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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)

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.

Keywords

  • Meteorological data
  • FAIR principles
  • Semantic metadata

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Notes

  1. 1.

    https://donneespubliques.meteofrance.fr/.

  2. 2.

    http://worldweather.wmo.int/fr/home.html.

  3. 3.

    https://www.infoclimat.fr.

  4. 4.

    http://www.meteociel.fr.

  5. 5.

    https://library.wmo.int/doc_num.php?explnum_id=10179.

  6. 6.

    https://w3id.org/dmo.

  7. 7.

    http://www.opengis.net/def/crs/EPSG/.

  8. 8.

    https://www.w3.org/ns/csvw.

  9. 9.

    https://www.w3.org/TR/eo-qb/.

  10. 10.

    https://www.w3.org/2005/Incubator/ssn/ssnx/meteo/aws.

  11. 11.

    https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32.

  12. 12.

    https://www.etalab.gouv.fr/wp-content/uploads/2014/05/Licence_Ouverte.pdf.

  13. 13.

    doi.org/10.5281/zenodo.4679704.

  14. 14.

    https://semiceu.github.io/GeoDCAT-AP/releases/2.0.0/.

  15. 15.

    https://fairassist.org/#!/.

References

  1. Arenas, H., Trojahn, C., Comparot, C., Aussenac-Gilles, N.: Un modèle pour l’intégration spatiale et temporelle de données géolocalisées. Revue Int. de Géomatique 28(2), 243 (2018)

    Google Scholar 

  2. Atmezing, G., et al.: Transforming meteorological data into linked data. Semant. Web 4(3), 285–290 (2013)

    CrossRef  Google Scholar 

  3. Benjelloun, O., Chen, S., Noy, N.: Google dataset search by the numbers. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 667–682. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_41

    CrossRef  Google Scholar 

  4. van den Brink, L., et al.: Best practices for publishing, retrieving, and using spatial data on the web. Semant. Web 10(1), 95–114 (2019)

    CrossRef  Google Scholar 

  5. Buttigieg, P.L., Morrison, N., Smith, B., et al.: The environment ontology: contextualising biological and biomedical entities. J. Biomed. Semant. 4, 43 (2013)

    CrossRef  Google Scholar 

  6. Clarke, D., et al.: FAIRshake: toolkit to evaluate the FAIRness of research digital resources. Cell Syst. 9(5), 417–421 (2019)

    CrossRef  Google Scholar 

  7. Devaraju, A., Huber, R., Mokrane, M., et al.: FAIRsFAIR data object assessment metrics, October 2020. https://doi.org/10.5281/zenodo.4081213

  8. Jacobsen, A., et al.: FAIR principles: interpretations and implementation considerations. Data Intell. 2(1–2), 10–29 (2020)

    CrossRef  Google Scholar 

  9. Guizzardi, G.: Ontology, ontologies and the “I’’ of FAIR. Data Int. 2(1–2), 181–191 (2020)

    Google Scholar 

  10. Janowicz, K., Haller, A., Cox, S.J., Le Phuoc, D., Lefrançois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Semant. 56, 1–10 (2019)

    CrossRef  Google Scholar 

  11. Karim, F., Vidal, M.-E., Auer, S.: Compact representations for efficient storage of semantic sensor data. J. Intell. Inf. Syst. 57(2), 203–228 (2021). https://doi.org/10.1007/s10844-020-00628-3

    CrossRef  Google Scholar 

  12. Koesten, L., Simperl, E., Blount, T., Kacprzak, E., Tennison, J.: Everything you always wanted to know about a dataset: studies in data summarisation. Int. J. Hum. Comput. Stud. 135, 102367 (2020)

    CrossRef  Google Scholar 

  13. Kremen, P., Necaský, M.: Improving discoverability of open government data with rich metadata descriptions using semantic government vocabulary. J. Web Semant. 55, 1–20 (2019). https://doi.org/10.1016/j.websem.2018.12.009

    CrossRef  Google Scholar 

  14. Lefort, L., Bobruk, J., Haller, A., Taylor, K., Woolf, A.: A linked sensor data cube for a 100 year homogenised daily temperature dataset. In: Proceedings of the 5th International Workshop on Semantic Sensor Networks, vol. 904, pp. 1–16 (2012)

    Google Scholar 

  15. Mons, B., Neylon, C., Velterop, J., et al.: Cloudy, increasingly fair; revisiting the FAIR data guiding principles for the European open science cloud. Inf. Serv. Use 37(1), 49–56 (2017)

    Google Scholar 

  16. Patroumpas, K., Skoutas, D., Mandilaras, G.M., Giannopoulos, G., Athanasiou, S.: Exposing points of interest as linked geospatial data. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases, pp. 21–30 (2019)

    Google Scholar 

  17. Raskin, R.: Development of ontologies for earth system science. In: Geoinformatics: Data to Knowledge. Geological Society of America (2006)

    Google Scholar 

  18. RDA Fair Data Maturity Model Working Group: FAIR Data Maturity Model. Specification and Guidelines, June 2020. https://doi.org/10.15497/rda00050

  19. Roussey, C., Bernard, S., André, G., Boffety, D.: Weather data publication on the LOD using SOSA/SSN ontology. Semant. Web 11(4), 581–591 (2020)

    CrossRef  Google Scholar 

  20. Suárez-Figueroa, M.C., Gómez-Pérez, A., Fernández-López, M.: The neon methodology framework: a scenario-based methodology for ontology development. Appl. Ontol. 10(2), 107–145 (2015). https://doi.org/10.3233/AO-150145

    CrossRef  Google Scholar 

  21. Wilkinson, M., Dumontier, M., Aalbersberg, I.J., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3(1), 1–9 (2016)

    CrossRef  Google Scholar 

  22. Wilkinson, M., Dumontier, M., Sansone, S.A., et al.: Evaluating FAIR maturity through a scalable, automated, community-governed framework. Sci. Data 6(1), 1–12 (2019)

    CrossRef  Google Scholar 

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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. https://doi.org/10.1007/978-3-030-98876-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-98876-0_7

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