How are we FAIR-ing? Creating a FAIR Self-Assessment Checklist for Data Repositories

In 2022, a team from a local grant-funded medical data repository team contacted the University of Pennsylvania Libraries’ Research Data & Digital Scholarship unit asking for guidance on evaluating the extent that their repository was FAIR enabling. After a consultation with the repository team, our research data experts discovered that many of the current self-assessments of the FAIR guidelines were for data creators rather than data repository managers. In addition, we wanted a self-assessment tool similar to the process and guidance created by CoreTrustSeal but with a focus explicitly on FAIR principles. In answer to their request, the Penn Libraries’ Research Data Engineer conducted a literature review and coalesced current guidance and assessment tools on the principles. After this review of the existing documentation, a small team consisting of the Research Data Engineer, the Head of Research Data Services, the Director of Data and Innovation Services, and the Bioinformatics Librarian developed through an iterative process a self-assessment tool for repository managers regarding FAIR principles. In addition to several iterations of the tool, we also met with the repository managers for feedback on ways to make the tool more understandable. Our discussions provided insights into the challenges of explaining the FAIR principles to those without information science backgrounds. The discussions we had and the development of this self-assessment tool helped to develop a more transparent and trustworthy repository.

This paper will discuss the development of the assessment, the goals for utilizing the tool, and lessons learned. Reporting our findings as they currently stand will prompt the research data management field to ruminate on FAIR principle adoption for data repositories. We also intend for this paper to encourage more conversation on the usability of the FAIR principles for professionals without an information science background.