Why: the resolution gap
Hawaiʻi has 5 counties but 33 moku districts. Federal baseline indicators — farm counts, unemployment rates, median income, rainfall, energy production — are published at county resolution through the Data Commons knowledge graph. The Mokunet platform presents these as island-effect baselines: they tell you where things stand at the island level. Research gaps often occur within the island model.
This repository is where Hawaiʻi researchers and community groups contribute environmental observations and community statistics to a shared research commons, sharpening the picture of what's actually happening in their moku. A water quality study in Maunalua Bay and a food security survey in Waiʻanae become comparable side by side — making district-level variation visible for the first time.
What: two pathways into the commons
Environmental observations observation
Water quality, soil tests, species surveys, air monitoring — samples that show what
conditions actually are at specific places. Each dataset carries a
sample_context block describing what was physically collected: soil, water,
sediment, air, tissue, sludge, or mixed matrices under one topic.
Community indicators indicator
Demographics, employment, wellbeing surveys, infrastructure — sub-county statistics that refine the county-level federal baselines at the moku level. Each dataset declares which Data Commons variables it supplements, and needs coordinates or explicit moku IDs so refinements resolve spatially.
Both research pathways resolve to specific moku districts and connect to shared sustainability goals. When your data includes coordinates, the platform anchors it to the right district automatically; observation data without coordinates needs only a text description of geographic scope.
What contributions are
- Version-controlled with full Git provenance
- Validated automatically via GitHub Actions
- Reviewed by community maintainers before ingestion
- Compared against county-level baselines from Data Commons
- Publicly accessible as commons data (default)
Who contributes
| Institution | Example labs / groups | Focus areas |
|---|---|---|
| UH Mānoa | SSRI, CTAHR, SOEST, WRRC | Demographics, agriculture, coastal, water |
| UH Hilo | Tropical Conservation Biology & Environment | Forestry, biodiversity |
| Hawaiʻi Pacific University | Natural Sciences, Marine Science | Biodiversity, coastal |
| Community organizations | Watershed partnerships, conservation districts | Water, land environment |
| State & federal agencies | DLNR, DOH, USGS, NOAA, USFWS | Multi-topic |
Your research already has a place in the commons. Just bring your data — the contribution pipeline is designed to handle geographic anchoring, sustainability goal linkage, and baseline comparison, and that ingestion path is being brought online now.
Hui Koʻe ʻĀina (huikoeaina.ainadesign.org) is the natural resource management portal where contributors and the learning community browse, query, and visualize this data on interactive maps. Governed under the UH Foundation, HKA provides the community entry point for University of Hawaiʻi and Hawaiʻi Pacific University research labs looking to de-silo their data.
Kuleana · commons reports (kuleana.ainadesign.org) is the companion public record of the work itself: who is caring for each moku, what they did, and what came of it. The first report tracks the 91 projects the State's fee on visitors paid for in fiscal year 2027. A commons report says what the work did; the observations contributed here show how the place is responding.
Why it matters: contributions join a shared map
Contributed datasets become part of the Mokunet shared map with their provenance intact — who gathered each record, where, and when. Geocoded observations are anchored to their moku and to location cells on the shared map, then tied to the UN Sustainable Development Goals they measure. Indicator datasets additionally track exactly which county baselines they refine. Both contribution types land on the same shared map, which is what enables cross-domain comparison at the district level.
The platform recognizes 33 traditional moku districts across 7 islands. You don't need to know these — the system assigns moku from coordinates automatically. If you're curious, see the moku districts reference.
Ready to bring your data in? The How to contribute page covers the requirements, topic taxonomy, quality levels, and licenses — and the interactive walkthrough on the dashboard takes you through GitHub step by step, no software installation required.