Get involved

Contribute to the Hub

The Hub grows because Centers, programmes and partners contribute — datasets, docs, tutorials, and the stories of how they get used. Pick a guide below.

Need help?

Unsure where to start, or stuck partway? Open an issue on GitHub with your question and the Hub team will pick it up.

GuideCenter · Project · Partner· ~2 days of preparation

Add a dataset to the Climate Data Hub

Datasets are added by opening a submission pull request against the Hub metadata registry. CI runs structural and semantic validation; once approved, the dataset is indexed, a DOI is minted, and a STAC item is published.

Confirm fit and licensing

Check that the dataset aligns with Hub scope (climate-relevant for agri-food systems) and that you can release it under CC-BY-4.0 or an approved equivalent. Need help? Open a scoping issue first.

Harmonise the format

Convert spatial rasters to Cloud-Optimised GeoTIFF or Zarr; cubes to Zarr. The COG authoring wiki has the validator.

Author the metadata record

Write the YAML record against the CDH schema — coverage, provenance, citation, and appropriate-use guidance — and validate it locally.

Shell

uvx check-jsonschema \
  --schemafile spec/schemas/profiles/cdh.schema.json my-record.yaml

Open a submission PR

Fork the metadata registry and open a PR. The template asks for provenance, QA notes and contact details. CI runs structural, schema and lineage checks.

Review & publish

A Hub maintainer and a domain reviewer assess the submission. Once approved, the dataset is merged, indexed in the catalog, and a DOI is minted.

Pre-submission checklist

Dataset uses an approved format and is COG/Zarr-compliant where applicable.
Record validates against the CDH schema.
Coverage, resolution, temporal extent, units and variables are documented.
Provenance and contributing institutions are listed; contacts are reachable.
Licence is CC-BY-4.0 or pre-approved; attribution requirements are recorded.
QA notes describe known caveats, gaps and recommended uses.

GuideAnyone· ~30 minutes

Edit a wiki page

Wikis live as Markdown files in the site repo. Edits flow through normal pull requests, so changes are reviewed, versioned and attributable.

Find the page in the repo

Each wiki page links to its source file on GitHub via the 'Edit on GitHub' link.

Open a PR with your edit

Fork or branch, edit the Markdown, and open a pull request. A preview build renders your change automatically.

Request review

Tag the relevant section maintainer (Standards, Methods, Concepts or Governance) for review.

Pre-submission checklist

Front-matter (title, section, description) is updated if needed.
Internal links resolve and external links are correct.
Code blocks specify a language for syntax highlighting.
Images include alt text.

GuideProject leads · Partners· ~1 hour to draft

Submit a use case story

Stories are short, factual write-ups of work that used the Hub. They help others understand what the Hub enables in practice and become a body of evidence for impact reporting.

Use the story template

Title, partners, sector, country, summary (≤120 words), 2–4 measurable impacts, and the datasets you used.

Open a PR to the stories folder

Drop your filled-in template into src/content/use-cases/ as a Markdown file. CI checks that referenced datasets exist.

Review & publish

The Hub comms lead reviews for clarity and accuracy. Published stories appear in the In use section.

Pre-submission checklist

All referenced datasets exist on the Hub.
Impacts are concrete (numbers, decisions, products).
Partners and contact persons are listed.

GuideAnyone comfortable with Python or R· ~2 hours

Write a tutorial

Tutorials are runnable, step-by-step guides to working with Hub data. They can be written as Markdown or contributed as an executed Jupyter notebook — both render the same way on the site, with styled prose, highlighted code, and saved outputs.

Pick a format

Markdown (.md) suits prose-led guides; a Jupyter notebook (.ipynb) suits analysis-led ones — commit it executed, and saved outputs (plots, tables, dataset previews) render right in the page. Either file goes in src/content/tutorials/.

Add the tutorial metadata

Markdown files take YAML front-matter. Notebooks carry the same fields in their notebook metadata under a "cdh" key (in Jupyter, edit it via the Property Inspector → Advanced Tools).

JSON

{
  "cdh": {
    "title": "Tropical Livestock Units from GLW4",
    "description": "Compute TLU from GLW4 species densities.",
    "audience": "Python & R",
    "topic": "Livestock",
    "time": "15 min",
    "updated": "2026-07-10",
    "datasets": ["glw4-2020"]
  }
}

Link the data you use

List the catalog record ids your tutorial works with in "datasets". The site cross-links both ways — your tutorial shows the records it uses, and each record page lists tutorials that use it.

Keep outputs static

Matplotlib figures, pandas tables and xarray previews all render on the site. Interactive outputs (Plotly, ipywidgets) don't — clear them, or talk to us about publishing that tutorial as an exported HTML page instead.

Open a PR

Fork or branch, add your file, and open a pull request. A preview build renders your tutorial so you can check it reads well.

Pre-submission checklist

Filename is lowercase (it becomes the URL, and the notebook download link).
Metadata is complete (title, description, audience, topic, time, updated).
Data is streamed from Hub endpoints, not bundled or downloaded ahead.
The notebook is committed executed, with only the outputs that teach.
Section headings (##) are present so the page gets a table of contents.
Linked dataset ids in "datasets" match catalog record ids.

ReportAnyone

Report a bug or issue

Spotted something off in a dataset, a broken link, or a glitch on this site? Tell us. Pick what you're reporting — each opens a prefilled GitHub issue on the right repository.

Be specific — what you expected vs what you got, with exact values and steps where possible. For data-quality issues, include the dataset id from its catalog page.