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Towards the FAIRification of Meteorological Data: a Meteorological Semantic Model (MTSR 2021)

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03683695
Contributor : Nathalie Aussenac-Gilles Connect in order to contact the contributor
Submitted on : Tuesday, May 31, 2022 - 6:49:50 PM
Last modification on : Monday, July 4, 2022 - 9:45:52 AM

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Amina Annane, Mouna Kamel, Cassia Trojahn, Nathalie Aussenac-Gilles, Catherine Comparot, et al.. Towards the FAIRification of Meteorological Data: a Meteorological Semantic Model (MTSR 2021). 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⟩

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