General Bathymetric Chart of the Oceans

GEBCO (General Bathymetric Chart of the Oceans) is a global terrain model providing elevation and bathymetry data in metres on a 15 arc-second interval grid, covering both ocean and land. The dataset includes a Type Identifier (TID) Grid indicating source data types, and incorporates under-ice topography for Greenland and Antarctica. Data are distributed as Cloud Optimised GeoTIFFs for annual releases.

bathymetry oceanography elevation ocean topography seafloor global-coverage

General Bathymetric Chart of the Oceans (GEBCO)

Dataset files (3 x .tif)

Source Cooperative product page

## README.md
# General Bathymetric Chart of the Oceans Data

Data originall accessed from the
[GEBCO website](https://www.gebco.net/data_and_products/gridded_bathymetry_data/).

GEBCO's gridded bathymetric data set, is a global terrain model for ocean and land, providing elevation data, in meters, on a 15 arc-second interval grid. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2024 Grid is based on.

This release includes a version of the grid with under-ice topography/bathymetry information for Greenland and Antarctica.

Find out information about [terms of use and attribution here](https://www.gebco.net/data_and_products/gridded_bathymetry_data/#a1).

Documentation on [data preparation is accessible here](https://github.com/AustralianAntarcticDivision/gebco).

## Access data

### Stream data

The cloud optimised geotiff can be used directly in QGIS or
other applications using this URL:
`https://data.source.coop/ausantarctic/gebco/GEBCO_<year>.tif`.

### Example to load as Xarray

```python
from odc.stac import load
from pystac import Item

# Bounding box around Tasmania
bbox = [140.0, -45.0, 150.0, -35.0]

# Get the item as a PySTAC Item
item = Item.from_file(
    "https://data.source.coop/ausantarctic/gebco/GEBCO_2024.stac-item.json"
)

# Load using odc.stac.load
data = load(
    [item],
    bbox=bbox,
    chunks={"latitude": 1024, "longitude": 1024},
)

# Plot an interactive map
data.elevation.odc.explore(vmin=-3000, vmax=3000, cmap="terrain")
```

### Downloading

While the idea of cloud native data formats is to not download, you can
download this data using the following URLs:

* 2024: `https://data.source.coop/ausantarctic/gebco/GEBCO_2024.tif`
* 2025: `https://data.source.coop/ausantarctic/gebco/GEBCO_2025.tif`
* 2026: `https://data.source.coop/ausantarctic/gebco/GEBCO_2026.tif`

providers: role [processor] for 'Australian Antarctic Division' is an AI guess — Hosts the data preparation pipeline repo (github.com/AustralianAntarcticDivision/gebco); assumed processor role based on that.. Verify before flipping to _state: reviewed.


GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)

GHRSST Level 4 MUR (Multi-scale Ultra-high Resolution) Global Foundation Sea Surface Temperature Analysis (v4.1) is a daily, 1-kilometer-resolution global ocean surface temperature dataset produced by NASA's Jet Propulsion Laboratory in collaboration with GHRSST. It fuses observations from multiple satellite-borne sensors (infrared and microwave radiometers) and in situ measurements to derive foundation SST values free of diurnal warming effects. Distributed as Cloud-Optimized GeoTIFFs with STAC metadata, it supports climate research, numerical weather prediction, and marine resource management.

oceanography remote-sensing climate weather-forecasting weather numerical-weather-prediction cloud-optimized-geotiff time-series sea-surface-temperature

NASA Jet Propulsion Laboratory (JPL) / PO.DAAC

Dataset files (37640 x .tif)

Source Cooperative product page

## README.md
# GHRSST Sea Surface Temperature Analysis

The GHRSST Level 4 MUR (Multi-scale Ultra-high Resolution) Global Foundation Sea Surface Temperature Analysis (v4.1) provides
a high-resolution, daily global assessment of ocean surface temperature. This dataset fuses observations from multiple
satellite-borne sensors (including infrared and microwave radiometers) and in situ measurements, offering a robust and
consistent picture of sea surface temperature conditions. The algorithm employs advanced interpolation and blending
techniques to fill gaps caused by cloud cover and sensor discrepancies, resulting in a seamless, 1-kilometer-resolution
global field. The “foundation” temperature represents the sea surface temperature free of diurnal warming effects,
making it particularly valuable for climate studies, numerical weather prediction, and a variety of marine applications.
The dataset is distributed by NASA’s Jet Propulsion Laboratory in collaboration with the Group for High Resolution Sea Surface
Temperature (GHRSST), and is widely used by researchers, operational agencies, and policy-makers for informed decision-making
in fields such as marine resource management, weather forecasting, and climate monitoring.

For more information, please refer to the [documentation](https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1).

## About this repository

The [code used to repackage this data is accessible here](https://github.com/auspatious/ghrsst-cogger).

Care has been taken to not change the source data, but just to repackage it into cloud optimised geotiffs with
STAC metadata. Each daily dataset is accessible in a `<year>/<month>/<day>` path, and file names are consistent with the
source data, e.g., `"{$Y%m%d}090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.stac-item.json`.

## Accessing data

### Using Python

To load a single day, you can do something like the following using Python:

```python
from pystac import Item
from odc.stac import load

path = "https://data.source.coop/ausantarctic/ghrsst-mur-v2/2025/02/09/20250209090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.stac-item.json"
item = Item.from_file(path)

data = load([item], chunks={}, anchor="center")
data
```

And then to plot it, do:

```python
sst_loaded = data["analysed_sst"].squeeze().compute()
sst_masked = sst_loaded.where(sst_loaded != -32768)

# Extract the scale and offset from the metadata
meta = item.assets["analysed_sst"].extra_fields["raster:bands"][0]
scale = meta["scale"]
offset = meta["offset"]
k_to_c = -273.15

sst_scaled = sst_masked * scale + offset + k_to_c
sst_scaled.plot.imshow(size=8, robust=True, cmap="inferno")
```

### Reading from STAC Parquet file

Alternately, you can use the STAC Parquet file as an index to all the STAC docs.

```python
import stacrs
import pystac
from odc.stac import load

url = "https://data.source.coop/ausantarctic/ghrsst-mur-v2/ghrsst-mur-v2.parquet"

center = 13, -61
buffer = 5

bbox = (
    center[1] - buffer,
    center[0] - buffer,
    center[1] + buffer,
    center[0] + buffer,
)

items = await stacrs.read(url)

# Or use .search to filter by time
# year = 2024
# items = stacrs.search(
#     url,
#     bbox=bbox,
#     datetime=f"{year}-01-01T00:00:00.000Z/{year}-12-31T23:59:59.999Z",
# )

items = [pystac.Item.from_dict(i) for i in items["features"]]

data = load(items, bbox=bbox, chunks={})
data
```

### Using R

The 'analysed_sst' files are templated by:

``` r
template <- "https://data.source.coop/ausantarctic/ghrsst-mur-v2/{format(date, '%Y/%m/%d')}/{format(date, '%Y%m%d')}090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1_analysed_sst.tif"
```

Example for a specific date:

``` r
date <- as.Date("2025-02-14")
(url <- glue::glue(template))
```

Strictly, we should check the catalogue first:

``` r
catalog <- arrow::read_parquet("https://data.source.coop/ausantarctic/ghrsst-mur-v2/ghrsst-mur-v2.parquet")

if (!url %in% catalog$assets$analysed_sst$href) {
  message("file does not exist, check available dates")
} else {
  dsn <- glue::glue("/vsicurl/{url}")
}
```

Read using the terra package:

``` r
library(terra)
(sst <- rast(dsn))
```

Or alternatively, using gdalraster:

``` r
library(gdalraster)
(ds <- new(GDALRaster, dsn))
```

Or with stars:

``` r
library(stars)
(sstars <- read_stars(dsn, proxy = TRUE))
```

To crop with the terra package:

``` r
crop(sst, ext(130, 150, -55, -42))
```

Or, more generally, project (by_util is very important for COG efficiency):

``` r
project(sst, rast(ext(130, 150, -55, -42), res = res(sst)), by_util = TRUE)
```

We can also change CRS and resolution as desired:

``` r
target <- rast(
  ext(c(-1, 1, -1, 1) * 6e5),
  res = 1000,
  crs = "+proj=laea +lon_0=147 +lat_0=-45"
)
project(sst, target, by_util = TRUE)
```

## License

See: [https://podaac.jpl.nasa.gov/CitingPODAAC](https://podaac.jpl.nasa.gov/CitingPODAAC)

> Data hosted by the PO.DAAC is openly shared, without restriction, in accordance with NASA's Earth Science program Data and Information Policy.

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.


Geometric Median & Median Absolute Deviation Mosaics for Small Island Developing States

Landsat-based cloud-free mosaics covering small island developing states across the world. Coverage is annual (using up to three years of data) and for all years from 2020-2025.


Land Use/Land Cover for Small Island Developing States


USGS Watershed Boundary Dataset

The USGS Watershed Boundary Dataset (WBD) is a nationally consistent geospatial dataset delineating hierarchical hydrologic unit boundaries across the United States and its territories. Published by the U.S. Geological Survey's National Geospatial Technical Operations Center, it defines watershed boundaries at multiple scales (HUC-2 through HUC-12) for use in water resource management, hydrologic modeling, and land stewardship.

watershed-boundaries hydrologic-units hydrology nhd water-resources stream-networks

Dataset files (1 x .filegdb)

Source Cooperative product page

providers: role [processor] for 'National Geospatial Technical Operations Center (NGTOC)' is an AI guess — NGTOC is the USGS division credited in the citation as compiling and distributing the WBD; assumed processor role.. Verify before flipping to _state: reviewed.


National Hydrology Dataset Plus High Resolution (NHDPlus HR)

NHDPlus HR is a national geospatial dataset depicting water flow across US landscapes and through the stream network. It integrates 1:24,000-scale National Hydrography Dataset High Resolution data, 10-meter 3D Elevation Program data, and the nationally complete Watershed Boundary Dataset. Coverage spans the United States and territories, providing hydrological features including streams, waterbodies, and drainage basins in File Geodatabase format.

hydrology watershed streams water-resources national-hydrography drainage-networks elevation

Dataset files (1 x .filegdb)

Source Cooperative product page

providers: role [producer, licensor] for 'U.S. Geological Survey (USGS)' is an AI guess — USGS is the known producer of NHDPlus HR (confirmed by API tags); licensor inferred from producer status as the originating federal agency, not explicitly stated.. Verify before flipping to _state: reviewed.


