Forked from: https://github.com/mkrapp/cookiecutter-reproducible-science 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:Huite/cookiecutter-reproducible-project
.
├── 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 external to the project.
│ ├── 2_interim <- Intermediate data that has been altered.
│ ├── 3_input <- The processed data sets, ready for modeling.
│ ├── 4_output <- Data dump from the model.
│ └── 5_visualization <- Post-processed data, ready for visualisation.
├── docs <- Documentation, e.g., doxygen or scientific papers (not tracked by git)
├── notebooks <- Jupyter 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 <- Install necessary software, dependencies, pull other git projects, etc.
├── 1_prepare <- Scripts and programs to process data, from 1_external to 2_interim.
├── 2_build <- Scripts to create model specific inputm from 2_interim to 3_input.
├── 3_model <- Scripts to run model and convert/compress model results, from 3_input to 4_output.
├── 4_analyze <- Scripts to post-process model results, from 4_output to 5_visualization.
└── 5_visualize <- Scripts for visualisation of your results, e.g., matplotlib, ggplot2 related.
This project is licensed under the terms of the BSD License