert ebenfalls keine Anwendungsontologie für CIDOC CRM. Zudem werden keine LIDO-Anwendungsprofile entwickelt. Sie dient vor allem zur Entwicklung von technischen Referenzmodellen für Metadaten und publizierten Forschungsdaten. Die Themengebiete erzeugen fachliche Profile als Module der OCMDP sowie fachspezifische Profil-Ontologien, die jeweils auf CIDOC CRM Klassen referenzieren, um die Kompatibilität mit dem N4O KG herzustellen und die Validierung positiv zu gestalten; die TWG entwickelt ein abgeglichenes gemeinsames harmonisiertes Metadaten- und Ontologieprofil, keine fachspezifischen Lösungen.
Primäres Ziel ist der Aufbau eines von der NFDI4Objects-Community getragenes Object Core Metadata Profile (OCMDP). Dieses Kerndatenschema stellt die Kompatibilität mit dem sich in Aufbau befindlichen NFDI Core Meta Data Profile (NFDI Sektionen) und dem NFDI Core (FIZ Karlsruhe) sicher, so dass Metadaten in NFDI4Objects innerhalb der NFDI-Konsortien interoperabel zur Verfügung stehen. Dazu müssen abgestimmte N4O eigene Elemente/Terms (properties) und Entitäten (classes) erzeugt und durch einen Cross-Walk mit anderen Schemata (u. a. schema.org, DCAT, DataCite, DublinCore) verknüpft werden. Der OCMDP ist die Basis für ein RDF-basiertes Austauschformat (Objects Metadata Junction Ontology - OMJO), welche Daten in einen N4O Metadata Knowledge Graph (N4O MKG) einspielt und diese FAIR zur Verfügung stellt. Zur FAIRifizierung von Forschungsdaten und Herstellung der Kompatibilität mit internationalen Cultural Heritage Daten auf Basis des CIDOC CRM und seiner Extensions wird eine Material Cultural Heritage Crosswalk Ontology (MaCHeCO) entwickelt, um hier gemeinsam mit der N4O Community Basis-Entitäten (classes)  in individuellen Ontologien durch einen hierarchischen CrossWalk zu CIDOC CRM als Crosswalk-Ontology im N4O Knowledge Graph (N4O KG) festzulegen. Zur Interaktion zwischen MaCHeCO und OMJO werden Anknüpfungsklassen zur föderierten Abfrage der beiden Knowledge Graphen (KG und MKG) definiert.
Das OCMDP und die MaCHeCO werden vor allem durch das fachliche Arbeitsprogramm - und damit dem Objektzyklus - der Task Areas 1-4 “Documentation”, “Collecting”, “Analysis & Experiments” sowie “Protecting” bestimmt und berühren dabei die Themengebiete Ausgrabung, (digitale) Rekonstruktion und Annotation, Sammlung, Kunstgeschichte, Restaurierung/Konservierung, naturwissenschaftliche Analysen, Denkmalpflege, Provenienzforschung, etc. Die Themengebiete erzeugen dabei fachliche Profile als Module der OCMDP, sowie fachspezifische Profil-Ontologien, die jeweils auf CIDOC CRM Klassen referenzieren (per cross-walk) um die Kompatibilität mit dem N4O KG herzustellen und die Validierung positiv zu gestalten.
Daraus ergeben sich als Aufgaben für die TWG u. a.:
Definition der Begrifflichkeiten zu einem Objects Core (N4O Proposal G6.5, p. 93; M6.3, p.95)
Festlegung von gemeinsamen Basis-Entitäten (classes) in individuellen Ontologien und hierarchischer CrossWalk u.A. auf CIDOC CRM 7.1.3 für MaCHeCO als Crosswalk-Ontologie im N4O Knowledge Graph
Entwicklung eines Object Core Metadata Profile (OCMDP) und Verknüpfung zu MaCHeCO und dem Knowledge Graph
Aufbau eines gemeinsamen Thesaurus zur Definition der Elemente (properties) und Entitäten (classes, zum Ontologieaustausch) im Object Core Metadata Profile (OCMDP)
OCMDP-Super-Element-CrossWalk zu NFDI Core Metadata Profile (schema.org / DataCite / DCAT / NFDI Core / CodeMeta) sowie DublinCore und Wikidata
Definition eines Austauschformats (CSV; XML; JSON)
Definition einer OCMDP Ontology (RDF Repräsentation des OCMDP) und der Object Metadata Junction Ontology (OMJO)
Elemente (properties) im Object Core Metadata Profile (OCMDP)
Gemeinsame Anknüpfungsentitäten (classes) zwischen OMJO und MaCHeO zur föderierten Abfrage der beiden Knowledge Graphen""" ;
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TWG 2024.3 Prototypische Modellierung eines Provenienzgazetteers zur FAIRen Bereitstellung in einer Wikibase""" ;
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    dct:description """Die digitale Transformation in den objektbezogenen Wissenschaften erfordert eine enge Verzahnung zwischen semantischer Modellierung und nachhaltiger Forschungssoftware-Entwicklung. Dieses Poster zeigt, wie die Community Cluster "Semantic Modelling & Linked Open Data" und "Research Software Engineering" des NFDI4Objects-Konsortiums gemeinsam ein interdisziplinäres federated Knowledge Graph Ecosystem schaffen.
Semantic Web als Fundament: Basierend auf CIDOC CRM und dem Wikiversum (Wikidata, Wikibases) werden heterogene objektbezogene Daten in interoperable Knowledge Graphs transformiert. Diese semantische Grundlage ermöglicht die Föderation von Forschungsdaten aus Archäologie, Cultural Heritage und Geowissenschaften in einem kohärenten NFDI4Objects Knowledge Graph.
FAIR4RS und TADA als Qualitätsframework: Die Anwendung der FAIR4RS-Prinzipien (Findable, Accessible, Interoperable, Reusable for Research Software) und TADA-Prinzipien (Transparent, Accessible, Documented, Auditable) auf FAIRification Tools gewährleistet nachhaltige Softwareentwicklung. Exemplarisch werden Tools wie das SPARQLing Unicorn QGIS Plugin und die Jupyter Python Minions vorgestellt, die als Brücke zwischen semantischen Datenstrukturen und praktischen Forschungsworkflows fungieren.
Jupyter-basierte Infrastrukturdienste: Durch die Integration in Base4NFDI Basic Services, wie Jupyter4NFDI, entstehen reproduzierbare Notebooks zur Abfrage von Triplestores (Wikidata, NFDI4Objects Knowledge Graph) und zur Datenanalyse. Diese ermöglichen es Forschenden, komplexe SPARQL-Queries in benutzerfreundlichen Python-Umgebungen auszuführen und zu visualisieren.
Cultural Heritage Software Catalog: In Kooperation mit der NFDI Section Metadata (WG Software Metadata) und der NFDI Section Common-Infrasstructures (WG RSE) wird ein Cultural Heritage Software Catalog entwickelt. Basierend auf open-archaeo.info werden Softwaremetadaten strukturiert erfasst und in den Basic Service nfdi.software integriert, wodurch die Auffindbarkeit und Nachnutzung domänenspezifischer Tools erheblich verbessert wird.
Community-Vernetzung: Das Projekt ist eingebettet in ein internationales Netzwerk der Computer Applications and Quantitative Methods in Archaeology (CAA), einschließlich der Special Interest Groups SSLA (Scientific Scripting Languages in Archaeology) und Data Dragons (Linked Open Data). Diese Community-getriebene Entwicklung gewährleistet praxisnahe und bedarfsorientierte Lösungen.
Technische Innovation: Tools wie das SPARQLing Unicorn QGIS Plugin zeigen die nahtlose Integration von Semantic Web-Technologien in etablierte GIS-Workflows. Das Plugin ermöglicht (Geo)SPARQL-Abfragen direkt aus QGIS heraus und transformiert Vektordaten in RDF-konforme Linked Open Data.
Nachhaltigkeit und Reproduzierbarkeit: Durch konsequente Anwendung der REAL-Prinzipien (Reproducible, Executable, Attributable, Literal) in Kombination mit community-driven Research Software Engineering entstehen lightweight, modulare FAIRification Tools, die sowohl wissenschaftliche Exzellenz als auch langfristige Nachhaltigkeit gewährleisten.
Dieses Poster zeigt, wie durch die synergetische Verbindung von Semantik und RSE ein robustes, interdisziplinäres Knowledge Graph Ecosystem entsteht, das als Modell für andere NFDI-Konsortien dienen kann.
Keywords: FAIR4RS, CIDOC CRM, Knowledge Graphs, Jupyter4NFDI, SPARQL, Cultural Heritage Software, Community-driven RSE"""@de ;
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    dct:title "Brückenbau zwischen LOD und RSE: Von CIDOC CRM bis Jupyter4NFDI - Ein interdisziplinäres federated Knowledge Graph Ecosystem"@de ;
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Semantic Web als Fundament: Basierend auf CIDOC CRM und dem Wikiversum (Wikidata, Wikibases) werden heterogene objektbezogene Daten in interoperable Knowledge Graphs transformiert. Diese semantische Grundlage ermöglicht die Föderation von Forschungsdaten aus Archäologie, Cultural Heritage und Geowissenschaften in einem kohärenten NFDI4Objects Knowledge Graph.
FAIR4RS und TADA als Qualitätsframework: Die Anwendung der FAIR4RS-Prinzipien (Findable, Accessible, Interoperable, Reusable for Research Software) und TADA-Prinzipien (Transparent, Accessible, Documented, Auditable) auf FAIRification Tools gewährleistet nachhaltige Softwareentwicklung. Exemplarisch werden Tools wie das SPARQLing Unicorn QGIS Plugin und die Jupyter Python Minions vorgestellt, die als Brücke zwischen semantischen Datenstrukturen und praktischen Forschungsworkflows fungieren.
Jupyter-basierte Infrastrukturdienste: Durch die Integration in Base4NFDI Basic Services, wie Jupyter4NFDI, entstehen reproduzierbare Notebooks zur Abfrage von Triplestores (Wikidata, NFDI4Objects Knowledge Graph) und zur Datenanalyse. Diese ermöglichen es Forschenden, komplexe SPARQL-Queries in benutzerfreundlichen Python-Umgebungen auszuführen und zu visualisieren.
Cultural Heritage Software Catalog: In Kooperation mit der NFDI Section Metadata (WG Software Metadata) und der NFDI Section Common-Infrasstructures (WG RSE) wird ein Cultural Heritage Software Catalog entwickelt. Basierend auf open-archaeo.info werden Softwaremetadaten strukturiert erfasst und in den Basic Service nfdi.software integriert, wodurch die Auffindbarkeit und Nachnutzung domänenspezifischer Tools erheblich verbessert wird.
Community-Vernetzung: Das Projekt ist eingebettet in ein internationales Netzwerk der Computer Applications and Quantitative Methods in Archaeology (CAA), einschließlich der Special Interest Groups SSLA (Scientific Scripting Languages in Archaeology) und Data Dragons (Linked Open Data). Diese Community-getriebene Entwicklung gewährleistet praxisnahe und bedarfsorientierte Lösungen.
Technische Innovation: Tools wie das SPARQLing Unicorn QGIS Plugin zeigen die nahtlose Integration von Semantic Web-Technologien in etablierte GIS-Workflows. Das Plugin ermöglicht (Geo)SPARQL-Abfragen direkt aus QGIS heraus und transformiert Vektordaten in RDF-konforme Linked Open Data.
Nachhaltigkeit und Reproduzierbarkeit: Durch konsequente Anwendung der REAL-Prinzipien (Reproducible, Executable, Attributable, Literal) in Kombination mit community-driven Research Software Engineering entstehen lightweight, modulare FAIRification Tools, die sowohl wissenschaftliche Exzellenz als auch langfristige Nachhaltigkeit gewährleisten.
Dieses Poster zeigt, wie durch die synergetische Verbindung von Semantik und RSE ein robustes, interdisziplinäres Knowledge Graph Ecosystem entsteht, das als Modell für andere NFDI-Konsortien dienen kann.
Keywords: FAIR4RS, CIDOC CRM, Knowledge Graphs, Jupyter4NFDI, SPARQL, Cultural Heritage Software, Community-driven RSE"""@de ;
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    bibo:abstract "Computational Archaeology plays an increasingly strategic role in research data management (RDM) within archaeology and the (digital) humanities [1], [2]. At the heart of these developments lies a growing recognition of Research Software Engineering (RSE) as a key enabler for the FAIRification of data and code. This contribution explores the role of RSE and domain-specific research software in archaeological data workflows, illustrating how computational archaeology drives innovation in semantic modelling, data transformation, and the handling of uncertainty, which are core challenges for the NFDI [3]. As a participant in the NFDI4Objects consortium, the German Chapter of the Computer Applications and Quantitative Methods in Archaeology (CAA-DE) [4] contributes substantially to structuring RDM processes. Through its involvement in the NFDI4Objects Community Clusters [5] \"Semantic Modelling & Linked Open Data\" [6] and \"Research Software Engineering\" [7] connected to the Humanities@NFDI group, CAA-DE experts help align domain practices with national strategies, including initiatives of the Base4NFDI consortium. Semantic modelling and Linked Open Data (LOD) [8], [9] are widely acknowledged as foundational elements for cross-disciplinary interoperability, as reflected in Base4NFDI's technical services TS4NFDI and the knowledge graph infrastructure KGI4NFDI. Internationally, the SIG on Scientific Scripting Languages in Archaeology (SIG SSLA) [10] and the SIG on Semantics and LOUD in Archaeology (SIG DataDragon) [11] within CAA International are pioneering community-driven efforts that bridge theory and implementation. These initiatives promote semantic interoperability by creating reusable ontologies, developing transformation pipelines, and sharing research software openly. Their collaborative structure demonstrates how communities shape infrastructures and standards from the bottom up. Research software developed in this context must be understood as both code and data [12], [13]. Tools such as the SPARQLing Unicorn Research Toolkit (c.f. Figure 1 and 2) [14], the modular Little Python Minions [15] (used, e.g. in Jupyter4NFDI workflows), and the RDFier (a data-to-RDF transformation engine) [16] serve as FAIRification services that encapsulate reusable logic and make RDM workflows transparent and repeatable. These tools embody FAIR4RS principles and reflect the needs of both domain researchers and infrastructure providers. Integrating Artificial Intelligence (AI) into archaeological workflows further highlights the need for explainable, reproducible, and FAIR data practices [17]-[20] in addition to traditional statistical methods [13], [21]-[28]. Semantic modelling and RSE provide the conceptual and technical foundation to ensure these AI applications can be responsibly integrated into RDM ecosystems. Beyond disciplinary boundaries, the methods and tools developed in computational archaeology are already influencing infrastructure efforts across NFDI consortia. As a real-world laboratory for semantic data practices, including uncertainties and vagueness in data modelling [29], [30] and research software development, the field contributes reusable components to broader initiatives like Base4NFDI. Initial feedback from community use has shown the importance of modularity, documentation, and training to lower entry barriers. These lessons highlight the value of aligning community-driven innovation with national RDM strategies. This contribution advocates for a more substantial recognition of Computational Archaeology as a driver for sustainable, FAIR, and AI-ready research data infrastructures—grounded in community, enabled by research software, and built for reuse across domains."@en ;
