Discussing Data: A UK-wide public consultation on the use of low fidelity synthetic data for research

IntroductionSynthetic data is an emerging tool for health and social care researchers which can be used to expedite research processes and enhance the safety of personal data handling. Although data owners across the UK and globally currently make synthetic datasets available, public awareness and perceptions of its use for research remain underexplored. ObjectivesWe aimed to agree a set of recommendations for data owners on producing and releasing synthetic data and communicating with the UK public about this topic. MethodsForty-four public members were invited to four deliberative workshops to co-create a set of recommendations for data owners. Notes and transcripts from workshops 1-3, along with public member survey feedback, were reviewed to identify key topics. ResultsThirty-seven individuals contributed. We developed ten recommendations spanning five areas: introducing synthetic data; explaining its purpose; creating datasets; managing access, use, and misuse; and clear communication. These were iteratively refined with public input during the final workshop. ConclusionsThis study offers a unique insight into public attitudes towards synthetic data use in health and administrative research. The recommendations developed are intended to support UK data owners in releasing synthetic data responsibly, aligning with public expectations and fostering trust in its use as a research innovation tool. Future research should continue to assess evolving public attitudes to both low and high-fidelity data as its use expands.

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Publication Details

Journal
International Journal for Population Data Science
Published
2025-11-27
DOI
https://doi.org/10.23889/ijpds.v10i1.2962
Primary Topic
Data Analysis and Archiving
Type
article
Field-Weighted Citation Impact
0.00

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article

Discussing Data: A UK-wide public consultation on the use of low fidelity synthetic data for research

Sean Johnson, Kim Munnery, Rob Trubey, Claire Nollett et al.
International Journal for Population Data Science
Data Analysis and Archiving
article

Discussing Data: A UK-wide public consultation on the use of low fidelity synthetic data for research

Sean Johnson, Kim Munnery, Rob Trubey, Claire Nollett, Joshua Stock, Michael Robling, Fiona Lugg‐Widger, Lucy Brookes‐Howell, Jim Fitzgibbon, Farheen Yameen
article en

Abstract

IntroductionSynthetic data is an emerging tool for health and social care researchers which can be used to expedite research processes and enhance the safety of personal data handling. Although data owners across the UK and globally currently make synthetic datasets available, public awareness and perceptions of its use for research remain underexplored. ObjectivesWe aimed to agree a set of recommendations for data owners on producing and releasing synthetic data and communicating with the UK public about this topic. MethodsForty-four public members were invited to four deliberative workshops to co-create a set of recommendations for data owners. Notes and transcripts from workshops 1-3, along with public member survey feedback, were reviewed to identify key topics. ResultsThirty-seven individuals contributed. We developed ten recommendations spanning five areas: introducing synthetic data; explaining its purpose; creating datasets; managing access, use, and misuse; and clear communication. These were iteratively refined with public input during the final workshop. ConclusionsThis study offers a unique insight into public attitudes towards synthetic data use in health and administrative research. The recommendations developed are intended to support UK data owners in releasing synthetic data responsibly, aligning with public expectations and fostering trust in its use as a research innovation tool. Future research should continue to assess evolving public attitudes to both low and high-fidelity data as its use expands.

International Journal for Population Data ScienceVol. 10(1)
Cardiff University (GB)
UK Research and Innovation
Openalex Percentile: Top 54%
Data Analysis and Archiving
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