1. Define the deliverable
Write down the task, model stage, modality, language, format, and intended use. Distinguish a fixed dataset from a managed service, a software subscription, or an interactive environment.
2. Inspect a representative sample
Agree on a sample-selection process that does not hide difficult cases. Review provenance, diversity, duplication, missing context, labels, and the link between examples and the task you need to solve.
3. Establish the rights
Have qualified counsel review the chain of rights, consent or other applicable basis, permitted uses, retention, deletion, sublicensing, and any limitations on trained models. A vendor’s public claim is not sufficient evidence for your particular dataset.
4. Make quality measurable
Define acceptance criteria before delivery: schema validity, label consistency, task coverage, held-out performance, contamination controls, and error remediation. For environments, include repeatability, reset behavior, and grader robustness.
5. Understand the full commercial scope
Request a proposal that separates data, collection, annotation, environment hosting, support, and renewal. Confirm minimum commitments, exclusivity, change requests, delivery milestones, and remedies for rejected data.
6. Keep a decision trail
Keep the source, license version, sample results, evaluation protocol, and approval record together. Recheck the offering at procurement time; a directory is a starting point, not a supplier audit.
This is general research guidance, not legal, financial, or procurement advice. Obtain advice appropriate to your project.
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