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    dct:description "Most scientific disciplines use research software to address relevant research questions, and in many cases this research software is tailored to the specific research questions. Thus, research software is defined as software created during the research process or for a research purpose [1]. Within the German National Research Infrastructure (NFDI), research software plays two important roles. First, the services and tools developed by the consortia can be considered research software. These are mostly related to infrastructure software as defined by Hasselbring et al., but not exclusively [2]. Second, research software that supports a research project is considered an important research artifact itself. Several consortia of the NFDI are consequently working to provide infrastructure for these artifacts. Their significance is evident in their crucial function of research software, for example, in natural sciences for climate and meteorological modeling, in social sciences for network analysis and sentiment analysis, in digital humanities for text mining and computational linguistics, and in engineering sciences for simulating complex systems. Whether being a service/tool or a research artifact, provenance and rich descriptions of research software are essential to improve scientific transparency, findability, citability, reproducibility, software security, and reusability. Therefore, to improve the FAIRness of research software [3], software metadata plays an important role in the implementation of the FAIR4RS principles [4], [5]. To enable good software metadata, common metadata standards play an important role. Consequently, the working group \"Research Software Metadata\" of the Section Metadata, Terminology, and Provenance of the NFDI focuses on metadata for research software. The overall goal of this working group is to provide a common core metadata vocabulary for research software that can be used to describe research software created in the various research domains of the NFDI consortia. The work will create a unified framework that can be used across the different NFDI consortia generating research software artifacts. This effort also addresses key interoperability challenges, such as aligning different metadata schemas, ensuring consistency across disciplines, and integrating with existing standards such as CodeMeta [6], Citation File Format (CFF) [7] and schema.org. To gain an overview of research software metadata schemas already in use in the different consortia, we have conducted a survey on the implementation of research software metadata in the NFDI consortia [8]. We found that only a few domain-specific metadata schemas for research software were in use by different consortia. In contrast, generic metadata schemas such as CodeMeta, Bioschemas, schema.org, and the CFF were most frequently used to describe research software. Relevant metadata elements for research software include algorithms, methods, software/hardware requirements, and tasks performed by the software. We subsequently performed an in depth analysis of the alignment of the generic metadata schemas by creating crosswalks, i.e. mapping the schemas onto each other. This crosswalk analysis provides the basis for further discussions with the wider RDM community regarding strategies for provenance and rich descriptions of research software. [1] https://doi.org/10.5281/zenodo.5504016. [2] https://doi.org/10.1109/MCSE.2025.3555023. [3] https://doi.org/10.5281/zenodo.10816032. [4] https://doi.org/10.15497/RDA00068. [5] https://doi.org/10.5281/zenodo.10246218. [6] https://codemeta.github.io/terms/ [7] https://doi.org/10.5281/zenodo.5171937. [8] https://doi.org/10.5281/zenodo.12704414."@en ;
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    bibo:abstract "Most scientific disciplines use research software to address relevant research questions, and in many cases this research software is tailored to the specific research questions. Thus, research software is defined as software created during the research process or for a research purpose [1]. Within the German National Research Infrastructure (NFDI), research software plays two important roles. First, the services and tools developed by the consortia can be considered research software. These are mostly related to infrastructure software as defined by Hasselbring et al., but not exclusively [2]. Second, research software that supports a research project is considered an important research artifact itself. Several consortia of the NFDI are consequently working to provide infrastructure for these artifacts. Their significance is evident in their crucial function of research software, for example, in natural sciences for climate and meteorological modeling, in social sciences for network analysis and sentiment analysis, in digital humanities for text mining and computational linguistics, and in engineering sciences for simulating complex systems. Whether being a service/tool or a research artifact, provenance and rich descriptions of research software are essential to improve scientific transparency, findability, citability, reproducibility, software security, and reusability. Therefore, to improve the FAIRness of research software [3], software metadata plays an important role in the implementation of the FAIR4RS principles [4], [5]. To enable good software metadata, common metadata standards play an important role. Consequently, the working group \"Research Software Metadata\" of the Section Metadata, Terminology, and Provenance of the NFDI focuses on metadata for research software. The overall goal of this working group is to provide a common core metadata vocabulary for research software that can be used to describe research software created in the various research domains of the NFDI consortia. The work will create a unified framework that can be used across the different NFDI consortia generating research software artifacts. This effort also addresses key interoperability challenges, such as aligning different metadata schemas, ensuring consistency across disciplines, and integrating with existing standards such as CodeMeta [6], Citation File Format (CFF) [7] and schema.org. To gain an overview of research software metadata schemas already in use in the different consortia, we have conducted a survey on the implementation of research software metadata in the NFDI consortia [8]. We found that only a few domain-specific metadata schemas for research software were in use by different consortia. In contrast, generic metadata schemas such as CodeMeta, Bioschemas, schema.org, and the CFF were most frequently used to describe research software. Relevant metadata elements for research software include algorithms, methods, software/hardware requirements, and tasks performed by the software. We subsequently performed an in depth analysis of the alignment of the generic metadata schemas by creating crosswalks, i.e. mapping the schemas onto each other. This crosswalk analysis provides the basis for further discussions with the wider RDM community regarding strategies for provenance and rich descriptions of research software. [1] https://doi.org/10.5281/zenodo.5504016. [2] https://doi.org/10.1109/MCSE.2025.3555023. [3] https://doi.org/10.5281/zenodo.10816032. [4] https://doi.org/10.15497/RDA00068. [5] https://doi.org/10.5281/zenodo.10246218. [6] https://codemeta.github.io/terms/ [7] https://doi.org/10.5281/zenodo.5171937. [8] https://doi.org/10.5281/zenodo.12704414."@en ;
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    dct:title "How are NFDI consortia using Knowledge Graphs? An overview of common functions and challenges by the Working Group “Knowledge Graphs”"@en ;
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    bibo:abstract "The NFDI Working Group \"Knowledge Graphs\" (WG KGs) operates within the cross-cutting Section Metadata to coordinate and facilitate exchange on best practices, use cases, and challenges in the creation and use of knowledge graphs (KGs) across different disciplines. In this poster, we follow up on previously published findings, such as the dataset of KGs and related metadata developed and used by different consortia [1]. Besides the successful proposal and initiation of a basic service for a KG infrastructure (KGI4NFDI) [2], members of the WG have continued to meet regularly to discuss the ways the basic service can best meet the requirements of individual disciplines, and the NFDI as a whole. Furthermore, the WG discusses concerns beyond the scope of the basic service, such as efforts within individual consortia focused on disciplinary tasks, or ongoing developments within related basic services, such as TS4NFDI [3] or PID4NFDI [4]. Based on agile requirements gathering, feedback, and practical exchange, the WG has coordinated the collection of functional use cases and ongoing challenges different consortia face, with the aim to identify commonalities and/or distinctions that can inform the development of current and future basic services. The core premise of KGs is to represent (meta)data and their relations in a machine-understandable format, which improves semantic interoperability and data integration by adopting standard ontologies and Linked Data principles (e.g., RDF, OWL). This drives the functional use cases observed in NFDI consortia. Following a literature review, the following core KG functionalities were categorised by members of the WG: a) Data integration; b) Data FAIRification; c) Metadata cataloguing; d) Data verification, enrichment and extension; e) Machine actionability and AI-readiness [5–9]. These core categories were specified further through an open call among the consortia represented in the WG KG. The call collected an extended dataset of use case examples from their KG projects, grouped according to functionality categories, and linked to the previously published KG overview dataset [10]. Examples include KGs established by individual consortia functioning as data and metadata catalogues for discipline-specific repositories and research data portals, while using dedicated semantic ontologies as in the case of NFDI4Culture, NFDI-MatWerk, NFDI4DataScience, MaRDI, and NFDI4Objects, among others. A more specific example of data integration is the capacity of the Semantic Kompakkt KG (NFDI4Culture) and the NFDI4Bioimage KG to semantically express the annotation of 3D models and Bioimaging file formats, respectively. Alongside the use cases that illustrate why different consortia adopt specific KG functionalities, common challenges were also identified and documented. These challenges include harmonizing diverse ontologies within or across consortia, ensuring data verification and quality, optimizing AI-ready tools to support KG (and data) (re)usage, as well as socio-technical challenges, such as data privacy, or managing access restrictions. By collecting concrete use case examples and identifying specific challenges, the WG KGs supports its mission to encourage broader adoption of KGs across the NFDI. This effort also contributes to building a \"one-stop-shop\" catalog of KG example implementations that the NFDI community can turn to when starting KG-driven projects."@en ;
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    dct:description "In the context of the German National Research Data Infrastructure (NFDI), Research Software Engineers (RSEs) and research software play crucial and increasingly formalised roles in enabling the creation, management, and sustainable reuse of research artefacts such as data and software [1], [2]. This contribution introduces a layered model of roles and responsibilities developed by the Working Group Research Software Engineering of the NFDI section Common Infrastructures [3]-[6] (INFRA-WG-RSE). The model reflects its implications for Research Data Management (RDM) and Research Data Infrastructures (RDI). It works with other sections and working groups, e.g. WG RSM [7] (Fig. 1) within NFDI. The core of this model (Fig. 2) is the recognition that RSE in RDI consists of three foundational pillars: P-I) Research Software as Research Artifact [8] P-II) Research Software as FAIRification Service P-III) Research Software as Infrastructure Service for FAIRification (e.g., Basic Services of Base4NFDI) [9], [10] These three pillars illustrate how research software acts as a tool and an integral and multifaceted component of digital research workflows. It may be a research output or contribute to generating it. In more mature cases, it can evolve into a reusable, community-maintained service within a portfolio of infrastructure services like NFDI. To support these roles, the RSE community engages in five cross-cutting tasks (CCT): A) Create Research Software Metadata to apply FAIR4RS [11] B) Implement Research Software (Quality) Criteria C) Contribute to the Research Software Infrastructure and Archiving D) Make Tools FAIR within a Research Software Marketplace E) Create Guidelines and give Training This structured approach forms the conceptual and operational foundation of the INFRA-WG-RSE. The group serves as a hub for coordination and networking across NFDI consortia, contributing to Base4NFDI services and fostering the adoption of FAIR4RS principles through shared practices, policies, and tooling. Precisely, CCT-C aligns with the Basic Service Jupyter4NFDI, supporting federated infrastructures for computational workflows. Contributions like the Jupyter Python Minions [12] from computational archaeology—as a use case from the humanities—offer reusable, modular FAIRification helpers in Jupyter-based environments. Similarly, CCT-D contributes to nfdi.software, a central Basic Service to interlink and sustain domain-specific software registries, such as those from NFDI4Objects, NFDI4Energy, and NFDI4ING (Betty's Research Engine), among others. The Basic Service TS4NFDI develops, e.g., widgets within the Terminology Service Suite [13] (P-III). Tools from the NFDI4Objects domain, e.g., the SPARQLing Unicorn QGIS Plugin [14] and the AMT [15], enable domain researchers to integrate data from the NFDI Knowledge Graph Ecosystem into established Open Source environments (P-II). Furthermore, the Alligator method [16] illustrates \"Research Software as Research Artifact\" (P-1). The WG RSM [7] within the NFDI section (Meta)data, Terminologies, Provenance complements these efforts by developing standardised metadata models to support interoperability and discoverability of research software also using nfdi.software, contributing directly to the realisation of FAIR4RS. We conclude with recommendations for more strongly integrating RSE roles into the strategic development of the NFDI, highlighting that software is more than a technical asset: it is a cultural, infrastructural, and epistemic cornerstone of modern research."@en ;
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    dct:title "RSE 4 Research Data Infrastructures The Role of Research Software Engineers in creating FAIR Data with FAIR4RS Code"@en ;
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    bibo:abstract "In the context of the German National Research Data Infrastructure (NFDI), Research Software Engineers (RSEs) and research software play crucial and increasingly formalised roles in enabling the creation, management, and sustainable reuse of research artefacts such as data and software [1], [2]. This contribution introduces a layered model of roles and responsibilities developed by the Working Group Research Software Engineering of the NFDI section Common Infrastructures [3]-[6] (INFRA-WG-RSE). The model reflects its implications for Research Data Management (RDM) and Research Data Infrastructures (RDI). It works with other sections and working groups, e.g. WG RSM [7] (Fig. 1) within NFDI. The core of this model (Fig. 2) is the recognition that RSE in RDI consists of three foundational pillars: P-I) Research Software as Research Artifact [8] P-II) Research Software as FAIRification Service P-III) Research Software as Infrastructure Service for FAIRification (e.g., Basic Services of Base4NFDI) [9], [10] These three pillars illustrate how research software acts as a tool and an integral and multifaceted component of digital research workflows. It may be a research output or contribute to generating it. In more mature cases, it can evolve into a reusable, community-maintained service within a portfolio of infrastructure services like NFDI. To support these roles, the RSE community engages in five cross-cutting tasks (CCT): A) Create Research Software Metadata to apply FAIR4RS [11] B) Implement Research Software (Quality) Criteria C) Contribute to the Research Software Infrastructure and Archiving D) Make Tools FAIR within a Research Software Marketplace E) Create Guidelines and give Training This structured approach forms the conceptual and operational foundation of the INFRA-WG-RSE. The group serves as a hub for coordination and networking across NFDI consortia, contributing to Base4NFDI services and fostering the adoption of FAIR4RS principles through shared practices, policies, and tooling. Precisely, CCT-C aligns with the Basic Service Jupyter4NFDI, supporting federated infrastructures for computational workflows. Contributions like the Jupyter Python Minions [12] from computational archaeology—as a use case from the humanities—offer reusable, modular FAIRification helpers in Jupyter-based environments. Similarly, CCT-D contributes to nfdi.software, a central Basic Service to interlink and sustain domain-specific software registries, such as those from NFDI4Objects, NFDI4Energy, and NFDI4ING (Betty's Research Engine), among others. The Basic Service TS4NFDI develops, e.g., widgets within the Terminology Service Suite [13] (P-III). Tools from the NFDI4Objects domain, e.g., the SPARQLing Unicorn QGIS Plugin [14] and the AMT [15], enable domain researchers to integrate data from the NFDI Knowledge Graph Ecosystem into established Open Source environments (P-II). Furthermore, the Alligator method [16] illustrates \"Research Software as Research Artifact\" (P-1). The WG RSM [7] within the NFDI section (Meta)data, Terminologies, Provenance complements these efforts by developing standardised metadata models to support interoperability and discoverability of research software also using nfdi.software, contributing directly to the realisation of FAIR4RS. We conclude with recommendations for more strongly integrating RSE roles into the strategic development of the NFDI, highlighting that software is more than a technical asset: it is a cultural, infrastructural, and epistemic cornerstone of modern research."@en ;
