Treffer: Clean Code Principles for Scientists: Enhancing Code Quality and Collaboration

In the scientific community, producing high-quality and maintainable code is crucial for accelerating research progress and facilitating collaboration. Clean code enhances readability, making scientific software more easy to understand and reuse by peers and can therefore play a key role in producing FAIR research software. By following clean code principles such as modularization and code organization, code becomes more accessible and reusable, enabling scientists to build upon existing work and share their code with others. Furthermore, clean code enhances maintainability, making it easier to update and modify code to adapt to changing research requirements. By embracing clean code, scientists can create software that is in line with the FAIR principles, fostering reproducibility, collaboration, and the advancement of scientific knowledge. The workshop will begin by providing an overview of the significance of clean code in scientific research, with a particular focus on the advantages it offers in terms of code quality, productivity, and collaboration. Participants will learn how clean code principles align with the unique challenges faced by scientists, such as changing requirements and the need for reproducibility. Throughout the workshop, participants will learn about the core concepts of clean code, covering topics such as naming conventions, code organization, and effective commenting. Practical exercises and examples will reinforce these principles, allowing participants to experience firsthand the impact of clean code on readability and maintainability. The workshop will emphasize the importance of refactoring techniques, teaching participants how to improve existing code without altering its functionality. Participants will explore code smells and anti-patterns commonly encountered in scientific codebases, and learn appropriate refactoring strategies to address them. Through hands-on exercises, participants will gain experience in extracting methods, reducing code duplication, and simplifying complex ...