A training source needs provenance and a documented use decision

Assign a stable source identifier and record the exact snapshot, retrieval time, provider, collection method, original locations where appropriate, and integrity hashes. Keep documentation supplied by the source, including stated licenses or terms, restrictions, attribution requirements, intended uses, and known gaps. Distinguish a provider's description of a collection from evidence about each underlying item when those differ. Datasheets for Datasets proposes documenting motivation, composition, collection, and recommended use; those fields help make the source understandable without turning documentation into a blanket authorization certificate.

The intended project matters: internal analysis, redistributed data, model training, and public release can pose different questions. Record the applicable decision and its scope through the responsible review process rather than inventing a universal answer from a file extension or domain name. Public accessibility does not establish permission for every possible use, and an unresolved record should not be relabeled approved merely because a parser succeeded. This lesson defines an evidence-preserving engineering workflow; it does not decide the legal status of a particular source or interpret unseen agreements.

Extraction, deduplication, redaction, and sampling create derivatives of a source. They should retain lineage to the original inventory and the transformation version. A duplicate cluster can contain copies from several locations with different documentation; keeping one text representative does not justify discarding every provenance path or selecting whichever condition is most convenient. Similarly, removing a sensitive span does not automatically settle unrelated use constraints. Keep admission metadata separate from content-quality scores so a highly fluent document cannot bypass an unresolved source decision.

Common Crawl documents different crawl artifacts and its own terms; using those artifacts still requires understanding their collection context and the material they contain. A release should state which snapshot and artifact types were used, what filtering occurred, and which sources were excluded or held. Avoid claiming a named dataset has a particular license unless the exact documentation and version establish that claim. If conditions change or a source must be retired, lineage lets the team identify affected shards, mixtures, and training runs instead of searching only the current text for a recognizable phrase.

Sample records to verify that source evidence, admission status, and derived-document lineage agree. Include excluded and unresolved material so the audit can detect accidental inclusion and excessive exclusion. A review sample supports a scoped claim with uncertainty; it does not certify every item. Record who or what made each policy decision and when the evidence was refreshed. The release process should fail visibly on missing required provenance rather than silently substituting an empty string or treating an unknown provider as the dominant source's identity.

The sampling lab examines only iid audit uncertainty. Reset, explicitly set BOTH defect probability p=0.1 and sample size n=20, predict sample-mean variance 0.0045, then Play or Step. Reset and re-set BOTH p=0.1 and n=100; predict 0.0009. Edits retain t; playback highlights exact binomial outcomes rather than sampling records. The CDF bound concerns a normal approximation, not permission verification. There are no source documents, agreements, provenance links, or legal determinations in this instrument, and correlated source errors can invalidate its iid premise.