MT9 Publishing Reproducible Code and Data: A Hands-on, Bring-Your-Own-Code CourseCourse Chairs: April Clyburne-Sherin, Outreach Scientist, Code Ocean
Instructor: April Clyburne-Sherin, Outreach Scientist, Code Ocean
Description: Creating research that is computationally reproducible is challenging but is increasingly expected and mandated by funders and journals. Fortunately, the process of publishing reproducible data and code has been made possible through new research tools. In this course, students will practice techniques for preparing a reproducible publication using their own data and code.
Over two sessions, this course will teach researchers how to create a Jupyter Notebook using their code and data, and how to publish their notebooks online so their code can be executed by anyone. In the first session, we will create Jupyter Notebooks following best practices for preparing data and code for sharing. In the second session, we will learn how to publish our Jupyter Notebooks using Code Ocean, an online computational reproducibility platform. Although we will focus on these tools for the course, the lessons generalize across platforms and languages.
After completing this course, students will be able to:
- Follow best practices for preparing code and data for publication.
- Overcome common barriers in preparing their own code and data for publication.
- Learn to use Jupyter Notebooks and Code Ocean to create a reproducible publication.
The audience for this course includes researchers and research support staff who are involved in the preparation and publication of research materials. Anyone with an interest in reproducible publications is welcome. This course is especially useful for those looking to learn practical steps for improving the computational reproducibility of their own research.
