Towards FAIR Research Data in Metrology




FAIR data principles, Research Data Management, Metrology, Data management plan, Semantic technologies, Ontologies, Measurement units


Good data management is necessary to maintain the trustworthiness and reliability of data. This is particularly important in metrology, the science of measurement, which ensures stable, comparable, coherent, and traceable measurement results. The digitalization of metrology has increased the demand for structured and harmonised research data management (RDM).

To meet this demand, the project TC-IM 1449 "Research data management in European metrology" was established in 2018. The project aims to promote good RDM practices underpinned by the FAIR principles, supporting  traceability and reproducibility of measurement results. For that purpose, the project is providing researchers with the knowledge, competency, awareness, and tools to implement good RDM practices.

The project has formulated a vision for RDM in metrology for the support of scientists by developing and disseminating recommendations and in the organisation of training. As part of this vision, the project has produced several deliverables, including a template research data management policy, guidelines for data documentation, creation of metadata, and quality assurance for data publication. The project is also creating a comprehensive guide to RDM, a checklist for project coordinators, and providing training modules.

The project's activities reflect the needs of metrologists that are collated and communicated by the technical experts from the relevant Technical Committees and European Metrology Networks. Furthermore, the project's deliverables will be an invaluable resource for researchers seeking to effectively manage and share their research data.


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BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OIML. International vocabulary of metrology | Basic and general concepts and associated terms (VIM). Joint Committee for Guides in Metrology, JCGM 200:2012. (3rd edition).

Hippolyte J-L, Romanchikova M, Bevilacqua M, Duncan P, Hunt SE, Grasso Toro F, Piette A-S, Neumann J. Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies. Metrology. 2023; 3(1):65-80.

(GO FAIR) GO FAIR Initiative. FAIRification Process. Available online:

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ISO 8000-1:2022 Data quality:




How to Cite

Lanza, G., Koval, M., Hippolyte, J.-L., Iturrate-García, M., Pellegrino, O., Piette, A.-S., & Grasso Toro, F. (2023). Towards FAIR Research Data in Metrology. Proceedings of the Conference on Research Data Infrastructure , 1.
Received 2023-04-26
Accepted 2023-06-29
Published 2023-09-07