Failed

Flowlines from the NHDPlus High Resolution (HR) dataset covering the South Fork Eel River watershed in northern California. Contains 5,966 stream and river flowline features spanning roughly 39.6°N–40.4°N, 123.4°W–124.1°W. Distributed as FlatGeobuf and MBTiles formats.

flowlines hydrology hydrography nhd rivers streams watersheds south-fork-eel-river

Dataset files (1 x .fgb)

Source Cooperative product page


South Eel River PM Tiles and .fgb Layers

Flowline data for the South Fork Eel River in Northern California, derived from the National Hydrography Dataset Plus High Resolution (NHDPlus HR). Distributed as PMTiles for web visualization and FlatGeobuf for analysis, covering stream network topology, routing attributes, and elevation profiles across the watershed.

hydrology flowlines river-network nhdplus watershed stream-topology

Eel_flow.fgb

[admin_code] Geographic Names Information System identifier for the named feature

[placename] GNIS-assigned name for the hydrographic feature (e.g. stream or river name)

ReachCode

[admin_code] NHD reach code uniquely identifying each hydrographic reach

Source Cooperative product page

providers: role [producer, licensor] for 'U.S. Geological Survey (USGS)' is an AI guess — NHDPlus HR is a USGS product; licensor assumed from standard USGS public-domain data policy, not stated in this dataset's metadata.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [Eel_flow.GNIS_Name (placename), Eel_flow.GNIS_ID (admin_code), Eel_flow.ReachCode (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


England Trees Outside Woodland

The National Trees Outside Woodland (TOW) V1 map is a vector product funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme produced under Forest Research’s Earth Observation for Trees and Woodlands (EOTW) project. The TOW map identifies canopy cover over 3m tall and 5m2 area which exists outside the National Forest Inventory (National Forest Inventory - Forest Research). Canopy cover is categorised into the following woodland types - lone trees, groups of trees and small woodlands. The data set was derived from the Vegetation Object Model (VOM) (Environment Agency, EA), the National Lidar Survey (EA), and Sentinel-2 (European Space Agency) imagery using spatial algorithms. The method is fully automated with no manual manipulation or editing. The map and its production method has been quality assured by DEFRA science assurance protocols and assessed for accuracy using ground truth data. Because the process classifies objects based on proximity to features within OS mapping, there could be some misclassifications of those objects not included in the OS (specifically: static caravans, shipping containers, large tents, marquees, coastal cliffs and solar farms). This is a first release of this dataset, the quality of the production methods will be reviewed over the next year, and improvements will be made where possible. The TOW map is available under open government licence and free to download from the Forestry Commission open data download website (Forestry Commission) and view online on the NCEA ArcGIS Online web portal (Trees Outside Woodland). A full report containing details on methodology, accuracy and user guide is available. TOW map web portal link : Trees Outside Woodland Public Map FR TOW map web page : Trees Outside Woodland Map - Forest Research


Cloud-Optimized derivatives of the California Department of Fish and Wildlife Areas of Conservation

Berkeley DSE optimization of California Areas of Conservation Concern (ACE). ACE was a CDFW Project providing an easily-accessible and standardized way to view a mapped summary of the best available statewide data on California's biological richness and biodiversity for use in conservation planning. (Public Domain)

biodiversity california conservation public-data


Multi-Resolution Land Characteristics (MRCL)

The Multi-Resolution Land Characteristics (MRLC) consortium is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management and modeling applications. The creation of this consortium has resulted in the mapping of the lower 48 United States, Hawaii, Alaska and Puerto Rico into a comprehensive land cover product termed, the National Land Cover Database (NLCD), from decadal Landsat satellite imagery and other supplementary datasets.

The primary objective of the MRLC NLCD is to provide the Nation with nationally complete, current, consistent and public domain information on the Nation's land cover. Land cover information is critical for local, state and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics and developing land management policies.

All MRLC NLCD data products are available at no charge to the user.

Cloud-optimized translations provided via DSE team, Work in Progress.  see official data at https://www.mrlc.gov/data


AOML Ocean GTS Observations

All available ocean observations from NOAA's Atlantic Oceanographic and Meteorological Laboratory (AOML) sourced from the WMO Global Telecommunication System (GTS) flattened stream, spanning 2012-01-01 to 2025-06-30. The dataset has been converted to Icechunk format as a single large Zarr-compatible chunked store with global spatial coverage.

ocean-observations oceanography gts climate weather icechunk zarr noaa meteorology

Dataset files (26 x .icechunk)

Source Cooperative product page

providers: role [producer] for 'NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML)' is an AI guess — Identified as producer from 'AOML' in dataset title and description; licensor role not stated. Verify before flipping to _state: reviewed.


CAMS Aerosol Analysis

CAMS Aerosol Analysis containing many wavelength and aerosol properties from the Copernicus CAMS operational archive, in Icechunk format. The goal of this is to give quicker access to some of the slow-access data, and focus on variables important for solar energy forecasting and related tasks.


Nordic Regional NWP Models

Hourly atmospheric analysis and forecast archive derived from two regional numerical weather prediction models: the Danish HARMONIE model at 2 km resolution covering Greenland and Iceland, and the MetCoOp MEPS model at 2.5 km resolution covering the broader Nordic region. Constructed by archiving the first three forecast steps of each model run to produce an effective hourly analysis product. Data is stored in Icechunk format for cloud-native multidimensional array access.

numerical-weather-prediction meteorology weather-forecasting weather atmospheric-modeling icechunk zarr arctic

Dataset files (16 x .icechunk)

Source Cooperative product page


Global Hydrology Estimate and Enterprise Rain Rate

Global Hydrology Estimate (GHE) and Enterprise Rain Rate (ERR) products from NOAA, stored in Icechunk/Zarr format. GHE provides satellite-derived global precipitation estimates; the Enterprise Rain Rate is its operational successor. Data spans June 2019 through mid-2025 at global coverage.

precipitation climate weather noaa meteorology remote-sensing global-coverage

Dataset files (3 x .icechunk)

Source Cooperative product page


Geostationary Full-Disk Imagery

Full-disk geostationary satellite imagery from GOES, Himawari, and G2-KA satellites, stored in Icechunk format and converted from public AWS archives. Data consists of Level 1b radiance/reflectance imagery processed by Satpy. Coverage spans historical and near-real-time imagery and is being actively expanded.

remote-sensing satellite-imagery weather climate earth-observation icechunk zarr noaa meteorology geostationary

Dataset files (81 x .icechunk)

Source Cooperative product page


GEOS Atmosphere Composition Analysis

GEOS-CF (Goddard Earth Observing System Composition Forecast) is a high-temporal-resolution global atmospheric composition analysis product from NASA's Global Modeling and Assimilation Office (GMAO). It provides gridded aerosol and atmospheric trace gas data at 0.25-degree spatial resolution and 15-minute temporal intervals covering the entire globe. Data are stored in Icechunk/Zarr format and updated with approximately one-day latency.

atmospheric-data meteorology atmospheric-modeling climate zarr icechunk global-coverage time-series

NASA Goddard Modeling and Assimilation Office (GMAO)

Dataset files (2 x .icechunk)

Source Cooperative product page


GFS Analysis and Forecast

Global Forecast System (GFS) analysis and forecast data stored in Icechunk (Zarr-based) format, covering global extent at 0.25-degree resolution. Includes both operational GFS analysis fields and a pseudo-reanalysis dataset constructed from the most recent available real-time forecast step. Temporal coverage spans 2016-01-01 through at least October 2025.

numerical-weather-prediction weather meteorology reanalysis gfs icechunk zarr global-coverage

Dataset files (4 x .icechunk)

Source Cooperative product page

providers: role [producer, licensor] for 'NOAA National Centers for Environmental Prediction (NCEP)' is an AI guess — GFS is operationally produced by NOAA/NCEP; assumed producer and licensor as US government data, but not stated in README. Verify before flipping to _state: reviewed.


Global Mosaic of Geostationary Images

A global mosaic of imagery assembled from multiple geostationary weather satellites at hourly cadence, covering latitudes from approximately -73° to +73°. Data is stored in Zarr v3 / IceChunk format and spans from July 2021 onward. Extended from a Hugging Face dataset originally produced by jacobbieker.

remote-sensing satellite-imagery meteorology weather zarr icechunk global-coverage geostationary

Dataset files (3 x .icechunk)

Source Cooperative product page


DWD ICON

Archive of global numerical weather prediction (NWP) model output from Deutscher Wetterdienst (DWD), Germany's national meteorological service. Covers the ICON Global and ICON ART Global (Aerosol and Reactive Trace gases) models, including ensemble runs and historical forecast data stored in GRIB2 format. Data is accessible via Icechunk/Zarr with near-global spatial coverage.

numerical-weather-prediction meteorology weather-forecasting weather ensemble-forecast atmospheric-modeling zarr icechunk

Dataset files (6 x .icechunk)

Source Cooperative product page


IFS Analysis

Global atmospheric analysis fields from the ECMWF Integrated Forecasting System (IFS), with a subset sourced from the NCAR/UCAR GDEX archive. Coverage spans January 2016 through June 2026 at global extent, comprising approximately 33 TB across nearly one million files. Data are stored in icechunk/Zarr format for cloud-native access.

meteorology numerical-weather-prediction global-coverage atmospheric-data icechunk zarr time-series atmospheric-modeling

European Centre for Medium-Range Weather Forecasts (ECMWF)

Dataset files (4 x .icechunk)

Source Cooperative product page


GPM IMERG Early and Final Half-Hourly Precipitation Datasets

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) Early and Final Half-Hourly precipitation products, stored as Zarr arrays with global coverage. The dataset spans from 2000-01-01 to present at 30-minute temporal resolution. Early run provides near-real-time estimates; Final run is a research-quality, latency-adjusted product.

precipitation gpm imerg remote-sensing zarr climate satellite-rainfall

Dataset files (3 x .zarr)

Source Cooperative product page

providers: role [producer] for 'JAXA' is an AI guess — JAXA is the co-lead agency on the GPM mission and contributes the core satellite; producer role inferred from mission structure, not stated in context.. Verify before flipping to _state: reviewed.


Copernicus Marine Wave and Ocean Products

A global collection of Copernicus Marine Service wave and ocean reanalysis and near-real-time analysis products at native model resolution, stored in Icechunk format. Temporal coverage spans 1980 to 2026 with near-global spatial extent. Select variables were rescaled to float16 to reduce storage footprint with minimal loss of precision.

oceanography reanalysis meteorology wave-modeling copernicus-marine sea-state ocean-analysis

Dataset files (3 x .icechunk)

Source Cooperative product page

providers: role [producer, licensor] for 'Copernicus Marine Service (CMEMS)' is an AI guess — CMEMS is the named source of these products; licensor role inferred from standard Copernicus Marine Service open-data terms, not explicitly stated here.. Verify before flipping to _state: reviewed.


MetOffice Global 10km and UK 2km Deterministic Analysis

A 2-year rolling archive of the UK Met Office's global 10 km and UK 2 km deterministic model analyses, providing hourly atmospheric data from 2024-01-01 to near-present. The global model additionally includes 5 hours of forecast outputs. Data is stored in Icechunk format for cloud-native multidimensional array access.

numerical-weather-prediction meteorology weather atmospheric-modeling weather-forecasting icechunk zarr global-coverage

Dataset files (7 x .icechunk)

Source Cooperative product page


MRMS Rainfall Measurements

Multi-Radar Multi-Sensor (MRMS) hourly precipitation accumulations for the United States, converted to Zarr format. Includes gauge-only measurements, MultiSensor MultiPass estimates, and derived precipitation rate products. Data is sourced from a network of NOAA weather radars and ground-based rain gauges.

mrms precipitation rainfall radar earth-observation weather remote-sensing hourly

NOAA National Severe Storms Laboratory (NSSL)

Dataset files (10 x .zarr)

Source Cooperative product page


Cloud-Optimized NREL National Solar Radiation Database

International solar radiation data from the NREL National Solar Radiation Database (NSRDB), reformatted into Icechunk/Zarr format and optimized for reading full spatial extents at single timesteps. The initial release covers India; the full dataset spans hourly and sub-hourly satellite-derived solar resource variables globally. Intended for photovoltaic modeling and climate/weather applications.

solar-radiation climate weather remote-sensing zarr icechunk

National Renewable Energy Laboratory (NREL)

Dataset files (2 x .icechunk)

Source Cooperative product page

providers: role [host] for 'OpenEI' is an AI guess — OpenEI S3 bucket (nrel-pds-nsrdb) cited as upstream source; assumed host/distributor of original files. Verify before flipping to _state: reviewed.


