The FAIR Principles define measures to make research data findable, accessible, interoperable and re-usable. Many players in science policy, including research funders, support the call for FAIR Data. The goal should be to ensure that research data is optimally prepared and accessible for both humans and machines, and that existing databases can be reused to answer new research questions - if technical and legal conditions allow.
RADAR supports the FAIR principles with a variety of measures and features, and we are continuously working on optimising their implementation. For example, in the last software release (v1.15) we focused on the FAIRness of RADAR datasets by integrating ROR (Research Organization Registry) and the GND (Integrated Authority File) as two further important standards data for metadata annotation, and by improving the machine-readability and -actionability of the RADAR landing pages in accordance with the signposting approach.
A revised synopsis of the implementation of the FAIR principles with RADAR is available here (PDF in German).