Improving FAIRness of the SYNOP meteorological data set with semantic metadata

Meteorological data, essential in a variety of applications, has been made available as open data through different portals, either governmental, associative or private ones. Making this data fully findable and reusable for experts from other domains than meteorology requires considerable efforts to guarantee compliance to the FAIR principles. Nowadays, most efforts in data FAIRification are limited to semantic metadata describing the overall features of data sets. However, such a description is not enough to fully address data interoperability and reusability by other scientific communities. This paper addresses this weakness by proposing a semantic model to represent different kinds of metadata, describing the data schema and the internal structure of a data set distribution, together with domain-specific definitions. This model is used to provide a reusable schema of the SYNOP data set, a largely used governmental meteorological data set in France. The impact of using the proposed model for improving FAIRness was evaluated. Copyright © 2023 Inderscience Enterprises Ltd.