Good Practices in Research Software Development

After attending this training, participants can ...

• understand key principles of good practices in research software development and their role in enabling open and reproducible research;
• be aware of common standards for code quality, including coding style, documentation, and maintainability;
• gain insight into the use of version control systems (e.g., Git) for collaborative and transparent research workflows;
• understand the importance of testing (unit testing, functional testing, and code coverage) and how it contributes to reliable scientific results;
• be familiar with approaches to ensure reproducibility in computational research, including workflow management and environment control;
• become aware of optimization strategies and when they are relevant in a research context;
• discover useful tools and resources across different programming languages (e.g., Python, R, C/C++, Fortran);
• connect with fellow researchers involved in research software development and exchange experiences across disciplines;
• learn best practices for deploying and sharing research software in a sustainable and reusable way.
• gain insight into the role of AI in software development.