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:dbrakenhoff/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
│ ├── 1-external <- Data from third party sources.
│ ├── 2-interim <- Intermediate data that has been transformed.
│ ├── 3-processed <- The final, canonical data sets for modeling.
│ └── 4-output <- Model output
├── docs <- Documentation, e.g., doxygen or scientific papers (not tracked by git)
├── notebooks <- Ipython 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
├── 0-setup <- setup environment etc.
├── 1-dataprep <- Scripts and programs to process data
├── 2-model <- Source code for building and running your own model
├── 3-analysis <- Scripts for post-processing/analysis of your results
├── 4-visualization <- Scripts for visualisation of your results
└── tools <- Any helper scripts go here
This project is licensed under the terms of the BSD License