FAIRification of Citizen Science Data Through Metadata-Driven Web API Development

Citizen Science (CS) implies a collaborative process to encourage citizens to collect data in CS projects and platforms. Unfortunately, these CS initiatives do not follow metadata nor data-sharing standards, which hampers their discoverability and reusability. To improve this scenario in CS is crucial to consider FAIR (Findability, Accessibility, Interoperability and Reusability) guidelines. Therefore, this paper defines a FAIRification process (i.e. make CS initiatives more FAIR compliant) which maps metadata of CS platforms' catalogues to DCAT and generates Web Application Programming Interfaces (APIs) for improving CS data discoverability and reusability in an integrated approach. An experiment in a CS platform with different CS projects shows the performance and suitability of our FAIRification process. Specifically, the validation of the DCAT metadata generated by our FAIRification process was conducted through a SHACL standard validator, which emphasises how the process could boost CS projects to become more FAIR compliant.