Place up to three profiles side by side. Compare their architectural role, lifecycle reach, and first limitation to test. No single standard covers every layer.
Working set
Choose profiles
1 of 3 selected
01
Data CardsFramework
Decision lens
Compare roles before choosing an implementation.
The useful question is not “Which standard wins?” It is “Which job must this part of the architecture perform, and what remains uncovered?”
01
Start with the job
Decide whether you need guidance, a domain payload, exchange, semantics, governance, or a reusable release.
02
Map lifecycle reach
Use the matrix to see where each profile has a direct role. A filled cell is coverage, not a quality score.
03
Test the boundary
Read what each option leaves unresolved before judging maturity or implementation fit.
Assessment
Review lifecycle coverage and practical fit.
Read left to right. Lifecycle reach comes first; a maturity label never overrides a scope mismatch.
01 · Lifecycle reach
Where each profile contributes directly
Coverage shows a recorded role at that readiness stage. It does not imply end-to-end implementation.
Readiness-stage coverage for Data Cards for AI Dataset Documentation
Structured, audience-aware summaries of dataset origins, collection and annotation, intended use, evaluation context, ethical considerations, and decisions affecting downstream performance.
Best fitHuman-facing readiness and release documentation for clinical, imaging, omics, laboratory, and real-world ML datasets.
Readiness stages
PlanAcquireHarmonizeExchangeLearn + reuse
AI-ready contribution
Makes the rationale and limitations that determine responsible reuse visible to human reviewers, while companion machine-readable metadata is still required for automation.
First limitation to test
There is no single mandatory schema or conformance test; narrative claims require linked evidence, ownership, review, and update controls.
Maturity
Scaling
Published and field-tested documentation framework; not a normative standard