Metadata Harmonization Starter

Use this skill to harmonize small metadata tables with inconsistent column names and categorical labels into one canonical TSV plus a compact JSON summary.

What This Skill Does

• reads one or more tabular metadata files
• applies a JSON mapping from source columns to canonical fields
• normalizes selected categorical values such as sex and condition
• writes a harmonized TSV and a machine-readable summary

When To Use It

• when you need a starter for metadata-harmonization
• when multiple small test fixtures use different metadata headers
• when you want deterministic harmonized outputs before validation or format conversion

Run

python3 skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/scripts/run_metadata_harmonization.py \
  --input skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/examples/cohort_a.tsv \
  --input skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/examples/cohort_b.tsv \
  --mapping skills/data-acquisition-and-dataset-handling/metadata-harmonization-starter/examples/column_mapping.json \
  --out-tsv scratch/metadata-harmonization/harmonized_metadata.tsv \
  --summary-out scratch/metadata-harmonization/harmonized_metadata_summary.json

Notes

• The starter keeps the mapping external so the same script can be reused for other tiny fixtures.
• Canonical rows are sorted by sample_id to keep committed outputs deterministic.