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    bibo:abstract "An important aspect for conservators and researchers is access to the latest research on examination methods, damage phenomena, conservation techniques and materials, as well as preventive measures. Professional exchange strengthens expertise in assessing object conditions and determining appropriate interventions. With the advancement of digitalisation, conservation is presented with increasing opportunities while simultaneously encountering the challenge of ensuring the digital accessibility and reusability of complex expertise and valuable data [1], [2]. Not only is conservation-related data scattered across isolated databases and stored in different formats but the information is also described using different terminologies. Experts from various specialisations use distinct terms for similar concepts, leading to inconsistencies that complicate data retrieval, comparison, and integration [3], [4]. This lack of standardisation significantly hampers interdisciplinary collaboration, knowledge sharing, and the development of interoperable digital resources. To address these challenges, the \"Conservation and Restoration Thesaurus for Archaeological Cultural Heritage\" was developed at the Leibniz-Zentrum für Archäologie (LEIZA) as part of NFDI4Objects [5]. This controlled vocabulary not only facilitates human communication by standardising terminology but also enables machine-readable, semantically structured data integration for the Semantic Web [4]. Based on the Simple Knowledge Organization System [6], it ensures interoperability and follows the FAIR principles (Findable, Accessible, Interoperable, Reusable) [7]. To further support research-specific needs, such as the citation of sources, the schema was extended with additional properties. In addition the thesaurus aims to align with ISO and DIN standards in its content classification [8], [9]. To support the creation of the complex vocabulary a simple web application was developed. This web application validates tabular vocabulary data against the SKOS-based schema, detecting errors like duplicate identifiers or incorrect hierarchies. It provides different visualisations to represent hierarchical structures and allows collaborative content development through an interactive comment function. The validated data can be exported as an RDF turtle file and integrated into central thesaurus repositories like DANTE [10] and SkoHub [11] or LOD-Clouds like Wikidata [12]. The collaboration between domain experts and software engineers ensured both user-friendliness for professionals and machine readability for digital applications. Beyond structured vocabulary development, the enrichment of the thesaurus content as well was supported by technical innovations, like Natural Language Processing (NLP). One key approach was the analysis of digital and retro-digitised conservation reports from approximately 170 years of conservation history at LEIZA (formerly the Römisch-Germanisches Zentralmuseum). Conservation-specific terminology was extracted from these textual resources, classified, and integrated based on hierarchical, equivalent, and associative relationships. At the Conference on Research Data Infrastructure 2025, the methodological and technical aspects of thesaurus development will be presented as a poster. At the same time, the practical benefits of subject-specific thesauri should be clearly outlined, thus emphasising the importance of standardised vocabularies for research data management. It is anticipated that the 1.0 version of the Conservation Thesaurus will be made available to the public prior to the commencement of the conference, demonstrating how semantic technologies can enhance research in the field of digital humanities by overcoming long-standing communication and data integration barriers."@en ;
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    dct:description """Die dreidimensionale Erfassung und digitale Dokumentation frühmittelalterlicher Inschriften-, Skulptur- und Kulturobjekte eröffnet neue Perspektiven für Forschung, Vermittlung und Erhaltung. Am Beispiel der irischen und schottischen Ogham-Steine, der Govan-Artefakte in Glasgow sowie der irischen Holy Wells werden unterschiedliche Aufnahmemethoden vorgestellt und verglichen – von citizen-science-basierten Structure-from-Motion-Erfassungen (z. B. mit der KIRI-Engine-App auf dem Smartphone) über klassische Photogrammetrie bis hin zu hochauflösenden Streifenlichtprojektionsscans professioneller Projekte wie Ogham in 3D und OG(H)AM. Ergänzend werden 3D-Modelle aus öffentlichen Plattformen (z. B. Sketchfab) sowie von Citizen Scientists systematisch dokumentierte Holy Wells einbezogen, die das Potenzial partizipativer Datenerhebung verdeutlichen.
Im Zentrum steht die Frage, wie diese heterogenen 3D-Modelle – mit unterschiedlichen Sensoriken, Granularitäten und Repositorien – als FAIR Digital Objects (FDOs) beschrieben, identifiziert und in ein föderiertes semantisches Wissensökosystem eingebunden werden können. Vorgestellt werden Metadaten- und Provenienzmodelle auf Basis von RDF/PROV-O und des Object Core Metadata Profile (OCMDP), die technische Erfassungsparameter (Methode, Auflösung, Software), Qualität und Bearbeitung, rechtliche Rahmenbedingungen (Lizenz, Rechte) sowie räumliche und kulturelle Kontextinformationen (Ort, Toponym, Denkmalkategorie, Identifikatoren) erfassen.
Der Beitrag zeigt, wie sich aus verteilten Quellen – etwa Sketchfab, Wikidata/Wikibase, institutionellen Datenbanken und Triplestores – ein federated Knowledge Graph bilden lässt. Durch Ontologie-Alignment und persistente Identifikatoren werden 3D-Daten zu nachnutzbaren Forschungsobjekten, die interdisziplinär ausgewertet werden können, etwa für paläographische Analysen (z. B. Groove Analysis an Ogham- und Govan-Steinen) oder räumliche Studien zu Holy-Wells-Netzwerken und Kulturlandschaften.
Ein besonderer Fokus liegt auf Citizen Science: Freiwillige erweitern den 3D-Korpus durch smartphonebasierte SfM-Modelle erheblich. Über FAIR-konforme Metadaten, Provenienzketten und Qualitätssicherung können diese Beiträge gleichwertig in wissenschaftliche Infrastrukturen integriert werden, ohne ihre Eigenständigkeit zu verlieren.
Damit verbindet der Vortrag 3D-Erfassung, semantische Modellierung und partizipative Datenerhebung zu einem integrativen Ansatz für nachhaltiges digitales Kulturerbe – und zeigt, wie selbst heterogene 3D-Daten (Ogham, Govan, Holy Wells) interoperabel, offen und zukunftsfähig in Forschung und Vermittlung eingebunden werden können."""@de ;
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    dct:description """Die 3D-Erfassung von Kulturgütern, Kunstwerken und vergänglichen Strukturen mit Smartphones eröffnet neue Perspektiven für partizipative digitale Dokumentation. Durch Structure-from-Motion-Verfahren (SfM) in mobilen Anwendungen wie der KIRI-Engine-App können Citizen Scientists Objekte ortsunabhängig und kostengünstig digitalisieren – von historischen Denkmälern über moderne Kunstwerke bis hin zu temporären Phänomenen wie Schnee- oder Holzskulpturen. Damit wird die digitale Erfassung zu einer alltagstauglichen Methode, um Objekte „zum Mitnehmen“ zu schaffen, die weit über das private Erinnerungsfoto hinausgehen.
Doch die reine Erfassung genügt nicht: Um 3D-Modelle nachhaltig nutzbar zu machen, müssen sie FAIR (Findable, Accessible, Interoperable, Reusable) sein. Hier setzt die SquirrelBase an – eine Wikibase-Instanz, die als semantische Metadaten- und Referenzplattform fungiert. Sie erweitert das Konzept der FAIR Digital Objects (FDOs) um eine leichtgewichtige, community-getriebene Lösung für Citizen Science und Forschung. Jedes 3D-Objekt erhält einen persistenten Identifier (Q-ID), kann mit externen Plattformen wie Sketchfab, 3DHOP, Wikidata oder OpenStreetMap verknüpft werden und wird damit Teil eines föderierten Wissensgraphen.
Die SquirrelBase speichert neben Basisinformationen (Erfassungsdatum, Methode, Software, Kamera) auch semantisch strukturierte Metadaten zu Georeferenz, Material, Objektkategorie, Urheber, Lizenz und Provenienz. Durch diese Modellierung lassen sich die 3D-Modelle maschinenlesbar beschreiben und in GIS- und LOD-Workflows integrieren – etwa über das SPARQLing Unicorn QGIS Plugin. Somit wird aus einem Smartphone-Scan ein vernetztes Forschungsobjekt, das interdisziplinär analysierbar und langfristig auffindbar ist.
Beispiele reichen von klassischen Kulturerbe-Objekten (z. B. Grenzsteine, Brunnen, Skulpturen) über urbane Kunstwerke bis hin zu ephemeren Strukturen in den Dolomiten. Gerade bei vergänglichen Objekten zeigt sich das Potenzial: Durch die Kombination von SfM-Erfassung, FAIR-Metadaten und semantischer Einbindung bleiben auch flüchtige Werke als digitale Erinnerungen erhalten und wissenschaftlich zitierbar.
Der Beitrag diskutiert zudem Herausforderungen der Datenqualität, Standardisierung und Langzeitarchivierung sowie mögliche Synergien mit Forschungsinfrastrukturen wie NFDI4Objects. Das vorgestellte Konzept zeigt, wie Low-Cost-Technologien, Open-Science-Prinzipien und semantische Modellierung zusammenwirken können, um eine nachhaltige, partizipative 3D-Dokumentation zu schaffen – offen, interoperabel und zukunftssicher."""@de ;
    dct:identifier "https://doi.org/10.5281/zenodo.18726246" ;
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    dct:issued "2026-02-25"^^xsd:date ;
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    dct:license <https://creativecommons.org/licenses/by/4.0/> ;
    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Von der Hosentasche in den Wissensgraph: Smartphone-3D als FAIR Digital Object in der SquirrelBase"@de ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """Die 3D-Erfassung von Kulturgütern, Kunstwerken und vergänglichen Strukturen mit Smartphones eröffnet neue Perspektiven für partizipative digitale Dokumentation. Durch Structure-from-Motion-Verfahren (SfM) in mobilen Anwendungen wie der KIRI-Engine-App können Citizen Scientists Objekte ortsunabhängig und kostengünstig digitalisieren – von historischen Denkmälern über moderne Kunstwerke bis hin zu temporären Phänomenen wie Schnee- oder Holzskulpturen. Damit wird die digitale Erfassung zu einer alltagstauglichen Methode, um Objekte „zum Mitnehmen“ zu schaffen, die weit über das private Erinnerungsfoto hinausgehen.
Doch die reine Erfassung genügt nicht: Um 3D-Modelle nachhaltig nutzbar zu machen, müssen sie FAIR (Findable, Accessible, Interoperable, Reusable) sein. Hier setzt die SquirrelBase an – eine Wikibase-Instanz, die als semantische Metadaten- und Referenzplattform fungiert. Sie erweitert das Konzept der FAIR Digital Objects (FDOs) um eine leichtgewichtige, community-getriebene Lösung für Citizen Science und Forschung. Jedes 3D-Objekt erhält einen persistenten Identifier (Q-ID), kann mit externen Plattformen wie Sketchfab, 3DHOP, Wikidata oder OpenStreetMap verknüpft werden und wird damit Teil eines föderierten Wissensgraphen.
Die SquirrelBase speichert neben Basisinformationen (Erfassungsdatum, Methode, Software, Kamera) auch semantisch strukturierte Metadaten zu Georeferenz, Material, Objektkategorie, Urheber, Lizenz und Provenienz. Durch diese Modellierung lassen sich die 3D-Modelle maschinenlesbar beschreiben und in GIS- und LOD-Workflows integrieren – etwa über das SPARQLing Unicorn QGIS Plugin. Somit wird aus einem Smartphone-Scan ein vernetztes Forschungsobjekt, das interdisziplinär analysierbar und langfristig auffindbar ist.
Beispiele reichen von klassischen Kulturerbe-Objekten (z. B. Grenzsteine, Brunnen, Skulpturen) über urbane Kunstwerke bis hin zu ephemeren Strukturen in den Dolomiten. Gerade bei vergänglichen Objekten zeigt sich das Potenzial: Durch die Kombination von SfM-Erfassung, FAIR-Metadaten und semantischer Einbindung bleiben auch flüchtige Werke als digitale Erinnerungen erhalten und wissenschaftlich zitierbar.
Der Beitrag diskutiert zudem Herausforderungen der Datenqualität, Standardisierung und Langzeitarchivierung sowie mögliche Synergien mit Forschungsinfrastrukturen wie NFDI4Objects. Das vorgestellte Konzept zeigt, wie Low-Cost-Technologien, Open-Science-Prinzipien und semantische Modellierung zusammenwirken können, um eine nachhaltige, partizipative 3D-Dokumentation zu schaffen – offen, interoperabel und zukunftssicher."""@de ;
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    dct:description """This poster presents an ongoing comparative study on the semantic modelling and utility of (geo-)spatial information across three key graph-based paradigms — Resource Description Framework (RDF), Labelled Property Graphs (LPGs), and Attributed Heterogeneous Information Networks (AHINs) — and explores their integration within the NFDI federated Knowledge Graph (KG) ecosystem. The work examines spatial data within these graph-based data models regarding semantic expressiveness, practical interoperability, integration in research workflows and computational performance. This excludes FAIR Digital Objects, which are used for, e.g., binary data of geophysical prospection or 3D documentation, and do not provide geo-spatial information at graph level.
Use cases reveal how RDF shows a tendency to excel in semantic richness and machine interpretability, whereas HINs and LPGs may handle high-volume or computationally demanding workflows more efficiently, albeit at the cost of standardisation and interoperability. Bridging these paradigms thus becomes crucial for a sustainable, federated research data infrastructure.
Within the NFDI federated KG ecosystem, we aim to combine the analytical power of AHINs with the semantic richness and flexibility of LPGs as well as the high standardisation of RDF-based KGs. We discuss schema-mapping strategies, identifier management (URIs vs. node IDs), and transformation workflows that connect semantically rich but slower RDF environments with performant HIN-based analytical pipelines. The goal is to survey the bi-directional interoperability between all three paradigms, exploring existing mappings between RDF, LPG and AHINs and open challenges, focusing on geo-spatial information.
From a research software engineering (RSE) perspective, this work contributes to RSE research, discussing Open Science, Research Data Management, Research Software usability and sustainability, Infrastructures for Scientific Computing within the Digital Humanities. It highlights design patterns and approaches for interlinking the three graph-based paradigms within the NFDI research data infrastructure, supporting scalable data exploration while maintaining FAIR principles. Spatial data serves as a cross-domain connector, linking research outputs from the humanities, natural sciences, geo-sciences and engineering through similarity of aligned spatial semantic concepts.
By aligning semantic interoperability with analytical performance, this poster demonstrates how graph architectures can unlock the full potential of RDF-based KGs, LPGs and AHINs for practical research applications. We outline next steps towards interfaces and interoperability, fostering a more robust, FAIR-compliant, and computationally efficient research-software ecosystem within the NFDI and beyond."""@en ;
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    dct:title "Semantic Modelling and Performance of (Geo-)Spatial Information in Knowledge Graphs and Heterogeneous Information Networks"@en ;
