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Article Dans Une Revue CODATA Data Science Journal Année : 2020

FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles

1 MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie
2 UM - Université de Montpellier
3 UT3 - Université Toulouse III - Paul Sabatier
4 INSERM - Institut National de la Santé et de la Recherche Médicale
5 UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations] - The University of Queensland
6 TU Graz - Graz University of Technology [Graz]
7 ICGM - Institut Charles Gerhardt Montpellier - Institut de Chimie Moléculaire et des Matériaux de Montpellier
8 WEB3 - WEB Architecture x Semantic WEB x WEB of Data
9 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
10 ULB - Hôpital Erasme [Bruxelles]
11 BFP - Biologie du fruit et pathologie
12 UB - Université de Bordeaux
13 INGV - Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Palermo
14 ISS - Istituto Superiore di Sanità = National Institute of Health
15 IMBE - Institut méditerranéen de biodiversité et d'écologie marine et continentale
16 AMU - Aix Marseille Université
17 IRD - Institut de Recherche pour le Développement
18 CNRS - Centre National de la Recherche Scientifique
19 AU - Avignon Université
20 CMU - Carnegie Mellon University [Pittsburgh]
21 SIB - Swiss Institute of Bioinformatics [Lausanne]
22 DGD.REVE - Direction générale déléguée à la Recherche, à l’Expertise, à la Valorisation et à l’Enseignement-Formation
23 PatriNat - Patrimoine naturel
24 GEODE - Géographie de l'environnement
25 UT2J - Université Toulouse - Jean Jaurès
26 LEASP - Epidémiologie et Analyses en Santé Publique : risques, maladies chroniques et handicap
Alison Specht

Résumé

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 paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding. These are essential milestones in developing adapted support and credit back mechanisms not yet in place.
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Dates et versions

hal-02483307 , version 1 (18-02-2020)
hal-02483307 , version 2 (12-08-2020)

Identifiants

Citer

Romain David, Laurence Mabile, Alison Specht, Sarah Stryeck, Mogens Thomsen, et al.. FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles. CODATA Data Science Journal, 2020, 19 (32), pp.1-11. ⟨10.5334/dsj-2020-032⟩. ⟨hal-02483307v2⟩
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