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Open Science Graphs Must Interoperate!

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ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium (TPDL 2020, ADBIS 2020)

Abstract

Open Science Graphs (OSGs) are Scientific Knowledge Graphs whose intent is to improve the overall FAIRness of science, by enabling open access to graph representations of metadata about people, artefacts, institutions involved in the research lifecycle, as well as the relationships between these entities, in order to support stakeholder needs, such as discovery, reuse, reproducibility, statistics, trends, monitoring, impact, validation, and assessment. The represented information may span across entities such as research artefacts (e.g. publications, data, software, samples, instruments) and items of their content (e.g. statistical hypothesis tests reported in publications), research organisations, researchers, services, projects, and funders. OSGs include relationships between such entities and sometimes formalised (semantic) concepts characterising them, such as machine-readable concept descriptions for advanced discoverability, interoperability, and reuse. OSGs are generally valuable individually, but would greatly benefit from information exchange across their collections, thereby improving their efficacy to serve stakeholder needs. They could, therefore, reuse and exploit the data aggregation and added value that characterise each OSG, decentralising the effort and capitalising on synergies, as no one-size-fits-all solution exists. The RDA IG on Open Science Graphs for FAIR Data is investigating the motivation and challenges underpinning the realisation of an Interoperability Framework for OSGs. This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) the definition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.

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Notes

  1. 1.

    ORCID, https://orcid.org.

  2. 2.

    ROR, https://ror.org.

  3. 3.

    GRID, https://grid.ac.

  4. 4.

    Re3data, http://re3data.org.

  5. 5.

    The Human Brain Project, https://www.humanbrainproject.eu.

  6. 6.

    Springer Nature SciGraph, https://scigraph.springernature.com.

  7. 7.

    CERIF, https://www.eurocris.org/cerif/main-features-cerif.

  8. 8.

    RDA Interest Group on Open Science Graphs for FAIR Data, https://rd-alliance.org/groups/open-science-graphs-fair-data-ig.

  9. 9.

    DataCite, https://datacite.org.

  10. 10.

    GraphQL, https://graphql.org.

  11. 11.

    DataCite API, https://api.datacite.org/graphql.

  12. 12.

    The OpenAIRE project, https://www.openaire.eu.

  13. 13.

    EOSC, https://www.eosc-portal.eu.

  14. 14.

    Access to the OpenAIRE Research Graph, http://develop.openaire.eu.

  15. 15.

    ORKG, http://orkg.org.

  16. 16.

    Scholexplorer, http://scholexplorer.openaire.eu.

  17. 17.

    Scholexplorer dump, https://zenodo.org/record/3541646.

  18. 18.

    RDA IG on Global Open Research Commons, https://www.rd-alliance.org/groups/global-open-research-commons-ig.

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Acknowledgements

This work was co-funded by the EU projects OpenAIRE-Advance (Grant agreement ID: 777541), FREYA (Grant agreement ID: 777523), ScienceGRAPH (Grant agreement ID: 819536), and the TIB Leibniz Information Centre for Science and Technology.

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Correspondence to Andrea Mannocci .

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Aryani, A., Fenner, M., Manghi, P., Mannocci, A., Stocker, M. (2020). Open Science Graphs Must Interoperate!. In: Bellatreche, L., et al. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL ADBIS 2020 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-030-55814-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-55814-7_16

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