Molecular data
Structure context · Sequence alignment · Review standards
01 / Find the failures
Data QA and expert evaluation identify sample errors, assay issues, and model outputs that miss domain criteria.
Assay & sample QA
Sample identity · Control coverage · Schema consistency
Model evaluation
Domain criteria · Expert review · Uncertainty
Data curation
Provenance · Version comparison · Review decisions
02 / Trace the data
Scroll to explore Match every result to its sample.
Link assay wells to sample records. Flag missing or mismatched identifiers.
- Sample mapping
- Source records
Illustrative review
Check the controls before the conclusion.
Review control coverage, missing measurements, and batch-level inconsistencies against the study's criteria.
- Control coverage
- Batch QA
Illustrative review
Make each curation decision traceable.
Keep expert judgments linked to source data, review criteria, and dataset versions.
- Domain review
- Dataset provenance
Illustrative review
03 / Improve the training data
Turn QA findings and expert judgments into curated datasets and targeted training examples.
01
Resolve flagged samples and annotations
02
Version the curated training data
03
Use expert feedback to guide model updates
04 / Reassess the model
Assess model updates against held-out references and expert criteria. Track changes alongside dataset versions.
01
Keep reference data and review criteria versioned
02
Compare outputs against the same checks
03
Send new findings back into data QA