Weather Observations

A collection of weather observations in Icechunk format. Data sources vary, but mostly from NOAA/EUMETSAT/ECMWF/MetOffice. Data includes radiosonodes, weather stations, and ocean observations.

icechunk zarr weather climate observation forecasting


Polar-Orbiting Satellite Imagery

Polar-orbiting satellite imagery from the JPSS (Joint Polar Satellite System) and MetOp satellite series, stored in Icechunk format. Data was converted from original instrument output using Satpy and covers weather, climate, and environmental observation use cases. The archive is continually expanding but is noted as incomplete.

satellite-imagery polar-orbiting jpss metop weather climate

Dataset files (6 x .icechunk)

Source Cooperative product page

providers: role [producer, licensor] for 'NOAA / NASA (JPSS Program)' is an AI guess — JPSS is a joint NOAA/NASA program; producer and licensor roles assumed as the originating government agencies for the raw instrument data. Verify before flipping to _state: reviewed.

providers: role [producer, licensor] for 'EUMETSAT (MetOp)' is an AI guess — MetOp is an EUMETSAT/ESA series; producer and licensor roles assumed as the originating agency for raw data. Verify before flipping to _state: reviewed.


International Precipitation Radar

Precipitation radar estimates from national weather services in the United Kingdom and Finland, comprising Finnish hourly 1-hour, 12-hour, and 24-hour precipitation accumulations alongside UK composite rain rate radar output. Data is stored in icechunk-optimized Zarr format and covers August 2023 through April 2026.

precipitation radar earth-observation meteorology weather zarr icechunk time-series

Dataset files (3 x .icechunk)

Source Cooperative product page


SILAM Global Dust Model Forecasts

Global atmospheric dust model forecasts produced by the Finnish Meteorological Institute (FMI) using the SILAM (System for Integrated modeLling of Atmospheric coMposition) model, stored in Zarr format at 0.1-degree spatial resolution with global coverage. Also includes FMI aerosol AWS forecast data in Icechunk format.

aerosols dust forecast numerical-weather-prediction atmospheric-modeling meteorology

Dataset files (3 x .zarr)

Source Cooperative product page


WeatherReal-filtered NOAA Integrated Surface Dataset

NOAA Integrated Surface Dataset (ISD) weather station observations filtered according to the WeatherReal dataset methodology and converted to Zarr format. Covers global surface meteorological station data spanning 1901 to 2025, including temperature, precipitation, and other surface weather variables.

Dataset files (11 x .zarr)

Source Cooperative product page

providers: role [producer, licensor] for 'NOAA National Centers for Environmental Information' is an AI guess — NOAA is the originating agency for ISD; licensor role assumed from standard NOAA open data policy. Verify before flipping to _state: reviewed.

providers: role [processor] for 'WeatherReal' is an AI guess — Assumed processor role based on the filtering methodology described as 'filtered as in the WeatherReal dataset paper'. Verify before flipping to _state: reviewed.


GLanCE is a global land cover training dataset containing nearly 2 million samples spanning 1984–2020 at 30 m spatial resolution derived from the Landsat archive, covering seven primary and twelve secondary land cover classes across all global ecoregions. Each sample includes up to 23 land cover characteristics and supports mapping of both abrupt and gradual land cover change over nearly four decades. The classification scheme is compatible with IPCC land use categories and the FAO Land Cover Classification System (LCCS).

land-use remote-sensing agriculture land-cover landsat change-detection time-series

Dataset files (1 x .geojson)

Source Cooperative product page

## README.md
# GLanCE: A Global Land Cover Training Dataset from 1984 to 2020
The Global Land Cover Estimation (GLanCE) project provides high-quality long-term records of land cover and land cover change at 30 m spatial resolution for the 21st century using the Landsat archive. The GLanCE land cover classification scheme is designed to be compatible with common land cover classification systems such as the IPCC land use categories for greenhouse gas inventory reporting and the FAO Land Cover Classification System (LCCS). As part of the GLanCE project, we present a new land cover training database that is designed to provide global coverage, ensure accuracy of land cover labels at 30 m spatial resolution, cover nearly four decades, and produce a geographically dense dataset.

## Download Dataset
This repository contains a [Cloud-Native Geospatial](https://cloudnativegeo.org/) [GeoParquet](https://geoparquet.org/) version as well as a [GeoJSON](https://geojson.org/) version of the data. You can use the [BROWSE link](https://beta.source.coop/boston-university/bu-glance) on the left hand side to navigate to the data and download it. You can also access the S3 bucket directly with S3 tools (aws cli, boto3, etc); the S3 URI is s3://us-west-2.opendata.source.coop/boston-university/bu-glance/

## Creator & Contact
* [Boston University Global Land Cover Estimation (GLanCE)](https://sites.bu.edu/measures/)
* rkstan@bu.edu

## Data License
The data is licensed under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).

## Citation & DOI
Stanimirova R., Tarrio K., Turlej K., McAvoy K., Stonebrook S., Hu K-T., Arévalo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z., Barber C.P., Souza C., Chen S., Wang J.A., Mensah F., Calderón-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S., Siampale A., and M.A. Friedl (2023) "A Global Land Cover Training Dataset from 1984 to 2020", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.x4xfh3

## Publication
Stanimirova, R., Tarrio, K., Turlej, K., McAvoy K., Stonebrook S., Hu K-T., Arévalo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z., Barber C.P., Souza C., Chen S., Wang J.A., Mensah F., Calderón-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S., Siampale A., and M.A. Friedl (2023) A global land cover training dataset from 1984 to 2020. Sci Data 10, 879 https://doi.org/10.1038/s41597-023-02798-5

| Column Name | Description |
| ------------- | ------------- |
| Lat  | Latitude  |
| Lon  | Longitude  |
| Start_Year  | Start year of segment, ranging from 1984 to 2020 (integer) |
| End_Year  | End year of segment, ranging from 1984 to 2020 (integer) |
| Glance_Class_ID_level1  | Level 1 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Barren/sparsely vegetated), 5 (Trees), 6 (Shrub), and 7 (Herbaceous). |
| Glance_Class_ID_level2  | Level 2 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Soil), 5 (Rock), 6 (Beach/sand), 7 (Deciduous), 8 (Evergreen), 9 (Mixed), 10 (Shrub), 11 (Grassland), 12 (Agriculture), and 13 (Moss/lichen). NaN values present. |
| Leaf_Type  | Tree leaf type: broadleaf (1), needleleaf (2), and mixed (3). NaN values present.  |
| Impervious_Percent  | Impervious percent for developed samples: low 0%-30% (1), medium 30%-60% (2), and high 60%-100% (3). NaN values present.  |
| Tree_Location  | Binary integer indicating whether trees are on the interior (0) or edge (1) of a forest. NaN values present.   |
| Veg_Density  | Vegetation density for trees and shrubs: sparse 0%-30% (1), open 30%-60% (2), and closed 60%-100% (3). NaN values present.   |
| Veg_Modifier | Vegetation modifiers, which can include one or more of the following: Cropland, Plantation, Wetland, Riparian/Flood, Mangrove, Greenhouse, and Trees/Shrub Present. NaN values present.    |
| Segment_Type  | Indicates whether a segment is stable (0) or transitional (1). See Section 1 for a detailed description. Land cover for transitional segments is recorded at both the beginning and end of the time segment - typically the first and last three years. NaN values present.    |
| Change  | Indicates presence (1) or absence (0) of land cover change for Level 1 land cover labels. Includes both abrupt change and gradual change (transitional segments (1) from the Segment_Type attribute) if it happened at any time for that training unit.   |
| LC_Confidence  | Interpreter confidence in the Level 1 land cover label from 1 (lowest) to 3 (highest). NaN values present.  |
| Level1_Ecoregion  | Ecoregion Level 1 number based on World Wildlife Fund definitions. For North America we used ecoregions based on the Environmental Protection Agency’s Ecoregions of North America product. |
| Level2_Ecoregion  | Ecoregion Level 2 number based on the Environmental Protection Agency’s Ecoregions of North America product. This field is available only for North America and is assigned a value of 0 for all other continents.   |
| Continent_Code  | Assigned continent number: North America (1), South America (2), Africa (3), Europe (4), Asia (5), and Oceania (6). |
| Dataset_Code |  Assigned dataset number: 1, 2, 3, 4, 5, 902, 999, 700, 701, 702, 703, 704, 705, 706, and 707. Numbers correspond to each Dataset as follows: STEP, CLUSTERING, LCMAP, ABoVE, MapBiomas, Feedback, Training_augment, MODIS_algo, GeoWiki, RadEarth, Collaborator_data, BU_team_collected, GLC30, LUCAS, ASB_crop.  For details see Scientific Data publication.    |
| Glance_ID  |  Unique ID for each sample.  |
| ID  |  ID for each unique combination of latitude and longitude. Change units have the same ID but different Glance_ID.   |

providers: role [host] for 'Radiant MLHub' is an AI guess — Original host inferred from dataset DOI domain (rdnt.x4xfh3) and citation referencing Radiant MLHub. Verify before flipping to _state: reviewed.


Clay Model v0 Embeddings

We provide here the embeddings for the 1M chips used to train Clay Model v0.
See documentation for details: https://clay-foundation.github.io/model/specification.html

remote sensing
earth
ai
foundational model


Plateau Tokyo Buildings in FlatCityBuf

Plateau Tokyo Buildings in FlatCityBuf (Updated 29 Jan 2026)


Plateau Tokyo Buildings in FlatCityBuf

Plateau Tokyo Buildings in FlatCityBuf


Tokyo 13999 district in FlatCityBuf

Tokyo 13999 district in FlatCityBuf


Geospatial ML

Visualizer components for geospatialml.com


Open Climate Risk

Building-level wildfire risk assessments across the continental United States, produced by CarbonPlan's Open Climate Risk project. The pipeline integrates USFS wildfire hazard potential raster inputs with Overture Maps building footprints and U.S. Census TIGER/Line administrative boundaries to compute risk metrics at building, Census block, tract, and county scales. Outputs are provided as Hive-partitioned Geoparquet, PMTiles, Icechunk tensor stores, GPKG, and CSV formats.

building-footprints climate census icechunk zarr geoparquet pmtiles wildfire-risk

Dataset files (10 x .icechunk)

Source Cooperative product page

## README.md
# Open Climate Risk

This Source Cooperative repository contains data products for the [Open Climate Risk](https://carbonplan.org/research/climate-risk) project. These data were used to build a scalable pipeline for calculating climate risk assessments at building-level resolution across the continental United States. Wildfire risk was calculated through a distributed processing system that can run locally or on cloud infrastructure. Technical documentation can be found [here](https://docs.carbonplan.org/ocr).

## Working with the data
Documentation for working with the raster and vector data can be found [here](https://docs.carbonplan.org/ocr/en/latest/how-to/work-with-data.html).

## Data Formats

**Tensor**
- Our raw input data consisted of either Zarr or TIFF files. We processed it into rechunked Icechunk stores.
- Our n-dimensional output data is stored as a single Icechunk store.

**Vector**
- Our raw input data was either GPKG or Geoparquet. All the vector pipeline inputs were converted to Geoparquet.
- Our output vector data is stored in multiple formats. See [our documentation on data access](https://docs.carbonplan.org/ocr/en/latest/access-data.html) for more details.
    - Hive-partitioned (state/county) Geoparquet file for risk sampled buildings across CONUS.
    - PMTiles layers for buildings, Census blocks, Census Tracts and counties.
    - Summary statistics files in GPKG and CSV

## Data structure

The data is split into `inputs` and `outputs,` where `inputs` are raw and processed data for the pipeline, and `outputs` are versioned dataset outputs.

```bash
├── input
│   └── fire-risk
│       ├── tensor
│       │   ├── USFS (Icechunk & TIFF)
│       │   │   ├── dillon-et-al-2023
│       │   │   ├── riley-et-al-2025
│       │   │   └── scott-et-al-2024
│       │   └── conus404-ffwi (Icechunk)
│       └── vector
│           ├── census-tiger (Geoparquet)
│           │   ├── LICENSE.txt (CC BY 4.0)
│           │   ├── blocks
│           │   ├── counties
│           │   └── tracts
│           └── overture-maps (Geoparquet)
│                └── LICENSE.txt (ODbL)
└── output
    └── fire-risk
        ├── tensor (Icechunk)
        │   ├── LICENSE.txt (CC BY 4.0)
        │   ├── NOTICE.txt
        │   └── production
        └── vector (Geoparquet, PMTiles, GPKG, csv)
            ├── LICENSE.txt (ODbL)
            ├── NOTICE.txt
            └── production

```

## Data Sources

The tensor outputs rely on data from [K Riley et al. 2025](https://doi.org/10.2737/RDS-2025-0006), [J Scott et al. 2024](https://doi.org/10.2737/RDS-2020-0016-2), and [R Rasmussen et al. 2023](https://doi.org/10.1175/BAMS-D-21-0326.1). The vector outputs additionally incorporate data from the [Overture Maps Foundation buildings dataset](https://docs.overturemaps.org/guides/buildings/) and [Census TIGER/Line](https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html). Read more about our data sources [here](https://docs.carbonplan.org/ocr/en/latest/reference/data-sources.html).

