Towards the FAIRification of Meteorological Data: a Meteorological Semantic Model - Méthodes et Ingénierie des Langues, des Ontologies et du Discours Accéder directement au contenu
Chapitre D'ouvrage Année : 2022

Towards the FAIRification of Meteorological Data: a Meteorological Semantic Model

Résumé

Meteorological institutions produce a valuable amount of data as a direct or side product of their activities, which can be potentially explored in diverse applications. However, making this data fully reusable requires considerable efforts in order to guarantee compliance to the FAIR principles. While most efforts in data FAIRification are limited to describing data with semantic metadata, such a description is not enough to fully address interoperability and reusability. We tackle this weakness by proposing a rich ontological model to represent both metadata and data schema of meteorological data. We apply the proposed model on a largely used meteorological dataset, the "SYNOP" dataset of Météo-France and show how the proposed model improves FAIRness.
Fichier principal
Vignette du fichier
MTSR_ER_FAIR_2021_final.pdf (1.02 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03683695 , version 1 (31-05-2022)

Identifiants

Citer

Amina Annane, Mouna Kamel, Cassia Trojahn, Nathalie Aussenac-Gilles, Catherine Comparot, et al.. Towards the FAIRification of Meteorological Data: a Meteorological Semantic Model. Emmanouel Garoufallou; María-Antonia Ovalle-Perandones; Andreas Vlachidis. Metadata and Semantic Research 15th International Conference, MTSR 2021, Virtual Event, November 29 – December 3, 2021, Revised Selected Papers ; ISBN: 978-3-030-98875-3, 1537, Springer International Publishing, pp.81-93, 2022, Communications in Computer and Information Science book series (CCIS), ⟨10.1007/978-3-030-98876-0_7⟩. ⟨hal-03683695⟩
136 Consultations
134 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More