FAIRness evaluation of WDCC for the paper "Recommendations for discipline-specific FAIRness evaluation derived from applying an ensemble of evaluation tools"

Project: Literature - Literature summary: Showcasing the FAIRness of a repositories' data management services requires the application of available FAIRness evaluation methods and tools. Results from these tools - in the form of FAIR scores - need to made available for transparency reasons. This data collection is comprised of any results obtained from evaluation the FAIRness of WDCC's data holdings. Some of these data form the basis of peer-reviewed publications (see associated data for more information). Results from five different FAIRness evaluation approaches applied to selected data collections archived in the WDCC. These data form the basis for the publication "Recommendations for discipline-specific FAIRness evaluation derived from applying an ensemble of evaluation tools" by Karsten Peters-von Gehlen et al., submitted to Data Science Journal in September 2021. The applied FAIRness evaluation tools are: 1) Checklist for Fitness for Use (CFU, Austin et al., 2019) 2) F-UJI (Devaraju Huber, 2020) 3) FAIR Maturity Evaluation Service (FMES, Wilkinson et al., 2019) 4) FAIRshake (Clarke et al., 2019) 5) Self Assessment based on Bahim et al. (2020) The spreadsheet belonging to each test is evident from the file name. Calculated mean FAIRscores and cross-correlations between tests are also included. Creative Commons Attribution 4.0 International (CC BY 4.0)