Developing and Publishing Code for Trusted Research Environments: Best Practices and Ways of Working
Year: 2021
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Chalstrey, Ed
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funders: Wellcome Trust, Scottish Government, Alan Turing Institute, UK Research and Innovation, British Heart Foundation, Department of Health and Social Care, Cardiff University, Swansea University, Public Health Agency, Chief Scientist Office, Scottish Government Health and Social Care Directorate, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division
Awards: G1168129227, G1240744640, G1543817132, G171866114, G1898492273, G2045002217, G2678174603, Application of machine learning to discover new multimorbidity phenotypes associated with poorer outcomes, G4906577935, G5342683040, G5697004987, Administrative Data Research Centres 2018, G6471925529, G920190840
OpenAlex ID: W3213619577