mne-tools / mne-tools/mne-python

DOC: Contributor credit improvements

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DOC
Dominant language
Python
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Merged PRs (30d)
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Description

Some improvements we should probably make to code credit (fairly easy):

  • Initial commits / early days of direct commit pushing should be accounted for. Only PR diffs are used at the moment. We could create a prs/0.json for example where we look at commits prior to, say, Aug 25, 2011 and credit existing code that way. And whatever GitHub says, I recall that fwd/inv/fiff code at that point should be credited to at least Alex Gramfort and Matti Hämäläinen.
  • We could consider adding contributor counts to the modules, like mne (123), mne.cov (45) or whatever the counts actually are.

Some improvements to credit beyond code (will be more work):

  • Add something having to do with PR comments, and issue comments. Not sure if we want to use number of comments, word count, etc. All of these metrics are flawed in different ways, so not sure the best thing here.
  • Add something about forum posts. This will require some discourse querying.

Just getting some ideas down that have been rattling around in my head, happy to add more ideas.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

The issue does not name files, tests, or an entry point. Start by locating the existing contributor-credit calculation and its PR-diff data, then clarify which single metric is in scope. Done should include an agreed implementation target and tests covering the selected credit source.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
tooling
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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