Projects of the Data Research, Access and Governance Network (DRAGoN)

An overview of our current and completed research projects since DRAGoN was formed in June 2020. In addition, we also provide regular training and consultancy.

Current projects

Qualidata Use and Confidential Knowledge (QUACK)

  • Project lead: Elizabeth Green
  • Project team: Professor Felix Ritchie
  • Partner organisations: ICPSR, University of Michigan, GESIS, University of Mannheim, Heidelberg University, UK Data Archive
  • Funding: UWE Bristol internal funding
  • Expected completion date: December 2022

When research data consists of personal identifiable information, there is a risk that publishing analyses will inadvertently release confidential information about data subjects. Quantitative data has well-established practices to manage this risk. As well as statistical theory, there are widely used practical guidelines and teaching materials. Research in statistical disclosure control (SDC) was mostly sponsored by national statistics institutes (NSIs), which provided both the motivation and the market for the research.

In contrast, there are almost no guidelines for qualitative data. Researchers working with qualitative data must trust to their own judgment and experience, often without any training or mentoring. As well as increasing the risk of confidentiality breach, this is inefficient, as each generation must learn the same lessons for itself.

One reason for this is the sheer range of qualitative data: ethnographic studies, social media analyses, interviews, videos, clinical case studies, court records. Guidelines in one field may be meaningless in another. A subsidiary reason is that there is no equivalent of the NSI network to sponsor qualitative data research. The Qualidata Use and Confidential Knowledge (QUACK) project seeks to scope and develop both principles and guidelines for working with disclosive qualitative data.

Completed projects

Wage and Employment Dynamics (WED) - Phases 2 and 3

The Wage and Employment Dynamics (WED) project’s primary aim is to develop a sustainable, documented ‘wage and employment spine’ with the potential to fundamentally transform UK research and policy analysis across a vast range of topics. Alongside the creation of data infrastructure, the project will also generate research findings of direct interest to policy makers. Public benefit will be maximised through the provision of high-quality metadata and training for users.

Key outputs:

ESRC Future Data Services Strategic Fellows' papers

  • Project lead: Elizabeth Green
  • Project team: Professor Felix Ritchie
  • Partner organisations: ICPSR, University of Michigan, GESIS, University of Mannheim, Heidelberg University, UK Data Archive
  • Funding: UWE Bristol internal funding
  • Expected completion date: December 2022

When research data consists of personal identifiable information, there is a risk that publishing analyses will inadvertently release confidential information about data subjects. Quantitative data has well-established practices to manage this risk. As well as statistical theory, there are widely used practical guidelines and teaching materials. Research in statistical disclosure control (SDC) was mostly sponsored by national statistics institutes (NSIs), which provided both the motivation and the market for the research.

In contrast, there are almost no guidelines for qualitative data. Researchers working with qualitative data must trust to their own judgment and experience, often without any training or mentoring. As well as increasing the risk of confidentiality breach, this is inefficient, as each generation must learn the same lessons for itself.

One reason for this is the sheer range of qualitative data: ethnographic studies, social media analyses, interviews, videos, clinical case studies, court records. Guidelines in one field may be meaningless in another. A subsidiary reason is that there is no equivalent of the NSI network to sponsor qualitative data research. The Qualidata Use and Confidential Knowledge (QUACK) project seeks to scope and develop both principles and guidelines for working with disclosive qualitative data.

EREOSDC (Outputs Statistical Disclosure Control)

Data is everywhere. Increasingly the data used for policy and analysis is confidential and needs to be protected. Analytical uses of data are moving to ‘trusted research environments’ (TREs), where users have great freedom to analyse data, but what they produce gets checked before release into the open to minimise confidentiality risks. UWE Bristol is a leader in telling people how to do this, but we are currently fragmented and running to stand still. This project is designed to allow us to

  • strengthen the theoretical foundations of what we do, including public engagement
  • develop re-usable resources for use by us (and others, with appropriate recognition
  • cement UWE Bristol’s reputation as a world leader in data governance
  • integrate and develop IT solutions UWE Bristol staff have built as pilots

There are four strands to output checking:

  • Checking of statistical outputs: UWE Bristol has the field almost to itself and is the leader
  • Checking of qualitative research outputs: an almost completely unresearched field
  • Checking of Artificial Intelligence (AI) outputs and machine learning models: a completely unresearched field
  • Statistical Disclosure Control (SDC) for national statistics; a well-established and very competitive field
 

Guidelines and Resources for Artificial Intelligence Model Access from Trusted Research Environments (GRAIMatter)

Project leads:

Project team:

Partner organisations:

  • University of Dundee (lead)
  • Swansea University
  • University of Edinburgh
  • University of Aberdeen
  • Durham University

Funding: DARE/Health Date Research UK

Completion date: August 2022

Researchers from the College of Arts, Technology and Environment at UWE Bristol are part of a consortium awarded £390K by the UKRI as funding for GRAIMatter (Guidelines and Resources for Artificial Intelligence Model Access from Trusted Research Environments) under a scheme run by DARE-UK which is developing a national research data infrastructure for the UK.

This project will investigate what technical, legal and ethical frameworks would enable Trusted Research Environments to safely manage the risk to individual’s privacy when allowing the release of AI-based models learned from the confidential data they host, such as medical records. Such models could have significant value for the public good - for example, diagnosing disease risk.

Professor Jim Smith (Computer Science Research Centre) is leading on technical AI-related aspects with Professor Felix Ritchie providing expertise on procedural/researcher-focused aspects and Dr Francesco Tava (College of Health, Science and Society) on ethical issues.

This project ties into the interdisciplinary DRAGoN which involves a number of College of Arts, Technology and Environment researchers, and which was awarded 100K investment from UWE Bristol's Expanding Research Excellence strategy.

Key outputs:

Process evaluation and R&D and innovation in data access and governance

Autumn school in data governance for low and middle-income countries

Advanced ethical models of data governance

The value of data governance

Review of statistical disclosure control options for output tables

Project lead: Elizabeth Green

Project team: Professor Felix Ritchie

Funder: HESA

Project duration: July 2021-March 2022

Output:

Externalities (wider costs and benefits) of data use (subcontractor)

Project lead: Damian Whittard

Project team: Professor Felix Ritchie

Partner organisation: Belmana Consulting (lead)

Funder: DCMS

Project duration: January 2021-March 2021

Output:

Automated disclosure control for research outputs

Project lead: Professor Felix Ritchie

Project team:

Funder: Eurostat

Project duration: January 2020-December 2020

Project summary: This project analysed whether and how output checking could be automated, and developed a proof-of-concept, ACRO, a working prototype for Stata users.

Output: 

Wage and Employment Dynamics (WED) - Phase 1

Project lead: Professor Felix Ritchie

Project team:

Partner organisations:

  • University College London (UCL)
  • Bayes Business School
  • University of Reading 
  • NIESR

Funder: ESRC

Project duration: October 2019-July 2022 (Phase 1)

Go to project website

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