## Terms of use

By viewing Open Climate Risk data, you agree to CarbonPlan’s [Terms of Use](https://carbonplan.org/terms) and [Privacy Policy](https://carbonplan.org/privacy).

This work contains information from the Overture Maps buildings database, which is made available here under the [Open Database License](https://opendatacommons.org/licenses/odbl) (ODbL).

Licenses for tensor ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0)) and vector ([ODbL](https://opendatacommons.org/licenses/odbl/1-0)) data are packaged with the data in colocated LICENSE.txt files.


30×30 state-level conservation policy implementation and progress across the United States

This repository contains a curated dataset of U.S. state-level 30×30 policy implementation and progress. Policy records were collected, reviewed, and standardized by the author from publicly available primary sources, then compiled into a single, harmonized dataset.


The Public Utility Data Liberation Project

The PUDL Project is an open source data processing pipeline that makes US energy data easier to access and use programmatically.

Hundreds of gigabytes of valuable data are published by US government agencies, but it's often difficult to work with. PUDL takes the original spreadsheets, CSV files, and databases and turns them into a unified resource. This allows users to spend more time on novel analysis and less time on data preparation.

energy
electricity
climate change
united states
natural gas
coal
emissions
finance
eia
ferc
epa


Demo of Source Cooperative

No Description Provided


California Disadvantaged Communities (DAC) and Economically Distressed Areas (EDA), 2023

California Department of Water Resources (DWR) layers identifying Disadvantaged Communities (DAC, MHI < 80% of statewide) and Economically Distressed Areas (EDA, MHI < 85% of statewide plus distress factors) at multiple Census geographies, joined to ACS 2019-2023 5-year estimates on 2020 Census boundaries.


California 30x30 data

No Description Provided


CalEnviroScreen

CalEnviroScreen 5.0 (Draft) is a cumulative environmental impact screening tool developed by California's Office of Environmental Health Hazard Assessment (OEHHA) that assigns a composite score to every California census tract based on pollution burden and population vulnerability. It integrates over 20 indicators spanning air quality, toxic releases, contaminated water and sites, and population health and socioeconomic factors into a single CES 5.0 Score ranging from 0–100. Draft version 5.0 was released January 2026 using 2020 Decennial Census tract geography covering 9,106 tracts statewide.

environmental-monitoring air-quality census demographics environmental-justice public-health geoparquet pmtiles

ces5.parquet

tract

[admin_code] Census Tract ID from 2020 Decennial Census

ZIP

[zipcode] Postal ZIP code for the census tract

AppoxLoc

[placename] Approximate city or unincorporated area name

county

[placename] California county name

region

[placename] California region name

Shape

[geometry] Census tract polygon geometry (EPSG:4326 / OGC:CRS84)

bbox

STRUCT(xmin DOUBLE, ymin DOUBLE, xmax DOUBLE, ymax DOUBLE)

calenviroscreen-5-0/ces5/hex

[spatial_index] H3 resolution 10 hexagonal cell index

[spatial_index] H3 resolution 8 parent hexagonal cell index

[spatial_index] H3 resolution 9 parent hexagonal cell index

ZIP

[zipcode] Postal ZIP code

county

[placename] California county name

region

[placename] California region name

[spatial_index] H3 resolution 0 parent hexagonal cell index

2 files sampled for this table

Source Cooperative product page

## README.md
# CalEnviroScreen 5.0 (Draft)

## Overview

CalEnviroScreen is a mapping tool developed by California's Office of Environmental
Health Hazard Assessment (OEHHA) to identify communities disproportionately burdened
by multiple sources of pollution. It combines environmental exposures, environmental
effects, sensitive population health indicators, and socioeconomic factors into a single
cumulative impact score for every California census tract.

Draft CalEnviroScreen 5.0 was released January 2026. It uses **2020 Decennial Census**
tract geography (9,106 tracts), updates all existing indicators, and adds two new
indicators (Children's Lead Risk from Housing and Small Air Toxic Sites) compared to
version 4.1.

The overall **CES 5.0 Score** is calculated as:

> **CES Score = Pollution Burden Score × Population Characteristics Score**

Both component scores are scaled 0–10, so the overall score ranges from 0–100.

## Data Formats & Access

**Base URL**: `https://s3-west.nrp-nautilus.io/public-calenviroscreen/`

| Format | Path | Description |
|--------|------|-------------|
| GeoParquet | `calenviroscreen-5-0/ces5.parquet` | Census-tract polygons with all attributes |
| PMTiles | `calenviroscreen-5-0/ces5.pmtiles` | Vector tiles for web mapping |
| H3 Hex Parquet | `calenviroscreen-5-0/ces5/hex/h0={cell}/data_0.parquet` | H3 resolution 10 with parent resolutions 9, 8, 0 |

## Usage Examples

### MapLibre GL JS

```javascript
map.addSource("ces5", {
  type: "vector",
  url: "pmtiles://https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.pmtiles",
});

map.addLayer({
  id: "ces5-fill",
  type: "fill",
  source: "ces5",
  "source-layer": "ces5",   // ← vector layer name inside the PMTiles
  paint: {
    "fill-color": [
      "interpolate", ["linear"],
      ["get", "CIscore_Pctl"],
      0, "#ffffcc", 50, "#fd8d3c", 100, "#800026"
    ],
    "fill-opacity": 0.7
  }
});
```

### DuckDB

```sql
-- Load spatial extension
INSTALL spatial; LOAD spatial;

-- Query top 10 most burdened tracts by CES score
SELECT tract, county, CIscore, CIscore_Pctl, Population
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet')
ORDER BY CIscore DESC NULLS LAST
LIMIT 10;

-- Find tracts in the top 10% of CES score (above 90th percentile)
SELECT tract, county, AppoxLoc, CIscore, CIscore_Pctl,
       Poverty, Asthma_Pctl, DieselPM_Pctl
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet')
WHERE CIscore_Pctl >= 90
ORDER BY CIscore_Pctl DESC;

-- Summary by county: average CES score and most burdened indicators
SELECT county,
       COUNT(*) AS num_tracts,
       ROUND(AVG(CIscore), 2) AS avg_ces_score,
       ROUND(AVG(Poverty), 2) AS avg_poverty_pct,
       ROUND(AVG(AirPM25), 4) AS avg_pm25
FROM read_parquet('https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet')
GROUP BY county
ORDER BY avg_ces_score DESC;
```

### Python (GeoPandas)

```python
import geopandas as gpd

ces = gpd.read_parquet(
    "https://s3-west.nrp-nautilus.io/public-calenviroscreen/calenviroscreen-5-0/ces5.parquet"
)

# Top burdened tracts
top_burden = ces.nlargest(100, "CIscore")[
    ["tract", "county", "CIscore", "CIscore_Pctl", "Poverty", "asthma"]
]
print(top_burden)
```

## Data Dictionary

Data source: [Draft CalEnviroScreen 5.0 Data Dictionary (PDF)](https://data.ca.gov/dataset/b38b4e2c-7ef0-47b3-bff0-29e0a80c8ed8/resource/428d0c6f-88cb-4209-bdd4-b1f44188cf01/download/draft-calenviroscreen-5.0-data-dictionary.pdf)

### Identification & Geography

| Column | Type | Description |
|--------|------|-------------|
| `tract` | DOUBLE | Census Tract ID from 2020 Decennial Census |
| `ZIP` | INTEGER | Postal ZIP code for the census tract |
| `AppoxLoc` | VARCHAR | Approximate city or unincorporated area (Esri Demographics 2024; for reference only) |
| `county` | VARCHAR | California county |
| `region` | VARCHAR | California region |
| `Population` | DOUBLE | 2023 5-year ACS population estimate |

### CES Score

| Column | Type | Description |
|--------|------|-------------|
| `CIscore` | DOUBLE | **Draft CES 5.0 Score** — Pollution Burden Score × Population Characteristics Score (range ~0–100) |
| `CIscore_Pctl` | DOUBLE | Percentile of the CES 5.0 Score (0–100) |

### Pollution Burden — Exposure Indicators

Each indicator has a raw value column and a `_Pctl` percentile column (0–100).

| Column | Type | Description |
|--------|------|-------------|
| `AirOzone` | DOUBLE | Mean of summer months (May–Oct) daily max 8-hour ozone concentration |
| `AirOzone_Pctl` | DOUBLE | Ozone percentile |
| `AirPM25` | DOUBLE | Annual mean PM2.5 concentration (µg/m³) |
| `AirPM25_Pctl` | DOUBLE | PM2.5 percentile |
| `ChildLead` | DOUBLE | Potential risk for lead exposure in children in low-income communities with older housing |
| `ChildLead_Pctl` | DOUBLE | Children's Lead Risk from Housing percentile |
| `DieselPM` | DOUBLE | Diesel PM emissions from on-road and non-road sources within and near populated blocks |
| `DieselPM_Pctl` | DOUBLE | Diesel PM percentile |
| `DrinkingWater` | DOUBLE | Drinking water contaminant index for selected contaminants |
| `DrinkingWater_Pctl` | DOUBLE | Drinking Water Contaminants percentile |
| `Pesticides` | DOUBLE | Total pounds of selected active pesticide ingredients per square mile (production agriculture) |
| `Pesticides_Pctl` | DOUBLE | Pesticide Use percentile |
| `ToxReleases` | DOUBLE | Toxicity-weighted air concentrations from chemical releases by large facilities |
| `ToxReleases_Pctl` | DOUBLE | Toxic Releases from Facilities percentile |
| `TrafficImp` | DOUBLE | Traffic density in vehicle-km/hr per road length within 150m of census tract boundary |
| `TrafficImp_Pctl` | DOUBLE | Traffic Impacts percentile |

### Pollution Burden — Environmental Effects Indicators

| Column | Type | Description |
|--------|------|-------------|
| `CleanupSites` | DOUBLE | Sum of weighted EnviroStor cleanup sites and NPL Superfund sites within buffered distances |
| `CleanupSites_Pctl` | DOUBLE | Cleanup Sites percentile |
| `gwthreats` | DOUBLE | Sum of weighted GeoTracker cleanup, land disposal, UST, produced water pond, dairy/feedlot sites within buffered distances |
| `GWThreats_Pctl` | DOUBLE | Groundwater Threats percentile |
| `HazWaste` | DOUBLE | Sum of weighted hazardous waste facilities and large quantity generators within buffered distances |
| `HazWaste_Pctl` | DOUBLE | Hazardous Waste percentile |
| `ImpWaters` | DOUBLE | Sum of pollutants across impaired water bodies within buffered distances |
| `ImpWaters_Pctl` | DOUBLE | Impaired Waters percentile |
| `SmAirToxSites` | DOUBLE | Sum of CEIDARS emissions sites and oil/gas well sites within buffered distances (new in v5.0) |
| `SmAirToxSites_Pctl` | DOUBLE | Small Air Toxic Sites percentile |
| `SolidWaste` | DOUBLE | Sum of weighted solid waste sites and facilities (SWIS) within buffered distances |
| `SolidWaste_Pctl` | DOUBLE | Solid Waste percentile |

