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Relevance 8/10Operations and WorkflowBeginner5 min read
Data Validation
Data validation checks labels and metadata against schema and quality constraints before export.
Why it matters for annotators
Validation prevents avoidable downstream pipeline failures.
Visual mental model
Annotated batch -> schema checks -> fix -> deliver.
Examples (bad vs good)
Scenario: Real annotation scenario involving Data Validation
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.