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.

A taxonomy-based data model is proposed to create a knowledge system for managing engineering skills within an organisation, motivated by the need to balance organisational expertise requirements and availability. The model, adapted from the “European Skills, Competences, Qualifications, and Occupations” framework, is designed to categorise and evaluate skills relevant to the engineering department of the National Physical Laboratory. This allows extraction of quantitative data on individual staff members' skills and competency levels, and the necessary skills for specific Job Title and Job Role combinations. It distinguishes between “Job Titles”, official job designations, and “Job Roles”, unofficial designations categorising staff according to their work areas, allowing the model to conform with inherent organisational rigiditiy. The model can cross-reference information using specific queries, such as extracting skills from specific individuals and assessing if they meet their current job functions. This model enhances existing skill management frameworks by allowing for a traceable pathway for skill allocation, allowing for future expansion by including other departments. Integrating validation procedures to assess staff skills, such as the inclusion of proof attached to skills, can also be considered. It offers operational benefits like enhanced capability planning, informed staff development, optimised resource allocation, and improved training programmes.

A common understanding of measurement data is fundamental to our society and the Système International (SI) is a central foundation in that regard. To prepare the SI for upcoming decades, its underpinning knowledge model needs to be accessible by software agents for interoperable use of measurement data. In this work, the knowledge model is extracted from the SI Brochure and implemented using Semantic Web standards. In addition, web services are developed around the knowledge model to showcase and simplify the use of the developed ontology. The quality of the implementation is tested based on key questions which the knowledge model will be expected to answer.