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We are SO excited to announce some massive news for the data.table community: data.table is now a NumFOCUS Sponsored Project!!!
For those who aren’t familiar, NumFOCUS is an incredible nonprofit organization that supports open source projects. There are some truly iconic projects on their roster: pandas, scikit-learn, the Julia language, and so many more.
data.table is the first R-only package to become a sponsored project!!! (Although other amazing existing multilingual projects do include R, such as igraph and jupyter.)
How can you get involved?
Did you know that anyone can make use of the NumFOCUS support system, not just data.table maintainers?
As a community member, you can submit a ticket to request support!
Some uses for this might be:
-
You’ve found a great funding opportunity, and you’d like help preparing, submitting, and administering it.
-
You’d like to organize a
data.tablerelated event, and you would like help planning and possibly funding it. -
You have some tech needs (e.g. Zoom, AWS) that would support a
data.tableventure if we provided them. -
You want to represent
data.tableas part of a broader NumFOCUS venture, such as the Open Source Science Initiative or a PyData Event.
To be clear – NumFOCUS provides administrative and structural support, not decision making. Decisions about how data.table funds are spent (and, of course, about the code) are still made according to the project governance document on GitHub. For full disclosure, all submitted tickets are also viewable by the Community Organizer (currently Kelly Bodwin) and the CRAN Maintainer (currently Tyson Barrett).
Whatever your idea is, we strongly encourage you to first discuss with the Community and Maintainers by creating an issue on GitHub.
Code of Conduct Reporting
Although we hope and anticipate this will never be needed, NumFOCUS also provides a better procedure for reporting any Code of Conduct violations in our project: https://numfocus.org/code-of-conduct
Reports are first viewed by a trained team at NumFOCUS, who then make recommendations to the data.table governance team about steps forward. In this way, reports can be more anonymized, and can be assessed by unbiased third parties before coming to the governance team.
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