### Pollution Burden — Component Score

| Column | Type | Description |
|--------|------|-------------|
| `Pollution` | DOUBLE | Average of percentiles from Pollution Burden indicators (Environmental Effects weighted at 0.5×) |
| `PollutionScore` | DOUBLE | Pollution Burden variable scaled 0–10 (used to calculate CES 5.0 Score) |
| `Pollution_Pctl` | DOUBLE | Pollution Burden percentile |

### Population Characteristics — Sensitive Population Indicators

| Column | Type | Description |
|--------|------|-------------|
| `asthma` | DOUBLE | Age-adjusted rate of emergency department visits for asthma |
| `Asthma_Pctl` | DOUBLE | Asthma percentile |
| `Cardiovascular` | DOUBLE | Age-adjusted rate of ED visits for heart attacks per 10,000 |
| `Cardiovascular_Pctl` | DOUBLE | Cardiovascular Disease percentile |
| `DiabetesPrev` | DOUBLE | Model-based prevalence of diabetes in adults ≥18 years old |
| `DiabetesPrev_Pctl` | DOUBLE | Diabetes Prevalence percentile |
| `LowBirthWeight` | DOUBLE | Percent of infants weighing less than 2,500g (~5.5 lbs) |
| `LowBirthWeight_Pctl` | DOUBLE | Low-Birth-Weight Infants percentile |

### Population Characteristics — Socioeconomic Factors

| Column | Type | Description |
|--------|------|-------------|
| `Education` | DOUBLE | Percentage of population over 25 with less than a high school education |
| `Education_Pctl` | DOUBLE | Educational Attainment percentile |
| `HousingBurden` | DOUBLE | Percentage of households that are low-income AND severely burdened by housing costs |
| `HousingBurden_Pctl` | DOUBLE | Housing Burden percentile |
| `LinguisticIso` | DOUBLE | Percentage of households where no one over age 14 speaks English well |
| `LinguisticIso_Pctl` | DOUBLE | Linguistic Isolation percentile |
| `Poverty` | DOUBLE | Percentage of people living below twice the federal poverty level |
| `Poverty_Pctl` | DOUBLE | Poverty percentile |
| `Unemployment` | DOUBLE | Percentage of people aged 16+ who are unemployed and eligible for the workforce |
| `Unemployment_Pctl` | DOUBLE | Unemployment percentile |

### Population Characteristics — Component Score

| Column | Type | Description |
|--------|------|-------------|
| `PopChar` | DOUBLE | Average of percentiles from Population Characteristics indicators |
| `PopCharScore` | DOUBLE | Population Characteristics variable scaled 0–10 (used to calculate CES 5.0 Score) |
| `PopChar_Pctl` | DOUBLE | Population Characteristics percentile |

### Demographic Profile

| Column | Type | Description |
|--------|------|-------------|
| `PopUnd_10` | DOUBLE | % of population under 10 years old (2023 5-year ACS) |
| `Pop10_64` | DOUBLE | % of population aged 10–64 years (2023 5-year ACS) |
| `PopOver_65` | DOUBLE | % of population 65 years and older (2023 5-year ACS) |
| `White_Pct` | DOUBLE | % non-Hispanic White |
| `Hispanic_Pct` | DOUBLE | % Hispanic or Latino |
| `Black_Pct` | DOUBLE | % non-Hispanic African American or Black |
| `NatAmeri_Pct` | DOUBLE | % non-Hispanic Native American |
| `Asian_Pct` | DOUBLE | % non-Hispanic Asian or Pacific Islander |
| `OtherMulti_Pct` | DOUBLE | % non-Hispanic "other" or multiple races |

### Geometry

| Column | Type | Description |
|--------|------|-------------|
| `Shape` | GEOMETRY | Census tract polygon (EPSG:4326) |
| `Shape_Length` | DOUBLE | Perimeter length |
| `Shape_Area` | DOUBLE | Area |
| `bbox` | STRUCT | Bounding box (xmin, ymin, xmax, ymax) |

## Notes on Zeros and Missing Values

- **NA (missing)**: Indicator value could not be calculated — due to no monitoring, unreliable estimates, or insufficient population. Census tracts with >4 missing Population Characteristics indicators receive NA for the overall CES score.
- **Zero raw value**: Monitoring occurred but no impact detected (e.g., no hazardous facilities within buffer distance). Zeros are excluded from percentile calculations to avoid inflating ranks.
- **Zero percentile**: Assigned to tracts with a raw value of zero, meaning no measurable impact for that indicator.
- Census tracts with <50 people, or <50 people living outside group quarters, receive NA for Population Characteristics.

See the [data dictionary PDF](https://data.ca.gov/dataset/b38b4e2c-7ef0-47b3-bff0-29e0a80c8ed8/resource/428d0c6f-88cb-4209-bdd4-b1f44188cf01/download/draft-calenviroscreen-5.0-data-dictionary.pdf) for full description of zeros and NAs by indicator.

## Citation

California Office of Environmental Health Hazard Assessment (OEHHA). *Draft CalEnviroScreen 5.0*. Released January 2026. Available at: https://oehha.ca.gov/calenviroscreen/general-info/draft-calenviroscreen-50

Contact: calenviroscreen@oehha.ca.gov

## Resources

- [Draft CalEnviroScreen 5.0 Homepage](https://oehha.ca.gov/calenviroscreen/general-info/draft-calenviroscreen-50)
- [CalEnviroScreen FAQs](https://oehha.ca.gov/calenviroscreen/calenviroscreen-faqs)
- [Data Dictionary PDF](https://data.ca.gov/dataset/b38b4e2c-7ef0-47b3-bff0-29e0a80c8ed8/resource/428d0c6f-88cb-4209-bdd4-b1f44188cf01/download/draft-calenviroscreen-5.0-data-dictionary.pdf)
- [California Open Data Portal](https://data.ca.gov/dataset/draft-calenviroscreen-5-0)
- [OEHHA CalEnviroScreen](https://oehha.ca.gov/calenviroscreen)

extent.spatial.bbox: omitted; spatial reference is categorical via [calenviroscreen-5-0.tract (admin_code), calenviroscreen-5-0.ZIP (zipcode), calenviroscreen-5-0.AppoxLoc (placename), calenviroscreen-5-0.county (placename), calenviroscreen-5-0.region (placename), calenviroscreen-5-0.Shape (geometry), calenviroscreen-5-0/ces5/hex.h10 (spatial_index), calenviroscreen-5-0/ces5/hex.h9 (spatial_index), calenviroscreen-5-0/ces5/hex.h8 (spatial_index), calenviroscreen-5-0/ces5/hex.h0 (spatial_index), calenviroscreen-5-0/ces5/hex.ZIP (zipcode), calenviroscreen-5-0/ces5/hex.AppoxLoc (placename), calenviroscreen-5-0/ces5/hex.county (placename), calenviroscreen-5-0/ces5/hex.region (placename)]. Add bbox once the placename → bbox gazetteer covers it.


Irrecoverable Carbon - Conservation International

Global raster dataset mapping irrecoverable carbon — ecosystem carbon stocks that cannot be recovered by 2050 if lost — along with vulnerable and manageable carbon stocks. Produced by Conservation International, the dataset spans 2010–2024 in two versioned releases (v1: 2021, v2: 2025 with corrections), delivered as Cloud-Optimized GeoTIFFs and H3 hexagonal Parquet files at global coverage.

conservation climate remote-sensing biodiversity global-coverage time-series h3 irrecoverable-carbon

Dataset files (10 x .tif)

Source Cooperative product page

## README.md
# Irrecoverable Earth: Global Carbon Reserves

## Overview

This dataset maps global "irrecoverable carbon" — carbon stocks that, if lost, cannot be recovered by 2050, making their conservation critical for meeting climate goals. The data also includes "manageable carbon" (stocks that can be influenced by human management) and "vulnerable carbon" (total stocks vulnerable to release upon land conversion).

This collection contains **two releases**:
- **v1 (2021 release)**: 2010 and 2018 data with biomass, soil, and total components (Zenodo 4091029)
- **v2 (2025 update)**: 2010, 2018, 2022, 2023, and 2024 data (total only); includes corrections to the 2018 baseline (Zenodo 17645053)

## Source & Attribution

**Source**: Conservation International
**Project Page**: https://www.conservation.org/irrecoverable-carbon

**v1 Data**: https://doi.org/10.5281/zenodo.4091029
**v2 Data (2025 update)**: https://doi.org/10.5281/zenodo.17645053

**Citation**: Noon, M.L., Goldstein, A., Ledezma, J.C. et al. Mapping the irrecoverable carbon in Earth's ecosystems. *Nat Sustain* **5**, 37–46 (2022). https://doi.org/10.1038/s41893-021-00803-6

**License**: Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0)

**Key findings from 2025 update**: 5.4% (7.4 Gt) of Earth's irrecoverable carbon has been lost between 2018–2024; global total is now 128.0 Gt.

---

## Data Formats & Access

**Base URL**: `https://s3-west.nrp-nautilus.io/public-carbon/`

---

## v2 Data (2025 Release — Zenodo 17645053)

Covers years **2010, 2018, 2022, 2023, 2024** for total carbon only. The 2018 baseline includes corrections to boreal grassland/shrubland estimates and updated coastal ecosystem extents. Note: irrecoverable carbon is available for all 5 years; vulnerable and manageable are available for 2010, 2018, and 2024 only.

### v2 Cloud-Optimized GeoTIFFs

**Path Pattern**: `v2/cogs/{category}_c_total_{year}.tif`

| File | Year | Description |
|------|------|-------------|
| `v2/cogs/irrecoverable_c_total_2024.tif` | 2024 | Irrecoverable carbon total (corrected baseline) |
| `v2/cogs/irrecoverable_c_total_2023.tif` | 2023 | Irrecoverable carbon total |
| `v2/cogs/irrecoverable_c_total_2022.tif` | 2022 | Irrecoverable carbon total |
| `v2/cogs/irrecoverable_c_total_2018.tif` | 2018 | Irrecoverable carbon total (corrected baseline) |
| `v2/cogs/irrecoverable_c_total_2010.tif` | 2010 | ⚠ **Not available** — source file has corrupt TIFF IFD (Zenodo upstream issue) |
| `v2/cogs/vulnerable_c_total_2024.tif` | 2024 | Vulnerable carbon total |
| `v2/cogs/vulnerable_c_total_2018.tif` | 2018 | Vulnerable carbon total |
| `v2/cogs/vulnerable_c_total_2010.tif` | 2010 | Vulnerable carbon total |
| `v2/cogs/manageable_c_total_2024.tif` | 2024 | Manageable carbon total |
| `v2/cogs/manageable_c_total_2018.tif` | 2018 | Manageable carbon total |
| `v2/cogs/manageable_c_total_2010.tif` | 2010 | Manageable carbon total |

All v2 COGs are compressed with ZSTD and use GoogleMapsCompatible tiling.

### v2 H3 Hexagonal Parquet

Aggregated carbon stocks at H3 resolution 8 (~0.74 km²), partitioned by H3 resolution 0 (h0) cells.

