Computational analysis is playing an increasingly central role in research. Journals, funders, and other researchers are calling for published research to include associated data and code. However, many researchers have not received training in best practices and tools for sharing code and data. This is a step-by-step, practical workshop to prepare research code and data for computationally reproducible publication. The workshop starts with some brief introductory information about computational reproducibility, but the bulk of the workshop is guided work with code and data. We cover the basic best practices for publishing code and data. Participants move through organizing their files, creating a codebook, preparing their code for reuse, documentation, and submitting their code and data to share using Code Ocean.
Presented by April Clyburne-Sherin, CodeOcean
Audience:
Active researchers who use code in their research and wish to share it, those who plan to do research using code, or those who support researchers.
Preparation:
Bring a laptop to fully participate.
Participants may bring their own data and code to work through during the workshop. Participants should be able to successfully run the code they bring themselves. If you don't have code and data of your own to bring, you will follow along with example code and data.
Workshop goals:
Define computational reproducibility and its relevance to researchers.
Learn best practices for file organization, documentation, and sharing.
Apply FAIR Principles to your research.
Assess possible tools for publishing code and data.
Submit your code and data for publishing on Code Ocean.
Workshop topics:
Essential information about computational reproducibility
