Home GO FAIR Today FAIR Convergence Matrix & FAIR Implementation Profiles

Tools for Accelerating Convergence on FAIR Implementations

The FAIR Principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines. 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 FAIR Convergence Matrix is a platform that systematically guides any self-identified community in the decision process leading to optimal FAIR implementations and practices. The resulting collection of FAIR implementation choices compose the FAIR Implementation Profile for that community. Community specific FAIR Implementation Profiles are themselves captured as FAIR datasets and are made openly available to other communities for reuse.

The FAIR Implementation Profiles representing the implementation strategies of various communities can be used as the basis to optimize the reuse of existing resources and interoperation within and between domains. Ready-made and well-tested FAIR Implementation Profiles created by trusted communities can find widespread reuse among other communities, and vastly accelerate convergence onto well-informed FAIR implementations.

FAIR Convergence Matrix Working Group

Initially introduced at the GO FAIR IN meeting 2019, the development of the FAIR Convergence Matrix and FAIR Implementation Profiles has been driven by a self-organized team of FAIR experts, now called the FAIR Convergence Matrix Working Group. The group tracks planning and development for the Matrix and FIPs, executing a development plan for a bottom-up, community driven approach that maximizes reuse of existing resources for implementing the FAIR components of EOSC and, more broadly, a global Internet of FAIR Data and Services. The FAIR Convergence Matrix Working Group has regular meetings, both remote and face-to- face. Active team members are listed below. If you are interested in joining the effort please contact office@go-fair.org.

Events

  • January 15-16, 2019: The GO FAIR Matrix was launched during the first GO FAIR
    Implementation Network meeting. Following the meeting, there was rapid progress in the development of Matrix components including the questionnaire, interfaces, semantics, as well as hosting solutions.
  • June 13, 2019: The first face-to-face Matrix Development Meeting for the FAIR Implementation Matrix was held at the GO FAIR International Support and Coordination Office in Leiden (notes).
  • September 2019: More than 40 European Research Infrastructures have tested early versions of the Matrix and produced FAIR Implementation Profiles (including 14 environmental RIs participating in the ENVRI-FAIR Project and 13 ESFRIs). These results triggered refinement of the Matrix questionnaire and the first analyses of cross-disciplinary FAIR Implementation Profiles.
  • September 19, 2019: A panel discussion was held at the CODATA 2019 Conference, Beijing, titled “The GO FAIR Implementation Matrix: Maximising the Reuse of Existing FAIR Resources” (slides).
  • October 22, 2019: A co-located event was held as part of the 14th RDA Plenary Meeting in Finland, Helsinki – titled “Building a Matrix about the Technologies Used by Research Infrastructures” (agenda, slides).
  • November 22, 2019: The second face-to-face Matrix Development Meeting was organized at the GO FAIR International Support and Coordination Office in Leiden.
  • December 11, 2019: A meeting was held at the GO FAIR International Support and Coordination Office in Leiden to launch detailed FIP creation, analysis, and formulation of Convergence strategies for distributed learning applications. Results were reported at the second annual GO FAIR IN meeting in Hamburg on January 23-24, 2020 (slides, notes).

Resources

FAIR Convergence Matrix Working Group

Members as of November 22, 2019

Core Development Team

  • Erik Schultes, GO FAIR International Support and Coordination Office
  • Hana Pergl Sustkova, GO FAIR International Support and Coordination Office
  • Kristina Hettne, Centre for Digital Scholarship, Leiden University Libraries
  • Barbara Magagna, Environment Agency Austria
  • Robert Pergl, Czech Technical University in Prague, Faculty of Information Technology
  • Jan Slifka, Czech Technical University in Prague, Faculty of Information Technology
  • Tobias Kuhn, VU University Amsterdam
  • Annika Jacobsen, Department of Human Genetics, Leiden University Medical Center
  • Peter McQuilton, FAIRsharing, Oxford e-Research Centre, Dept. of Engineering Science, University of Oxford

Active Members​ (contributing/consulting)

  • Susanna-Assunta Sansone, Oxford e-Research Centre, Dept. of Engineering Science, University of Oxford
  • Natalie Meyers, University of Notre Dame
  • Carlo Maria Zwölf, Observatoire de Paris
  • Melanie Imming, SURF
  • Egon Willighagen, Maastricht University
  • Xiaofeng Liao, VU University Amsterdam
  • Mark Musen, Stanford Center for Biomedical Informatics Research
  • Markus Stocker, TIB Leibniz InformationCentre for Science and Technology

Observers

  • Simon Hodson, CODATA
  • Bert Meerman, GO FAIR Foundation
  • Barend Mons, CODATA and GO FAIR International Support and Coordination Office
  • Catharina Wassner, GO FAIR International Support and Coordination Office
  • Larry Lannom, Corporation for National Research Initiatives
  • Guido Aben, AARNet
  • Jakub Moscicki, CERN
  • Peter Thijsse, MARIS BV
  • Philipp Conzett, UiT The Arctic University of Norway
  • Hugh Shanahan, Royal Holloway, University of London
  • Zhiming Zhao, VU University Amsterdam
  • Margareta Hellström, Lund University
  • Riccardo Rabissoni, National Institute of Geophysics and Volcanology Pisa
  • Sara Decoster, KU Leuven
  • Peter Wittenburg, Max Planck Computing and Data Facility
  • Konstantinos Repanas, Open Science & European open science cloud, European Commission