How to assess FAIRness to improve crediting and rewarding processes for data sharing? A step forward towards an extensive assessment grid

• DOI: 10.5281/zenodo.2625721
• Publication Date: 2019-04-02
• Authors: Romain David; Laurence Mabile; Mohamed Yahia; Anne Cambon‐Thomsen; Anne‐Sophie Archa...; Louise Bezuidenhout; Sofie Bekaert; Gabrielle Bertier; Elena Bravo; Jane Carpenter; Anna Cohen-Nabeiro; Aurélie Delavaud; Laurent Dollé; Michele De Rosa; Fiona Murphy; Florencia Grattarola; Sophie Pamerlon; Alison Specht; Anne-Marie Tassé; Mogens Thomsen; Martina Zilioli
• Keywords: Data Sharing

The SHARC (SHAring Reward & Credit) interest group (IG) is an interdisciplinary set up in the framework of RDA (Research Data Alliance) to improve crediting and rewarding mechanisms sharing process throughout data life cycle. Notably, one objectives promote activities research assessment schemes at national European levels. To this aim, RDA-SHARC IG developing grids using criteria establish if are compliant FAIR principles (findable /accessible / interoperable reusable). The grid aiming be extensive, generic trans-disciplinary. It meant used by evaluators assess quality practice researcher/scientist over a given period, taking into account means support available that period. displays mind-mapped tree-graph structure based on previous works management (Reymonet et al., 2018; Wilkinson 2016; E.U.Guidelines about FAIRness Management Plans). work from FORCE 11*, Open Science Career Assessment Matrix designed EC Working Rewards under science. organised 5 clusters: ‘Motivations for sharing’; ‘Findable’, ‘Accessible’, ‘Interoperable’ ‘Reusable’. For each criterion, 4 graduations proposed (‘Never Not Assessable’; ‘If mandatory’; ‘Sometimes’; ‘Always’). Only value must selected per criterion. Evaluation should done cluster; final overall will sum number ticked total ‘motivations sharing’ appreciated qualitatively interpretation. goals develop graduated researcher literacy help identifying needs build guidelines capacity researchers.