Preparing Code and Data for Computational Reproducibility

Computational analyses are playing an increasingly central role in research and are a feature of many advanced digital libraries. Journals, sponsors, and researchers, including in the digital library field, are calling for published research to include associated data and code. However, many involved in research have not received training in best practices and tools for building systems (e.g., using containers) and implementing methods that facilitate sharing code and data. This tutorial aims to address this gap in training while also providing those who support researchers with curated best practices guidance and tools. This tutorial is unique compared to other reproducibility events due to its practical, step-by-step design. It is comprised of hands-on exercises to prepare research code and data for computationally reproducible publication. Although the tutorial starts with some brief introductory information about computational reproducibility, the bulk of the tutorial is guided work with data and code. The basic best practices for publishing code and data are covered with curated resources. Examples will include from the digital library and information retrieval domains. Participants move through preparing research for reuse, organization, documentation, automation, and submitting their code and data to share. Tools to support reproducibility will be introduced but all lessons will be platform agnostic.