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# System prepended metadata

title: Contributor Journey Comics - NumPy

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# Contributor Journey Comics - NumPy


## About your organization

NumPy is very widely used Python library in pretty much every field of science and engineering. [Over 32,000 packages on GitHub](https://github.com/numpy/numpy/network/dependents) depend on NumPy, and 6 million users visit our website every month. It is the universal standard for working with numerical data in Python and is at the core of the scientific Python and PyData ecosystems. It provides `ndarray`, a homogeneous n-dimensional array object with methods to efficiently operate on it. Its user base spans from beginner coders to experienced researchers doing state-of-the-art scientific and industrial R&D.  The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image, and most other data science and scientific Python packages. The API and concepts are also replicated in deep learning frameworks (e.g., Tensorflow, PyTorch) and in array computing libraries for other programming languages.


## About your project


### Your project’s problem

It can be difficult for contributors to know that there are many different ways to contribute to NumPy. Especially for first-time contributors, there is uncertainty how to become a regular contributor, a reviewer or become a maintainer. Additionally, contributors may not be aware of non-code contributions like documentation fixes, triaging issues, and reviewing pull requests, among others. The NumPy community would like to make clear these various options. These conversations have been recorded and discussed in community calls but not disseminated widely, especially not to potential contributors who are not attending community meetings. This project aims to document these various pathways to being a contributor. By reducing this intial friction and exciting the reader in their future journey of open-source, this project aims to increase the number and diversity of contributions in NumPy. Long-term contributors gained through this effort would also improve the project's sustainability.

### Your project’s scope

- This project would produce a comprehensive series of informative digital illustrations that would reside on the NumPy documentation website.
- These illustrations would serve a purpose similar to a corporate job level diagram. However, rather than mapping a hierarchical career, these illustrations would map an open-ended journey in open-source.

### Measuring your project’s success
- Increase in first-time attendees to the NumPy community meetings
- Increase in first-time contributors becoming repeating contributors
- Increase in number of responses in issues by non-maintainers
- Increase in number of reviews made by non-maintainers
- Increase in number of people given repository rights

### Skills the technical writer would need

Must have: 
- Mapping undocumented processes
- Communicating with community members
- Creating visual metaphors for technical concepts
- Familiarity with Github
- Illustration skills

Nice to have:
- Understanding of how NumPy works under the hood

### Timeline

We expect that the technical writer will spend 6 months on the project, between May and November 2023, working 5 hours per week on average. During these hours, the technical writer will:
- Attend NumPy community meetings
- Make drafts; revise and edit them according to discussions with community members
- Publish and spread awareness of the project

## Project budget

| Budget item | Amount | Running Total | 
|-------------|--------|---------------|
Technical writer  | 2880.00 | 2880.00 	
TOTAL | | 2880.00 	


## Additional information

There is a potential candidate who could fulfill this project. This candidate is Mars Lee (@MarsBarLee).
- She is active in the NumPy community and Open Source Design community.
- She has written and illustrated comics about open-source contributions.
- She has also run virtual and in-person sprints.
- A sample of her work can be [viewed here](https://github.com/alt-text-task-force/.github/blob/main/profile/scipy-2022-comic.md).



NumPy has participated in Google Season of Docs in 2019, 2020, and 2021. You can read the successful project reports here:

- [NumPy Documentation for Community Education (GSoD 2020)](https://developers.google.com/season-of-docs/docs/2020/participants/project-numpy-cooperrc)
- [High level restructuring and end user focus (GSoD 2020)](https://developers.google.com/season-of-docs/docs/2020/participants/project-numpy-kubedoc)
- [High level restructuring and end user focus (GSoD 2021)](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2021:-NumPy-Case-Study)

The mentors for this project, Mukulika Pahari and Ross Barnowski, are both members of the NumPy Documentation Team, with experience mentoring and creating documentation, as well as around the tooling and infrastructure involved in creating the documentation for NumPy. Ross was also in this role for Google Season of Docs 2021 and Mukulika was the technical writer for that year's project.
 
Current documentation:

- Latest version (tutorial, reference guide, developer docs): https://www.numpy.org/devdocs/index.html
- Historical archive of released documentation: https://docs.scipy.org/doc/
- NumPy website: http://www.numpy.org/
- SciPy ecosystem website (which NumPy is at the core of): https://scipy.org/
- GitHub repository (all sources for code and docs): https://github.com/numpy/numpy
- Jupyter Notebook repository (not integrated into the main documentation): https://github.com/numpy/numpy-tutorials

Please visit https://numpy.org/contribute for contact information. You can also write directly to numpy-scipy-gsod@googlegroups.com with specific questions.
