openlibhums / openlibhums/janeway

Allow authors to submit requests for metadata revisions

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Metadata new feature
Dominant language
Python
Stars
239
Forks
97
Avg merge
9d 1h
Merged PRs (30d)
8

Description

Is your feature request related to a problem? Please describe.
Sometimes authors might require to make changes to an article's metadata at various stages of the workflow. Currently, authors have to email the editors outside the system to communicate any desired changes. Then, the editor have to go and make the changes to the article metadata themselves and might need to go back and forth over email while the process takes place.

Describe the solution you'd like
A new mechanism for authors to submit metadata revisions to any of their submissions. These revisions will be stored in Janeway and a notification will be fired to the editorial team. Then, if approved, the metadata changes will be applied onto the article object.

Additional context
The metadata revision requests can be preserved even after being accepted so that editors can refer back to previous versions of the metadata or even restore the article metadata to a previous state.
It might be worth cosidering the current wofklow for preprints here, since the process seems it would be very similar.

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

Start by reviewing Janeway's existing preprint workflow, which the issue identifies as a possible model for this feature. Trace how metadata changes and editorial notifications currently move through that workflow. Done should include author-submitted revision requests, editorial notification and approval handling, and preservation of accepted revisions for later reference or restoration.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
backend
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
30/100

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