Thank you for joining + Resources: Open Know-How (OKH) Meeting 24.01.18



For links shared during the meeting, as well as additional discussion details, please see the full AGENDA.

Recap of Action Items

Prior to the next OKH Initiative Group Meeting:

Meeting Notes

  1. Welcome

  2. Recap/Review of previous action items

  1. New Business / Items to Discuss
  1. Action Items (frontloaded on post)

  2. Wrap-Up / Reminder of Next Meeting

Thu Feb 15
16:00PM GMT
Join Link (BBB): OKH Working Group

The OKH maintenance working group currently meets every third Thursday of the month at 4pm UTC .

Everyone is welcome to sit in on these meetings! You do not have to already be a part of the group - we look forward to new faces and voices joining the conversation! :v:t4: Below is a schedule through Spring 2024 so you can mark your calendars:

  • Feb 15
  • Mar 21
  • Apr 18
  • May 16

For a listing of all upcoming IOPA events, please visit our upcoming events calendar, which supports export via ics download or direct add to Google calendar. :spiral_calendar:

Also of note: @hoijui and @Jbutler-helpful brought up the topic of being able to readily visualize how active an OSS project is by taking a look at commits. During the meeting, I brought up the rich club phenomenon, and the subsequent methodological approach employed by Gasparini et al. in their 2019 Analyzing Rich-Club Behavior in Open Source Projects.

I mentioned during this meeting that I had pulled that methodology to investigate reproducibility for the purpose of conducting an analysis on commits to a single project in GitHub (as opposed to analyzing multiple projects - the 100 most popular, in that research group’s case). Here are the basic steps for replicating this method for creation of a dataset with the purpose of analysis and visualization of commits to a specific project:

  1. Creation of the Dataset: Cloning, Import, Enrichment

  2. Graph Generation

  3. Rich-club coefficient calculation (not necessary in this case - unless there is specific interest)

Data collection in this previous case I constructed is based on API commits (very specific); I started with importing Git repositories into a relational database using Gitana, and drawing from supabase.

Long story short: there is a way to script a vis of how active an OSH project is. I don’t usually lead with the tech-based solution - typically I look to “what are you looking to do” and “why is this thing that you want to do import to accomplish/what will it help?”

A vis that I shared during the meeting earlier today (from the Gasparini et al. article):

So - you can hold that above image in your mind and think - would this be useful for a quick visualization of how active a project is? Sure, you can quickly scan a repo to see how many commits total - but with a bespoke process in place, you could see where the commits are coming from, and identify specific clusters (around institutions, other known projects, etc).

There was also an inquiry during the meeting - “what is rich club?”

Well. If you’d like to know more, indicate your interested here. There’s a video presentation from 2021 that @max_w and I did for the IASC virtual conference that will unpack everything. Just ask. If you want to follow up on applying that methodology for analysing specific projects @Jbutler-helpful or @hoijui, you can ask me directly - it is a fairly intensive thing to grok and admittedly, I have unanswered questions myself.