Making Science Reproducible - A Capstone Course

This course is for learners who have already taken previous courses in our Reproducible Research Workflows Specialization. It provides hands-on activities to allow students to test out the skills that they learned in the Specialization including the ITCR Training Network’s Reproducibility in Cancer Informatics, Advanced Reproducibility in Cancer Informatics, Containers for Scientists, and GitHub Automation for Scientists courses. This capstone course builds on these foundational concepts. Students of this specialization learn about version control, containerization, and automation to help make their research more transparent and reproducible. The activities in the capstone will cover: - Making and storing secrets on GitHub (using sensitive API keys, tokens, passwords in code securely) - Creating GitHub Actions (automating tasks including running and testing code such as for continuous integration and deployment) - Building Docker images (creating and sharing computing environments to run code) These activities will help students to create a computing environment that can be shared with others and used to test out code in your repositories on GitHub. Unique features of this course: - Provides hands-on exercises with evaluation to prove that learners were successful - Practical everyday tips - Troubleshooting tips - Links out to our other courses for more information The content will help learners to more reproducibly work with data for scientific research. Key Words Reproducibility, Containers, Version Control, GitHub Actions, Continuous Integration, Docker Intended Audience - The course is intended for researchers and research staff who might be interested in learning about best practices and skills for reproducible science. - Students should be comfortable with basic GitHub operations, including creating repositories, making commits, and opening pull requests before beginning this capstone. - The expectation is that learners will have taken the other