# CGIAR Climate Action Data Hub

> A curated catalog of quality-assured climate and agricultural datasets, described consistently, openly licensed, and published in cloud-native formats. Use the catalog to discover datasets, inspect metadata, cite original publishers, and access Zarr, Cloud-Optimized GeoTIFF, and Parquet assets plus STAC metadata directly.

The Hub also provides practical tutorials and wiki documentation for finding, evaluating, accessing, and using its datasets.

For an expanded, single-file representation of the Hub's documentation and dataset metadata, see [llms-full.txt](https://cgiar-climate-data-hub.github.io/llms-full.txt).

Each dataset record has stable machine-readable JSON metadata and embedded schema.org/Dataset markup. Cite datasets using the citation on each record page, which preserves credit to the original publisher.

Links below use plain Markdown where available. Dataset and Markdown-authored page twins live at the page URL plus index.md.

## Datasets

- [Monthly and Seasonal Precipitation from CHIRPS v3 (Africa)](https://cgiar-climate-data-hub.github.io/catalog/africa-precipitation-monthly-seasonal/index.md): Total precipitation (PTOT) aggregated from CHIRPS v3.0 and delivered as per-pixel monthly Cloud Optimized GeoTIFFs and per-year rolling 3-month (seasonal) sums over Africa, 1981 to present. This is the rainfall backbone of the KE-ENSO Explorer's seasonal/ENSO analysis: the twelve seasonal windows (JFM, FMA, ..., DJF) are the tri-month sums the notebook compares against ENSO phase and against the drought (SPEI), vegetation (NDVI) and flood layers. A derived aggregation product, not a redistribution of the CHIRPS daily archive.
- [CHIRPS v3 Daily Precipitation](https://cgiar-climate-data-hub.github.io/catalog/chirps-v3-daily/index.md): CHIRPS (Climate Hazards center InfraRed Precipitation with Stations), version 3, is a quasi-global daily precipitation dataset produced by the Climate Hazards Center (UC Santa Barbara), blending 0.05° satellite infrared cold-cloud-duration imagery with in-situ station observations. It spans 1981 to near-present, covering 50°S–50°N at all longitudes, and is widely used for drought monitoring, hydrological modeling, and trend/seasonal analysis. Values represent daily accumulated precipitation in millimeters.
- [CHIRTS-ERA5 Daily](https://cgiar-climate-data-hub.github.io/catalog/chirts-era5-daily/index.md): CHIRTS-ER5 daily provides global, high-resolution (0.05°) daily maximum and minimum 2-meter air temperature estimates from 1983 to present. It blends ERA5 reanalysis with station observations using the CHIRTS algorithm, producing a consistent long-term record suitable for climate trend analysis, heat-stress monitoring, and agricultural modeling.
- [Gridded livestock density for 2020 (GLW4)](https://cgiar-climate-data-hub.github.io/catalog/glw4-2020/index.md): Gridded Livestock of the World, version 4 (GLW 4) maps modelled densities of six livestock species: buffalo, cattle, sheep, goats, pigs, and chickens. This data is for the 2020 reference year. Densities are estimated with a Random Forest model that downscales harmonized subnational census counts onto a regular grid, so the values represent modelled distributions rather than direct observations. All of the values are number of animals per km^2
- [MAPSPAM 2020](https://cgiar-climate-data-hub.github.io/catalog/spam2020/index.md): Global Spatially-Disaggregated Crop Production Statistics Data for 2020 Version 2.0 Release 2, represented as a gridded crop production data cube. The record describes crop area, harvested area, production, and yield by crop and production technology at 5 arc-minute resolution.

## Documentation

- [Licensing and attribution](https://cgiar-climate-data-hub.github.io/wikis/licensing-and-attribution/index.md): Permitted licences, attribution requirements, and DOI policy for Hub datasets.

## Tutorials

- [Getting started with the Climate Data Hub](https://cgiar-climate-data-hub.github.io/tutorials/getting-started/index.md): What the Hub is, how to find the right dataset, and how to get data into your tool — Python, R, a desktop GIS, or none of the above.
- [Tropical Livestock Units from GLW4](https://cgiar-climate-data-hub.github.io/tutorials/tlu_glw4/): Compute Tropical Livestock Units (TLU) by combining GLW4 species density layers with standard conversion factors, streaming directly from the Hub's Zarr store.

## Use cases

- [\[Example — replace me\] How \[partner\] used Hub data in \[country\]](https://cgiar-climate-data-hub.github.io/in-use/example-story/index.md): [One- or two-sentence summary: who used what data, for which decision, and what came of it. This renders on the story card.]

## Reference

- [Catalog](https://cgiar-climate-data-hub.github.io/catalog/): Browse and filter all datasets
- [Catalog index](https://cgiar-climate-data-hub.github.io/catalog.json): Machine-readable schema.org DataCatalog of every record (per-record raw metadata at /catalog/<id>.json)
- [For AI & agents](https://cgiar-climate-data-hub.github.io/ai/): Agent skills and every machine-readable endpoint, documented
- [Agent skills docs](https://cgiar-climate-data-hub.github.io/skills/): Ready-made skills (open Agent Skills format) that teach assistants Hub workflows, with install guides
- [FAQ](https://cgiar-climate-data-hub.github.io/faq/): Access, licensing, formats, and contributing
- [About](https://cgiar-climate-data-hub.github.io/about/): What the Hub is and who runs it
- [Contribute](https://cgiar-climate-data-hub.github.io/contribute/): How to submit a dataset