3922069
doi
10.5281/zenodo.3922069
oai:zenodo.org:3922069
user-rda-sharcig
user-rda-related
Mabile, Laurence
INSERM-Université Paul Sabatier Toulouse III
Specht, Alison
SEES-TERN, the University of Queensland
Stryeck, Sarah
Graz University of Technology, Institute for Interactive Systems and Data Science
Thomsen, Mogens
INSERM-Université Paul Sabatier Toulouse III
Yahia, Mohamed
INIST-CNRS
Jonquet, Clement
LIRMM, University of Montpellier, CNRS
Dollé, Laurent
BBMRI.be, Erasme Hospital
Jacob, Daniel
INRAE, UMR BFP, Université de Bordeaux
Bailo, Daniele
EPOS-ERIC / Istituto Nazionale di Geofisica e Vulcanologia
Bravo, Helena
Istituto Superiore Sanità
Gachet, Sophie
Aix Marseille Univ, CNRS, Avignon Université, IRD, IMBE
Gunderman, Hannah
Carnegie Mellon University Pittsburgh
Hollebecq, Jean-Eudes
MISTEA, INRAE, Montpellier SupAgro, Université de Montpellier
Ioannidis, Vassilios
SIB Swiss Institute of Bioinformatics
Le Bras, Yvan
DGD-REVE, UMS PatriNat, MNHN
Lerigoleur, Emilie
CNRS, UMR GEODE, Université Toulouse 2
Cambon-Thomsen, Anne
INSERM-Université Paul Sabatier Toulouse III
SHARC Community
Research Data Alliance - SHAring Reward & Credit (SHARC) Interest Group
Templates for FAIRness evaluation criteria - RDA-SHARC ig
David, Romain
MISTEA, INRAE, Montpellier SupAgro, University of Montpellier, ERINHA AISBL
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
FAIR principles; FAIRness literacy; FAIR assessment, Research data sharing; FAIRification; Pre-FAIRification;
<p>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.</p>
Zenodo
2020-06-29
info:eu-repo/semantics/other
3922068
user-rda-sharcig
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https://zenodo.org/records/3922069/files/Templates for FAIRness evaluation criteria - RDA-SHARC ig .ods
public
10.5281/zenodo.3922068
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doi