PolicyEngine / PolicyEngine/microcosm
Publish a small L0 Populace dataset for web app and Colab
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 0
- Forks
- 4
- Avg merge
- 1d 3h
- Merged PRs (30d)
- 94
Description
Context
The full Populace replacement for legacy policyengine-us-data needs a smaller companion artifact for interactive use in the web app, examples, and Colab notebooks.
Goal
Produce and publish a small Populace US dataset around 20k records using L0 sparsification, derived from the same validated full-release pipeline rather than a separate hand-built sample.
Acceptance criteria
- Starts from a certified full Populace release artifact.
- Uses an L0/sparsification step that preserves calibrated fiscal/geographic behavior as well as possible for its size.
- Exposes diagnostics against the standard current target surface, including congressional-district targets, with clear comparison to the full release.
- Is small enough for web app and Colab use.
- Has an explicit release manifest entry and documented intended use, so it is not confused with the full production replacement dataset.
Notes
This should not block the big-dataset policyengine-us-data replacement, but it should be tracked as part of making Populace usable across production, web app, and notebook contexts.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the validated full Populace release pipeline, its certified artifact, and the existing L0 or sparsification entry point. Define the small dataset from that pipeline, compare diagnostics against the standard target surface and full release, then add the release manifest entry and intended-use documentation for web app and Colab use.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-engineering
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100