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:shjenkins94/cookiecutter-reproducible-science
.
├── bin <- Most code goes here.
│ ├── external <- Any external source code, like git repos or
| | libraries (ignored)
| | └── README.md <- Source of any external source code.
│ ├── src <- Anything that needs to be compiled
│ ├── tools <- Any helper scripts go here.
│ └── visualization <- Scripts for visualisation of results.
├── config <- Configuration files.
| └── environment.yaml <- conda env export
├── data
| ├── Makefile <- File that does simple data processing.
│ ├── external <- Data from third party sources.
│ | └── README.md <- Source of any external data.
│ ├── intermediate <- Intermediate data that has been transformed.
│ ├── practice <- Data used to try things out and test code.
│ ├── raw <- Original proprietary data.
│ └── target <- The final product.
├── docs <- Text things go here
│ ├── notebooks <- Ipython or R notebooks.
│ └── reports <- For a manuscript source.
│ └── figures <- Figures for the manuscript or reports
├── AUTHORS.md
├── LICENSE
└── README.md
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