Data provenance, curation and quality in metrology

TL;DR

This paper explores the role of data provenance and curation in enhancing data quality within scientific and industrial metrology, highlighting gaps between current standards and emerging needs.

Contribution

It provides a comprehensive survey of provenance technologies and standards relevant to metrology, and identifies key gaps and future research directions.

Findings

Provenance is crucial for data quality assessment in metrology.

Current standards have limited adoption in scientific and industrial contexts.

Identified gaps suggest need for tailored provenance solutions for metrology.

Abstract

Data metrology -- the assessment of the quality of data -- particularly in scientific and industrial settings, has emerged as an important requirement for the UK National Physical Laboratory (NPL) and other national metrology institutes. Data provenance and data curation are key components for emerging understanding of data metrology. However, to date provenance research has had limited visibility to or uptake in metrology. In this work, we summarize a scoping study carried out with NPL staff and industrial participants to understand their current and future needs for provenance, curation and data quality. We then survey provenance technology and standards that are relevant to metrology. We analyse the gaps between requirements and the current state of the art.