**Path Pattern**: `v2/{dataset-name}/hex/h0={cell}/data_0.parquet`

| Dataset | Path prefix |
|---------|-------------|
| Irrecoverable carbon 2024 | `v2/irrecoverable-carbon-2024/hex/` |
| Irrecoverable carbon 2023 | `v2/irrecoverable-carbon-2023/hex/` |
| Irrecoverable carbon 2022 | `v2/irrecoverable-carbon-2022/hex/` |
| Irrecoverable carbon 2018 | `v2/irrecoverable-carbon-2018/hex/` |
| Vulnerable carbon 2024    | `v2/vulnerable-carbon-2024/hex/` |
| Vulnerable carbon 2018    | `v2/vulnerable-carbon-2018/hex/` |
| Vulnerable carbon 2010    | `v2/vulnerable-carbon-2010/hex/` |
| Manageable carbon 2024    | `v2/manageable-carbon-2024/hex/` |
| Manageable carbon 2018    | `v2/manageable-carbon-2018/hex/` |
| Manageable carbon 2010    | `v2/manageable-carbon-2010/hex/` |

### v2 Usage Examples

**DuckDB — compare irrecoverable carbon across years:**
```sql
SELECT
    year,
    SUM(carbon) / 1e9 AS total_Gt_C
FROM (
    SELECT 2024 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2024/hex/**/*.parquet')
    UNION ALL
    SELECT 2023 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2023/hex/**/*.parquet')
    UNION ALL
    SELECT 2022 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2022/hex/**/*.parquet')
    UNION ALL
    SELECT 2018 AS year, carbon FROM read_parquet('https://s3-west.nrp-nautilus.io/public-carbon/v2/irrecoverable-carbon-2018/hex/**/*.parquet')
)
GROUP BY year ORDER BY year;
```

---

## v1 Data (2021 Release — Zenodo 4091029)

Covers years **2010 and 2018** with biomass, soil, and total components separately.

### v1 Cloud-Optimized GeoTIFFs

**Path Pattern**: `cogs/{category}_c_{component}_{year}.tif`

**Categories**: `irrecoverable`, `manageable`, `vulnerable`
**Components**: `biomass`, `soil`, `total`
**Years**: `2010`, `2018`

Examples:
- `cogs/irrecoverable_c_total_2018.tif`
- `cogs/manageable_c_biomass_2010.tif`
- `cogs/vulnerable_c_soil_2018.tif`

### v1 H3 Hexagonal Parquet

| Dataset | Path |
|---------|------|
| Irrecoverable carbon (total 2018) | `irrecoverable-carbon/hex/h0={cell}/data_0.parquet` |
| Vulnerable carbon (total 2018)    | `vulnerable-carbon/hex/h0={cell}/data_0.parquet` |

---

## Data Dictionary

### Hexagonal Parquet Schema

| Column | Type | Description |
|--------|------|-------------|
| `carbon` | Float | Total carbon stock in the hex cell (Mg C) |
| `h8` | String | H3 hexagon cell ID at resolution 8 |
| `h0` | String | H3 resolution 0 parent cell ID (partition key) |

**Units**: Megagrams of Carbon (Mg C). One Mg = 1 metric ton.

### Terminology

- **Irrecoverable Carbon**: Ecosystem carbon that, if lost, could not be recovered by 2050 through natural regeneration or restoration.
- **Vulnerable Carbon**: Total carbon stocks (biomass + soil) that are vulnerable to release upon land conversion.
- **Manageable Carbon**: Carbon stocks in ecosystems that can be managed by human activities (forestry, agriculture, conservation).

### v2 Corrections vs v1

The v2 (2025) update includes:
1. **Bug fix**: Corrected overestimation of irrecoverable carbon in boreal grasslands and shrublands in Asia (reduces 2018 total from 139.1 Gt to 135.3 Gt)
2. **Updated coastal extents**: Global Mangrove Watch 2022 and UNEP-WCMC 2022 for seagrasses and salt marshes
3. **Annual tracking**: New years 2022, 2023, 2024 for irrecoverable; 2024 for vulnerable and manageable
4. **Updated protection status**: Uses WDPA data through January 2023–October 2025

---

## Background & Methodology

The concept of "irrecoverable carbon" was developed to identify carbon stocks where prevention of loss is more effective than restoration. These areas represent the overlap between:

1. **High carbon density** — Significant climate mitigation value
2. **Low recoverability** — Cannot regenerate by 2050
3. **High threat** — Significant risk of conversion

The dataset integrates:
- Biomass data from multiple sources (Spawn et al., Walker et al.)
- Soil carbon from SoilGrids250m
- Ecosystem vulnerability assessments
- ESA CCI Land Cover for annual conversion tracking (v2 only)

extent.temporal: None (probe sample) widened to ['2010-01-01T00:00:00Z', '2024-01-01T00:00:00Z'] using README-stated coverage ['2010', '2024'].


US Census Geographic Boundaries

Cloud-optimized formats for Draft CalEnviroScreen 5.0 (released January 2026) is a cumulative environmental pollution and health vulnerability screening tool developed by California's Office of Environmental Health Hazard Assessment (OEHHA).


California Geological Survey (CGS) Datasets

Cloud Optimized versions of Geologic, geophysical, and neotectonic data products published by the California Geological Survey (CGS), often co-produced with the U.S. Geological Survey. Each child collection corresponds to a published Geologic Data Map (GDM) series volume.


Conservation Policy Spending in Federal Legislation

Federally funded conservation restoration and resilience projects under two major US legislative acts: the Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA). Sourced from conservation.gov, the dataset covers over 2,100 project locations across the United States and territories. Available in cloud-optimized formats including GeoParquet, FlatGeobuf, and PMTiles.

conservation federal-funding restoration resilience land-management land-use policy bipartisan-infrastructure-law inflation-reduction-act

Dataset files (2 x .geojson)

Source Cooperative product page

## README.md
# Federally Funded Restoration & Resilience Projects

Based on data on https://apps.conservation.gov/atbatlas/explorer/

## [Bipartisan Infrastructure Law Restoration & Resilience Projects](https://www.conservation.gov/datasets/58ccf74d49a8493a917dc1f81c2e534a_0/about)

## [Inflation Reduction Act Projects](https://www.conservation.gov/datasets/bc8b539412284f3e987cd1f0c1c71c3e_0/about)

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.

providers: role [producer, licensor] for 'conservation.gov (USDA)' is an AI guess — Assumed USDA as producer and licensor based on the conservation.gov domain; README does not explicitly state the agency or license terms.. Verify before flipping to _state: reviewed.


California Protected Areas Database

Spatial database of California's protected areas including parks, reserves, and conservation lands. Version 2025b contains approximately 26 million polygon features representing designated protected areas across the state. Available in cloud-optimized Parquet, Shapefile, and other spatial formats.

protected-areas conservation land-use boundaries

Dataset files (9 x .parquet)

Source Cooperative product page


WWF Terrestrial Ecoregions of the World (2017)

Cloud-optimized versions of Global terrestrial ecoregions from the World Wildlife Fund (WWF), based on the landmark Olson et al. (2001) classification. The dataset delineates 847 ecoregions grouped into 14 biomes and 9 biogeographic realms. Each ecoregion represents a distinct community of plants and animals sharing evolutionary history.


EPA Water Datasets


Wildfire Boundary Polygons from USGS and CalFire in cloud native formats

Snapshots of fire polygon boundaries from USGS Monitoring Trends in Burn Severity (MTBS) and the California Department of Forestry and Fire Protection (CalFire), including burned area extents and controlled burn treatments through 2022. Also includes Wildfire Hazard Potential raster data for the contiguous United States at 270-m resolution. Distributed in cloud-native formats including PMTiles and GeoParquet.

wildfire conservation fire-perimeters burned-area wildfire-hazard pmtiles

Dataset files (1 x .filegdb)

Source Cooperative product page

## README.md
# fire

Snapshots of fire polygons data from multiple sources.  Cloud-optimized formats include PMTiles and (Geo)Parquet serializations.

## USGS MTBS Fire perimiters:

- Original shapefiles for burned area extent downloaded from: https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/MTBS_Fire/data/composite_data/burned_area_extent_shapefile/mtbs_perimeter_data.zip

## Calfire

- Original geodatabase file up to year 2022 downloaded from: https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/gis-mapping-and-data-analytics

PMTiles created using gdal 3.8.4 via

```
ogr2ogr -dsco MAX_SIZE=50000000 -dsco MAX_FEATURES=20000000 calfire-2022.pmtiles CALFIRE/fire22_1.gdb
```

[Example map](https://data.source.coop/cboettig/fire/fire-pmtiles.html) created using [map.py](https://data.source.coop/cboettig/fire/map.py)

## Wildfire Hazard Potential

> Dillon, Gregory K. 2023. Wildfire Hazard Potential for the United States (270-m), version 2023. 4th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2015-0047-4

providers: role [producer] for 'USDA Forest Service Research Data Archive' is an AI guess — Cited as publisher of the Wildfire Hazard Potential dataset (DOI 10.2737/RDS-2015-0047-4); assumed producer role. Verify before flipping to _state: reviewed.

providers: role [processor] for 'cboettig' is an AI guess — Source Cooperative account that produced the cloud-native PMTiles and GeoParquet serializations from original source files. Verify before flipping to _state: reviewed.


Parquet Snapshots of the FishBase Database

Versioned parquet and CSV snapshots of the FishBase and SeaLifeBase MySQL backend databases, exported roughly annually and curated for programmatic access via the rOpenSci rfishbase package. The dataset comprises dozens of raw relational tables covering fish and marine species biology, taxonomy, ecology, abundance, and fisheries statistics. Data structure reflects the original MySQL schema and may require multi-table joins for common queries.

fish fisheries ecology biodiversity marine-biology species-database conservation

abnorm.parquet

Location

[placename] Named location where the abnormality or disease was observed

abnormref.parquet

abundance.parquet

Locality

[placename] Named locality where the abundance record was collected

[admin_code] Country code for the abundance record location

abundancedelta.parquet

acknowledge.parquet

withoutpicture.parquet

withphotos.parquet

wpicture.parquet

zweb_picturetype.parquet

zweb_speciesgroups.parquet

Source Cooperative product page

## README.md
---
license: cc-by-nc-4.0
---

# Dataset Card for FishBase

<!-- Provide a quick summary of the dataset. -->

Snapshots of FishBase data tables used by the [rOpenSci package `rfishbase`](https://github.com/ropensci/rfishbase) and the FishBase API.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** Carl Boettiger
- **License:** CC-BY-NC, FishBase.org Team

### Dataset Sources

<https://fishbase.org>, <https://sealifebase.org>, via exclusive export of backend MySQL databases.

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

This data is provided to facilitate access to fishbase statistics through `rfishbase`.
Curation has evolved over the past decade and now provides basically direct access to versioned snapshots of the fishbase backend data.

### Out-of-Scope Use

Reuse of this data is not licensed for commerical applications. Developers should contact the FishBase team directly. See <https://fishbase.org> for details.

## Dataset Structure

Data provided here are raw CSV and parquet exports from the MySQL database backend powering <https://fishbase.org> and its sister site, <https://sealifebase.org>.
Please note that there are many tables here which have evolved over decades and were not developed with the intent of ever supporting direct access by end users.
The data structure can be highly unintuitive and common tasks may require joins across multiple tables.  These files represent a snapshot in time as indicated
by the version numbers (e.g. v24.07 is data snapshot dated to July of 2024).

## Dataset Creation

Dataset creation relies on ingesting a MySQL dump provided roughly annually from the FishBase team to this maintainer.  Current process imports this into
a MySQL (mariadb) server in docker, connects a the `duckdb` `mysql` extension and exports as parquet and csv, as shown in the `creation.py` script.

### Source Data

See <https://fishbase.org> for details.

license: Licensed CC-BY-NC by the FishBase.org Team; commercial reuse requires contacting FishBase directly.

extent.spatial.bbox: omitted; spatial reference is categorical via [abundance.Locality (placename), abundance.C_Code (admin_code), abnorm.Location (placename)]. Add bbox once a placename → bbox gazetteer is available.


