# Data quality research plan

Quality is multidimensional and purpose-dependent. A single score must not replace the underlying
measurements or review decisions.

## Required dimensions

| Dimension | Example evidence | Common failure |
|---|---|---|
| Integrity | content digest, byte count, decode errors | silent truncation or corruption |
| Duplication | exact and near-duplicate rates by source and split | evaluation leakage |
| Structure | parse success, field completeness, document boundaries | merged or fragmented records |
| Language | versioned detector plus manual sample | dialect and code-switch misclassification |
| Content quality | transparent heuristics and rejection counts | opaque quality score |
| Privacy | detector results plus human review | identifiers missed by automation |
| Safety | category definitions, sample review, subgroup counts | context-blind filtering |
| Coverage | domain, time, language, geography, and format distribution | hidden sampling bias |
| Contamination | train/eval overlap checks and hash strategy | benchmark memorisation |
| Reproducibility | fixed inputs, code revision, configuration, hashes | irreproducible `latest` snapshot |

## Reporting rules

- Report absolute counts and rates.
- Break results down by source and split; aggregates can hide failures.
- Record every exclusion reason and overlapping filter count.
- Version detectors and thresholds, and retain calibration evidence.
- Use deterministic sampling for manual review and record the sampling method.
- Publish known blind spots and uncertainty.
- Never call a source clean, safe, representative, or unbiased without scoped evidence.