spikyclip/cookiecutter-biology-experiment

A simplified project structure for biology experiments based off the cookiecutter data science template.

Cookiecutter Biology Experiment

A simplified project structure for biology experiments based off the cookiecutter data science template

Project homepage

Requirements to use the cookiecutter template:

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• Python 2.7 or 3.5+
• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

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cookiecutter -c v1 https://github.com/SpikyClip/cookiecutter-biology-experiment

The resulting directory structure

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The directory structure of your new project looks like this:

    ├── LICENSE
    ├── README.md          <- The top-level README for developers using this project.
    ├── 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.
    │
    ├── dist               <- Folder for repo python package distribution archives
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-jqp-initial-data-exploration`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
    │                         generated with `pip freeze > requirements.txt`
    ├── setup.cfg          <- Makes project pip installable (pip install -e .) so src can be imported
    ├── pyproject.toml     <- Tells build tools what is required to build project
    │
    ├── src                <- Source code for use in this project.
    │   ├── __init__.py    <- Makes src a Python module
    │   │
    │   ├── data           <- Scripts to download, generate or clean data
    │   │
    │   ├── models         <- Scripts to train models and then use trained models to make
    │   │                     predictions
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │
    └── tests              <- folder for unit testing