About This Course

Research software engineering is crucial in health data research because it helps ensure that the software used to analyse and interpret health data is reliable, robust, and accurate. In health data research, software plays a critical role in analysing and interpreting large, complex and sensitive data sets. The software used in health data research must be able to handle vast amounts of data, perform complex computations, and produce results that are both accurate and trustworthy.

Using these resources

This course is designed to provide you with an understanding of software engineering practices and principles that are particularly relevant to the research community.

You will learn about various software development methodologies, tools, and techniques that are commonly used in research software engineering. You will also learn about best practices for software design, coding, testing, and documentation, and how to apply these principles to your own software projects. Furthermore, you will be introduced to the key challenges and considerations that are unique to the research software engineering domain, such as ensuring reproducibility and collaboration, and balancing the trade-offs between performance and code maintainability.

By the end of this course, you will have a solid introduction to research software engineering and the skills you need to develop high-quality, reliable software for your research projects.

Intended audience

This course is for aspiring and practising research software engineers in health data research.

Instructors

Heidi Seibold

Open Science Trainer

Digital Research Academy

Ted Laderas

Bioinformatics Trainer

DNAnexus

Ted Laderas is a bioinformatics trainer for DNAnexus. He trains ... Read more

Ben Busby

Principal Scientist

DNAnexus

Bob Turner

Senior Research Software Engineer

University of Sheffield

Jan Simson

Trainer

Digital Research Academy

Jan is a doctoral candidate at the LMU Munich and the Munich Cen... Read more