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search.filters.applied.f.access: openAccess × Author: Stocker, Markus × DDC: 020 × Type: Article × Type: Text ×

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    FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources
    (Cambridge, MA : MIT Press, 2020) Sustkova, Hana Pergl; Hettne, Kristina Maria; Wittenburg, Peter; Jacobsen, Annika; Kuhn, Tobias; Pergl, Robert; Slifka, Jan; McQuilton, Peter; Magagna, Barbara; Sansone, Susanna-Assunta; Stocker, Markus; Imming, Melanie; Lannom, Larry; Musen, Mark; Schultes, Erik
    The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.
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    Persistent Identification Of Instruments
    (Ithaka : Cornell University, 2020) Stocker, Markus; Darroch, Louise; Krahl, Rolf; Habermann, Ted; Devaraju, Anusuriya; Schwardmann, Ulrich; D'Onofrio, Claudio; Häggström, Ingemar
    Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
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    ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph
    (Sofia : Pensoft, 2021) Auer, Sören; Stocker, Markus; Vogt, Lars; Fraumann, Grischa; Garatzogianni, Alexandra
    This document is an edited version of the original funding proposal entitled 'ORKG: Facilitating the Transfer of Research Results with the Open Research Knowledge Graph' that was submitted to the European Research Council (ERC) Proof of Concept (PoC) Grant in September 2020 (https://erc.europa.eu/funding/proof-concept). The proposal was evaluated by five reviewers and has been placed after the evaluations on the reserve list. The main document of the original proposal did not contain an abstract.