Datasets:

hsilvosa
/

aemps-cima

license: other
language:
  - es
pretty_name: AEMPS CIMA Research Dataset
tags:
  - medicine
  - public-data
  - regulatory
  - knowledge-graph
configs:
  - config_name: active_ingredients
    data_files:
      - split: train
        path: data/active_ingredients/*.parquet
  - config_name: administration_routes
    data_files:
      - split: train
        path: data/administration_routes/*.parquet
  - config_name: atc_codes
    data_files:
      - split: train
        path: data/atc_codes/*.parquet
  - config_name: document_links
    data_files:
      - split: train
        path: data/document_links/*.parquet
  - config_name: documents
    data_files:
      - split: train
        path: data/documents/*.parquet
  - config_name: excipients
    data_files:
      - split: train
        path: data/excipients/*.parquet
  - config_name: medications
    data_files:
      - split: train
        path: data/medications/*.parquet
  - config_name: photos
    data_files:
      - split: train
        path: data/photos/*.parquet
  - config_name: presentations
    data_files:
      - split: train
        path: data/presentations/*.parquet

AEMPS CIMA Research Dataset

This directory builds a research-ready snapshot of CIMA, the medicine information system maintained by the Spanish Agency of Medicines and Medical Devices (AEMPS).

The source exposes official information about authorized and non-authorized medicines, commercial presentations, active ingredients, ATC codes, pharmaceutical forms, administration routes, regulatory status, safety-related indicators, segmented summaries of product characteristics and patient leaflets.

The project is intended exclusively for research and data exploration. It is not medical advice and must not be used to make prescribing, dispensing, diagnosis, treatment, regulatory, or safety decisions. Users should consult AEMPS and qualified healthcare professionals for current authoritative information.

Original source and credit

All source records are provided by Agencia Española de Medicamentos y Productos Sanitarios (AEMPS) through CIMA.

CIMA: https://cima.aemps.es/cima/publico/home.html
REST API documentation, version 1.23: https://cima.aemps.es/cima/resources/docs/CIMA_REST_API.pdf
AEMPS open data statement: https://sede.aemps.gob.es/datos-abiertos/

AEMPS describes these data as public and reusable, subject where applicable to source attribution. The generated provenance file records the exact retrieval time and source endpoints. Before public release, the maintainer must review the current AEMPS legal notice and set the precise Hugging Face license metadata if a standard license identifier applies.

Hugging Face contents

The staging process creates one dataset configuration per relational table. Every configuration uses a single train split because CIMA is an entity and document collection, not a predefined supervised-learning benchmark.

Configuration	Unit	Main content
`medications`	One medicine registration	Names, regulatory state, prescription and safety flags, dose and pharmaceutical form
`presentations`	One commercial presentation	National code, status, commercialization and supply flags
`active_ingredients`	One medicine–ingredient edge	Ingredient identifiers and names
`excipients`	One medicine–excipient edge	Excipient identifiers, amounts, units, and ordering
`atc_codes`	One medicine–ATC edge	ATC code, description and level
`administration_routes`	One medicine–route edge	Route identifiers and descriptions
`documents`	One segmented document section	Product information or patient leaflet section in HTML and plain text
`document_links`	One source document reference	Document type, URL and segmented-content availability
`photos`	One source image reference	Packaging or pharmaceutical-form image URLs and update times
No raw API responses are uploaded. The release also includes machine-readable schema, profile, quality, provenance, and checksum files.

This first release is a complete current-state snapshot at the retrieval date. Historical change events are intentionally excluded: they are not required to represent the current catalogue, and collecting the full event register would substantially delay publication. A future release may expose history as a separate dataset configuration.

Installation

Python 3.11 or newer is recommended.

cd aemps-cima
python -m venv .venv
python -m pip install -e ".[dev]"

Usage

Run a small end-to-end sample first:

cima-pipeline run --config configs/sample.json

Run the full snapshot:

cima-pipeline run --config configs/default.json

The run command downloads, normalizes, profiles, validates, and stages the release. Individual stages are also available:

cima-pipeline download --config configs/default.json
cima-pipeline normalize --config configs/default.json
cima-pipeline analyze --config configs/default.json
cima-pipeline stage --config configs/default.json

The default configuration excludes the historical change register. Set include_change_register to true only when intentionally building a separate historical release; that endpoint contains more than one million events and is not needed for the current-state snapshot.

Inspect hf_staging/ before publishing. Uploading is deliberately separate and explicit:

cima-pipeline upload --config configs/default.json --repo-id ORGANIZATION/DATASET_NAME

Set HF_TOKEN in the environment or authenticate with the Hugging Face CLI. The upload command never includes data/raw.

Storage lifecycle

After a release is verified on Hugging Face, delete data/raw, data/processed, and hf_staging locally. They are reproducible and ignored by Git. Keep the code, configuration, schemas, tests, and lightweight artifacts metadata in the repository.

Splits, classes, and limitations

There is no target label and therefore no class count. Fields such as ATC group, regulatory state, prescription status, and pharmaceutical form are categorical attributes, not benchmark classes. All configurations contain a 100% train split. Researchers creating prediction tasks should define their own patient-safe, leakage-aware, and preferably temporal evaluation design.

CIMA changes over time. A snapshot can become outdated, records may be corrected retrospectively, text availability varies by medicine, and relationships reflect the source representation at retrieval time. Dates returned by the API require careful timezone interpretation. The pipeline preserves source values and records its normalization decisions.