CE_28C: Everyday Reproducibility: Simple Flexible Tools for Making Analyses more Accessible and Reproducible (Added Fee)

Ensuring that analyses are reproducible is important for statisticians from academics to industry. The ability to reproduce third-party results is fundamental to the scientific process itself as well as to the public confidence in this process. In addition to ensuring that analyses are reproducible, it is important that analyses can be easily shared, accessed, and explored. While critically important, building analyses that are computationally reproducible, shareable, and accessible is not trivial. This 'reproducibility crisis' been recognized in popular science and professional statistical societies alike. Indeed, the desire to make analyses reproducible, shareable, and accessible, has led to significant development of computational tools and discussion of best practices within the statistical community. This half-day course exposes participants both to a conceptual discussion of reproducibility and its role in statistics and data analysis, as well as providing a concrete survey of pragmatic tools and practices that can be straight-forwardly adopted by practitioners to help enhance the reproducibility, share-ablility, and accessibility of analyses they create. This course covers a selection of practical tools and paradigms that can be widely adopted by statisticians across an array of fields, computing environments, and reproducibility goals. Participants must have basic familiarity with some computing language/environment.