F-UJI score as indicator for the FAIRness of a dataset further increased

As announced in summer, we recently took part in the virtual support measure "FAIRness assessment challenge for datasets and semantic artefacts" of the EU project FAIR-IMPACT. We managed to further optimize the FAIRness of RADAR datasets and thus consistently continue our previous development work in this area in 2023.

As representatives of all RADAR datasets, we selected four RADAR4Chem datasets and analyzed their FAIRness using the "F-UJI - Automated FAIR Data Assessment Tool". F-UJI is a web service for programmatically assessing the FAIRness of research data objects at dataset level based on the FAIRsFAIR Data Object Assessment Metrics and was developed as part of the FAIRsFAIR project.

Supported by mentors, we were able to further optimize the RADAR software during the support measure and thus increase the F-UJI score as an indicator of the FAIRness of the selected RADAR4Chem datasets to values of up to 87%. The highest FAIR level "advanced" was achieved for the individual criteria "Findable" and "Accessible". For the "Interoperable" and "Reusable" criteria, the FAIR level "moderate" was achieved for the most part.

See the result for the RADAR4Chem dataset 10.22000/1752 as an example (Status: 7.11.2023):

On the other hand, the FAIR evaluation of the various RADAR datasets once again highlighted the importance of detailed metadata annotation using persistent identifiers for the FAIRness of a dataset. For example, if data providers do not link to a related resource (e. g. the DOI of a scientific article based on research data in the dataset), this leads to lower F-UJI scores. 

We are currently discussing with the RADAR User Advisory Board the possibility of integrating a "FAIRness check" with corresponding instructions for data providers during the metadata annotation process. 

* RADAR4Chem is funded by NFDI4Chem (DFG Project Number 441958208)