Some may know that I’m involved, as part of building an app ecosystem, in figuring out how to measure Free Software communities. We formed a working group within the CHAOSS project.
One of the things that we want to do is figure out community metrics for both KDE and GNOME. This post is related to GNOME but should be universally applicable.
Going forward, the importance of making decisions through data is going to be more important. We know that we are under-resourced, and we vaguely know where we are under-resourced but we have no data to conclusively show anything about our community.
If we have successes, we should be able to demonstrate that. If people want to donate or be part of an advisory board – then they have something to assess. And yes, the data could also prove or disprove aspects of our community if we are honest with ourselves. Metrics should be thoughtful and tell the right and accurate story.
Collecting data has always been a challenge for Free Software – this is because we are all rightly skittish about giving up our privacy. We should still be able to collect data without compromising privacy and by showing what decisions this data is driving and what questions are being answered.
Doing this can make us more goal-oriented. That’s going to be important.
To that end, in addition to working with the CHAOSS project, I’m also working with Georg of Bitergia and publishing the metrics that come from the GNOME GitLab. Right now, the work will require looking at metrics collections, making sure that it is accurate, and then building an engine that can be used by the community, maintainers, foundation staff, and Directors of the GNOME Foundation based on conversations within the community. I hope to do a lot of that at this years GUADEC.
There will be a series of blog posts where I’ll be talking about these metrics and going through the ramifications of building community metrics.
We are using Cauldron whose components are all developed under an OSI approved license. You can find the metrics for the GNOME community at https://cauldron.io/project/6416.
If you’re interested in being a fledgling data scientist, community manager, or just love to look at data – feel free to reach out. We would love to have you.
This series will also be cross posted to dev.to as well.
I read this post a while back and Brave browser just released some information on a new data collection system they’re working on. Seems like it would be a great fit for what you’re trying to accomplish giving strong privacy protection without requiring as much trust.
https://brave.com/privacy-updates/19-star/
“Brave’s new system STAR protects user privacy by ensuring the data users contribute are never unique to that user. This property, sometimes called k-anonymity, ensures that the data collector can only see a submitted value if the same value has also been submitted by some number of other users. K-anonymity (and thus the STAR system) prevents the data collector from ever seeing values that are unique—this means the values can’t be used to identify users.”