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NumPy Community Survey Topics

This is an overview of the topics we would like to explore with the inaugural NumPy Community Survey:

Community Demographics
Contribution
Mentorship
Project Priorities
Future of NumPy

NumPy Community Demographics

NumPy has an estimated 10-15 million users. How representative is the NumPy community of the scientific computing and open source communities?

What field of science has the largest number of NumPy users? And what industry?

What is their programming experience?

What do they use NumPy for?

We know NumPy was used to image a black hole. What other major scientific and commercial projects depend on NumPy?

Why do users choose NumPy over other available alternatives?

To what extent are they engaged with, or even aware of, the NumPy development and maintenance, and the philosophy and values of open source software?

Contribution

NumPy is supported largely by 10-15 active maintainers. (Not bad for a project with 10-15 million users!) To continue maintaining it and add significant new features that support our user base and the whole ecosystem, we need a lot more help from the open source community. Besides programming and writing documentation, we are looking for volunteers experienced in web development, marketing, community coordination, fundraising and grant writing, developing educational materials, and much more. Therefore we would like to find out the following:

Why do people decide to contribute to open source?

How do they choose a project for contribution?

Why have they chosen to contribute to NumPy?

What prevents them from contributing to NumPy?

Mentorship

Mentorship is critical to open source community sustainability and growth. What motivates experienced contributors to become mentors, and how do they select mentees?

How do beginners connect with mentors within the open source community?

Project Priorities

What do NumPy users and contributors deem to be high priority for the NumPy project in the next 12 months (e.g. specific new features, documentation, website, performance, reliability, packaging, etc.)?

Future of NumPy

What kind of future do NumPy users and contributors envision for NumPy (further applications, maintenance model, etc.)?