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.
cookiecutter command line:
pip install cookiecutter
To start a new science 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 │ ├── 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