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    bibo:abstract """This poster presents an ongoing comparative study on the semantic modelling and utility of (geo-)spatial information across three key graph-based paradigms — Resource Description Framework (RDF), Labelled Property Graphs (LPGs), and Attributed Heterogeneous Information Networks (AHINs) — and explores their integration within the NFDI federated Knowledge Graph (KG) ecosystem. The work examines spatial data within these graph-based data models regarding semantic expressiveness, practical interoperability, integration in research workflows and computational performance. This excludes FAIR Digital Objects, which are used for, e.g., binary data of geophysical prospection or 3D documentation, and do not provide geo-spatial information at graph level.
Use cases reveal how RDF shows a tendency to excel in semantic richness and machine interpretability, whereas HINs and LPGs may handle high-volume or computationally demanding workflows more efficiently, albeit at the cost of standardisation and interoperability. Bridging these paradigms thus becomes crucial for a sustainable, federated research data infrastructure.
Within the NFDI federated KG ecosystem, we aim to combine the analytical power of AHINs with the semantic richness and flexibility of LPGs as well as the high standardisation of RDF-based KGs. We discuss schema-mapping strategies, identifier management (URIs vs. node IDs), and transformation workflows that connect semantically rich but slower RDF environments with performant HIN-based analytical pipelines. The goal is to survey the bi-directional interoperability between all three paradigms, exploring existing mappings between RDF, LPG and AHINs and open challenges, focusing on geo-spatial information.
From a research software engineering (RSE) perspective, this work contributes to RSE research, discussing Open Science, Research Data Management, Research Software usability and sustainability, Infrastructures for Scientific Computing within the Digital Humanities. It highlights design patterns and approaches for interlinking the three graph-based paradigms within the NFDI research data infrastructure, supporting scalable data exploration while maintaining FAIR principles. Spatial data serves as a cross-domain connector, linking research outputs from the humanities, natural sciences, geo-sciences and engineering through similarity of aligned spatial semantic concepts.
By aligning semantic interoperability with analytical performance, this poster demonstrates how graph architectures can unlock the full potential of RDF-based KGs, LPGs and AHINs for practical research applications. We outline next steps towards interfaces and interoperability, fostering a more robust, FAIR-compliant, and computationally efficient research-software ecosystem within the NFDI and beyond."""@en ;
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    prism:volume "8" ;
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        <https://w3id.org/squirrelpapers/person/0009-0008-2877-3204> ;
    dct:description """In geoscientific and geoarchaeological research, analytical workflows often remain locked in Excel-based environments. While such approaches are convenient, they limit reproducibility, transparency, and interoperability. This paper presents how Research Software Engineering (RSE) methodology can transform these “black box” workflows into reproducible, FAIR-compliant and semantically interoperable processes by reimplementing established analytical routines in open, script-based environments using Python and R.
The work builds on data from tephra and sediment analyses in the Eifel region, focusing on EPMA and LA-ICP-MS. Traditionally, these datasets were processed and visualised in Excel, making version control, reproducibility, and methodological transparency difficult.
Building on methods from ELSAinteractive++ (PHd by B.W. Diensberg), a C++-based application for visualising sediment cores, the project replaces static, closed workflows with modular Python scripts based on pandas and matplotlib. Each processing step, from data import to statistical transformation and plotting, is now reproducible and extensible.
Further integration draws on an Eifel GDGT dataset and analytical workflow to demonstrate how existing R code for biomarker-based temperature reconstructions can be re-implemented in Python for enhanced cross-platform reproducibility. Together, these examples illustrate the practical implementation of the FAIR4RS principles, Findable, Accessible, Interoperable, and Reusable research software.
Beyond reproducible computation, the project also explores semantic data publication. Laboratory results and analytical metadata are modelled as RDF triples to ensure machine-actionable interoperability with Cultural Heritage and Earth Science data. Python scripts assist in transforming tabular outputs into RDF using common vocabularies, while the SPARQLing Unicorn Toolkit enables the conversion of these RDF datasets into human-readable HTML pages automatically published on GitHub Pages. This dual approach, semantic and computational FAIRification, bridges the gap between traditional analytical workflows and Linked Open Data infrastructures.
In contrast to spreadsheet-based workflows, the RSE-driven approach offers:
Versioned and transparent analysis pipelines via Git-based code;
Parameter reproducibility through explicit scripting;
Scalability and automation for high-volume datasets; and
Semantic interoperability through FAIR metadata and RDF publication.
By showcasing examples from the Campanian Ignimbrite and Eifel tephra research, the paper highlights how RSE methodology enhances both reproducibility and semantic interoperability across disciplinary borders. It demonstrates that adopting RSE principles in geoscientific and archaeological workflows is not just a technical improvement but a cultural shift, from isolated Excel sheets to open, machine-actionable, and verifiable research."""@en ;
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    dct:title "Two Sides of the RSE Coin: Reproducible and Semantically Interoperable Workflows in Climate and Geoarchaeological Research"@en ;
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        sqpt:poster ;
    bibo:abstract """In geoscientific and geoarchaeological research, analytical workflows often remain locked in Excel-based environments. While such approaches are convenient, they limit reproducibility, transparency, and interoperability. This paper presents how Research Software Engineering (RSE) methodology can transform these “black box” workflows into reproducible, FAIR-compliant and semantically interoperable processes by reimplementing established analytical routines in open, script-based environments using Python and R.
The work builds on data from tephra and sediment analyses in the Eifel region, focusing on EPMA and LA-ICP-MS. Traditionally, these datasets were processed and visualised in Excel, making version control, reproducibility, and methodological transparency difficult.
Building on methods from ELSAinteractive++ (PHd by B.W. Diensberg), a C++-based application for visualising sediment cores, the project replaces static, closed workflows with modular Python scripts based on pandas and matplotlib. Each processing step, from data import to statistical transformation and plotting, is now reproducible and extensible.
Further integration draws on an Eifel GDGT dataset and analytical workflow to demonstrate how existing R code for biomarker-based temperature reconstructions can be re-implemented in Python for enhanced cross-platform reproducibility. Together, these examples illustrate the practical implementation of the FAIR4RS principles, Findable, Accessible, Interoperable, and Reusable research software.
Beyond reproducible computation, the project also explores semantic data publication. Laboratory results and analytical metadata are modelled as RDF triples to ensure machine-actionable interoperability with Cultural Heritage and Earth Science data. Python scripts assist in transforming tabular outputs into RDF using common vocabularies, while the SPARQLing Unicorn Toolkit enables the conversion of these RDF datasets into human-readable HTML pages automatically published on GitHub Pages. This dual approach, semantic and computational FAIRification, bridges the gap between traditional analytical workflows and Linked Open Data infrastructures.
In contrast to spreadsheet-based workflows, the RSE-driven approach offers:
Versioned and transparent analysis pipelines via Git-based code;
Parameter reproducibility through explicit scripting;
Scalability and automation for high-volume datasets; and
Semantic interoperability through FAIR metadata and RDF publication.
By showcasing examples from the Campanian Ignimbrite and Eifel tephra research, the paper highlights how RSE methodology enhances both reproducibility and semantic interoperability across disciplinary borders. It demonstrates that adopting RSE principles in geoscientific and archaeological workflows is not just a technical improvement but a cultural shift, from isolated Excel sheets to open, machine-actionable, and verifiable research."""@en ;
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    dct:description """Research Software Engineers (RSEs) have become an essential driving force behind the sustainable creation, curation, and FAIRification of research data. Within the German National Research Data Infrastructure (NFDI), this role is gaining increasing formal recognition – not only as software developers but as integral contributors to infrastructure design, metadata standards, and cross-consortia interoperability.
The Working Group “Research Software Engineering” (WG RSE) of the NFDI’s Section Common Infrastructures brings together RSEs from interdisciplinary NFDI consortia. It develops a shared understanding of RSE roles, best practices, and common frameworks for research software within data infrastructures. The group’s conceptual foundation builds on three pillars that position RSEs as:
Creators of Research Software as Research Artefacts,
Providers of FAIRification Services,
and Contributors to Infrastructure Services such as Base4NFDI’s basic services (nfdi.software, Jupyter4NFDI, TS4NFDI).
Across these pillars, the WG coordinates five cross-cutting tasks (CCTs) that define practical RSE activities in research data infrastructures:
(A) creating metadata and applying FAIR4RS principles;
(B) implementing software quality and sustainability criteria;
(C) contributing to archiving and infrastructure integration;
(D) making tools FAIR and visible through marketplaces; and
(E) creating shared guidelines and training opportunities.
This structured approach serves as both a coordination framework and a point of connection between RSEs in the NFDI and the wider international RSE community. The paper presents this model and invites discussion about its transferability beyond the NFDI context – for instance, how similar structures could support RSE communities in other national or institutional frameworks.
By showcasing examples such as the SPARQLing Unicorn QGIS Plugin, the Jupyter Python Minions, and the Academic Meta Tool (all from the archaeological domain), the paper visualises how RSE work in NFDI4Objects, NFDI4ING, or NFDI4Energy bridges domain boundaries and fosters reproducible, FAIR, and open research workflows. These examples demonstrate that the technical and social aspects of Research Software Engineering are deeply intertwined: developing FAIR software is not only about compliance, but about creating communities of practice.
This paper serves as an invitation to dialogue: How can the broader RSE community and the NFDI ecosystem learn from one another? How can international RSE initiatives and infrastructures collaborate to strengthen FAIR4RS implementation and Research Software sustainability across disciplinary borders?"""@en ;
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    dct:title "RSE across the NFDI - Connecting Infrastructure and Community through FAIR4RS Practice"@en ;
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    bibo:abstract """Research Software Engineers (RSEs) have become an essential driving force behind the sustainable creation, curation, and FAIRification of research data. Within the German National Research Data Infrastructure (NFDI), this role is gaining increasing formal recognition – not only as software developers but as integral contributors to infrastructure design, metadata standards, and cross-consortia interoperability.
The Working Group “Research Software Engineering” (WG RSE) of the NFDI’s Section Common Infrastructures brings together RSEs from interdisciplinary NFDI consortia. It develops a shared understanding of RSE roles, best practices, and common frameworks for research software within data infrastructures. The group’s conceptual foundation builds on three pillars that position RSEs as:
Creators of Research Software as Research Artefacts,
Providers of FAIRification Services,
and Contributors to Infrastructure Services such as Base4NFDI’s basic services (nfdi.software, Jupyter4NFDI, TS4NFDI).
Across these pillars, the WG coordinates five cross-cutting tasks (CCTs) that define practical RSE activities in research data infrastructures:
(A) creating metadata and applying FAIR4RS principles;
(B) implementing software quality and sustainability criteria;
(C) contributing to archiving and infrastructure integration;
(D) making tools FAIR and visible through marketplaces; and
(E) creating shared guidelines and training opportunities.
This structured approach serves as both a coordination framework and a point of connection between RSEs in the NFDI and the wider international RSE community. The paper presents this model and invites discussion about its transferability beyond the NFDI context – for instance, how similar structures could support RSE communities in other national or institutional frameworks.
By showcasing examples such as the SPARQLing Unicorn QGIS Plugin, the Jupyter Python Minions, and the Academic Meta Tool (all from the archaeological domain), the paper visualises how RSE work in NFDI4Objects, NFDI4ING, or NFDI4Energy bridges domain boundaries and fosters reproducible, FAIR, and open research workflows. These examples demonstrate that the technical and social aspects of Research Software Engineering are deeply intertwined: developing FAIR software is not only about compliance, but about creating communities of practice.
This paper serves as an invitation to dialogue: How can the broader RSE community and the NFDI ecosystem learn from one another? How can international RSE initiatives and infrastructures collaborate to strengthen FAIR4RS implementation and Research Software sustainability across disciplinary borders?"""@en ;
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    dct:description """Research software plays an increasingly important role in the Cultural Heritage domain, where digital tools and computational methods support data analysis, visualisation, and knowledge integration across archaeology, heritage conservation, and the humanities. However, the discoverability, sustainability, and FAIRness of such software are still limited, particularly due to missing metadata harmonisation and a lack of central catalogues tailored to this research area.
To address this gap, chublets.software is currently being developed within the Community Cluster “Research Software Engineering” of NFDI4Objects as a prototype for a domain-specific FAIR software marketplace. The platform aims to enhance the findability and reusability of Cultural Heritage–related research software by connecting existing infrastructures, standards, and initiatives, including open-archaeo.info and the NFDI Basic Service nfdi.software provided by Base4NFDI.
At its core, chublets.software relies on a Wikibase instance hosted on wikibase.cloud as a structured and community-editable knowledge base for research software metadata. The system is designed to ingest metadata both manually and automatically. Planned Python-based scripts will harvest metadata from software repositories (e.g., GitHub, GitLab) by parsing Citation File Format (CFF) and CodeMeta files, which are among the most common open standards for research software metadata. This process follows the crosswalks and mappings developed by the NFDI Working Group “Research Software Metadata” (Section Metadata, Terminology & Provenance) to ensure interoperability between different metadata schemas.
