Lessons learned from implementing FAIRification workflows in diabetes research in Germany.

ABSTRACT: The FAIR principles guide data stewardship towards maximizing the value of scientific data while offering a high level of flexibility to accommodate differences in standards and scientific practices. Research communities have developed and implemented domain-specific workflows to make their data FAIR. This work compares the implementation of two externally developed structured FAIRification workflows-a generic workflow and a domain-specific workflow- using the example of metadata captured in diabetes research in Germany and applying the FAIR data maturity model developed by the Research Data Alliance. Interestingly, the implementation of both workflows required similar resources and led us to achieve the same FAIRness rating. We therefore conclude that the adaptations made in the FAIRifica

SUBMITTER: Inau ET 

PROVIDER: S-EPMC12799184 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature