Catalog

Once a dataset is in the registry, anyone in the organization (and every org, for public datasets) can find it, pin a version, and pull it. The Hub and the CLI are two views of the same data.

List datasets in your organization

dn dataset list

acme/[email protected] private - Labeled support tickets for intent classification.
acme/[email protected] private - Prompt-injection canaries for regression checks.
acme/[email protected] public - Multilingual question answering.

Add --include-public to see every organization’s public datasets alongside yours:

dn dataset list --include-public

--search <text> filters on name or description; --limit N caps the result count; --json emits the raw response for scripting.

Inspect a dataset

dn dataset info acme/support-prompts

acme/[email protected] private - Labeled support tickets for intent classification.
  versions: 1.2.0, 1.1.0, 1.0.0, 0.1.0

info shows the latest version’s summary and the full version history. Pass a specific version to fetch that record (dn dataset info acme/[email protected]).

Pinned references

org/name@version is the canonical way to refer to a dataset. Every downstream consumer resolves this same shape:

Where	Example
Training job config	DatasetRef(name="support-prompts", version="1.2.0")
SDK pull	dn.pull_package(["dataset://acme/support-prompts:1.2.0"])
SDK load	dn.load_package("dataset://acme/[email protected]")
CLI pull	dn dataset pull acme/[email protected]

Evaluation manifests don’t resolve dataset refs directly — they take inline rows (see Evaluations → Inputs). Pull the dataset and shape the rows into the manifest when you need a registry dataset as eval input.

Omit @version for “latest visible” — handy for interactive inspection, but avoid it in automation. A moving latest turns reruns into moving targets.

When the dataset lives in your own organization, the org/ prefix is optional. The CLI, SDK, and evaluation manifests resolve bare names against your active org.

Pull a dataset locally

dn dataset pull acme/[email protected] --output ./data.parquet

Without --output, the CLI prints a pre-signed URL you can use with curl, a browser, or a restore script:

dn dataset pull acme/[email protected]
# Download URL (expires 2026-04-21T18:23:00Z):
# https://...

Pull one split instead of the whole artifact:

dn dataset pull acme/[email protected] --split test --output ./test.parquet

Splits must exist in the manifest — dn dataset info lists them. When the dataset has no splits, --split is not needed.

Browse in the Hub

The Hub shows the same listings with facet filters (tags, license, task categories, format, size category), a card/table view toggle, and a per-version detail panel with schema and file list. The Hub and dn dataset list reflect the same registry — authoring happens through the CLI or SDK, discovery happens through either.

Scope tabs

Three tabs above the listing decide which datasets you see. Each carries a count that reflects your active filters.

Tab	Shows
Mine	Datasets you created
Your organization	Every dataset in the organization, whoever created it
Public	Public datasets from every organization, including your own

The middle tab carries your organization’s name — acme in the examples above — and is the default.

These are independent lenses, not a hierarchy. Everything under Mine also sits under your organization, but your organization and Public overlap without either containing the other: your org’s public datasets appear under both, another org’s public datasets appear only under Public, and your org’s private datasets appear only under your organization’s tab.

The CLI cuts the same data differently. dn dataset list --include-public returns your organization’s datasets plus every organization’s public ones — a superset of your org tab, and not the same set as Public, which excludes your org’s private datasets. No CLI flag reproduces the Public tab on its own.

What to reach for next

• Cut a new version or change visibility → Publishing
• Consume the pulled dataset in Python → Using in code
• Every CLI verb → dn dataset