This work explores the use of Trusted Research Environments for the secure analysis of sensitive, granular data. When secure infrastructure and overall data governance practices are in place, accredited researchers are able to access a wealth of detailed data and resources to facilitate more targeted local policy analysis. Working with data within such infrastructure as part of a larger research project involves advanced planning and coordination to work efficiently. As new and novel granular data resources become available (e.g. administrative digital health records or consumer data), a wealth of local policy insights can be gained across issues of public health or local economic vitality. Much of these new forms of data however often come with a large degree of sensitivity around issues of personal identifiability and how the data is used for public facing research.
Within this context, our work presents the Local Data Spaces (LDS) project, which was a targeted rapid response and cross-disciplinary collaborative initiative related to understanding local COVID-19 out- comes and recovery challenges. The project allowed accredited LDS researchers to access granular data in a secured TRE - the Office for National Statistics’ Secured Research Service (SRS), for localized tracking of public health and economic outcomes over the course of the pandemic. Embedded researchers worked on co-producing a range of locally focused insights and reports built on secured data and made appropri- ately open and available to the public and all local stakeholders for wider use.