Biological data, clean and curated.

Reliable machine learning depends on biological data that is harmonized, quality controlled, documented, and ready for reuse.

ML-readiness check

Sample metadata is complete Ontology or controlled vocabulary mapped Batch effects and QC flags documented Provenance and reuse permissions captured

25% ready: start with metadata completion.

Normalize formats, labels, sample attributes, assay metadata, and clinical variables so downstream teams know what each field means.

Document missingness, outliers, batch effects, measurement limits, duplicated records, and lineage across source systems.

Create data dictionaries, feature tables, versioned exports, and model-ready packages that can survive handoff.