The marketplace is conceptually aligned with nfdi.software, which provides a central registry and harvesting infrastructure for research software across the NFDI consortia. While nfdi.software ensures a cross-domain backbone for the discovery of research software, chublets.software extends this approach by offering a domain-specific layer that captures disciplinary context and metadata elements relevant for Cultural Heritage research – for example, links to datasets, 3D repositories, terminologies, and digital excavation tools.
By building upon open-archaeo.info as an existing community-driven catalogue of archaeological software, chublets.software adds a semantic and infrastructural dimension that connects research software metadata with the broader NFDI ecosystem. The resulting Linked Open Data approach allows Cultural Heritage researchers, Research Software Engineers (RSEs), and data stewards to collaboratively curate, search, and interlink tools in a sustainable and machine-actionable way.
This paper will introduce the conceptual framework and technical design of chublets.software, discuss its alignment with FAIR4RS principles, and demonstrate how it contributes to improving the visibility, interoperability, and long-term accessibility of software artefacts in Cultural Heritage research. In doing so, it showcases how research software engineering practices and community-driven infrastructure development can effectively bridge the gap between disciplinary specificity and cross-domain interoperability within the NFDI."""@en ;
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    dct:title "chublets.software – Building a FAIR Software Marketplace for Cultural Heritage"@en ;
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    bibo:abstract """Research software plays an increasingly important role in the Cultural Heritage domain, where digital tools and computational methods support data analysis, visualisation, and knowledge integration across archaeology, heritage conservation, and the humanities. However, the discoverability, sustainability, and FAIRness of such software are still limited, particularly due to missing metadata harmonisation and a lack of central catalogues tailored to this research area.
To address this gap, chublets.software is currently being developed within the Community Cluster “Research Software Engineering” of NFDI4Objects as a prototype for a domain-specific FAIR software marketplace. The platform aims to enhance the findability and reusability of Cultural Heritage–related research software by connecting existing infrastructures, standards, and initiatives, including open-archaeo.info and the NFDI Basic Service nfdi.software provided by Base4NFDI.
At its core, chublets.software relies on a Wikibase instance hosted on wikibase.cloud as a structured and community-editable knowledge base for research software metadata. The system is designed to ingest metadata both manually and automatically. Planned Python-based scripts will harvest metadata from software repositories (e.g., GitHub, GitLab) by parsing Citation File Format (CFF) and CodeMeta files, which are among the most common open standards for research software metadata. This process follows the crosswalks and mappings developed by the NFDI Working Group “Research Software Metadata” (Section Metadata, Terminology & Provenance) to ensure interoperability between different metadata schemas.
The marketplace is conceptually aligned with nfdi.software, which provides a central registry and harvesting infrastructure for research software across the NFDI consortia. While nfdi.software ensures a cross-domain backbone for the discovery of research software, chublets.software extends this approach by offering a domain-specific layer that captures disciplinary context and metadata elements relevant for Cultural Heritage research – for example, links to datasets, 3D repositories, terminologies, and digital excavation tools.
By building upon open-archaeo.info as an existing community-driven catalogue of archaeological software, chublets.software adds a semantic and infrastructural dimension that connects research software metadata with the broader NFDI ecosystem. The resulting Linked Open Data approach allows Cultural Heritage researchers, Research Software Engineers (RSEs), and data stewards to collaboratively curate, search, and interlink tools in a sustainable and machine-actionable way.
This paper will introduce the conceptual framework and technical design of chublets.software, discuss its alignment with FAIR4RS principles, and demonstrate how it contributes to improving the visibility, interoperability, and long-term accessibility of software artefacts in Cultural Heritage research. In doing so, it showcases how research software engineering practices and community-driven infrastructure development can effectively bridge the gap between disciplinary specificity and cross-domain interoperability within the NFDI."""@en ;
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    dct:description """Abstract
Community-basierte Geodatenplattformen wie OpenStreetMap (OSM), Wikidata (Schmidt, Thiery, und Trognitz 2022) und weitere Wikibase-Instanzen stellen eine der wertvollsten frei verfügbaren Quellen raumbezogener Informationen dar. In der wissenschaftlichen Nutzung, insbesondere im Bereich des Cultural Heritage und der Geowissenschaften, besteht jedoch weiterhin die Herausforderung, diese heterogenen Daten strukturiert, semantisch konsistent und maschinenlesbar in Forschungsinfrastrukturen zu integrieren. Der Beitrag zeigt, wie Community-Geodaten automatisiert extrahiert, transformiert und in das Federated Knowledge Graph Ecosystem (Fischer u. a. 2025; Rossenova u. a. 2025; Thiery u. a. 2025) wie die Knowledge Graph Infrastructure (KGI4NFDI) und den Terminology Service (TS4NFDI) eingebunden werden können.
Die Datenextraktion erfolgt über Overpass Turbo (für OSM-Daten) sowie über SPARQL-Endpoints der Wikibase-Systeme (z. B. Wikidata, fuzzy-sl Wikibase, FactGrid). Anschließend werden die Datensätze mithilfe von Python-Skripten und der Bibliothek rdflib in RDF transformiert und innerhalb von Jupyter Notebooks dokumentiert, um eine reproduzierbare, FAIR4RS-konforme Prozesskette sicherzustellen. Die resultierenden RDF-Graphen werden gemäß CIDOC CRM und GeoSPARQL 1.1 modelliert und über die Material Cultural Heritage Crosswalk Ontology (MaCHeCO) mit dem NFDI4Objects Knowledge Graph interoperabel gemacht. Ergänzend erzeugt das SPARQLing Unicorn Toolkit (Homburg und Thiery 2024b; 2024a) eine HTML-Dokumentation der RDF-Ergebnisse, während das zugehörige SPARQLing Unicorn QGIS-Plugin die Re-Integration der Linked Data in QGIS ermöglicht. Anhand konkreter Use Cases wird das Vorgehen illustriert:
(1) Cultural Heritage Irish Sites – etwa der Ogham Stone Coumeenole North (Q126503090; node:5145413640), cgl. Thiery und Thiery (2023), via https://overpass-turbo.eu/s/2eM0 oder St. Lachtain’s Well (Q121840779; way:935503837), via https://overpass-turbo.eu/s/2eM2 – werden aus OSM und Wikidata abgerufen, semantisch harmonisiert und mit dem NFDI4Objects-Graph verknüpft.
(2) Geoscience Sites – beispielsweise die Franchthi Cave (node:1221172611; Q1441331) oder Crvena Stiljena Cave (node:10879170567; Q121418883), vgl. Thiery und Schenk (2023), sowie die Bohrkerne AU3, AU4 und DE3 (Schenk u. a. 2024) – werden als (duale) geologische und archäologische Entitäten modelliert.
(3) Zur Rekonstruktion georegionaler Kontexte (z. B. der Eifel-Region) werden Fluss- und Grenzrelationen wie der Rhein (123924), die Mosel (390416) oder der Parc naturel des Hautes-Fagnes – Eifel (10601027) integriert, um „fuzzy boundaries“ semantisch zu repräsentieren.
Der Beitrag zeigt, wie durch die Nutzung offener Standards, freier Software (Python, QGIS) und offener Schnittstellen (Overpass, SPARQL) eine durchgängige Pipeline von Community-Daten zu semantisch vernetzten, FAIRen Forschungsdaten entsteht. Damit wird eine Brücke zwischen der FOSSGIS-Community und den NFDI-Forschungsinfrastrukturen geschlagen – ein Beitrag zur nachhaltigen Nutzung, Verknüpfung und Langzeitverfügbarkeit offener Geodaten in Wissenschaft und Kulturerbe.
Referenzen
Fischer, Kristina, Anja Gerber, Thomas Koprucki, Andreas Noback, Marco Reidelbach, Torsten Schrade, und Florian Thiery. 2025. „Windows on Data: Federating Research Data with FAIR Digital Objects and Linked Open Data“. In Proceedings of the Conference on Research Data Infrastructure 2025, herausgegeben von York Sure-Vetter und Paul Groth, 7(5), 𝒬3, 7(5), 𝒬3. Aachen: Squirrel Papers. https://doi.org/10.5281/zenodo.16736221.
Homburg, Timo, und Florian Thiery. 2024a. „SPARQL Unicorn Ontology Documentation“. Squirrel Papers 6 (2): #2. https://doi.org/10.5281/zenodo.10780476.
———. 2024b. „sparqlunicorn/sparqlunicornGoesGIS-ontdoc: Version 0.17“. Squirrel Papers 6 (2): #2. https://doi.org/10.5281/zenodo.8190763.
Rossenova, Lozana, Fidan Limani, Carsten Fortmann-Grote, Angelika Kaplan, Florian Thiery, Atif Latif, Renat Shigapov, u. a. 2025. „How are NFDI consortia using Knowledge Graphs? An overview of common functions and challenges by the Working Group “Knowledge Graphs”“. In Proceedings of the Conference on Research Data Infrastructure 2025, herausgegeben von York Sure-Vetter und Groth Paul, 7(5), 𝒬5, 7(5), 𝒬5. Aachen: Squirrel Papers. https://doi.org/10.5281/zenodo.16736077.
Schenk, Fiona, Ulrich Hambach, Sarah Britzius, Daniel Veres, und Frank Sirocko. 2024. „A Cryptotephra Layer in Sediments of an Infilled Maar Lake from the Eifel (Germany): First Evidence of Campanian Ignimbrite Ash Airfall in Central Europe“. Quaternary 7 (2): 17. https://doi.org/10.3390/quat7020017.
Schmidt, Sophie C., Florian Thiery, und Martina Trognitz. 2022. „Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata“. Digital 2 (3): 333–64. https://doi.org/10.3390/digital2030019.
Thiery, Florian, Lozana Rossenova, Daniel Mietchen, Timo Homburg, und Peter Thiery. 2025. „Distributed Research Data Knowledge Graphs - Challenges of federated queries using the Wikiverse and OpenStreetMap within the NFDI Knowledge Graph Ecosystem“. In Proceedings of the Conference on Research Data Infrastructure 2025, herausgegeben von York Sure-Vetter und Paul Groth, 7(5), 𝒬2, 7(5), 𝒬2. Aachen: Squirrel Papers. https://doi.org/10.5281/zenodo.16736047.
Thiery, Florian, und Fiona Schenk. 2023. „Modelling of Uncertainty in Geo Sciences Sites“. Squirrel Papers 5 (1): #4. https://doi.org/10.5281/zenodo.10255259.
Thiery, Florian, und Peter Thiery. 2023. „Linked Open Ogham. How to publish and interlink various Ogham Data?“ Archeologia e Calcolatori 34 (1): 105–14. https://doi.org/10.19282/ac.34.1.2023.12."""@de ;
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    dct:publisher <https://w3id.org/squirrelpapers/org/research-squirrel-engineers> ;
    dct:title "Von OpenStreetMap zu semantisch vernetzten Wissensgraphen: Integration von Community-Geodaten in das Federated Knowledge Graph Ecosystem"@de ;
    dct:type sqpt:conference-paper,
        sqpt:presentation ;
    bibo:abstract """Abstract
Community-basierte Geodatenplattformen wie OpenStreetMap (OSM), Wikidata (Schmidt, Thiery, und Trognitz 2022) und weitere Wikibase-Instanzen stellen eine der wertvollsten frei verfügbaren Quellen raumbezogener Informationen dar. In der wissenschaftlichen Nutzung, insbesondere im Bereich des Cultural Heritage und der Geowissenschaften, besteht jedoch weiterhin die Herausforderung, diese heterogenen Daten strukturiert, semantisch konsistent und maschinenlesbar in Forschungsinfrastrukturen zu integrieren. Der Beitrag zeigt, wie Community-Geodaten automatisiert extrahiert, transformiert und in das Federated Knowledge Graph Ecosystem (Fischer u. a. 2025; Rossenova u. a. 2025; Thiery u. a. 2025) wie die Knowledge Graph Infrastructure (KGI4NFDI) und den Terminology Service (TS4NFDI) eingebunden werden können.
Die Datenextraktion erfolgt über Overpass Turbo (für OSM-Daten) sowie über SPARQL-Endpoints der Wikibase-Systeme (z. B. Wikidata, fuzzy-sl Wikibase, FactGrid). Anschließend werden die Datensätze mithilfe von Python-Skripten und der Bibliothek rdflib in RDF transformiert und innerhalb von Jupyter Notebooks dokumentiert, um eine reproduzierbare, FAIR4RS-konforme Prozesskette sicherzustellen. Die resultierenden RDF-Graphen werden gemäß CIDOC CRM und GeoSPARQL 1.1 modelliert und über die Material Cultural Heritage Crosswalk Ontology (MaCHeCO) mit dem NFDI4Objects Knowledge Graph interoperabel gemacht. Ergänzend erzeugt das SPARQLing Unicorn Toolkit (Homburg und Thiery 2024b; 2024a) eine HTML-Dokumentation der RDF-Ergebnisse, während das zugehörige SPARQLing Unicorn QGIS-Plugin die Re-Integration der Linked Data in QGIS ermöglicht. Anhand konkreter Use Cases wird das Vorgehen illustriert:
(1) Cultural Heritage Irish Sites – etwa der Ogham Stone Coumeenole North (Q126503090; node:5145413640), cgl. Thiery und Thiery (2023), via https://overpass-turbo.eu/s/2eM0 oder St. Lachtain’s Well (Q121840779; way:935503837), via https://overpass-turbo.eu/s/2eM2 – werden aus OSM und Wikidata abgerufen, semantisch harmonisiert und mit dem NFDI4Objects-Graph verknüpft.
(2) Geoscience Sites – beispielsweise die Franchthi Cave (node:1221172611; Q1441331) oder Crvena Stiljena Cave (node:10879170567; Q121418883), vgl. Thiery und Schenk (2023), sowie die Bohrkerne AU3, AU4 und DE3 (Schenk u. a. 2024) – werden als (duale) geologische und archäologische Entitäten modelliert.
(3) Zur Rekonstruktion georegionaler Kontexte (z. B. der Eifel-Region) werden Fluss- und Grenzrelationen wie der Rhein (123924), die Mosel (390416) oder der Parc naturel des Hautes-Fagnes – Eifel (10601027) integriert, um „fuzzy boundaries“ semantisch zu repräsentieren.
Der Beitrag zeigt, wie durch die Nutzung offener Standards, freier Software (Python, QGIS) und offener Schnittstellen (Overpass, SPARQL) eine durchgängige Pipeline von Community-Daten zu semantisch vernetzten, FAIRen Forschungsdaten entsteht. Damit wird eine Brücke zwischen der FOSSGIS-Community und den NFDI-Forschungsinfrastrukturen geschlagen – ein Beitrag zur nachhaltigen Nutzung, Verknüpfung und Langzeitverfügbarkeit offener Geodaten in Wissenschaft und Kulturerbe.
Referenzen
Fischer, Kristina, Anja Gerber, Thomas Koprucki, Andreas Noback, Marco Reidelbach, Torsten Schrade, und Florian Thiery. 2025. „Windows on Data: Federating Research Data with FAIR Digital Objects and Linked Open Data“. In Proceedings of the Conference on Research Data Infrastructure 2025, herausgegeben von York Sure-Vetter und Paul Groth, 7(5), 𝒬3, 7(5), 𝒬3. Aachen: Squirrel Papers. https://doi.org/10.5281/zenodo.16736221.
Homburg, Timo, und Florian Thiery. 2024a. „SPARQL Unicorn Ontology Documentation“. Squirrel Papers 6 (2): #2. https://doi.org/10.5281/zenodo.10780476.
———. 2024b. „sparqlunicorn/sparqlunicornGoesGIS-ontdoc: Version 0.17“. Squirrel Papers 6 (2): #2. https://doi.org/10.5281/zenodo.8190763.
Rossenova, Lozana, Fidan Limani, Carsten Fortmann-Grote, Angelika Kaplan, Florian Thiery, Atif Latif, Renat Shigapov, u. a. 2025. „How are NFDI consortia using Knowledge Graphs? An overview of common functions and challenges by the Working Group “Knowledge Graphs”“. In Proceedings of the Conference on Research Data Infrastructure 2025, herausgegeben von York Sure-Vetter und Groth Paul, 7(5), 𝒬5, 7(5), 𝒬5. Aachen: Squirrel Papers. https://doi.org/10.5281/zenodo.16736077.
Schenk, Fiona, Ulrich Hambach, Sarah Britzius, Daniel Veres, und Frank Sirocko. 2024. „A Cryptotephra Layer in Sediments of an Infilled Maar Lake from the Eifel (Germany): First Evidence of Campanian Ignimbrite Ash Airfall in Central Europe“. Quaternary 7 (2): 17. https://doi.org/10.3390/quat7020017.
Schmidt, Sophie C., Florian Thiery, und Martina Trognitz. 2022. „Practices of Linked Open Data in Archaeology and Their Realisation in Wikidata“. Digital 2 (3): 333–64. https://doi.org/10.3390/digital2030019.
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    owl:sameAs <https://orcid.org/0000-0002-7613-4123> ;
    foaf:familyName "Voß" ;
    foaf:givenName "Jakob" ;
    foaf:name "Jakob Voß" ;
    schema:identifier <https://orcid.org/0000-0002-7613-4123> .

