Scientific communities produce a valuable amount of data as a direct or side product of their research, which can be potentially explored in many different applications. However, making data open and accessible requires considerable efforts in order to guarantee the data quality and compliance to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability).
Semantics4FAIR is one of the projects selected by the French National Research Agency (ANR) on the FLASH CALL Open Science entitled "research practices and open research data". It aims at facilitating the tasks of finding and accessing scientific data that results from both research and production by a scientific community, in order to support the development of new usages by other scientific communities. The originality of the proposed approach is twofold: (i) a human factor method to capture user’s needs and vocabularies; and (ii) a semantic approach takes up the findability challenge.
We plan to build and reuse several ontologies to account for the various points of view on the data and the relations between these views: one ontology will account for the data producers’ view, and the users’ vocabulary refers to a different ontology. These ontologies will be then used to describe the data, its provenance and usages, and will be the basis for the development of services querying and consuming data.
This work will rely on the collaboration of a computer science lab (IRIT) and a human-factor institute (MSH-T) with scientific communities that want to make their datasets FAIR, and scientific communities that want to reuse this data for their own research projects. We propose to test the approach thanks to a joint work with the atmospheric scientific community (OMP and CNRM) as meteorology data providers, and the Palynologist community (GET) and meteorology data exploitation (MeteoFrance) services as two data user communities.
Amina Annane, Mouna Kamel, Nathalie Aussenac-Gilles, Cassia Trojahn, Catherine Comparot, Christophe Baehr. Un modèle sémantique en vue d’améliorer la FAIRisation des données météorologiques. Journées Francophones d’Ingénierie des Connaissances (IC) Plate-Forme Intelligence Artificielle (PFIA 2021), Collège SIC (Science de l’Ingénierie des Connaissances) de l’AFIA, Jun 2021, Bordeaux, France. pp.20-29.
Amina Annane, Mouna Kamel, Cassia Trojahn, Nathalie Aussenac-Gilles, Catherine Comparot, et Christophe Baehr. SYNOP Data Evaluation Using FAIR Maturity Model. [Research Report] IRIT/RR–2021–03–FR, IRIT - Institut de Recherche en Informatique de Toulouse. 2021.
Louis Mendy. Spécialisation d’un logiciel de gestion de métadonnées sémantiques pour la description des jeux de données. [Internship Report], IRIT - Institut de Recherche en Informatique de Toulouse. 2021.
Alexandre Champagne. Utilisation d’ontologies pour la recherche de jeux de données météorologiques. [Internship Report], IRIT - Institut de Recherche en Informatique de Toulouse. 2021.
post-doctoral position at Paul Sabatier-Toulouse3 University
research engineer at CNRS
assistant professor at Perpignan University
Student at Paul Sabatier-Toulouse3 University
Student at Paul Sabatier-Toulouse3 University
assistant professor at Jean Jaures-Toulouse2 University
master student at Jean Jaures-Toulouse2 University
researcher at CNRS
OMP-SEDOO, research engineer
research engineer at CNRS
research engineer at CNRS
This project has received funding under grant agreement No ANR-19-DATA-0014-01 from the French National Research Agency (ANR).