DAVID, Romain; MABILE, Laurence; SPECHT, Alison; STRYECK, Sarah; THOMSEN, Mogens; YAHIA, Mohamed; JONQUET, Clement; DOLLÉ, Laurent; JACOB, Daniel; BAILO, Daniele; BRAVO, Elena; GACHET, Sophie; GUNDERMAN, Hannah; HOLLEBECQ, Jean-Eudes; IOANNIDIS, Vassilios; LE BRAS, Yvan; LERIGOLEUR, Emilie; CAMBON-THOMSEN, Anne

doi:10.5334/dsj-2020-032

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DAVID, Romain
Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie [MISTEA]
European Research Infrastructure on Highly Pathogenic Agents [ERINHA-AISBL]

MABILE, Laurence
Trajectoires d'innovations en santé : enjeux bioéthiques et impact en santé publique [Equipe 4 - BIOETHICS]

SPECHT, Alison
The University of Queensland [UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations]]

Langue

Article de revue

Ce document a été publié dans

CODATA Data Science Journal. 2020-08-11, vol. 19, n° 32, p. 1-11

Committee on Data for Science and Technology (CODATA)

Résumé en anglais

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.< Réduire

Mots clés en anglais

FAIR principles

FAIRness literacy

FAIR assessment

Research data sharing

FAIRification

Pre-FAIRification

DOI

http://dx.doi.org/10.5334/dsj-2020-032

Projet Européen

The European plug-in to the global Research Data Alliance
European Plant Phenotyping Network 2020
Providing an open collaborative space for digital biology in Europe
ERINHA-Advance - Advancing European Research Infrastcuture on Highly Pathogenic Agents

Project ANR

Des Données aux Connaissances en Agronomie et Biodiversité
Centre français de phénomique végétale
Building New Tools for Data Sharing and Reuse through a Transnational Investigation of the Socioeconomic Impacts of Protected

Origine

Importé de hal

Unités de recherche

Biologie du Fruit & Pathologie (BFP) - UMR 1332