Available Datasets

The catalog is live and growing — it is the source of truth, not this page. At the time of writing it holds 70 top-level collections spanning ~325 datasets: protected areas, biodiversity, carbon, hydrology, land cover, census and demography, hazards, marine and ocean governance, and more. Any hardcoded list here would be out of date within a week.

Browse the catalog

Explore everything interactively in the STAC browser:

➡️ browser.moregeo.it — Boettiger Lab Geospatial Datasets

The underlying root catalog document is:

https://s3-west.nrp-nautilus.io/public-data/stac/catalog.json

Each collection carries its own STAC metadata — asset hrefs (Parquet, PMTiles, COG), spatial extent, column schemas, licensing, and dataset-specific aggregation guidance.

A few examples

Representative top-level collections, to give a sense of the range. This is a sample, not an inventory — browse the catalog for the full list.

collection_id	Description
protected-planet	UNEP-WCMC WDPA + WD-OECM global protected & conserved areas
pad-us-4.1	PAD-US 4.1 — national inventory of US protected areas
iucn-richness-2025	IUCN Red List species richness & range-weighted richness
gbif-derived	GBIF occurrences hexed at H3 res 0–10, plus derived products
ncp-biodiversity	Nature's Contributions to People biodiversity indicators
irrecoverable-carbon	Irrecoverable, manageable, and vulnerable carbon stocks
wetlands-global-unified	Ramsar, GLWD, and NWI wetlands
hydrobasins-v1c	HydroSHEDS HydroBasins global watershed boundaries
overture-divisions	Overture Maps administrative boundaries (country/region/county)
us-census	TIGER/Line boundaries and American Community Survey tables
land-cover	National and global land-cover classifications
dem	Digital elevation models (Copernicus GLO-30 and others)
high-seas	Areas beyond national jurisdiction & ocean governance layers
kba	World Database of Key Biodiversity Areas
ca30x30	California 30x30 conservation-initiative datasets

Let the agent discover them

Never hardcode or guess an S3 path. The workflow is always:

1. browse_stac_catalog — list the collections currently in the catalog.
2. get_stac_details(dataset_id) — resolve Parquet paths, column schemas, and any dataset-specific aggregation rules.
3. query(sql) — run DuckDB SQL over the paths returned in step 2, copied verbatim.

Because discovery is dynamic, newly published datasets become usable with no change to the server, the client, or this page.

H3 spatial indexing

Most tabular datasets are indexed on Uber's H3 hexagonal grid, which makes cross-dataset joins a cheap key equality instead of a spatial predicate.

Column	Resolution	Cell area
h8	Resolution 8	~0.74 km²
h4	Resolution 4	~1,771 km²
h0	Resolution 0	~4,357,449 km²

Area calculations

H3 cells are not equal-area — true cell area varies with latitude and icosahedral distortion (res-8 cells range ~0.55–0.82 km²), so a nominal per-resolution constant introduces a systematic error (~6% for California). For a region, feature, or per-group area, sum the exact per-cell area over distinct cells:

-- Exact area in km² (the proper method)
SELECT SUM(h3_cell_area(h8, 'km^2')) AS area_km2
FROM (SELECT DISTINCT h8, h0 FROM read_parquet('s3://...') WHERE ...);

Only for unscoped global aggregates over millions of cells — where materializing every distinct cell would defeat the fast approximate path — fall back to multiplying an approximate count by the nominal constant (APPROX_COUNT_DISTINCT(h8) * 0.737327598, accurate to ~1–2% globally).

See h3-guide.md for the full area guidance.

Cross-dataset joins

Always include h0 in join conditions to enable partition pruning:

SELECT a.h8, a.value, b.other_value
FROM read_parquet('s3://dataset-a/**') a
JOIN read_parquet('s3://dataset-b/**') b
  ON a.h8 = b.h8 AND a.h0 = b.h0   -- h0 required for pruning

Omitting h0 forces a full scan of both datasets and is 5–20× slower.

Adding a dataset

Datasets are published by the companion data-workflows project, which converts legacy and proprietary sources into cloud-native Parquet/PMTiles/COG with rich STAC metadata. To propose a dataset, open an issue there.