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.

• 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

• Typeset equations

• Example 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.

• Template for general Python projects

• Template for Datascience projects

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

JupyterCon 2018: Challenges and Guidelines for Reproducible Research and Interactive Education with Jupyter Notebook

Reproducible Data Science Workflows using Docker Container

• Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. Rule A, Birmingham A, Zuniga C, Altintas I, Huang SC, Knight R, Moshiri N, Nguyen MH, Rosenthal SB, Pérez F, Rose PW. PLoS Comput Biol. 2019 Jul 25;15(7):e1007007. doi: 10.1371/journal.pcbi.1007007.
• 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).