Evidence map›Paper›PMID 41923947›Full record

ArticleInternational journal of population data science2025

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

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

Abstract read
In one paragraph

Article in International journal of population data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Claire NollettCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Fiona Lugg-WidgerCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Joshua StockCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Lucy Brookes-HowellCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Jim FitzgibbonPublic contributor.
Sean JohnsonCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Kim MunneryCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Michael RoblingCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.
Farheen YameenPublic contributor.
Rob TrubeyCentre for Trials Research, Cardiff University, 7th Floor, Neuadd Meirionnydd, Heath Park Campus, Cardiff, CF14 4YS UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Synthetic 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. Objectives: We 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. Methods: Forty-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. Results: Thirty-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. Conclusions: This 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.

Indexed as

Information DisseminationPublic OpinionCommunicationHumansUnited Kingdompublic attitudespublic deliberationpublic patient engagementpublic recommendationssynthetic data

Identifiers

PMID41923947
PMCPMC13037561

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.