This page features resources submitted by U-M data science researchers. Ensuring reproducible data science is no small task: computational environments may vary drastically and can change over time; specialized workflows might require specialized infrastructure not easily available; sensitive projects might involve restricted data; the robustness of algorithmic decisions and parameter selections varies widely; crucial steps (e.g. wrangling, cleaning, mitigating missing data issues, preprocessing) where choices are made might not be well-documented. Our resource collection will help researchers tackle some of these challenges. If you would like to submit tools, publications and other resources to be included in this page, please email email@example.com.