Speaker
Description
This semester marks the third time we have offered the course "FYS-8805: Collaborative Coding and Reproducible Research," which has typically attracted 5–7 PhD students per iteration. The course addresses the increasing importance of robust programming practices, collaborative workflows, and reproducible methodologies in modern research, equipping participants with essential skills in research software engineering that are applicable across diverse scientific disciplines.
Through a combination of interactive lectures, hands-on exercises, and collaborative problem-solving, participants gain practical experience with tools and practices such as version control (e.g., Git and GitHub), automated testing, code quality standards, and the use of high-performance computing resources. These skills are particularly valuable for researchers working with large datasets, complex simulations, or computational models. The course features a collaborative home exam, where students work in groups to develop and document a codebase, applying the principles of reproducibility and teamwork.
Over the past editions of the course, we have observed how students have successfully applied the skills learned in this course to their research and daily work. For example, one participant developed a remarkable GitHub repository as part of their research, creating a platform to systematically align the training and testing of state-of-the-art breast cancer risk prediction models in mammography. This platform not only streamlined data handling and documentation of popular open datasets for this task but also allows for the integration of new models. The student is now preparing to submit this work as an open software publication to a journal.
Open to PhD students and researchers from UiT, this 5 ECTS credit course is designed to meet the growing demand for computational proficiency and reproducible research.