NEP 48 — Spending NumPy project funds — NumPy Enhancement Proposals

Ralf Gommers <ralf.gommers@gmail.com>
Inessa Pawson <inessa@albuscode.org>
Stefan van der Walt <stefanv@berkeley.edu>
The NumPy project has historically never received significant unrestricted funding. However, that is starting to change. This NEP aims to provide guidance about spending NumPy project unrestricted funds by formulating a set of principles about what to pay for and who to pay. It will also touch on how decisions regarding spending funds get made, how funds get administered, and transparency around these topics.
NumPy is a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit organization headquartered in Austin, TX. Therefore, for all legal and accounting matters the NumPy project has to follow the rules and regulations for US nonprofits. All nonprofit donations are classified into two categories: unrestricted funds which may be used for any legal purpose appropriate to the organization and restricted funds, monies set aside for a particular purpose (e.g., project, educational program, etc.).
For the detailed timeline of NumPy funding refer to NumPy funding – history and current status.
Since its inception and until 2020, the NumPy project has only spent on the order of $10,000 USD of funds that were not restricted to a particular program. Project income of this type has been relying on donations from individuals and, from mid 2019, recurring monthly contributions from Tidelift. By the end of 2020, the Tidelift contributions increased to $3,000/month, and there’s also a potential for an increase of donations and grants going directly to the project. Having a clear set of principles around how to use these funds will facilitate spending them fairly and effectively. Additionally, it will make it easier to solicit donations and other contributions.
A key assumption this NEP makes is that NumPy remains a largely volunteer-driven project, and that the project funds are not enough to employ maintainers full-time. If funding increases to the point where that assumption is no longer true, this NEP should be updated.
In scope for this NEP are:
Principles of spending project funds: what to pay for, and who to pay.
Describing how NumPy’s funds get administered.
Describing how decisions to spend funds get proposed and made.
Out of scope for this NEP are:
Making any decisions about spending project funds on a specific project or activity.
Principles for spending funds that are intended for NumPy development, but don’t fall in the category of NumPy unrestricted funds. This includes most of the grant funding, which is usually earmarked for certain activities/deliverables and goes to an Institutional Partner rather than directly to the NumPy project, and companies or institutions funding specific features. Rationale: As a project, we have no direct control over how this work gets executed (at least formally, until issues or PRs show up). In some cases, we may not even know the contributions were funded or done by an employee on work time. (Whether that’s the case or not should not change how we approach a contribution). For grants though, we do expect the research/project leader and funded team to align their work with the needs of NumPy and be receptive to feedback from other NumPy maintainers and contributors.
NumPy will likely always be a project with many times more volunteer contributors than funded people. Therefore having those funded people operate in ways that attract more volunteers and enhance their participation experience is critical. That key principle motivates many of the more detailed principles given below for what to pay for and whom to pay.
The approach for spending funds will be:
first figure out what we want to fund,
then look for a great candidate,
after that’s settled, determine a fair compensation level.
The next sections go into detail on each of these three points.
Pay for things that are important and otherwise won’t get done. Rationale: there is way more to be done than there are funds to do all those things. So count on interested volunteers or external sponsored work to do many of those things.
Plan for sustainability. Don’t rely on money always being there.
Consider potential positive benefits for NumPy maintainers and contributors, maintainers of other projects, end users, and other stakeholders like packagers and educators.
Think broadly. There’s more to a project than code: websites, documentation, community building, governance - it’s all important.
For proposed funded work, include paid time for others to review your work if such review is expected to be significant effort - do not just increase the load on volunteer maintainers. Rationale: we want the effect of spending funds to be positive for everyone, not just for the people getting paid. This is also a matter of fairness.
When considering development work, principle (1) implies that priority should be giving to (a) the most boring/painful tasks that no one likes doing, and to necessary structural changes to the code base that are too large to be done by a volunteer in a reasonable amount of time.
There are also many tasks, activities, and projects outside of development work that are important and could enhance the project or community - think of, for example, user surveys, translations, outreach, dedicated mentoring of newcomers, community organizing, website improvements, and administrative tasks.
Time of people to perform tasks is also not the only thing that funds can be used for: expenses for in-person developer meetings or sprints, hosted hardware for benchmarking or development work, and CI or other software services could all be good candidates to spend funds on.
All else being equal, give preference to existing maintainers/contributors.
When looking outside of the current team, consider this an opportunity to make the project more diverse.
Pay attention to the following when considering paying someone:
the necessary technical or domain-specific skills to execute the tasks,
communic