The workflow at a glance
- Register as a contributor (GitHub issue — one time only)
- Fork the repository — this creates your own copy to edit
- Add your data to
contributions/{your-slug}/{dataset-slug}/ - Include a
metadata.json(use the template) - Open a pull request — GitHub Actions validates automatically
New to GitHub, or want each step spelled out with what you'll see on screen? Use the interactive walkthrough on the dashboard — it walks the same workflow one step at a time. If your lab already uses Git for code, the same workflow applies to data; the full written guide is in CONTRIBUTING.md.
Tools that can help
New to GitHub?
A plain-language walkthrough for researchers — no software installation required.
Working with ArcGIS Online?
Step-by-step instructions for discovering, downloading, and transforming ArcGIS feature services into the contribution format.
Using Claude Code?
A project-local skill prepares a contribution from a raw lab return — PDF report, xlsx form, lab CSV, or GeoJSON — through translation, geographic anchoring, metadata assembly, and validation.
What you provide — and what you don't
| Field | Required | Description |
|---|---|---|
| Data file | Yes | CSV or GeoJSON with your research data |
| Title | Yes | Human-readable name for the dataset |
| Contribution type | Yes | observation, indicator, or spatial_overlay |
| Topics | Yes | What domain your research addresses (e.g. water, demographics) |
| License | Yes | Open data license (CC-BY-4.0 recommended) |
| Quality level | Yes | preliminary, verified, or peer_reviewed |
| Schema | Yes | Column names and types in your data file |
| Sample context | For observations | What was physically collected (soil, water, sediment, etc.) |
| Baseline context | For indicators | Which Data Commons variables your data supplements |
| Coverage | When needed | Text description of geographic scope (only if data lacks coordinates) |
You do NOT need to provide: moku district IDs (auto-derived from coordinates, or inferred from your coverage description), SDG codes (auto-derived from your topic selections), or baseline comparisons (the platform links your data to county-level Data Commons indicators automatically).
Topic taxonomy
Contributions are tagged with topics from a controlled vocabulary. Each topic maps to ISO 37101 community sustainability issues and aligns with Data Commons baseline indicators. SDG codes are assigned automatically from your topic selections.
| Topic | Typical type | Description | Example datasets |
|---|---|---|---|
| land_environment | observation | Terrestrial substrates and environmental characterization | Soil surveys, sediment analysis, substrate testing |
| water | observation | Water quality and hydrology | Water quality, streamflow, effluent |
| biodiversity | observation | Species and ecosystem surveys | Species surveys, habitat mapping |
| agriculture | either | Crop and production data | Crop trials (obs), farm production by moku (ind) |
| coastal | observation | Shoreline and marine systems | Erosion rates, sea level, coral |
| climate | either | Weather, emissions, and energy | Weather stations (obs), solar generation by district (ind) |
| forestry | observation | Forest and canopy systems | Canopy cover, native species |
| food_safety | observation | Contamination and pathogen testing | Contamination testing, pathogens |
| infrastructure | indicator | Built systems and capacity | Water system capacity, broadband coverage |
| demographics | indicator | Population and governance data | Population by moku, employment by census tract |
| community_wellbeing | indicator | Community quality of life and social determinant indicators | QOL surveys, food insecurity rates, social trust indices |
See the topics reference for the full taxonomy, contribution type guidance, and metadata examples — including how contributions are wired into the governance graph.
Quality levels
| Level | Meaning | Review |
|---|---|---|
| preliminary | Raw field data, not yet QA'd | Spot-check |
| verified | QA'd by contributor, methods documented | Standard review |
| peer_reviewed | Published or reviewed by external party | Fast-track |
Preliminary data is welcome — the quality level is a declaration, not a gatekeep. Labs can upgrade quality by submitting a follow-up PR with updated metadata.
Licenses
All contributions must use an open data license:
- CC-BY-4.0 (recommended)
- CC-BY-SA-4.0
- CC-BY-NC-4.0
- CC0
- ODbL
Wondering where your data ends up, or whether it belongs here? The What & why page explains the resolution gap the commons closes, the two contribution pathways, and how datasets join the shared Mokunet map with provenance intact.