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

## TL;DR

This study compares two FAIRification workflows for diabetes research data in Germany and finds that domain-specific adjustments improve efficiency but not FAIRness scores.

## Contribution

The study provides a comparative analysis of FAIRification workflows and identifies key requirements for FAIRifying core health data.

## Key findings

- Both generic and domain-specific FAIRification workflows required similar resources and achieved the same FAIRness rating.
- Domain-specific adaptations improve efficiency but do not necessarily increase FAIRness scores for core data sets.
- Early planning of FAIR data strategies is emphasized for effective implementation.

## 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 FAIRification workflow for health research data improve efficiency but do not necessarily lead to higher FAIRness scores when applied to core data sets. Based on the results of our workflow comparison, we identified a list of requirements that should be met for the FAIRification of a core data set regardless of the workflow employed. In the future, FAIR data strategies and infrastructure should be planned and implemented as early as possible in the FAIRification journey. It is anticipated that this comparative analysis will help establish standard operating procedures for the FAIRification of core data sets for health studies.

Implementation of the FAIR principles in research data management improves the yield of scientific data across diverse research contexts. This study compared two structured FAIRification workflows—one generic and one specific to health research—using diabetes research data from Germany. Both workflows required similar effort and resulted in the same FAIRness score. This suggests that while domain-specific adjustments can improve efficiency, they do not necessarily increase FAIRness for core data sets. The authors outline key requirements for FAIRifying core health data and emphasize that FAIR data planning should begin early. Their findings aim to support the development of standard procedures for FAIRifying core data sets in health research.