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:mkrapp/cookiecutter-reproducible-science
.
├── AUTHORS.md
├── LICENSE
├── README.md
├── bin <- Your compiled model code can be stored here (not tracked by git)
├── config <- Configuration files, e.g., for doxygen or for your model if needed
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── 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
├── data <- scripts and programs to process data
├── external <- Any external source code, e.g., pull other git projects, or external libraries
├── models <- Source code for your own model
├── tools <- Any helper scripts go here
└── visualization <- Scripts for visualisation of your results, e.g., matplotlib, ggplot2 related.
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