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Towards the FAIRification of Meteorological Data: A Meteorological Semantic Model

Part of the Communications in Computer and Information Science book series (CCIS,volume 1537)


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.


  • Meteorological data
  • FAIR principles
  • Semantic metadata

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  • DOI: 10.1007/978-3-030-98876-0_7
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Correspondence to Cassia Trojahn .

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Annane, A., Kamel, M., Trojahn, C., Aussenac-Gilles, N., Comparot, C., Baehr, C. (2022). Towards the FAIRification of Meteorological Data: A Meteorological Semantic Model. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham.

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