How research data is made FAIR

In a new publication, researchers from the German Center for Diabetes Research (DZD) and the University Medical Center Greifswald show how research data can be processed to be FAIR on a step-by-step basis, i.e. such that they can be found, accessed and reused in an interoperable manner. Using the DZD basic data set as an example, they compare different FAIRification work flows, evaluate their strengths and challenges and provide concrete recommendations for the scientific community.

Clinical research generates extensive and diverse data, for example relating to blood values, body measurements or previous illnesses. However, this data can only realize its full potential if it can be found, accessed and reused in an interoperable manner. These properties are described by the FAIR principles: findable, accessible, interoperable, reusable (FAIRification). The FAIR principles not only facilitate the exchange of data between research groups, but also enable faster comparisons, new findings and more efficient use of existing resources. FAIRification thus forms the basis for the sustainable use of clinical research data. The DZD is therefore working intensely to standardize data from diabetes research nationwide and make it usable for other medical issues.

DZD Core Data Set – an important step towards FAIR research data

But what steps are necessary to make data FAIR? The DZD has taken an important step towards FAIRification with its core data set for diabetes and metabolism research. An open license grants permission to make interoperable use of central parameters of clinical diabetes research in the DZD basic data set. 

To make it easier for other researchers to obtain FAIR data, the DZD, in collaboration with the University Medical Center Greifswald, has systematically described and compared the different work flows for FAIRification of data using the example of the DZD basic data set. The study derives minimum requirements that can reduce time and effort as well as costs. The authors describe how FAIRification can be made more efficient and, if need be, be optimized in the future. Many of the findings are also transferable to other clinical data sets.

Planning FAIRification right from the start

“As subsequent FAIRification is very time-consuming, research projects should always be planned FAIR right from the start – including infrastructure and clearly coordinated processes between all parties involved,” emphasizes Dr. Lars Oest, Head of Bioinformatics and Data Management at the DZD. However, further research is needed to automate semantic enrichment and integrate prospective data management plans.