Data provenance, curation and quality in metrology

James Cheney; Adriane Chapman; Joy Davidson; Alistair Forbes

doi:10.1142/9789811242380_0009

Data provenance, curation and quality in metrology

James Cheney, Adriane Chapman, Joy Davidson, Alistair Forbes

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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.

Original language	English
Title of host publication	Advanced Mathematical and Computational Tools in Metrology and Testing XII
Editors	F Pavese, A B Forbes, N F Zhang, A G Chunovkina
Place of Publication	Singapore
Publisher	World Scientific Press
Pages	167-187
Number of pages	21
ISBN (Electronic)	978-981-124-239-7, 978-981-124-238-0
ISBN (Print)	978-981-124-237-3
DOIs	https://doi.org/10.1142/9789811242380_0009
Publication status	Published - 17 Feb 2022

Publication series

Name	Advances in Mathematics for Applied Sciences
Volume	90
ISSN (Print)	1793-0901

Keywords

interval computations
measurement uncertainty
NP-hard problems
monotonicity
indirect measurements
uncertainty quantification

Access to Document

10.1142/9789811242380_0009Licence: All Rights Reserved

Cite this

Cheney, J., Chapman, A., Davidson, J., & Forbes, A. (2022). Data provenance, curation and quality in metrology. In F. Pavese, A. B. Forbes, N. F. Zhang, & A. G. Chunovkina (Eds.), Advanced Mathematical and Computational Tools in Metrology and Testing XII (pp. 167-187). (Advances in Mathematics for Applied Sciences; Vol. 90). World Scientific Press. https://doi.org/10.1142/9789811242380_0009

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Cheney, J, Chapman, A, Davidson, J & Forbes, A 2022, Data provenance, curation and quality in metrology. in F Pavese, AB Forbes, NF Zhang & AG Chunovkina (eds), Advanced Mathematical and Computational Tools in Metrology and Testing XII. Advances in Mathematics for Applied Sciences, vol. 90, World Scientific Press, Singapore, pp. 167-187. https://doi.org/10.1142/9789811242380_0009

Data provenance, curation and quality in metrology. / Cheney, James; Chapman, Adriane; Davidson, Joy et al.

Advanced Mathematical and Computational Tools in Metrology and Testing XII. ed. / F Pavese; A B Forbes; N F Zhang; A G Chunovkina. Singapore : World Scientific Press, 2022. p. 167-187 (Advances in Mathematics for Applied Sciences; Vol. 90).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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AU - Chapman, Adriane

AU - Davidson, Joy

AU - Forbes, Alistair

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N2 - 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.

AB - 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.

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KW - measurement uncertainty

KW - NP-hard problems

KW - monotonicity

KW - indirect measurements

KW - uncertainty quantification

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Cheney J, Chapman A, Davidson J, Forbes A. Data provenance, curation and quality in metrology. In Pavese F, Forbes AB, Zhang NF, Chunovkina AG, editors, Advanced Mathematical and Computational Tools in Metrology and Testing XII. Singapore: World Scientific Press. 2022. p. 167-187. (Advances in Mathematics for Applied Sciences). https://doi.org/10.1142/9789811242380_0009

Data provenance, curation and quality in metrology

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Data provenance, curation and quality in metrology

Abstract

Publication series

Keywords

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Fingerprint

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Turing Fellowship

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