Metrology for data in life sciences, healthcare and pharmaceutical manufacturing: Case studies from the National Physical Laboratory




NMI, metrology, digital pathology, medicines manufacturing, metadata standards, data quality, ontologies, FAIR principles


Data metrology, i.e., the evaluation of data quality and its fitness-for-purpose, is an inherent part of many disciplines including physics and engineering. In other domains such as life sciences, medicine, and pharmaceutical manufacturing these tools are often added as an afterthought, if considered at all. The use of data-driven decision making and the advent of machine learning in these industries has created an urgent demand for harmonised, high-quality, content rich, and instantly available datasets across domains. The Findable, Accessible, Interoperable, Reproducible principles are designed to improve overall quality of research data. However, these principles alone do not guarantee that data is fit-for-purpose. Issues such as missing data and metadata, insufficient knowledge of measurement conditions or data provenance are well known and can be aided by applying metrological concepts to data preparation to increase confidence. This work conducted by National Physical Laboratory Data Science team showcases life sciences and healthcare projects where data metrology has been used to improve data quality.






Research Papers