process-latvia-data

To structure Latvian ZVA product JSON for vocabulary mapping, run a Python script that groups products by active substance and brand into sanitized per-product folders with metadata files. This creates a consistent directory layout under data/latvia/products/ for downstream mapping.

Organizing raw ZVA registry data involves converting HumanProducts.json into sanitized product folders. The script generates an info.txt file with standardized fields like active_substance and atc_code, plus dated TSV exports for each grouped product.

You prepare Latvian drug registry data for RxNorm or OMOP mapping by grouping products by active substance and brand. The script creates per-product directories containing standardized info.txt metadata and dated TSV files that downstream mapping scripts can consume.

Yes, you can use Python to group ZVA products by active substance and brand. The organize_products.py script processes HumanProducts.json locally within the repository, creating sanitized folder names and emitting consistency warnings when INFO fields differ across grouped records.

When structuring Latvian ZVA product data, the script generates an info.txt file per product containing standardized fields including short_name, active_substance, marketing_authorisation_holder, manufacturer, atc_code, package_leaflet, and summary_of_product_characteristics.

The ZVA data processing script emits consistency warnings when INFO fields differ across grouped records within the same product. This alerts researchers to potential data discrepancies in the raw registry JSON before proceeding with downstream vocabulary mapping and review workflows.