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Published June 29, 2020 | Version 1.1
Dataset Open

Templates for FAIRness evaluation criteria - RDA-SHARC ig

  • 1. MISTEA, INRAE, Montpellier SupAgro, University of Montpellier, ERINHA AISBL
  • 2. INSERM-Université Paul Sabatier Toulouse III
  • 3. SEES-TERN, the University of Queensland
  • 4. Graz University of Technology, Institute for Interactive Systems and Data Science
  • 5. INIST-CNRS
  • 6. LIRMM, University of Montpellier, CNRS
  • 7. BBMRI.be, Erasme Hospital
  • 8. INRAE, UMR BFP, Université de Bordeaux
  • 9. EPOS-ERIC / Istituto Nazionale di Geofisica e Vulcanologia
  • 10. Istituto Superiore Sanità
  • 11. Aix Marseille Univ, CNRS, Avignon Université, IRD, IMBE
  • 12. Carnegie Mellon University Pittsburgh
  • 13. MISTEA, INRAE, Montpellier SupAgro, Université de Montpellier
  • 14. SIB Swiss Institute of Bioinformatics
  • 15. DGD-REVE, UMS PatriNat, MNHN
  • 16. CNRS, UMR GEODE, Université Toulouse 2
  • 17. Research Data Alliance - SHAring Reward & Credit (SHARC) Interest Group

Description

The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This document is a reusable template that aims to support FAIRification assessment with human understandable criteria. The level of compliance for each criterion can be used to prioritise the most appropriate and sufficient training, support and actions.

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