Model-Driven Approach for Making Citizen Science Data FAIR

Abstract

Citizen Science (CS) initiatives have proliferated in different scientific and social fields, producing vast amounts of data. Existing CS projects usually adopt PPSR Core as a data and metadata standard. However, these projects are still not FAIR (Findable, Accessible, Interoperable and Reusable)-compliant. We propose to use DCAT as a data and metadata standard since it helps to improve the interoperability of CS data catalogs and all the FAIR features. For this purpose, in this paper we present a model-driven approach to make CS data FAIR. Our approach has the following contributions: (i) the definition of a metamodel based on PPSR Core, (ii) the definition of a DCAT profile for CS, (iii) a definition of set of automated transformations from PPSR Core to DCAT. Finally, the implementation of the model-driven process has been validated by evaluating several FAIR metrics. The results show that our proposal has significantly improved the FAIR quality of CS projects.