Data Use Code Snippets (DUCS) — trusted, dataset-specific Python and R code examples for NASA Earthdata datasets, generated with AI, verified by actually running them against real data.
Note
Status: early planning. The pipeline described below is not yet implemented. This repository will host the published, verified snippets and their index; the pipeline itself is developed in a separate repository.
| Path | Contents |
|---|---|
examples/ |
Example draft snippets that predate the pipeline, for various data formats and structures. They show the intended shape of a DUCS snippet. They are not pipeline output. |
Verified snippets and a machine-readable index will be added here as the pipeline starts publishing.
NASA's Earthdata archive holds 11,000+ datasets. A researcher who finds the right dataset still faces potential challenges: how do you authenticate, search this particular collection, navigate its particular file structure, and read its particular format?
Good examples exist, but only for a small fraction of the archive. Hand-writing and maintaining dataset-specific examples for every dataset, and keeping them working as libraries and data formats change, is not feasible manually.
Alongside code generation, DUCS treats "does this code actually run?" as a core value. Every snippet is executed against a real granule before publication.
- Generate — dataset metadata (CMR/UMM-C), a format→library lookup table, and validated prior examples are assembled into a prompt; an LLM writes a snippet covering authentication · search · file-structure navigation · data access.
- Validate — the snippet runs in a version-pinned environment against a real granule. On failure, the traceback is fed back for a bounded number of correction attempts.
- Review — snippets that never converge go to a human review queue rather than being published broken.
- Publish — verified snippets are committed here alongside a machine-readable index.
Re-validation runs on a schedule, so snippets that break due to upstream changes are caught rather than degrading without notice.
The project is in its setup phase; contribution guidelines are still being written.
Funded under the 2026 NASA ESDS AI Innovation Call, as a collaboration across NASA DAACs and the Earthdata community.
Not yet finalized.