<https://w3id.org/squirrelpapers/person/0000-0002-8155-7702> a foaf:Person,
        schema:Person ;
    rdfs:label "Dirk Seidensticker" ;
    owl:sameAs <https://orcid.org/0000-0002-8155-7702> ;
    foaf:familyName "Seidensticker" ;
    foaf:givenName "Dirk" ;
    foaf:name "Dirk Seidensticker" ;
    schema:identifier <https://orcid.org/0000-0002-8155-7702> .

<https://w3id.org/squirrelpapers/person/0000-0002-9695-0240> a foaf:Person,
        schema:Person ;
    rdfs:label "Valeria Vitale" ;
    owl:sameAs <https://orcid.org/0000-0002-9695-0240> ;
    foaf:familyName "Vitale" ;
    foaf:givenName "Valeria" ;
    foaf:name "Valeria Vitale" ;
    schema:identifier <https://orcid.org/0000-0002-9695-0240> .

<https://w3id.org/squirrelpapers/person/0000-0002-9823-5507> a foaf:Person,
        schema:Person ;
    rdfs:label "Nicole High-Steskal" ;
    owl:sameAs <https://orcid.org/0000-0002-9823-5507> ;
    foaf:familyName "High-Steskal" ;
    foaf:givenName "Nicole" ;
    foaf:name "Nicole High-Steskal" ;
    schema:identifier <https://orcid.org/0000-0002-9823-5507> .

