Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies - Jean-Laurent Hippolyte; Marina Romanchikova; Maurizio Bevilacqua; Paul Duncan; Samuel E. Hunt; Federico Grasso Toro; Anne-Sophie Piette; Julia Neumann (Metrology, 2023)
Achieving the highest levels of compliance with the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship requires machine-actionable semantic representations of data and metadata. Human and machine interpretation and reuse of measurement datasets rely on metrological information that is often specified inconsistently or cannot be inferred automatically, while several ontologies to capture the metrological information are available, practical implementation examples are few. This work aims to close this gap by discussing how standardised measurement data and metadata could be presented using semantic web technologies. The examples provided in this paper are machine-actionable descriptions of Earth observation and bathymetry measurement datasets, based on two ontologies of quantities and units of measurement selected for their prominence in the semantic web. The selected ontologies demonstrated a good coverage of the concepts related to quantities, dimensions, and individual units as well as systems of units, but showed variations and gaps in the coverage, completeness and traceability of other metrology concept representations such as standard uncertainty, expanded uncertainty, combined uncertainty, coverage factor, probability distribution, etc. These results highlight the need for both (I) user-friendly tools for semantic representations of measurement datasets and (II) the establishment of good practices within each scientific community. Further work will consequently investigate how to support ontology modelling for measurement uncertainty and associated concepts.