Computation is playing an increasingly central role in research. Journals, funders, and other researchers are calling for published research to include associated data and code. But what are the best practices and tools for sharing code and data? This workshop will explore the current landscape of reproducibility and introduce steps to prepare research code and data for computationally reproducible publication. The workshop starts with an analysis of computational reproducibility and its relevance to researchers, followed by guided work with code and data: preparing code for reuse, documentation, and submitting their code and data to share. We will finish with an in-depth demonstration of 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. Participants should bring a laptop to fully participate.
This free workshop is open to the Princeton community.
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.
Shahar is a user experience specialist, creative technologist, and software developer; he holds a Master's degree from the Interactive Telecommunications Program (ITP) at New York University, and a B.Sc. in Neuroscience from the Hebrew University in Jerusalem.