aprilcs/byocode-2hr

Hands-on, step-by-step workshop to prepare your research for a computationally reproducible publication.

Preparing your data and code for computationally reproducible publication: A hands-on workshop for researchers

Computational analyses are 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.

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.

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.

Prerequisites

• Encapsulate all files within one directory.
• Separate code and data into folders named "code" and "data".
• Create GitHub account and upload code to GitHub.
• To take advantage of the BYOCode aspect, you should bring R code of your own that runs + the data it runs on. Feel free to come if you don't have code - you may follow along with example code and data.
• Bring a laptop to fully participate.

Topics

• Essential information about computational reproducibility
• Organizing your code and data
• Preparing your data for publication
• Preparing your code for publication
• Documenting your research for reuse
• Sharing your code and data

Instructor bio

April Clyburne-Sherin is an epidemiologist, methodologist, and expert in open science tools, methods, training, and community stewardship. She holds an MS in Population Medicine (Epidemiology). Since 2014, she has focussed on creating curriculum and running workshops for scientists in open and reproducible research methods (Center for Open Science, Sense About Science, SPARC). In her current role as Outreach Scientist at Code Ocean, she trains scientists in computational reproducibility best practices.

Questions

If you have any questions or feedback, please open an issue or contact April Clyburne-Sherin ([email protected]).