“Teaching Reproducible Research Inspiring New Researchers to Do More Robust and Reliable Science”, Karl Broman, University of Wisconsin, Mine Çetinkaya-Rundel, Duke University.
With recent emphasis on robust and reliable science, a minimal standard for data analysis and other scientific computations is that they be reproducible—that the code and data are assembled in a way that all the results can be re-created (e.g., the figures in a paper). While adopting a workflow that will make results reproducible will ultimately make a researcher’s life easier, this goal will not be easy to achieve without the right tools and organization.
In this webinar, three reproducible research experts share how they teach undergraduate and graduate students to make their research reproducible. They recommend instilling best practices in students as early as possible and teaching data analysis at all levels of a science curriculum using a completely reproducible framework. In this way, new researchers will know no other workflow than a reproducible one. They also urge statisticians to marshal efforts to promote reproducible data analysis practices in other disciplines. While all this might sound like a tall order at first, modern tools for literate programming (e.g., R Markdown) and systems for version control (e.g., GitHub, Open Science Framework) paired with carefully designed curricula make this goal easier to attain than ever before.
Registration: This webinar is free, but registration is required: