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Un modèle sémantique en vue d’améliorer la FAIRisation des données météorologiques

Abstract : Making meteorological data FAIR in order to ease its reuse is a strategic issue because this data is essential to advance research in many fields. This work proposes a semantic model which combines a metadata model and a data model for describing meteorological observation data. Indeed, modeling (meta)data is an essential step towards their FAIRification. We use the SYNOP open dataset made available by Météo-France to illustrate how difficult data access and understanding can be, and how the use of the proposed model to represent meteorological data improves their compliance with the "F", "I" and "R" principles.
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Contributor : Florent Breuil Connect in order to contact the contributor
Submitted on : Monday, June 14, 2021 - 4:13:48 PM
Last modification on : Wednesday, November 16, 2022 - 11:46:09 AM
Long-term archiving on: : Thursday, September 16, 2021 - 8:23:34 AM


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  • HAL Id : emse-03260061, version 1


Amina Annane, Mouna Kamel, Nathalie Aussenac-Gilles, Cassia Trojahn, Catherine Comparot, et al.. Un modèle sémantique en vue d’améliorer la FAIRisation des données météorologiques. Journées Francophones d'Ingénierie des Connaissances (IC 2021) @ Plate-Forme Intelligence Artificielle (PFIA 2021), Collège SIC (Science de l’Ingénierie des Connaissances) de l’AFIA, Jun 2021, Bordeaux (en distanciel), France. pp.20-29. ⟨emse-03260061⟩



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