EM-DAT-04 · Subject model · W2
Data quality and context coverage
Quality rule, measurement, violation, coverage and remediation plan. Source completeness, field fill rate and truth of facts are different indicators.
Queued for research
Claude: not-started; Grok: not-started.
Research note, in Russian: Entire research brief pending
Subject boundary and candidate types
- DataQualityRule
- QualityEvaluation
- QualityIssue
- CoverageAssessment
Deep research questions
- How does completeness differ from accuracy?
- How to measure coverage without a known denominator?
- What does freshness mean for a specific purpose?
Verifiable invariants
- An assessment pins the rule and the sample
- An unknown denominator is explicit
- A correction does not rewrite a past assessment
End-to-end acceptance scenario
A complete but stale dataset and a partial trustworthy sample receive separate assessments.
Negative case
A 100% field fill rate is declared 100% accuracy.
Approaches to compare
- W3C DCAT: dataset, distribution, catalog
- W3C PROV and Data Cube: provenance and observation
- SDMX and BI/data catalog practice: indicators, breakdowns, definitions and releases
Candidates in the live catalogue
- WM-DAT-007 · Data Quality Assessment · 0.3.0-research.1 · installable
Semantic fit requires boundary research; a published model does not by itself complete this card.
Result requirements
Every card is executed together with the full research contract: definitions, fields and cardinalities, lifecycle, sources, data mastership, rights, the five object facets, at least eight invariants, positive and negative examples, dependencies, migration and applicability limits.