[ANN] SciML Small Grants Program: Funded Open Source Contributions!

I am very happy to announce the launch of the SciML Small Grants program! This is an open source contributions program to help improve the Julia SciML organization and some of the issues that have traditionally been overlooked. Funded projects include: (1) improving compilation and startup times, (2) creating and maintaining benchmarks, (3) improvements to package structures. No numerical/scientific knowledge needed for many of these projects. If you’ve been looking contribute and needed an impetus to get started, let this be your call to arms!

For more information, see SciML Small Grants Program Current Project List.

P.S. If you want your pet peeve to become a small grants project, get in touch with us about donating to the program! Donate for SciML

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Are applications from outside of US also possible?

[The initiative sounds great, especially for maybe also for students, e.g., the benchmarking task could be great for someone learning PDE numerics.]

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Any country that OpenCollective/NumFOCUS can work with is fine, which is a pretty broad list.

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My questions:

  1. Can newbies to open source apply?
  2. Do I need to proficient at Julia to apply?
  3. I self-studied finite difference and pseudospectral PDE discretiations. Will that suffice? I have no opportunity to enrol in a course related to it at my university.
  4. How do I maximize my chance of acceptance? I am particularly interested in Fix and Update the “Simple Handwritten PDEs as ODEs” Benchmark Set.

Let me start by quoting the web page:

However, it is also expected that the contributor can work fairly independently with guidance from the reviewer. Contributors are expected to be comfortable enough in the area of expertise to work through the errors and test failures on their own. The SciML Small Grants Program is not a training program like Google Summer of Code or the SciML Fellowship, and thus the reviewer is not expected to mentor or teach the contributor how to solve the problem. The reviewer is in no obligation to help the contributor work through tests, bug fixing, etc. as these projects were chosen due to the fact that the maintainers have not been able to find the time to cover these areas. The obligation of the reviewer is to give a timely feedback on what the requirements for merging would be (i.e. what tests are required to be added, whether certain code meets style demands, etc.) so that the contributor can achieve a mergable PR within the time frame, but there is no expectation that the reviewer will “go the extra mile” to teach the contributor how the package or mathematics works.

Yes, it’s not so hard to pick up the basics. You can watch something like the following:

and within an hour you’re up and running. that’s fine.

You need to be able to demonstrate that you can do the project without sufficient help. Maybe do one of the 8 and show that you can do it. That wouldn’t be very much work? But if someone showed up with no background in the subject and no prior contributions to any Julia open source then I think we’d ask to see some proof that this is a serious application, like a notebook showing some pseudospectral PDEs wrtitten to solve or something.

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