<https://w3id.org/squirrelpapers/person/0000-0003-1349-2131> a foaf:Person,
        schema:Person ;
    rdfs:label "Frank von Hagel" ;
    owl:sameAs <https://orcid.org/0000-0003-1349-2131> ;
    foaf:familyName "von Hagel" ;
    foaf:givenName "Frank" ;
    foaf:name "Frank von Hagel" ;
    schema:identifier <https://orcid.org/0000-0003-1349-2131> .

<https://w3id.org/squirrelpapers/person/0000-0003-3085-3031> a foaf:Person,
        schema:Person ;
    rdfs:label "Atif Latif" ;
    owl:sameAs <https://orcid.org/0000-0003-3085-3031> ;
    foaf:familyName "Latif" ;
    foaf:givenName "Atif" ;
    foaf:name "Atif Latif" ;
    schema:identifier <https://orcid.org/0000-0003-3085-3031> .

<https://w3id.org/squirrelpapers/person/0000-0003-3986-0510> a foaf:Person,
        schema:Person ;
    rdfs:label "Leyla Jael Castro" ;
    owl:sameAs <https://orcid.org/0000-0003-3986-0510> ;
    foaf:familyName "Castro" ;
    foaf:givenName "Leyla Jael" ;
    foaf:name "Leyla Jael Castro" ;
    schema:identifier <https://orcid.org/0000-0003-3986-0510> .

<https://w3id.org/squirrelpapers/person/0009-0002-8700-3505> a foaf:Person,
        schema:Person ;
    rdfs:label "Sebastian Gampe" ;
    owl:sameAs <https://orcid.org/0009-0002-8700-3505> ;
    foaf:familyName "Gampe" ;
    foaf:givenName "Sebastian" ;
    foaf:name "Sebastian Gampe" ;
    schema:identifier <https://orcid.org/0009-0002-8700-3505> .

<https://w3id.org/squirrelpapers/person/0009-0006-0497-2327> a foaf:Person,
        schema:Person ;
    rdfs:label "Beate Hetényi" ;
    owl:sameAs <https://orcid.org/0009-0006-0497-2327> ;
    foaf:familyName "Hetényi" ;
    foaf:givenName "Beate" ;
    foaf:name "Beate Hetényi" ;
    schema:identifier <https://orcid.org/0009-0006-0497-2327> .

<https://w3id.org/squirrelpapers/person/0009-0009-9101-5833> a foaf:Person,
        schema:Person ;
    rdfs:label "Angelika Kaplan" ;
    owl:sameAs <https://orcid.org/0009-0009-9101-5833> ;
    foaf:familyName "Kaplan" ;
    foaf:givenName "Angelika" ;
    foaf:name "Angelika Kaplan" ;
    schema:identifier <https://orcid.org/0009-0009-9101-5833> .

<https://w3id.org/squirrelpapers/person/behrens-sarah> a foaf:Person,
        schema:Person ;
    rdfs:label "Sarah Behrens" ;
    foaf:familyName "Behrens" ;
    foaf:givenName "Sarah" ;
    foaf:name "Sarah Behrens" .

<https://w3id.org/squirrelpapers/person/boochs-frank> a foaf:Person,
        schema:Person ;
    rdfs:label "Frank Boochs" ;
    foaf:familyName "Boochs" ;
    foaf:givenName "Frank" ;
    foaf:name "Frank Boochs" .

<https://w3id.org/squirrelpapers/person/colavizza-giovanni> a foaf:Person,
        schema:Person ;
    rdfs:label "Giovanni Colavizza" ;
    foaf:familyName "Colavizza" ;
    foaf:givenName "Giovanni" ;
    foaf:name "Giovanni Colavizza" .

<https://w3id.org/squirrelpapers/person/cuy-sebastian> a foaf:Person,
        schema:Person ;
    rdfs:label "Sebastian Cuy" ;
    foaf:familyName "Cuy" ;
    foaf:givenName "Sebastian" ;
    foaf:name "Sebastian Cuy" .

<https://w3id.org/squirrelpapers/person/dufner-matthias> a foaf:Person,
        schema:Person ;
    rdfs:label "Matthias Dufner" ;
    foaf:familyName "Dufner" ;
    foaf:givenName "Matthias" ;
    foaf:name "Matthias Dufner" .

<https://w3id.org/squirrelpapers/person/godeke-anna-katharina> a foaf:Person,
        schema:Person ;
    rdfs:label "Anna-Katharina Gödeke" ;
    foaf:familyName "Gödeke" ;
    foaf:givenName "Anna-Katharina" ;
    foaf:name "Anna-Katharina Gödeke" .

<https://w3id.org/squirrelpapers/person/hammitzsch-martin> a foaf:Person,
        schema:Person ;
    rdfs:label "Martin Hammitzsch" ;
    foaf:familyName "Hammitzsch" ;
    foaf:givenName "Martin" ;
    foaf:name "Martin Hammitzsch" .

<https://w3id.org/squirrelpapers/person/heinz-guido> a foaf:Person,
        schema:Person ;
    rdfs:label "Guido Heinz" ;
    foaf:familyName "Heinz" ;
    foaf:givenName "Guido" ;
    foaf:name "Guido Heinz" .

<https://w3id.org/squirrelpapers/person/kallas-nathalie> a foaf:Person,
        schema:Person ;
    rdfs:label "Nathalie Kallas" ;
    foaf:familyName "Kallas" ;
    foaf:givenName "Nathalie" ;
    foaf:name "Nathalie Kallas" .

<https://w3id.org/squirrelpapers/person/kiesling-j-b> a foaf:Person,
        schema:Person ;
    rdfs:label "J.B. Kiesling" ;
    foaf:familyName "Kiesling" ;
    foaf:givenName "J.B." ;
    foaf:name "J.B. Kiesling" .

<https://w3id.org/squirrelpapers/person/klammt-anne> a foaf:Person,
        schema:Person ;
    rdfs:label "Anne Klammt" ;
    foaf:familyName "Klammt" ;
    foaf:givenName "Anne" ;
    foaf:name "Anne Klammt" .

<https://w3id.org/squirrelpapers/person/kunz-axel> a foaf:Person,
        schema:Person ;
    rdfs:label "Axel Kunz" ;
    foaf:familyName "Kunz" ;
    foaf:givenName "Axel" ;
    foaf:name "Axel Kunz" .

<https://w3id.org/squirrelpapers/person/papazoi-elli> a foaf:Person,
        schema:Person ;
    rdfs:label "Elli Papazoi" ;
    foaf:familyName "Papazoi" ;
    foaf:givenName "Elli" ;
    foaf:name "Elli Papazoi" .

<https://w3id.org/squirrelpapers/person/quintana-miguel-angel-dilena> a foaf:Person,
        schema:Person ;
    rdfs:label "Miguel Angel Dilena Quintana" ;
    foaf:familyName "Quintana" ;
    foaf:givenName "Miguel Angel Dilena" ;
    foaf:name "Miguel Angel Dilena Quintana" .

<https://w3id.org/squirrelpapers/person/rutenik-tobias-arera> a foaf:Person,
        schema:Person ;
    rdfs:label "Tobias-Arera Rütenik" ;
    foaf:familyName "Rütenik" ;
    foaf:givenName "Tobias-Arera" ;
    foaf:name "Tobias-Arera Rütenik" .

<https://w3id.org/squirrelpapers/person/schmidle-wolfgang> a foaf:Person,
        schema:Person ;
    rdfs:label "Wolfgang Schmidle" ;
    foaf:familyName "Schmidle" ;
    foaf:givenName "Wolfgang" ;
    foaf:name "Wolfgang Schmidle" .

<https://w3id.org/squirrelpapers/person/schneider-nico> a foaf:Person,
        schema:Person ;
    rdfs:label "Nico Schneider" ;
    foaf:familyName "Schneider" ;
    foaf:givenName "Nico" ;
    foaf:name "Nico Schneider" .

<https://w3id.org/squirrelpapers/person/schwarzkopf-christopher> a foaf:Person,
        schema:Person ;
    rdfs:label "Christopher Schwarzkopf" ;
    foaf:familyName "Schwarzkopf" ;
    foaf:givenName "Christopher" ;
    foaf:name "Christopher Schwarzkopf" .

<https://w3id.org/squirrelpapers/person/skole-john> a foaf:Person,
        schema:Person ;
    rdfs:label "John Skole" ;
    foaf:familyName "Skole" ;
    foaf:givenName "John" ;
    foaf:name "John Skole" .

<https://w3id.org/squirrelpapers/person/taani-rania> a foaf:Person,
        schema:Person ;
    rdfs:label "Rania Taani" ;
    foaf:familyName "Taani" ;
    foaf:givenName "Rania" ;
    foaf:name "Rania Taani" .

<https://w3id.org/squirrelpapers/person/thiery-susanne> a foaf:Person,
        schema:Person ;
    rdfs:label "Susanne Thiery" ;
    foaf:familyName "Thiery" ;
    foaf:givenName "Susanne" ;
    foaf:name "Susanne Thiery" .

<https://w3id.org/squirrelpapers/person/unold-martin> a foaf:Person,
        schema:Person ;
    rdfs:label "Martin Unold" ;
    foaf:familyName "Unold" ;
    foaf:givenName "Martin" ;
    foaf:name "Martin Unold" .

<https://w3id.org/squirrelpapers/person/weisser-bernhard> a foaf:Person,
        schema:Person ;
    rdfs:label "Bernhard Weisser" ;
    foaf:familyName "Weisser" ;
    foaf:givenName "Bernhard" ;
    foaf:name "Bernhard Weisser" .

<https://w3id.org/squirrelpapers/place/alte-universitat-heidelberg> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Alte Universität" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/alte-universitat-heidelberg#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/alte-universitat-heidelberg#geometry> ;
    owl:sameAs wd:Q436344 ;
    dcat:centroid "POINT(8.70611 49.4111)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Alte Universität Heidelberg" ;
    schema:latitude 4.94111e+01 ;
    schema:longitude 8.70611e+00 .

<https://w3id.org/squirrelpapers/place/alte-universitat-heidelberg#geometry> a geo:Geometry ;
    geo:asWKT "POINT(8.70611 49.4111)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/aula-convegni-cast-center-for-advanced-studies> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Chieti" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/aula-convegni-cast-center-for-advanced-studies#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/aula-convegni-cast-center-for-advanced-studies#geometry> ;
    owl:sameAs wd:Q13138 ;
    dcat:centroid "POINT(14.167428 42.351114)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q38 ;
    schema:description "Aula Convegni CAST - Center for Advanced Studies and Technology Campus Universitario Chieti Scalo, Chieti, Italy" ;
    schema:latitude 4.235111e+01 ;
    schema:longitude 1.416743e+01 .

