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Multi-party trusted research environment federation: Establishing infrastructure for secure analysis across different clinical-genomic datasets

Authors: Nik-Zainal, Professor Serena; Seeger, Thorben; Fennessy, Rosanna; Hall, Eleanor; Moss, Parker; Coles, Geoff; Dunford, Maria Chatzou; +3 Authors

Multi-party trusted research environment federation: Establishing infrastructure for secure analysis across different clinical-genomic datasets

Abstract

Combining data assets for research has historically involved the movement of data between organisations. Trusted Research Environments (TREs) now offer an alternative to data sharing by providing secure environments for approved researchers to access and analyse data. However, data are still held separately. Even where researchers have permission to use data held in two separate TREs, moving data between organisations from one TRE to another can be costly, complicated and time consuming. The purpose of this project is to demonstrate how TREs can be enabled to ‘talk to each other’ to facilitate analysis across separate databases as if they were one, a process known as ‘federation.’ This project aimed to deliver the UK’s first demonstration of genomic data federation by bridging the TREs of the NIHR Cambridge Biomedical Research Centre and Genomics England without moving original data, only the combined analysis results. This report contains learnings from the project and sets out opportunities for future work to unlock unprecedented possibilities for research and discovery for long-term patient and public benefit. This work was funded by UK Research & Innovation [Grant Number MC_PC_21026] as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, which is delivered in partnership with Health Data Research UK (HDR UK) and ADR UK (Administrative Data Research UK).

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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