rich metadata proposal in VOTable
- Dominant language
- Python
- Stars
- 88
- Forks
- 64
- Avg merge
- 38m
- Merged PRs (30d)
- 1
Description
Dear astroquery team!
in the VO context, we promote a rich VOTable called Mango (see https://github.com/ivoa/dm-usecases/wiki/mango).
It is not yet a standard, but we are very interested to have astroquery feedbacks :-) .
The idea consists to add an annotation block in VOTable to describe celestial sources who are in the table.
The serialization is based on the Mango DM (not a standard yet) and highlights measures (like position, photometry, parallaxes or other quantities) - The XML annotation uses "VOdmlInstance" which seems difficult at first glance but which enables to serialize complex models.
Among the new capabilities, we could provide the photometry and their filter assignment.
In the VizieR prototype, columns describing the same quantity are grouped in a measure (for instance a velocity measure which groups the velocity column and columns like a quality flag; an error, etc.) -
more details are available here : https://github.com/ivoa/dm-usecases/wiki/mango
example : http://viz-beta.u-strasbg.fr/viz-bin/Mango?-out.max=10&-source=II/305/archive
* An other development concerns the Provenance of the VizieR catalogue.
Today provenance information in VOTable is very poor, so we developed a Provenance output dedicated for users (e.g: authors, article, filter assignment..).
The output is still in test and can evolve - it is built on the IVOA ProvDM.
example:
https://cdsarc.unistra.fr/viz-bin/provenance?cat=II/305&filter=true&out=yaml
https://cdsarc.unistra.fr/viz-bin/provenance?cat=II/305&filter=true&
Contributor guide
No contributing guide indexed for this repository
Research direction
The issue names no repository files, tests, or entry points. Start by reviewing the linked Mango use cases and provenance examples, then clarify the desired scope and acceptance criteria before implementation; completion cannot be determined from the current proposal.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, xml
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100