In a new publication, researchers from the German Center for Diabetes Research (DZD) and the University Medical Center Greifswald have compared and evaluated different workflows for the FAIR preparation of clinical data. Using the DZD basic data set as an example, they derive concrete recommendations on how research data can be made findable, accessible, interoperable and reusable. The aim is to reduce the effort and facilitate the reuse of existing data in clinical research.

Clinical research generates large amounts of heterogeneous data – from blood values to anthropometric measurements to pre-existing conditions. However, this data only reaches its full potential if it complies with the FAIR principles: findable, accessible, interoperable and reusable. FAIRification facilitates the exchange of data between research groups, enables faster comparisons and new findings, and promotes sustainable use of resources.

The DZD is promoting the standardization and FAIRification of diabetes and metabolism data nationwide in order to make them usable for other medical issues. A key milestone is the DZD data set, which provides interoperable central parameters of clinical diabetes research for national and international reuse under an open license.

The new study systematically describes and compares different FAIRification workflows using the concrete example of the basic data set. The authors analyse the strengths, weaknesses and efforts of the individual approaches and derive minimum requirements that can reduce costs and time. Many findings can be transferred to other clinical datasets.

“Subsequent FAIRification is extremely time-consuming,” emphasizes Dr. Lars Oest, Head of Bioinformatics and Data Management at the DZD. “That’s why FAIR must be planned from the very beginning – with the right infrastructure and clearly coordinated processes between all those involved.” However, there is still a need for research to automate semantic enrichment and integrate prospective data management plans.

The publication underlines that FAIRification is not only a technical but a strategic topic for modern clinical research. It creates the conditions for more efficient, reproducible and collaborative science and contributes to faster progress in diabetes and metabolism research in the long term.

Original Paper:

Lessons learned from implementing FAIRification workflows in diabetes research in Germany | PLOS Digital Health

Editor: X-Press Journalistenbüro GbR

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