This page contains in no particular order a list of interesting blog posts, tutorials, and papers by some of which this guide was inspired by.
- Wilson, Greg, et al. “Good enough practices in scientific computing.” - PLoS computational biology 13.6, 2017
- Orozco, Valérie, et al. “How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics.” - No. 18-933. Toulouse School of Economics (TSE), 2018
- Broman, Karl. “Initial steps toward reproducible research”
- Vanderplas, Jake. “Reproducible data analysis in Jupyter”, 2017
- “Reproducibility Guide”, ropensci
- “Reproducible Research”, CRAN Task View
- “Data Science Cookiecutter Template”
- “Reproducible Research”, Coursera
- Bryan, Jennifer. “Excuse me, do you have a moment to talk about version control?,” The American Statistician 72.1, 2018: 20-27
- Ellis, Shannon E., and Jeffrey T. Leek. “How to share data for collaboration.” The American Statistician 72.1, 2018: 53-57
- Kitzes, Justin, et al. “The Practice of Reproducible Research”
- Boettiger, Carl. “An introduction to Docker for reproducible research (with examples from the R environment).” ACM SIGOPS Operating Systems Review 49.1, 2015: 71-79
- Stodden, Victoria, and Sheila Miguez. “Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research”, 2013
- “Comparing Workflows (Git)”, Atlassian.com
- Monroy, Tania S. “Down the rabbit hole. A 101 on reproducible workflows with Python”, Youtube.com, 2018