Developing and Publishing Code for Trusted Research Environments: Best Practices and Ways of Working
It addresses practical challenges for researchers working with sensitive data in Trusted Research Environments, offering incremental guidance.
The report provides recommendations for software engineering and data science best practices tailored to researchers in the Wales Multimorbidity Machine Learning project, focusing on development workflows for Trusted Research Environments and advice for publishing code used with sensitive data.
This report discusses 3 distinct, but overlapping topics. Firstly, it recommends the tools and best practices for research software engineering and data science that are most relevant to the researchers working on the Wales Multimorbidity Machine Learning (WMML) project. Secondly, it expands upon these recommendations for the specific use case of Trusted Research Environments (TREs), with development workflows for computational research in TREs offered that respect and complement existing best practices. Finally, it discusses the considerations around publishing research code that is developed to run within a TRE on sensitive data, offering practical advice that researchers using TREs can follow.