Improving FAIRness of the SYNOP meteorological data set with semantic metadata

Amina Annane (1) , Mouna Kamel (2, 3) , Cassia Trojahn (2, 4) , Nathalie Aussenac-Gilles (2, 5) , Catherine Comparot (2, 4) , Christophe Baehr (6)

1 Geotrend
2 IRIT-MELODI - MEthodes et ingénierie des Langues, des Ontologies et du DIscours
3 UPVD - Université de Perpignan Via Domitia
4 UT2J - Université Toulouse - Jean Jaurès
5 CNRS - Centre National de la Recherche Scientifique
6 CNRM - Centre national de recherches météorologiques

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 datasets. 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 dataset distribution, together with domain-specific definitions. This model is used to provide a reusable schema of the SYNOP dataset, a largely used governmental meteorological dataset in France. The impact of using the proposed model for improving FAIRness was evaluated.

Keywords: Metadata; Ontologies; Metereological data; FAIR principles

Domains: Artificial Intelligence [cs.AI]; Environmental Sciences

Amina Annane, Mouna Kamel, Cassia Trojahn, Nathalie Aussenac-Gilles, Catherine Comparot, et al.. Improving FAIRness of the SYNOP meteorological data set with semantic metadata. International Journal of Metadata, Semantics and Ontologies, 2023, 16 (2), pp.118-137. ⟨10.1504/IJMSO.2023.135332⟩. ⟨hal-04420766⟩