H3-Indexed cloud-optimized snapshots of GBIF

Cloud-optimized GeoParquet snapshots of Global Biodiversity Information Facility (GBIF) occurrence records covering over 3 billion biodiversity observations worldwide. Records are enriched with H3 hierarchical hexagonal spatial index keys at resolutions 0–10 and partitioned by H3 resolution-0 cell for fast spatial aggregations. Includes derived products: taxonomic aggregation counts per hexagon and a spatial join with historical US redlining (Mapping Inequality) polygons.

biodiversity h3 geoparquet global-coverage ecology species-occurrences environmental-monitoring

2024-10-01

countrycode

[admin_code] ISO 2-letter country code of occurrence

stateprovince

[placename] State or province name of occurrence

decimallatitude

[latlon] Decimal latitude of occurrence record

decimallongitude

[latlon] Decimal longitude of occurrence record

geom

[geometry] OGC CRS84 point geometry of occurrence

5 files sampled for this table

taxonomy

[spatial_index] H3 resolution-0 hexagonal cell index for taxonomic aggregation grouping

4 files sampled for this table

tmp.parquet

[spatial_index] H3 resolution-0 hexagonal cell index (partition key); h1–h11 also present at finer resolutions

Source Cooperative product page

## README.md
# GBIF Occurrence Data & Derived Products

Processed GBIF occurrence data hexed at H3 resolutions 0-10, plus derived products.

GBIF releases new snapshots periodically. Releases are versioned by `year-month` (e.g., `2025-06`) so multiple versions can coexist in the bucket during transitions. **Always use the most recent release for new work.**

## Current Release: 2025-06

**Path:** `s3://public-gbif/2025-06/hex/`

Global GBIF occurrences partitioned by H3 resolution 0 cell. 94 non-empty h0 partitions covering the globe.

### Schema

- Taxonomic columns: `gbifid`, `datasetkey`, `occurrenceid`, `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `infraspecificepithet`, `taxonrank`, `scientificname`, `verbatimscientificname`, `verbatimscientificnameauthorship`
- Spatial/occurrence columns: `countrycode`, `locality`, `stateprovince`, `occurrencestatus`, `individualcount`, `decimallatitude`, `decimallongitude`, `coordinateuncertaintyinmeters`, `coordinateprecision`, `elevation`, `depth`, `eventdate`, `day`, `month`, `year`, `taxonkey`, `specieskey`, `basisofrecord`, `institutioncode`, `collectioncode`, `catalognumber`, `recordnumber`, `identifiedby`, `dateidentified`, `license`, `rightsholder`, `recordedby`, `typestatus`, `establishmentmeans`, `lastinterpreted`, `mediatype`, `issue`, `geom`
- H3 hex columns: `h0` (VARCHAR, partition key), `h1`-`h10` (UBIGINT)

### DuckDB Example

```sql
INSTALL h3 FROM community; LOAD h3;
INSTALL httpfs; LOAD httpfs;
SET s3_endpoint = 's3-west.nrp-nautilus.io';
SET s3_url_style = 'path';

-- Count occurrences by family in a single h0 cell
SELECT family, COUNT(*) AS n
FROM read_parquet('s3://public-gbif/2025-06/hex/h0=8009fffffffffff/part/*.parquet')
GROUP BY family
ORDER BY n DESC
LIMIT 20;
```

## Deprecated: Legacy Hex (pre-2025)

**Path:** `s3://public-gbif/hex/` — **DEPRECATED, do not use for new work.**

This is the original hex partition with all h0-h11 columns stored as VARCHAR strings. It is retained only for backward compatibility with existing applications. It will be removed once no active apps depend on it. See [GitHub issue](https://github.com/boettiger-lab/data-workflows/issues) for tracking.

## Other Assets

### GBIF Occurrences in Redlined Cities
**File:** `s3://public-gbif/redlined_cities_gbif.parquet`

Spatial join of GBIF occurrences with "Mapping Inequality" (Redlining) polygons for US cities.

**Schema:** `gbifid`, `scientificname`, `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `recordedby`, `date`, `coordinateuncertaintyinmeters`, `city`, `state`, `grade` (A-D), `residential`, `commercial`, `industrial`

### Taxonomic Aggregations by H3 Hexagon
**Prefix:** `s3://public-gbif/taxonomy/` (partitioned by `h0`)

Aggregated counts of taxa within H3 resolution 0 hexagons.

**Schema:** `scientificname`, `kingdom`, `phylum`, ... `species`, `n` (count), `h0`

### Taxa List
**File:** `s3://public-gbif/taxa.parquet`

Reference list of all taxa found in the dataset.

## Source & Citation

- **Producer:** Global Biodiversity Information Facility (GBIF)
- GBIF.org (2025). GBIF Occurrence Download. <https://www.gbif.org/>
- Nelson, R. K., et al. (2023). Mapping Inequality. (for redlined cities asset)

## License

- **GBIF Data:** See [GBIF Data Use Agreement](https://www.gbif.org/terms). Individual records may have specific licenses (CC0, CC-BY, CC-BY-NC).
- **Redlining Data:** CC-BY-NC-SA 4.0.

extent.spatial.bbox: omitted; spatial reference is categorical via [..decimallatitude (latlon), ..decimallongitude (latlon), ..geom (geometry), ..h0 (spatial_index), ..countrycode (admin_code), ..stateprovince (placename), 2024-10-01.decimallatitude (latlon), 2024-10-01.decimallongitude (latlon), 2024-10-01.geom (geometry), 2024-10-01.h0 (spatial_index), 2024-10-01.countrycode (admin_code), 2024-10-01.stateprovince (placename), taxonomy.h0 (spatial_index)]. Add bbox once the placename → bbox gazetteer covers it.


Global Fishing Watch Annual Fishing Effort v3 (2012–2024)


Wildlife Habitat Corridors

Ungulate wildlife habitat corridors in the Western United States, derived from the USGS Ungulate Migrations of the Western United States, Volume 2. The dataset covers migration routes and corridors for elk and other large ungulates across the western US, distributed in vector formats including Shapefile, GeoParquet, GeoPackage, and PMTiles.

wildlife biodiversity habitat-corridors ungulates migration conservation western-us

Dataset files (96 x .shp)

Source Cooperative product page

## README.md
# Wildlife Habitat Corridors

[Ungulate corridors in Western US from Conservation.gov](https://www.conservation.gov/datasets/0deb6a49c1a74ca0a9d52577134e5e14_0/about), based on
USGS Ungulate Migrations of the Western United States, Volume 2.

> Kauffman, M.J., Lowrey, B., Beck, J., Berg, J., Bergen, S., Berger, J., Cain, J., Dewey, S., Diamond, J., Duvuvuei, O., Fattebert, J., Gagnon, J., Garcia, J., Greenspan, E., Hall, E., Harper, G., Harter, S., Hersey, K., Hnilicka, P., Hurley, M., Knox, L., Lawson, A., Maichak, E., Meacham, J., Merkle, J., Middleton, A., Olson, D., Olson, L., Reddell, C., Robb, B., Rozman, G., Sawyer, H., Schroeder, C., Scurlock, B., Short, J., Sprague, S., Steingisser, A., and Tatman, N., 2022, Ungulate Migrations of the Western United States, Volume 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9TKA3L8.

Additional data appears to be available in the USGS record


iNaturalist Species Ranges & Taxonomy


Cloud native versions of LandMark: Indigenous & Community Land Rights (v202509)


Climate and Economic Justice Screening Tool (cloud-native formats)

The Climate and Economic Justice Screening Tool (CEJST) identifies U.S. census tracts considered disadvantaged based on climate, energy, health, housing, legacy pollution, transportation, water, and workforce burden indicators. Mandated by the January 2021 Executive Order on Tackling the Climate Crisis, it is used by federal agencies to prioritize historically overburdened and underserved communities for program benefits. This repository republishes the data in cloud-native PMTiles and GeoParquet formats for streaming analysis and interactive mapping.

environmental-justice climate disadvantaged-communities census-tracts census federal-policy land-use

U.S. Digital Service / Council on Environmental Quality

Dataset files (1 x .filegdb)

Source Cooperative product page

## README.md
# Climate and Economic Justice Screening Tool

The official tool is hosted at https://screeningtool.geoplatform.gov

This source.coop repository provides access to this data as PMTiles and geoparquet.  PMTiles provides static vector tiles enabling many popular client-side libraries to quickly and easily render interactive maps involving very large vector datasets. The Geoparquet format is optimized for cloud-based (i.e. streaming) analysis workflows without the need to download and store the data.

## Using PMTiles

[example map](https://data.source.coop/cboettig/justice40/justice40.html)

See [map.py](https://data.source.coop/cboettig/justice40/map.py) for soruce code

## Using geoparquet

```python
import ibis # pip install ibis-framework
from ibis import _

summary = (ibis
    .read_parquet("https://data.source.coop/cboettig/justice40/disadvantaged-communities.parquet")
    .group_by(_.StateName)
    .agg(fraction_disadvantaged = (_.Disadvan * _.SHAPE_Area).sum() / _.SHAPE_Area.sum())
    .order_by(ibis.desc(_.fraction_disadvantaged))
    .to_pandas()
)

summary
```

providers: role [processor] for 'Carl Boettiger (cboettig)' is an AI guess — Inferred as processor: converted original FileGDB to cloud-native PMTiles and GeoParquet for this repository. Verify before flipping to _state: reviewed.


Land Cover Datasets

Cloud-optimized versions of global and US alnd cover data. Currently GLS-LC100 CC-BY-4.0 + NLCD. public-domain


Mapping Inequality Data -- Redlining

Historical data on residential neighborhood designations and lending discrimination practices in US cities, derived from Home Owners' Loan Corporation (HOLC) maps created between 1935-1940. Maps document how federal lending policies and racial/economic discrimination shaped neighborhoods and property investment eligibility.

redlining housing-discrimination historical-maps urban-history residential-investment us-cities

University of Richmond Digital Scholarship Lab

Dataset files (7 x .parquet)

Source Cooperative product page


Map of Biodiversity Importance, NatureServe

Demonstration of generating and serving static raster tiles with GDAL using Map of Biodiversity Importance (MOBI) data from NatureServe, (2022) https://www.natureserve.org/map-biodiversity-importance, original data files from https://geohub-natureserve.opendata.arcgis.com/search?q=mobi

raster cog tiles static biodiversity gdal


Nature's Contributions to People (NCP) - Biodiversity Indicators


OBIS: Ocean Biodiversity Information System (2024)

Snapshot of the official OBIS parquet, see https://obis.org/data/access/


Overture Maps Divisions


United States Protected Areas Database (PAD-US) 3.0

The Protected Areas Database of the United States (PAD-US) 3.0 is a national inventory of protected lands compiled by the USGS Gap Analysis Project. This processed version is subset to the continental US and restricted to Fee and Easement feature classes plus tribal Proclamation boundaries, serialized to GeoParquet and PMTiles formats for efficient remote access. It covers approximately 883,000 polygon features representing federally, state, locally, and tribally managed protected areas.

protected-areas conservation biodiversity land-management land-use

Dataset files (1 x .filegdb)

Source Cooperative product page

## README.md
---
license: pddl
---

# United States Protected Areas Database

> U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022, Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B.

The PAD database classifications are complex and include overlapping and apparently duplicate polygons. For instance, national park boundaries can be found are listed in Proclamation feature class and also in Fee class (showing internal holes within boundaries). The processed PAD data layers here have been subset to the continental US (matching availability in MOBI data), and restricted to feature classes of "Fee" and "Easements", except for tribal boundaries which are taken from the "Proclamations."  See [ProtectedLands.net](https://www.protectedlands.net/pad-us-data-structure-attributes/) for a more detailed discussion of the data structure and classification involved in PAD.  Code for reproducing this processing can be found at [https://github.com/boettiger-lab/pad-us-3](https://github.com/boettiger-lab/pad-us-3)

USGS PAD-v3 is distributed as an ESRI Geodatabase.  After subsetting the data and labelling according to the additional classification listed above based
on manager type indicated in the data, core attributes are serialized into [PMTiles](https://docs.protomaps.com/pmtiles/) and [Geoparquet](https://geoparquet.org/)
formats hosted on [source.coop](https://beta.source.coop/repositories/cboettig/pad-us-3/description/).  PMTiles allows for rapid rendering of vector objects
at the appropriate zoom level in an interactive map from remote servers.  Geoparquet provides a high-performance serialization that can be efficiently queried directly from
remote https addresses without download using software such as [duckdb](https://duckdb.org), widely available in python, R, and other languages.

