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.
- Add specific chapters to this guide, e.g. Deploy your notebooks
- Flesh out or update materials
- Explain details with code snippets or figures
- Demonstrate guidelines through example notebooks
- Organize content
- Setup this repo as a Jupyter Book
- See Open Source Guides for some inspiration
- Anything else to strengthen the community of Jupyter Notebooks users
For suggestions please open an issue. To contribute, fork this repository and send pull-requests.
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Deploy your notebooks: How to share your notebooks
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Parameterize your notebooks: How to pass in parameters to notebooks
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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.
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
- Jupyter Notebooks – a publishing format for reproducible computational workflows (2016) Jupyter Dev. Team, IOS Press, doi: 10.3233/978-1-61499-649-1-87.
- Exploration and Explanation in Computational Notebooks, A. Rule, et al. (2018) Proc. of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, doi: 10.1145/3173574.3173606.
- Enabling Reproducible NGS Analysis Through Automated Jupyter Pipelines, A. Birmingham (2017) presentation
- Binder 2.0 - Reproducible, interactive, sharable environments for science at scale, Project Jupyter, et al. (2018) Proc. of the 17th Python in Science Conf. (SCIPY 2018).