How are we FAIR-ing? Creating a FAIR self-assessment checklist for data repositories | IASSIST Quarterly

Abstract:  In 2023, a team from a local grant-funded medical data repository requested guidance from Penn Libraries on evaluating the extent to which 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 focusing explicitly on the 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 developed a FAIR Principles self-assessment tool for repository teams. In addition to several iterations of the tool, we also met with the repository team for feedback on making the tool more understandable. Our conversation provided insights into the challenges of explaining the FAIR Principles to those without information or data science backgrounds. The discussion and creation of this self-assessment tool helped develop a more transparent and trustworthy repository. This paper will discuss our process for developing the assessment, the goals for utilizing the tool, and the lessons learned. Reporting our findings as they currently stand will prompt the research data management field to ruminate on the adoption of FAIR Principles for data repositories. We also intend to encourage conversation on the usability of the FAIR Principles for professionals without an information or data science background.