<https://w3id.org/squirrelpapers/place/aula-convegni-cast-center-for-advanced-studies#geometry> a geo:Geometry ;
    geo:asWKT "POINT(14.167428 42.351114)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/bordeaux-france> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Bordeaux" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/bordeaux-france#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/bordeaux-france#geometry> ;
    owl:sameAs wd:Q1479 ;
    dcat:centroid "POINT(-0.579444 44.837778)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q142 ;
    schema:description "Bordeaux, France" ;
    schema:latitude 4.483778e+01 ;
    schema:longitude -5.79444e-01 .

<https://w3id.org/squirrelpapers/place/bordeaux-france#geometry> a geo:Geometry ;
    geo:asWKT "POINT(-0.579444 44.837778)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/bournemouth-england> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Bournemouth" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/bournemouth-england#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/bournemouth-england#geometry> ;
    owl:sameAs wd:Q170478 ;
    dcat:centroid "POINT(-1.88 50.72)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q145 ;
    schema:description "Bournemouth, England" ;
    schema:latitude 5.072e+01 ;
    schema:longitude -1.88e+00 .

<https://w3id.org/squirrelpapers/place/bournemouth-england#geometry> a geo:Geometry ;
    geo:asWKT "POINT(-1.88 50.72)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/dbm-bochum-germany> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "German Mining Museum" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/dbm-bochum-germany#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/dbm-bochum-germany#geometry> ;
    owl:sameAs wd:Q896952 ;
    dcat:centroid "POINT(7.21722 51.4892)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "DBM Bochum, Germany" ;
    schema:latitude 5.14892e+01 ;
    schema:longitude 7.21722e+00 .

<https://w3id.org/squirrelpapers/place/dbm-bochum-germany#geometry> a geo:Geometry ;
    geo:asWKT "POINT(7.21722 51.4892)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/deutsches-bergbau-museum-bochum-germany> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "German Mining Museum" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/deutsches-bergbau-museum-bochum-germany#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/deutsches-bergbau-museum-bochum-germany#geometry> ;
    owl:sameAs wd:Q896952 ;
    dcat:centroid "POINT(7.21722 51.4892)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Deutsches Bergbau-Museum, Bochum, Germany" ;
    schema:latitude 5.14892e+01 ;
    schema:longitude 7.21722e+00 .

<https://w3id.org/squirrelpapers/place/deutsches-bergbau-museum-bochum-germany#geometry> a geo:Geometry ;
    geo:asWKT "POINT(7.21722 51.4892)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/dresden-germany> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Dresden" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/dresden-germany#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/dresden-germany#geometry> ;
    owl:sameAs wd:Q1731 ;
    dcat:centroid "POINT(13.738144 51.049329)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Dresden, Germany" ;
    schema:latitude 5.104933e+01 ;
    schema:longitude 1.373814e+01 .

<https://w3id.org/squirrelpapers/place/dresden-germany#geometry> a geo:Geometry ;
    geo:asWKT "POINT(13.738144 51.049329)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/edinburgh-scotland-uk> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Edinburgh" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/edinburgh-scotland-uk#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/edinburgh-scotland-uk#geometry> ;
    owl:sameAs wd:Q23436 ;
    dcat:centroid "POINT(-3.189167 55.953333)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q145 ;
    schema:description "Edinburgh, Scotland, UK" ;
    schema:latitude 5.595333e+01 ;
    schema:longitude -3.189167e+00 .

<https://w3id.org/squirrelpapers/place/edinburgh-scotland-uk#geometry> a geo:Geometry ;
    geo:asWKT "POINT(-3.189167 55.953333)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/fort-collins-usa> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Fort Collins" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/fort-collins-usa#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/fort-collins-usa#geometry> ;
    owl:sameAs wd:Q490732 ;
    dcat:centroid "POINT(-105.083333 40.566667)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q30 ;
    schema:description "Fort Collins, USA" ;
    schema:latitude 4.056667e+01 ;
    schema:longitude -1.050833e+02 .

<https://w3id.org/squirrelpapers/place/fort-collins-usa#geometry> a geo:Geometry ;
    geo:asWKT "POINT(-105.083333 40.566667)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/fraunhofer-fokus-fraunhofer-institute-for-open> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Berlin" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/fraunhofer-fokus-fraunhofer-institute-for-open#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/fraunhofer-fokus-fraunhofer-institute-for-open#geometry> ;
    owl:sameAs wd:Q64 ;
    dcat:centroid "POINT(13.383333 52.516667)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Fraunhofer FOKUS – Fraunhofer Institute for Open Communication Systems, Berlin, Germany" ;
    schema:latitude 5.251667e+01 ;
    schema:longitude 1.338333e+01 .

<https://w3id.org/squirrelpapers/place/fraunhofer-fokus-fraunhofer-institute-for-open#geometry> a geo:Geometry ;
    geo:asWKT "POINT(13.383333 52.516667)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/georg-august-universitat-gottingen-germany> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "University of Göttingen" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/georg-august-universitat-gottingen-germany#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/georg-august-universitat-gottingen-germany#geometry> ;
    owl:sameAs wd:Q152838 ;
    dcat:centroid "POINT(9.93792 51.53398)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Georg-August-Universität Göttingen, Germany" ;
    schema:latitude 5.153398e+01 ;
    schema:longitude 9.93792e+00 .

<https://w3id.org/squirrelpapers/place/georg-august-universitat-gottingen-germany#geometry> a geo:Geometry ;
    geo:asWKT "POINT(9.93792 51.53398)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/gesellschaft-der-freunde-des-romisch> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Mainz" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/gesellschaft-der-freunde-des-romisch#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/gesellschaft-der-freunde-des-romisch#geometry> ;
    owl:sameAs wd:Q1720 ;
    dcat:centroid "POINT(8.273611 49.999444)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Gesellschaft der Freunde des Römisch-Germanischen Zentralmuseums in Mainz e.V. Mainz, Germany" ;
    schema:latitude 4.999944e+01 ;
    schema:longitude 8.273611e+00 .

<https://w3id.org/squirrelpapers/place/gesellschaft-der-freunde-des-romisch#geometry> a geo:Geometry ;
    geo:asWKT "POINT(8.273611 49.999444)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/graz-austria#geometry> a geo:Geometry ;
    geo:asWKT "POINT(15.438611 47.070833)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/hamburg-germany> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Hamburg" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/hamburg-germany#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/hamburg-germany#geometry> ;
    owl:sameAs wd:Q1055 ;
    dcat:centroid "POINT(10.0 53.55)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Hamburg, Germany" ;
    schema:latitude 5.355e+01 ;
    schema:longitude 1e+01 .

<https://w3id.org/squirrelpapers/place/hamburg-germany#geometry> a geo:Geometry ;
    geo:asWKT "POINT(10.0 53.55)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/heidelberg-germany> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Heidelberg" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/heidelberg-germany#geometry> ;
    geo:hasGeometry <https://w3id.org/squirrelpapers/place/heidelberg-germany#geometry> ;
    owl:sameAs wd:Q2966 ;
    dcat:centroid "POINT(8.694724 49.409358)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Heidelberg, Germany" ;
    schema:latitude 4.940936e+01 ;
    schema:longitude 8.694724e+00 .

<https://w3id.org/squirrelpapers/place/heidelberg-germany#geometry> a geo:Geometry ;
    geo:asWKT "POINT(8.694724 49.409358)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/historisches-seminar-der-universitat-munster> a dct:Location,
        geo:Feature,
        schema:Place ;
    rdfs:label "Münster" ;
    geo:hasDefaultGeometry <https://w3id.org/squirrelpapers/place/historisches-seminar-der-universitat-munster#geometry> ;
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/hochschule-fur-technik-und-wirtschaft-dresden#geometry> ;
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/jgu-mainz-germany#geometry> ;
    owl:sameAs wd:Q1720 ;
    dcat:centroid "POINT(8.273611 49.999444)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "JGU, Mainz Germany" ;
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    schema:longitude 8.273611e+00 .

<https://w3id.org/squirrelpapers/place/jgu-mainz-germany#geometry> a geo:Geometry ;
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/johannes-gutenberg-universitat-mainz#geometry> ;
    owl:sameAs wd:Q161982 ;
    dcat:centroid "POINT(8.241667 49.993056)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Johannes Gutenberg Universität Mainz, Fachbereich 07, Klassische Archäologie" ;
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<https://w3id.org/squirrelpapers/place/johannes-gutenberg-universitat-mainz#geometry> a geo:Geometry ;
    geo:asWKT "POINT(8.241667 49.993056)"^^geo:wktLiteral .

<https://w3id.org/squirrelpapers/place/julius-maximilians-universitat-wurzburg> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/julius-maximilians-universitat-wurzburg#geometry> ;
    owl:sameAs wd:Q161976 ;
    dcat:centroid "POINT(9.935278 49.788056)"^^geo:wktLiteral ;
    schema:addressCountry wd:Q183 ;
    schema:description "Julius-Maximilians-Universität Würzburg" ;
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<https://w3id.org/squirrelpapers/place/julius-maximilians-universitat-wurzburg#geometry> a geo:Geometry ;
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<https://w3id.org/squirrelpapers/place/kit-karlsruhe> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/kit-karlsruhe#geometry> ;
    owl:sameAs wd:Q1040 ;
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    schema:addressCountry wd:Q183 ;
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    schema:latitude 4.901667e+01 ;
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<https://w3id.org/squirrelpapers/place/krakow-poland> a dct:Location,
        geo:Feature,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/krakow-poland#geometry> ;
    owl:sameAs wd:Q31487 ;
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<https://w3id.org/squirrelpapers/place/leibniz-zentrum-fur-archaologie-leiza-mainz> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/leibniz-zentrum-fur-archaologie-leiza-mainz#geometry> ;
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<https://w3id.org/squirrelpapers/place/leibnizhaus-hannover-germany> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/leibnizhaus-hannover-germany#geometry> ;
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<https://w3id.org/squirrelpapers/place/mainz-germany#geometry> a geo:Geometry ;
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<https://w3id.org/squirrelpapers/place/munich-germany#geometry> a geo:Geometry ;
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/oslo-norway#geometry> ;
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<https://w3id.org/squirrelpapers/place/polytechnic-university-of-valencia-universitat> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/potsdam-germany#geometry> ;
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<https://w3id.org/squirrelpapers/place/rgk-frankfurt-am-mainz> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/rgk-frankfurt-am-mainz#geometry> ;
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<https://w3id.org/squirrelpapers/place/rwth-aachen-c-a-r-l-aachen-germany> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/rwth-aachen-c-a-r-l-aachen-germany#geometry> ;
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<https://w3id.org/squirrelpapers/place/sapienza-university-of-rome-italy> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/sapienza-university-of-rome-italy#geometry> ;
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/stuttgart-germany#geometry> ;
    owl:sameAs wd:Q1022 ;
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<https://w3id.org/squirrelpapers/place/technische-universitat-hamburg-tuhh> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/technische-universitat-hamburg-tuhh#geometry> ;
    owl:sameAs wd:Q1060 ;
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<https://w3id.org/squirrelpapers/place/universidad-de-barcelona-spain> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/universidad-de-barcelona-spain#geometry> ;
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<https://w3id.org/squirrelpapers/place/universita-di-torino-italy> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/universita-di-torino-italy#geometry> ;
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<https://w3id.org/squirrelpapers/place/universitat-zu-koln-koln-deutschland> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/universitat-zu-koln-koln-deutschland#geometry> ;
    owl:sameAs wd:Q54096 ;
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    schema:addressCountry wd:Q183 ;
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<https://w3id.org/squirrelpapers/place/universitat-zu-koln-koln-deutschland#geometry> a geo:Geometry ;
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<https://w3id.org/squirrelpapers/place/university-college-cork-ucc-cork-ireland> a dct:Location,
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    geo:hasGeometry <https://w3id.org/squirrelpapers/place/university-college-cork-ucc-cork-ireland#geometry> ;
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<https://w3id.org/squirrelpapers/place/university-of-glasgow-glasgow-united-kingdom> a dct:Location,
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    owl:sameAs wd:Q192775 ;
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