## Contents Overview

- `PADUS3` contains files extracted from the orginal zip archive and associated geodatabase distributed by the USGS.
- `pad-us3-combined.parquet` contains a geoparquet version of the geodatabase protected areas layer
- `pad-us3-combined.fgb` contains a FlatGeobuff version of the geodatabase protected areas layer

PMTiles are computed with GDAL 3.8.4 up to max zoom 10 as follows:

```
ogr2ogr -dsco MAX_SIZE=90000000 -dsco MAX_FEATURES=50000000 -dsco MAXZOOM=10 public.pmtiles public_protected.fgb
```

## Examples

Examples are generated with the `map.py` script.

[Dark map - full page](https://data.source.coop/cboettig/pad-us-3/protected-areas-dark.html)

license: Open Data Commons Public Domain Dedication and License (ODC PDDL); README frontmatter specifies 'pddl'.


Protected Areas of the United States (PAD-US)


Population & Demography Data


Rangeland vegetation cover (RAP / rangeland-s2)


American Rivers Datasets


Carbon Stocks of Individual Trees in African Drylands: Allometry and Output Data -- Cloud formats

Cloud-optimized GeoTIFF versions of allometry and output data characterizing carbon stocks of individual trees across African drylands, spanning the Sahel and Sahara regions. The dataset provides tree-level carbon estimates derived via machine learning from satellite imagery, covering approximately West to East Africa (18°W–42°E, 9°N–24°N). Original data published at ORNL DAAC by Tucker et al. (2022).

carbon-stocks trees drylands sahel remote-sensing

Tucker, C.J., M. Brandt, P. Hiernaux, A. Kariryaa, J.L. Small, and J. Meyers

Dataset files (3 x .tif)

Source Cooperative product page


Social Vulnerability Index

Cloud-native copy of the CDC/ATSDR Social Vulnerability Index (SVI) for 2020, aggregated at the US Census tract level. The SVI quantifies community vulnerability to external stressors using 15 US Census variables grouped into four themes: socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation. Available in cloud-optimized formats (GeoParquet and PMTiles).

social-vulnerability census-tracts census public-health demographics

CDC / ATSDR (Agency for Toxic Substances and Disease Registry)

Dataset files (11 x .pmtiles)

Source Cooperative product page

## README.md
# CDC Social Vulnerability Index (2020)

Copy of the CDC Social Vulnerability Index (2020) in cloud native formats. Original data and documentation at: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

## Social Vulnerability Measures

![](https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI-Variables.png)

See column definitions in [CDC documentation](https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2020Documentation_08.05.22.pdf)

## [Interactive Map](https://data.source.coop/cboettig/social-vulnerability/map.html)

providers: role [processor] for 'cboettig' is an AI guess — Account holder on Source Cooperative; assumed to have converted original CDC data to cloud-native formats. Verify before flipping to _state: reviewed.


Taxonomic names

A consolidated dataset of taxonomic naming authorities drawn from multiple biodiversity databases, including Catalogue of Life (COL), GBIF, IUCN, ITIS, NCBI, and Open Tree of Life (OTT). Each record links scientific names, vernacular (common) names, taxonomic rank, and taxonomic status across the full Linnaean hierarchy (kingdom through infraspecific epithet). The dataset contains over 27 million records distributed across 9 Parquet files in both Darwin Core and simplified common-name formats.

taxonomy biodiversity species-names nomenclature taxonomic-classification vernacular-names

National Center for Biotechnology Information (NCBI)

common_col_part_0.parquet

common_gbif_part_0.parquet

country

[placename] Country name associated with a vernacular name usage in GBIF

countryCode

[admin_code] ISO 3166-1 alpha-2 country code associated with a vernacular name usage in GBIF

common_itis_part_0.parquet

common_ncbi_part_0.parquet

dwc_col_part_0.parquet

dwc_gbif_part_0.parquet

dwc_itis_part_0.parquet

dwc_ncbi_part_0.parquet

dwc_ott_part_0.parquet

Source Cooperative product page

providers: role [producer] for 'International Union for Conservation of Nature (IUCN)' is an AI guess — Listed in API description as a source but no IUCN-specific asset is visible in the file listing.. Verify before flipping to _state: reviewed.

providers: role [producer] for 'Integrated Taxonomic Information System (ITIS)' is an AI guess — Inferred as producer from asset names (common_itis, dwc_itis); not explicitly named in the API description.. Verify before flipping to _state: reviewed.

providers: role [producer] for 'Open Tree of Life Taxonomy (OTT)' is an AI guess — Inferred as producer from asset name (dwc_ott); not explicitly named in the API description.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [common_gbif_part_0.country (placename), common_gbif_part_0.countryCode (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


US Federal Trails Network


US Political Boundaries, Including Domestic Sovereign Nations (Federally-Recognized Tribes)

US political and administrative boundaries at state and county level (2020), plus federally-recognized tribal land areas. Provided in geoparquet and pmtiles formats for efficient geographic analysis and visualization.

administrative-boundaries tribal-lands state-boundaries county-boundaries

US Department of Agriculture - Natural Resources Conservation Service

Dataset files (4 x .parquet)

Source Cooperative product page

## README.md
# US Political Boundaries

Includes [geoparquet](https://geoparquet.org/) and [pmtiles](https://docs.protomaps.com/pmtiles/) formatted versions of the following data:

- State Boundaries (2020) [original record](https://www.conservation.gov/datasets/e0021066318948eca81e233b3773e4d6_0/about)
- County Boundaries (2020) [original record](https://www.conservation.gov/datasets/17b89622df5643cda5339ae6649247a6_0/about)

## US Domestic Sovereign Nations: Land Areas of Federally-Recognized Tribes (BIA)

[original record](https://www.conservation.gov/datasets/245ffcb63a0b44cb9ed467bbd5f9d7ea_0/about)

See also:

- Cloud-optimized rendering of census-tract (2020) level data can be found in:
  * The [Social Vulnerability Index](https://beta.source.coop/repositories/cboettig/social-vulnerability)
  * The [Climate and Economic Justice Screening Tool](https://beta.source.coop/repositories/cboettig/justice40)


USGS National Hydrography Dataset in cloud-optimized formats

Experimental cloud-optimized vector tiles and geoparquet for USGS NHD dataset (2024) https://www.usgs.gov/national-hydrography/access-national-hydrography-products


U.S. Fish and Wildlife Service (USFWS) Datasets


Wetlands


Forest Carbon Project Boundaries

A global database of 575 nature-based carbon offset project boundaries across 55 countries, including avoided deforestation, afforestation/reforestation, and improved forest management projects. Boundaries were aggregated from carbon project registries and manual georeferencing with high accuracy validation (IoU score 0.98±0.015).

forest-carbon carbon-offsets nature-based-solutions climate-change-mitigation avoided-deforestation reforestation forest-management project-boundaries

# A Global Database of Nature-Based Carbon Offset Project Boundaries

Provides 575 global forest carbon project boundaries. The files in this repository have been converted to a fiboa GeoParquet file from a database originally published by Akshata Karnik, Jack B. Kilbride, Tristan R.H. Goodbody, Rachael Ross, Elias Ayrey.

## Dataset Information
- **Converted by:** Alex Logan
- **Original Authors:** Akshata Karnik, Jack B. Kilbride, Tristan R.H. Goodbody, Rachael Ross, Elias Ayrey (Corresponding Author)
- **Date:** June 5, 2024
- **License:** CC-BY 4.0 See https://zenodo.org/records/11459391

## Overview

Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies. However, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data.

The original database provided the Geospatial boundaries of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (n=433, 75.3%) as well as manual georeferencing and digitization (n=127, 22.1%). Database entries include three varieties of carbon projects:
- Avoided deforestation
- Afforestation, reforestation and re-vegetation
- Improved forest management

An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 ± 0.015).

## Important Notes for Users

- **Area Definitions:**
  - **Project Area:** The geographical area where project participants implement activities to reduce deforestation
  - **Project Accounting Area:** The geographical area used to calculate carbon credit issuance

- This database does not represent a census of all nature-based carbon projects and does not contain all varieties of such projects

- Users should verify that any georeferencing inaccuracies will not significantly impact their analyses

- The boundaries included reflect data available in the registries at the time of access, with some projects regularly updating their information

- We were unable to assess the accuracy of boundaries constructed from linear features or from developer-provided protocols

## Database Schema

Each record in the GeoPackages contains the following attributes:

| Attribute | Description |
|-----------|-------------|
| **Project Name** | Name of the carbon project as given in the documentation |
| **ProjectID** | Combination of registry abbreviation and project number |
| **Registry Name** | Registry hosting project information (American Carbon Registry, BioCarbon Registry, Climate Action Reserve, EcoRegistry, Gold Standard, Verra) |
| **Methodology** | Name of the methodology used for the project's implementation |
| **Project Type** | Type of forestry carbon offset program (ARR, AD, IFM) |
| **Country** | Country where the project is located |
| **Project Developer Name** | Entity or individual organizing the carbon offset project (first name only if multiple) |
| **Project Start Date** | Start date of the crediting period (mm/dd/yyyy) |
| **Project End Date** | End date of the crediting period (mm/dd/yyyy) |
| **Date of Entry** | Date when project information was added to the database (mm/dd/yyyy) |
| **Processing Approach** | Method used to obtain boundary data (Official, Georeferenced, Linear, or Method) |
| **PD Declined to Provide** | Whether the project developer declined to provide geometry information (Yes, No, N/A) |
| **Geometry Type** | Type of geometry in the database (Point or Polygon) |
| **Project Area** | WKT representation of the project implementation area |
| **Project Accounting Area** | WKT representation of the area used to calculate carbon credits |
| **Project Reference Region** | WKT representation of the area used for historical and current deforestation trends |
| **Comment** | Notes about manual referencing or other relevant information |

## Methodology: Manual Georeferencing & Digitization

Project boundaries were manually georeferenced using QGIS (v3.32.2) through the following process:

1. The map with the clearest boundary within the PDD was identified and overlaid on OpenStreetMap and Bing Satellite Imagery basemaps
2. Each map was georeferenced with the basemap layers using at least 4 ground control points
3. Geometries depicted on the georeferenced map were manually digitized
4. Digitized geometries were consolidated into a single geospatial layer

**Special Cases:**

- **Linear Features (9 projects):**
  - Linear geometries were buffered by 30m
  - Holes were eliminated using the "Delete Holes" tool in QGIS
  - An inverse buffer of 29m was applied to obtain the final polygon

- **Protocol-Based (6 projects):**
  - Project boundaries were recreated following steps in the PDD
  - Used resources like ASTER Global Digital Elevation Model (GDEM) and Copernicus Global Land Cover (CGLC) dataset

- **Point Geometries (42 projects):**
  - For projects with insufficient boundary information
  - General location and project type were identified
  - Entered into database as point geometry

Source Cooperative product page

README

Dataset files (6 × .parquet)

Karnik, A., Kilbride, J. B., Goodbody, T. R. H., Ross, R., & Ayrey, E. (2024). A Global Database of Nature-Based Carbon Offset Project Boundaries. Zenodo. https://zenodo.org/records/11459391


Open Administrative Boundaries

Country-level administrative boundaries from Overture Maps Foundation. Contains 219 country records in GeoParquet format covering global coverage.

administrative-boundaries countries geoparquet overture divisions

Dataset files (1 x .parquet)

Source Cooperative product page


Some random aois for testing

No Description Provided