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

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