New course supports researchers to build more reproducible analytical workflows

A new online training course has been launched to help researchers develop more reliable and reproducible research workflows using Python and R.

The free, self-paced course, Testing in Research Workflows, introduces practical approaches to testing research code, helping researchers identify errors, improve the quality of their analyses and ensure their work remains reliable as projects develop.

The course introduces researchers to automated testing and defensive programming, demonstrating how these approaches can help identify problems with code, data and analytical outputs.

Through a practical healthcare case study, participants will learn how to write and run tests using Python and R, explore different testing approaches, and use tools such as GitHub Actions to automate checks within their research workflows.

It also includes supporting code and examples that participants can follow during the course and revisit afterwards.

The course is designed for anyone who writes or maintains analytical code using Python or R and wants to improve the reliability and reproducibility of their work.

It is self-paced and assumes some basic familiarity with writing code in either language.

The course was developed by Amy Heather, Postdoctoral Research Associate and Tom Monks, Associate Professor of Health Data Science, both based within the Peninsula Collaboration for Health Operational Research and Data Science (PenCHORD) at the University of Exeter, with production and editing support from HDR UK.

The course was developed as part of the STARS project (Sharing Tools and Artefacts for Reproducible and Reusable Simulations in Healthcare), which aims to support good practice in healthcare simulation modelling and make research workflows more transparent, reliable and reproducible.

The project is supported by the Medical Research Council and the National Institute for Health and Care Research (NIHR), through the ARC South West.