A boilerplate for reproducible and transparent science with close resemblances to the philosophy of Cookiecutter Data Science: A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Install cookiecutter command line: pip install cookiecutter
To start a new science project:
cookiecutter gh:justinvasel/cookiecutter-reproducible-science
.
├── README.md
├── data
├── docs <- Documentation, e.g., doxygen or scientific papers (not tracked by git)
├── notebooks <- Ipython or R notebooks
├── reports <- For a manuscript source, e.g., LaTeX, Markdown, etc., or any project reports
│ └── figures <- Figures for the manuscript or reports
└── src <- Source code for this project
Check out my latest research project, which successfully applied the cookiecutter philosophy: SEMIC: an efficient surface energy and mass balance model applied to the Greenland ice sheet.
This project is licensed under the terms of the BSD License