DCAT Question: Fed Trade commision
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Description
- [ ] Answer question in comments for this ticket
- [ ] Add Q & A to FAQ list with your initials and last edited date: https://github.com/GSA/dcat-us/wiki
'Xie, Juanhui' via Data.gov Help
10:05 AM (2 hours ago)
to DataGovHelp@gsa.gov
Good morning,
Please see my question below. Could you help address the question or provide further guidance on the topic? Thanks!
In the “Full inventory JSON Example”. Starting on Page 49 of DCAT-US Schema v3.0 Implementation Guide (The Guide), it included two datasets and a dataset series for this two datasets. But the Dataset series does not seem to have fulfill it’s intended purpose, that is, to centralize attributes shared among member datasets and thus reduce duplication of these information in each dataset. Rather, it seems that many common attributes are repeated in each Dataset class, thus make the file extremely “verbose” for a simple example like this (it only contain two datasets), without fulfilling DatasetSeries’s purpose of reducing duplication. Could you shed some light on why this is the case, AND, are there other examples that demonstrate more efficient metadata expression via the use of DatasetSeries?
Giving the end of September deadline for v3.0 complaint data.json, I would really appreciate if you can provide answer/guidance as soon as possible.
Joanne Xie
Program Manager - Data Governance and Artificial Intelligence
Federal Trade Commission
Contributor guide
Research direction
Start with the DCAT-US Schema v3.0 Implementation Guide, especially the “Full inventory JSON Example” beginning on page 49, and compare how DatasetSeries is used with its member datasets. Provide the explanation in a comment, then add the question and answer to the data.gov wiki FAQ with your initials and last edited date.
Written by the indexing model from the issue text.
Assessment
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100