About us

Transforming how researchers securely access and analyse data across multiple locations.

Trusted Research Environments (TREs) are secure locations in which data are safely available for researchers to analyse; however, it is difficult for researchers to perform analyses across multiple TREs, especially for cross-sector research. To meet this challenge, federation has emerged as a solution to enable independent organisations to collaborate through agreed-upon standards and protocols, while maintaining control over their own data, services, and platforms. However, variability amongst TRE implementations and analysis tools can prevent a federated approach to data access and research. To enable federated, cross-sector research, approaches are required that:

• are straightforward for independent systems to adopt,
• cater to a variety of system implementations, and
• operate using standards to facilitate safe access and use of data.

Federation in research is an approach to mitigate the challenges inherent when conducting research on sensitive and disparate data. This approach emphasises data always remaining with the collecting organisation. Here, code written by the researchers is sent to the data. In this system, all data analysis takes place within highly secure computer systems called TREs. A researcher writes their computer code, and this is then sent to each TRE, analysing only the data kept in that one TRE. The results from each TRE are then returned to the researcher and combined. There is a spectrum of federation in research:

• Discovery enables researchers to discover datasets across various data sources.
• Analytics enables researchers to perform analysis on data from disparate data sources.
• Learning enables researchers to use machine learning methods across different datasets.

Strategy

Foundationally, we are guided by the 20-year vision of Health Data Research (HDR) UK for large-scale data to benefit every interaction with patients, every clinical trial and every biomedical discovery, and to transform public health, where we align with HDR UK current mission is to accelerate trustworthy use of health data to enable discoveries that improve people’s lives.

Through federated research, new research data infrastructure can be deployed that supports health research; however, these solutions go beyond health and have the potential to enable more complex research across more diverse populations. From this, there are three questions that guide what good federated research is:

• What is possible?
• What is useful?
• What is acceptable?

These questions inform the objectives across the Federated Research programme:

• Secure infrastructure: Integrating and leveraging opportunities across the infrastructure programmes to add value and maximise user benefit.
• Safe tools and services: Creating a framework for federated services to advance health data science and support the deployment of advanced analytics by researchers.
• FAIR and transparency: Improving transparency around technical engineering and governance of federated research to make it easier for developers from other disciplines to get involved.
• Outreach and engagement: Collaborating with partner organisations to establish a community that can drive standards and best-practice while encouraging innovative approaches to federation and its application.

Collaborators

The core project team are composed of members from:

• Swansea University
• University of Dundee
• University of Manchester
• University of Nottingham

Capabilities

The Fed A programme is a multi-disciplinary, cross-institutional collaboration that has unique capabilities to enable Federated research, while considering what is possible, acceptable and useful.

Federated infrastructure

• Implementation of infrastructure that supports local TRE processing, using standardised APIs and layers to enable coordinated analytics across sites.
• Integration of best-practice infrastructure to promote scalability, compliance, and sustainability.

Federated architecture

• Developing scalable architecture that efficiently incorporates new partners/ datasets, which is resilient, flexible, and compliant.
• Preserving data privacy and sovereignty in-situ, thereby reducing legal and ethical concerns.
• Enabling collaborative research without the requirement of data centralisation, thereby accelerating data provisioning.

FAIR data

• Enhancing research credibility by developing transparent services to document methods, data sources, and analysis, for reproducibility.
• Promoting stakeholder trust by incorporating processes to support usage auditing, accountability, and data control.
• Fostering innovation with oversight, through inclusive cross-collaborative procedures to address ethical and legal obligations and mitigate any risks to data security.

Data standardisation

• Enabling cross-institutional research through common data structures, reducing need for site-specific transformation.
• Enabling federated research at scale, through use of common tools, services and methods that enhance data quality, comparability, and consistency.

Adoption and implementation

• Development of federated solutions that incorporate common data models, schemas, governance, and software that supports interoperability across distributed environments.
• Foster trust and engagement amongst stakeholders through collaborative objectives, and transparent decision-making processes.

Scalability and feasibility

• Federated deployment, through standardised platforms and workflows, which will support expansion while maintaining performance.
• Adapting to diverse technical environments by integrating with local TREs, which supports participation without hindering functionality.
• Reducing resource demands by leveraging existing systems and standards, where feasibility is being validated by use-cases.