Guide for Reproducible Research and Data Science in Jupyter Notebooks
This guide is a community-resource of crowdsourced guidelines and tutorials for reproducible research in Jupyter Notebooks. This resource is a companion to the high-level guide TenRulesJupyter and paper Ten Simple Rules for Reproducible Research in Jupyter Notebook to keep up with the rapidly evolving Jupyter project and to provide in-depth tutorials and examples.
For suggestions please open an issue. To contribute, fork this repository and send pull-requests.
Parameterize your notebooks: How to pass in parameters to notebooks
Test your notebooks: How to validate your to notebooks
Deploy your notebooks: How to share your notebooks
Other sections (to be written)
Cookiecutters are project templates to create skeleton repositories for Python and other languages. Here are a couple of examples you may find useful.
A Practical Introduction to Reproducible Computational Workflows
Putting the science back in data science
Reproducible research best practices @JupyterCon
Data Carpentry - Reproducible Research using Jupyter Notebooks
Reproducible Data Analysis in Jupyter
Reproducible Computational Research
Education Technology - Jupyter and Reproducibility
Reproducible Computational Research
On Writing Reproducible and Interactive Papers
Software Development Best Practices for Computational Chemistry
Reproducible Data Science Workflows using Docker Container