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FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

Hana Pergl SustkovaHana Pergl Sustkova is the Operations Officer at the GO FAIR International Support and Coordination Office (GFISCO). Hana supports key activities of the GFISCO including the FAIR Convergence Matrix coalition coordination. Prior to joining the GFISCO, Hana worked as project manager for the ELIXIR research infrastructure, which marked her transition from an international corporation to the academic sphere. Her background is business administration and managementKristina HettneCentre for Digital Scholarship, Leiden University Libraries, Leiden, The NetherlandsPeter WittenburgMax Planck Computing and Data Facility, Gießenbachstraße 2, 85748 Garching, GermanyAnnika JacobsenLeiden University Medical Center, Leiden, 2333 ZA, The NetherlandsTobias KuhnDepartment of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 11051081 HV Amsterdam, The NetherlandsRobert PerglCzech Technical University in Prague, Faculty of Information Technology (FIT CTU), 160 00 Prague 6, Czech RepublicJan SlifkaJan Slifka got his Master's degree in Software Engineering at the Czech Technical University in Prague. He is now a PhD student there, focusing on evolvable systems and functional programming. He acquired hands-on experience from the industry while working as a senior developer for several startups with a wide range of technologies. He is also a member of ELIXIR-CZ Interoperability Platform where he works as a chief User Interface developer on the Data Stewardship Wizard. He collaborates with GO FAIR, building the FAIR Funding EcosystemPeter McQuiltonPeter McQuilton holds a 1st class BSc (Hons) degree in Genetics from the University of Leeds (2000) and a PhD (2004) in Drosophila neurodevelopment from the University of Cambridge. Peter has spent over 15 years working in the fields of bioinformatics, biocuration and data wrangling, first as a genetic literature curator at FlyBase, the premier database on drosophila genes and genomes, and then as part of the Data Readiness Group at the Oxford e-Research Centre. As part of the Data Readiness Group, Peter leads the FAIRsharing project. FAIRsharing is a manually curated, searchable portal of interlinked standards, databases and policies, from all domainsBarbara MagagnaEnvironment Agency Austria, A-1090 Vienna, AustriaSusanna‐Assunta SansoneSusanna-Assunta Sansone is an Associate Director, Associate Professor and Principal Investigator at the Oxford e-Research Centre, part of the Department of Engineering Science at the University of Oxford. She is one of the authors of the FAIR principles and an active contributor to a variety of community-driven FAIR-enabling efforts. Her group researches and develops methods and tools to improve data reuse, for data transparency, research integrity and the evolution of scholarly publishing: Markus Stockera Diploma (MSc) in Informatics from the University of Zurich, Switzerland. His research interests lie at the intersection between research infrastructures and research communities, and how such infrastructures acquire, maintain, and share scientific knowledge about human and natural worldsMelanie ImmingSURF, Utrecht 3511 EP, The NetherlandsLarry LannomCorporation for National Research Initiatives (CNRI), Reston, Virginia 20191, USAMark A. MusenStanford Center for Biomedical Informatics Research, Stanford, CA 94305, USAErik SchultesGO FAIR International Support and Coordination Office, Leiden, The Netherlands

Data Intelligencejournal2019en

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Annotatsiya

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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Research Data Management Practices Scientific Computing and Data Management Data Quality and Management

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DOI: 10.1162/dint_a_00038

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