Health data can help researchers spot diseases earlier, improve treatments, and make healthcare more personal. Yet accessing it is slow, and working together is difficult. The Data Matryoshka aims to help speed up health research by introducing progressive data layers—dataset versions each with the right detail and protection for different research stages.
Picture the traditional Matryoshka doll—shaped like an egg, and when you lift the top half, there’s a smaller but identical one inside. Our data doll works the same way. At the very centre is the precious patient data, safely protected. The next layer out is anonymised data ready for researchers. On the outside is safe, synthetic data.
What is synthetic data? Sometimes called fake, artificial or simulated, it looks like real data but is built from scratch. Researchers who access it first understand what the real data looks like. They can plan their work better, learn from others, and even share examples showing how an analysis was done.
During the COVID-19 pandemic, research teams wanted NHS data but waited months while NHS teams prepared and anonymised it. That delay cost time we did not have. Now we use synthetic data to build catalogues of what we hold, to work together across the UK’s growing network of secure environments, and to publish examples of the data used for scientific research.
Researchers should be able to prepare, test and improve their methods before requesting access to real patient records. With realistic examples first, they can design better studies, test their software, and spot problems early. Research becomes faster and more efficient. Patient privacy stays protected at every stage.
The Matryoshka team brings together University College London, the Alan Turing Institute, University College London Hospitals NHS Foundation Trust, Great Ormond Street Hospital, OneLondon (the secure data environment serving London’s health services) and Synthetic Data UK. We combine health research, secure data infrastructure, data science, governance and patient involvement expertise. Using OMOP (Observational Medical Outcomes Partnership), a shared standard for organising health records, we show how organisations work with data safely and consistently, in one hospital, across the UK, or internationally.
What The Data Matryoshka aims to achieve
Upon completion, the Matryoshka team will create practical tools and guidance for using synthetic data safely throughout the health research journey. We will:
- Connecting synthetic and real-world research – create layered workflows that move researchers from synthetic data through to real data access, using OMOP as the shared language
- Help generate synthetic data – build and refine tools that generate useful synthetic versions of complex NHS datasets
- Transparent governance – develop clear governance frameworks that show patients, researchers and NHS teams how decisions about data access are made
- Building confidence in synthetic data – create training materials explaining the benefits, risks and safeguards of synthetic data for researchers and information governance professionals
- Enabling cross-Trusted Research Environment (TRE) collaboration – show how TREs across the UK can work together using reusable, scalable approaches
- Involve the public – work with patients and community members throughout, so their perspectives shape everything we design
Progress so far
In the first six months, the Matryoshka team has:
- Built the progressive data-layer model showing how public synthetic data, private synthetic data and real data each play a role at different research stages
- Shown how synthetic data lets researchers explore data structures, test software and refine methods before they request access to real patient records
- Started developing a synthetic-to-real pathway using OMOP, so the same approach works across different organisations
- Brought together our partners to shape the project’s technical design and practical implementation
- Worked with the UCLH NHS Data Trust Committee, a group of patients and staff, who contributed to governance design, reviewed example datasets and helped shape how we explain synthetic data
- Found that private synthetic data needs careful safeguards even though it contains no identifiable patient records, because realistic data can still reveal sensitive information about population groups or rare conditions
In the final six months, we will test reusable workflows and governance frameworks, refine example datasets with public contributors, and share practical learning on how organisations can adopt transparent, scalable approaches to secure health-data research.
Relevance to the DARE UK vision
Data Matryoshka develops new capabilities for TREs that make sensitive data research more useful, transparent and trustworthy. Our tools, governance approaches, and learning will help the research community prepare studies earlier and work together more effectively. Access to more sensitive data stays proportionate, secure, and accountable.
About the DARE UK Next-Gen Catalysts
The DARE UK Next-Gen Catalysts are early-stage, exploratory projects developing and testing the next generation of capabilities for Trusted Research Environments (TREs) across the UK. Bringing together researchers, technology specialists, public contributors and other partners, these projects explore bold new ideas that could expand how sensitive data is accessed, analysed and governed while maintaining privacy, security and public trust. Their role is to generate evidence, prototypes and practical learning that can help shape the future of secure, trustworthy data research for public benefit.
