Research Data Alliance - Related Documents
Published May 22, 2024 | Version 1.4
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Templates for FAIRness evaluation criteria - RDA-SHARC ig V1.4

Authors/Creators

  • 1. ROR icon European Research Infrastructure on Highly Pathogenic Agents
  • 2. ROR icon Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
  • 3. ROR icon Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale
  • 4. ROR icon Université Toulouse III - Paul Sabatier
  • 5. Terrestrial Ecosystem Research Network
  • 6. Technische Universität Graz
  • 7. ROR icon Institut Charles Gerhardt Montpellier
  • 8. Institut de Chimie Moléculaire et des Matériaux de Montpellier
  • 9. INSERM
  • 10. VIB
  • 11. Ghent University
  • 12. Biothèque Wallonia-Bruxelles
  • 13. Université de Bordeaux
  • 14. Istituto Superiore di Sanità
  • 15. ROR icon EMGO Institute for Health and Care Research
  • 16. Department of Clinical Genetics

Description

In order to foster data sharing, the RDA-SHARC (SHAring Rewards & Credit) interest group has been set up to unpack and improve crediting and rewarding mechanisms in the data/resources sharing process. As part of the objectives, 
Two assessment grids have been developed using criteria to establish if data are compliant to  the F.A.I.R principles (findable /accessible / interoperable / reusable). The criteria used are based on the work from FORCE 11*, and on the basis of the Open Science Career Assessment Matrix designed by the EC Working group on Rewards under Open science.

This complete template displays the criteria that must be applied by researchers to attest to their FAIR practice. Criteria are organized in 5 clusters: ‘Sharing motivations’; ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’. For each criterion, 4 choices are proposed (‘Never / Not Assessable’; ‘Mandatory’; ‘Sometimes’; ‘Always’). ‘Mandatory’ is selected if researchers know how to apply the criterion but only do so if they are mandated to; ‘Sometimes’ is selected when stakeholders know that FAIR compliance is important, but that the criterion is not applied every time; ‘Always’ is the case of an optimal organisation (as infrastructure design or as community consensus) and quality processes are maintained for long term compliance. At least one choice but one only should be selected per criteria. Evaluation needs to be done for each cluster F/A/I/R; The final evaluation will be the sum of [number of each ticked degree] / [total number of appliable criteria] in each cluster; the ‘sharing motivations’ should be appreciated qualitatively in the final interpretation. The level of compliance for each criterion can be used in a final report to prioritise the most appropriate and sufficient training, support and actions. 

Files

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Additional details

Related works

Is described by
Poster: 10.5281/zenodo.1995645 (DOI)
Is supplement to
Journal article: 10.5334/dsj-2020-032 (DOI)

Dates

Accepted
2020
This grid is related to a paper
Submitted
2019

References

  • David, R., Mabile, L., Specht, A., Stryeck, S., Thomsen, M., Yahia, M., Jonquet, C., Dollé, L., Jacob, D., Bailo, D., Bravo, E., Gachet, S., Gunderman, H., Hollebecq, J.-E., Ioannidis, V., Le Bras, Y., Lerigoleur, E., Cambon-Thomsen, A. and Alliance – SHAring Reward and Credit (SHARC) Interest Group, T.R.D. (2020) 'FAIRness Literacy: The Achilles' Heel of Applying FAIR Principles', Data Science Journal, 19(1), p. 32. Available at: https://doi.org/10.5334/dsj-2020-032.