The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary group endorsed by Research Data Alliance. It intends to improve crediting and rewarding mechanisms for scientists who work towards sharing their data for potential reuse. In this perspective, assessing the compliance with FAIR practices and increasing the understanding of FAIRness criteria are critical steps. To that aim, a FAIR criteria assessment survey has been launched to seek feedback from the scientific community on intelligible, realistic and human-readable assessment criteria that could help guiding the scientist to follow FAIR practices as much as possible. It should also help the evaluator to objectively achieve his/her task. The criteria to be assessed by the respondents to the survey are organised in 5 groups. In addition to ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’ criteria, ‘Motivations for Sharing’ has been included. For each criterion, 4 choices are proposed (‘Never / Not Assessable’; ‘If mandatory only’; ‘Sometimes’; ‘Always’). One choice and only one, must be ticked for each criterion. The final score consists of the sum of the number of each ticked degree compare to the total number of criteria in each group; the ‘sharing motivations’ are appreciated qualitatively in the final interpretation. This survey is aimed at gathering comments to build realistic and usable tools for scientists and evaluators. It will help to construct an evaluation tool for FAIR practices that will enable to consider data sharing activities as a valuable research output and will foster this activity by promoting fairness literacy and dedicated support for data management. The poster will present the first results of the survey. Special emphasis will be put on highlighting the differences in understanding the human readable FAIR compliance criteria between different users (researchers and evaluators, young and senior researchers, IT people and others…).

References Reymonet N et al. Réaliser un plan de gestion de données « FAIR » : modèle, 2018. 〈sic_01690547v2 Wilkinson MD et al. (2018). A design framework and exemplar metrics for FAIRness. Scientific data, 5, 180118. doi:10.1038/sdata.2018.118 Wilkinson MD, The FAIR Guiding Principles for scientific data management and stewardship.Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18. E.U. European Commission Directorate-General for Research and Innovation report: Evaluation of Research Careers fully acknowledging Open Science Practices; Rewards, incentives and/or recognition for researchers practicing Open Science. 2017 E.U. European Commission Directorate-General for Research and Innovation report: H2020 Programme Guidelines on FAIR Data Management in Horizon 2020, Version 3.0, 26 July 2016