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Guide for Reproducible Research and Data Science in Jupyter Notebooks

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# 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](https://github.com/jupyter-guide/ten-rules-jupyter) and paper [Ten Simple Rules for Reproducible Research in Jupyter Notebook](https://arxiv.org/abs/1810.08055) to keep up with the rapidly evolving Jupyter project and to provide in-depth tutorials and examples.

## How to Contribute

* 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](https://predictablynoisy.com/jupyter-book-guide/)
* See [Open Source Guides](https://github.com/github/opensource.guide) for some inspiration
* Anything else to strengthen the community of Jupyter Notebooks users

For suggestions please open an [issue](https://github.com/jupyter-guide/jupyter-guide/issues). To contribute, fork this repository and send [pull-requests](https://github.com/jupyter-guide/jupyter-guide/pull/new/master).

## Guides and Tutorials

* [__Parameterize your notebooks__](chapters/parameterize.md): How to pass in parameters to notebooks

* [__Test your notebooks__](chapters/test.md): How to validate your to notebooks

* [__Deploy your notebooks__](chapters/deployment.md): How to share your notebooks

* [__Typeset equations__](chapters/equations.md)

* [__Example Notebooks__](notebooks)

* Other sections (to be written)

## Cookiecutters

[Cookiecutters](https://cookiecutter.readthedocs.io/en/latest/index.html) 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__](https://github.com/MolSSI/cookiecutter-cms)

* [__Template for Datascience projects__](http://drivendata.github.io/cookiecutter-data-science/)

## Related Resources
[A Practical Introduction to Reproducible Computational Workflows](https://github.com/ISMB-ECCB-2019-Tutorial-AM4/reproducible-computational-workflows)

[Putting the science back in data science](https://www.oreilly.com/ideas/putting-the-science-back-in-data-science?imm_mid=0ec94e&cmp=em-data-na-na-newsltr_20170125)

[Reproducible research best practices @JupyterCon](https://www.kaggle.com/rtatman/reproducible-research-best-practices-jupytercon)

[Data Carpentry - Reproducible Research using Jupyter Notebooks](https://reproducible-science-curriculum.github.io/workshop-RR-Jupyter/)

[Reproducible Data Analysis in Jupyter](https://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/)

[Reproducible Computational Research](https://wrdrd.github.io/docs/consulting/data-science#tensimplerules-for-reproducible-computational-research)

[Education Technology - Jupyter and Reproducibility](https://wrdrd.github.io/docs/consulting/education-technology#jupyter-and-reproducibility)

[Reproducible Computational Research](https://wrdrd.github.io/docs/consulting/data-science#tensimplerules-for-reproducible-computational-research)

[On Writing Reproducible and Interactive Papers](https://codeocean.com/2018/06/28/on-writing-reproducible-and-interactive-papers/metadata)

[Software Development Best Practices for Computational Chemistry](https://github.com/choderalab/software-development)

[JupyterCon 2018: Challenges and Guidelines for Reproducible Research and Interactive Education with Jupyter Notebook](https://www.slideshare.net/pwrose/challenges-and-guidelines-for-reproducible-research-and-interactive-education-with-jupyter-note)

[Reproducible Data Science Workflows using Docker Container](https://www.youtube.com/watch?v=oO8n3y23b6M)

## Further Reading
* 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](https://doi.org/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](https://dx.doi.org/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](https://doi.org/10.1145/3173574.3173606).
* Enabling Reproducible	NGS Analysis Through Automated Jupyter Pipelines, A. Birmingham (2017) [presentation](http://compbio.ucsd.edu/wp-content/uploads/2016/10/20170206_reproducible_analysis_thru_jupyter_notebooks.pdf)
* 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)](http://conference.scipy.org/proceedings/scipy2018/pdfs/project_jupyter.pdf).

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