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.

Approach

Federated Research is driven to transform how researchers securely access and analyse data across multiple locations. Achieved by promoting independent organisations to work together through agreed-upon standards, protocols, principles, while keeping control over their own data, services, or platforms. There are four themes that guide the Federated Research collaborative:

Adopt Standards

• Use open standards to provide quality, consistent and reliable processes to reduce friction in deployment
• Developers and communities are supported in developing and connecting tools to a broader ecosystem

Build Trust

• Incorporate best-practice principles to guide handling of sensitive data
• Involvement with leading organisations to design and develop suitable research data infrastructure.
• Collaborate with the public to promote transparency, garner acceptance and ensure operation within social licence

Enable Federation

• Federation is more than software or any specific platform. It’s many, working together to form an ecosystem
• Beyond technical possibilities, there are elements of trust, autonomy, and governance to inform what is acceptable and useful
• There is no one-size-fits-all model of federation. How these networks are deployed is determined by trust, data maturity, technical infrastructure, and resources

Accelerate Research

• Conducting research on data that remains at the source maintains safety and security for people
• Expansion of what data is available promotes research that is inclusive and representative of diverse populations
• Improved efficiency of research lifecycle

Capabilities

Federated Research is an approach to leverage unique capabilities across multi-disciplinary and cross-institutional collaborators, to understand what good federated research is.

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.