Skip to main content
A source identifies where material comes from. A dataset holds its versioned content. A collection makes permitted datasets available to a workspace.

​
Choose the collection route

Start with a small source you can inspect. Check the Source choices in the job form and the selected bundle’s parameters. Use only adapters offered by your deployment. A source family’s presence in your task does not establish adapter support. For an unsupported source, use a supported export or custom ingestion job. For social collection, an item limit means sampling. Without an item limit, collection still has provider, spend and runtime bounds. Check comment and reply settings separately. Date filters and comment collection do not have identical behaviour across platforms.

​
How collected material becomes evidence

Collection first accepts material into the dataset. Processing then creates addressable fragments, searchable text and eligible enrichment results. Each derived result identifies its inputs and versions. A collected file can remain unavailable for search while extraction runs. Empty results can reflect access, collection scope, sampling or processing gaps. Inspect processing before interpreting them.

​
Access follows the resource

Workspace reach gives members Reader access to the dataset. Ingestion and changes need a Contributor grant on the dataset itself. Every attached dataset must fit the workspace security level. Secrets are named centrally. An Admin sets tenant secrets in Console; an Operator manages platform-provided secrets. Jobs record the resolved version without showing its value. Attaching a dataset creates no new ingestion job or secret. Continue with Organise data through collections or Set up ingestion with Kato.