1st Workshop on schemes, vocabularies, and ontologies for FAIR

Tuesday, September 13, 2022 - 14:45

Organized by:

  • Luiz Olavo Bonino da Silva Santos, University of Twente and Leiden University Medical Center, the Netherlands
  • Giancarlo Guizzardi, Free University of Bozen-Bolzano, Italy & University of Twente, the Netherlands
  • Clement Jonquet, French National Research Institute for Agriculture, Food and Environment, Mathematics, Informatics and STatistics for Environment and Agronomy research unit, Montpellier, France
  • Cassia Trojahn, Institut de Recherche en Informatique de Toulouse, France http://www.irit.fr/~Cassia.Trojahn, cassia.trojahn@irit.fr



Making the huge and diverse kinds of data produced by researchers, data stewards, and service providers, fully reusable and understood requires specific efforts. The Findable, Accessible, Interoperable and Reusable (FAIR) principles were elaborated to address these issues, describing a set of requirements for data reusability and interoperability. These principles have been gaining increasing attention in a range of different areas and applications, including in the industrial area. A key aspect in making data FAIR is the ability of machines to automatically find, access, interoperate, and reuse data with none or minimal human intervention. For that, the ability of properly and semantically describing data is essential. The workshop has the following main goals:

  • to bring together leaders from academia, industry and user institutions to discuss the adoption of FAIR principles in real-world requirements.
  • to serve to inform industry and user representatives about existing research efforts that may meet their requirements. 1
  • to investigate how the FAIR principles are supported by the use of schemes, vocabulaires, and ontologies that ideally are themselves FAIR.
  • to discuss the challenges and perspectives in adopting FAIR principles. The topics of interest include, but are not limited to, the following:
  • schemes, ontologies and vocabulaires for FAIR data and metadata;
  • domain and cross-domain ontologies for FAIR data;
  • making vocabularies and ontologies FAIR;
  • alignment of schemes, vocabulaires and ontologies for FAIR;
  • data management for FAIR data;
  • best practices for implementing the FAIR principles;
  • FAIRification process and use cases;
  • metrics for FAIRness assessment;
  • provenance in FAIR environments;
  • FAIR principles and open science;
  • FAIR principles and linked open data;
  • FAIR in industry, scientific communities (life science, digital humanities, health, smart cities, etc.).



FAIR data has a major impact for data producers and consumers. A key step toward improving FAIRness of data is the use of schemes, vocabularies, and ontologies for both data and metadata representation, which are at the core of the semantic web. These aspects are crucial in knowledge sharing at the crosssector for both research and business.

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