Open source, serverless, and local-first Data Platform for Gitcoin Grants Data. This data hub improves data access and empower data scientists to conduct research that guides community driven analysis.
Datasets are living as Parquet files on IPFS! You can get them all at the IPFS CID pointer available in this repository, surfaced also in the Data Portal Data section.
The following command will give you a working URL to explore the available tables.
# Get the latest IPFS CID
LATEST_IPFS_CID=$(curl https://raw.githubusercontent.com/davidgasquez/gitcoin-grants-data-portal/main/data/IPFS_CID)
# Print the Gateway URL with all the tables
echo https://ipfs.filebase.io/ipfs/$LATEST_IPFS_CID/data/You can also go to ipns://k51qzi5uqu5dhn3p5xdkp8n6azd4l1mma5zujinkeewhvuh5oq4qvt7etk9tvc, which points to the latest available data via IPNS.
You can now read the files from your favorite tools. E.g: pd.read_parquet('https://grantsdataportal.xyz/data/allo_rounds.parquet')
The repository contains code and artifacts to help process Gitcoin Grants data from the Grants Stack Indexer API. It is an instance of Datadex allowing you and everyone else to:
- Add new data sources to the portal, collaborate on better models (ala Dune) or submit an interesting analysis.
- All in a permissionless way. Don't ask, fork it and improve the models, add a new source or update any script.
- Declarative stateless transformations tracked in git, executed in GitHub Actions and published to IPFS. Data, artifacts (like the entire DuckDB database), and models all version controlled.
- Share and explore dashboards and report with the world!
Tip
You can read more on the motivation and the approach on my blog!
The fastest way to start working on the Data Portal is via VSCode Remote Containers. Once inside the develpment environment, you can run make dev to spin up the Dagster UI.
The development environment can also run in your browser thanks to GitHub Codespaces! Just click on the badge below to get started.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. There are multiple interesting ways to contribute to this project:
- Propose a new dataset
- Add new data sources
- Improve dbt project models
- Write a one off report
MIT.
