Class materials
Session |
Lecture slides |
Interactive session |
Reflection |
|---|---|---|---|
| Morning | Reproducible workflows | Plan an analysis | Go with the flow |
| Afternoon | Workflow organization | Make spaghetti | Exploration and iteration |
Further reading
Stoudt, Vásquez, and Martinez (2021) describe how analytical workflows evolve, what they look like in the end, and how they’re necessary for data-intensive science.
Lowndes et al. (2017) present a case study on their own team-based research. They show how an emphasis on reproducibility improves efficiency as well as transparency.
References
Lowndes, Julia S. Stewart, Benjamin D. Best, Courtney Scarborough, Jamie C. Afflerbach, Melanie R. Frazier, Casey C. O’Hara, Ning Jiang, and Benjamin S. Halpern. 2017. “Our Path to Better Science in Less Time Using Open Data Science Tools.” Nature Ecology & Evolution 1 (6). https://doi.org/10.1038/s41559-017-0160.
Stoudt, Sara, Váleri N. Vásquez, and Ciera C. Martinez. 2021. “Principles for Data Analysis Workflows.” Edited by Patricia M. Palagi. PLOS Computational Biology 17 (3): e1008770. https://doi.org/10.1371/journal.pcbi.1008770.