Introducing Students to Research and Reproducibility with Open Science Tools

Abstract The adoption of transparent, reproducible, and open research can result in increased credibility and quality of research outputs for peers to confidently reuse. There are many digital open science tools and platforms that help organize lab notes, protocols, and code for open dissemination, but how confident can students or new researchers practice reproducible research with these tools? This presentation outlines how open science tools were integrated into a short summer course for undergraduate students. In this course, students were introduced to research first through the basics of the scientific method, the specific stages of the research lifecycle, and how open science practices can be applied at each stage. Simultaneously, students practiced research skills through Python exercises with data in Jupyter Notebooks. The course culminated with a reproducibility study, where students attempted to reproduce and build on samples of computational research, where outputs were prepared with and without open science tools. By doing this, students could experience and evaluate two different approaches to research dissemination. The reflections and products of students that participated in the course offered insight into how students adopt these tools and how these tools impact reproducibility of computational research.