Training data provenance records where data came from and how it reached its current form. A file hash can identify bytes, but it cannot establish permission to use them, explain how labels were assigned or resolve an unknown source. A useful handoff preserves those distinctions without retaining more personal information than the task requires.
Keep source identity separate from permission
Record an authorized source reference, collection method, relevant dates and the owner responsible for reviewing usage terms. Do not assume that public accessibility grants unrestricted training rights. If the permitted use is unresolved, mark the gap and route it to the qualified owner rather than changing the status to approved. This is engineering guidance, not legal advice.
Describe transformations that change meaning
Filtering, deduplication, redaction, normalization, translation and annotation can affect what an example represents. Preserve the transformation version and enough configuration to explain the resulting dataset. Where a manual decision changes a label or exclusion, retain its reason and authorized reviewer instead of only the final file.
Identify derived and synthetic examples
A generated example should not become indistinguishable from a collected one. Record its generation method and relevant model or template version when authorized. If an example derives from restricted material, synthetic generation does not automatically remove the original constraints. Investigate the permitted-use boundary before distributing the derived dataset.
Use a manifest without copying every secret
An illustrative manifest can connect an example identifier to a source reference, processing version, permission status and quality-review outcome. Store sensitive source material behind the existing access control rather than placing it in a broadly shared spreadsheet. References, checksums and redacted notes can support investigation without duplicating an entire production record.