Information: 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 your 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. Participants move through best practices for organizing their files, automating their analyses, documentation, and submitting their code and data for publication. Although workshop participants will be using the Code Ocean platform, the exercises and best practices are platform and discipline agnostic.
Prerequisites: Participants should bring their own code, data, and laptop. Example code and data will available for those without.
Audience: Researchers who use code in their research and wish to share it.
April 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) and is co-author of FOSTER's Open Science Training Handbook. In her current role as Outreach Scientist at Code Ocean, she trains scientists in computational reproducibility best practices.