Thank you for your interest in hosting a reproducibility workshop at your institution. We are currently offering two types of reproducibility workshops:
Description: 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.
Description: With the aim of improving the reproducibility of the research they publish and fund, journals and funders are increasingly calling for published research to include associated data and code. However, many researchers have not received training in the best practices and tools for reproducibly sharing their research. This workshop introduces reproducibility best practices that are applicable across disciplines. We will provide a survey of the history and current landscape of reproducibility and identify the common barriers. We will present practical solutions and resources. Finally, we will demonstrate tools that help researchers overcome barriers to reproducibility and practice using these tools during hands-on activities, including how to share their code and data using Code Ocean.
Audience: Researchers, students, librarians, and those interested in research best practices.