| --- |
| license: other |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-retrieval |
| - question-answering |
| tags: |
| - regulatory |
| - pharmaceuticals |
| - medicines |
| - rag |
| - healthcare |
| - global |
| pretty_name: Zebrafish Global Drug Regulatory RAG Dataset |
| configs: |
| - config_name: default |
| data_files: |
| - split: documents |
| path: all_countries/documents_all.jsonl |
| - split: chunks |
| path: all_countries/chunks_all.jsonl |
| --- |
| |
| # Zebrafish Global Drug Regulatory RAG Dataset |
|
|
| Pharmaceutical regulatory intelligence aggregated from **200 countries** and **202 regulatory authorities** worldwide. Built for retrieval-augmented generation over global medicines regulation. |
|
|
| ## Dataset summary |
|
|
| | Metric | Value | |
| |---|---| |
| | Countries covered | **200** | |
| | Regulatory authorities | **202** | |
| | Documents | **12,533** | |
| | RAG chunks | **30,768** | |
| | Total size | **273.2 MB** | |
| | Created | 2026-04-28 | |
| | Schema version | 1.0 | |
|
|
| ## Data sources |
|
|
| | Source | Documents | |
| |---|---| |
| | Direct web scraping (national authority sites) | 10,360 | |
| | API integrations (FDA, OpenFDA, etc.) | 8,966 | |
| | Secondary sources (gap-fill from WHO, regional bodies, peer references) | 142 | |
|
|
| ## Layout |
|
|
| ``` |
| . |
| ├── README.md (this file) |
| ├── all_countries/ |
| │ ├── documents_all.jsonl (all 12,533 documents) |
| │ └── chunks_all.jsonl (all 30,768 chunks) |
| ├── by_country/ |
| │ ├── Afghanistan/ |
| │ │ ├── documents.jsonl |
| │ │ └── chunks.jsonl |
| │ ├── Albania/ |
| │ ├── ... (200 country folders) |
| │ └── Zimbabwe/ |
| └── hf_export_summary.json |
| ``` |
|
|
| ## Document schema |
|
|
| Each line in `documents.jsonl` is a single JSON object: |
|
|
| ```json |
| { |
| "document_id": "uuid", |
| "source": { |
| "country": "AZ", |
| "country_name": "Azerbaijan", |
| "authority": "ASMP", |
| "authority_full_name": "Agency for Standardization Metrology and Patents", |
| "tier": 3, |
| "url": "https://...", |
| "scrape_timestamp": "2026-04-28T..." |
| }, |
| "content": { |
| "title": "...", |
| "content_type": "GUIDELINE | LEGISLATION | PUBLICATION | ...", |
| "category": "HUMAN_MEDICINES | MEDICAL_DEVICES | ...", |
| "subcategory": "", |
| "language": "en", |
| "original_language": "en", |
| "is_translated": false |
| }, |
| "freshness_metadata": { |
| "document_date": "...", |
| "effective_date": "...", |
| "scrape_date": "2026-04-28", |
| "status": "ACTIVE", |
| "freshness_score": "CURRENT" |
| }, |
| "text": "...", |
| "sections": [{ "heading": "...", "level": 1, "content": "..." }], |
| "tags": ["legislation", "human-medicines", "pharmacovigilance"], |
| "word_count": 297, |
| "chunk_ready": true |
| } |
| ``` |
|
|
| ## Chunk schema |
|
|
| Each line in `chunks.jsonl` is one chunk ready for vector-store ingestion: |
|
|
| ```json |
| { |
| "chunk_id": "uuid", |
| "document_id": "parent uuid", |
| "chunk_index": 0, |
| "total_chunks": 5, |
| "context_prefix": "[Country: ... | Authority: ...]", |
| "text": "...", |
| "token_count": 512, |
| "metadata": { |
| "country": "AZ", |
| "authority": "ASMP", |
| "tier": 3, |
| "content_type": "LEGISLATION", |
| "category": "HUMAN_MEDICINES", |
| "language": "en", |
| "status": "ACTIVE" |
| } |
| } |
| ``` |
|
|
| ## Authority tiers |
|
|
| - **Tier 1**: WHO Listed Authorities (WLAs) and EU Medicines Regulatory Network — mature, high-capacity regulators (FDA, EMA, MHRA, PMDA, MFDS, HSA, Swissmedic, etc.). |
| - **Tier 2**: ML3/ML4 / transitional WLAs — well-functioning regulators in major emerging economies (ANVISA, CDSCO, NMPA, ANMAT, etc.). |
| - **Tier 3**: National regulators across the rest of the world. |
|
|
| ## Coverage methodology |
|
|
| The dataset combines four content acquisition strategies: |
|
|
| 1. **Direct scraping** of national authority websites (Tier 1, 2, and 3 with available URLs). |
| 2. **API integration** with FDA OpenFDA, ICH, WHO, and regional harmonisation bodies. |
| 3. **Connection-based gap filling** for countries without scrapable national portals — pulls from WHO Global Benchmarking Tool data, regional regulatory cooperation memberships (EAC, ASEAN, GCC, CIS, Council of Europe, Arab League, etc.), and recognised reference-authority relationships (FDA, EMA, WHO-PQ, TGA). |
| 4. **Recovery pass** for countries that were scraped but produced no accepted documents — uses the same connection-based approach. |
|
|
| ## Pipeline |
|
|
| Documents pass through a 5-stage pipeline before inclusion: |
|
|
| ``` |
| SCRAPE → CLEAN → NORMALIZE → ENRICH → VALIDATE → EXPORT |
| ``` |
|
|
| - **Clean**: HTML→markdown, noise removal, mojibake fixes. |
| - **Normalize**: ISO-codification of country names, date standardisation, language detection. |
| - **Enrich**: Freshness scoring, category classification, related-document linking, tag generation. |
| - **Validate**: Length, scope (human medicines), freshness, deduplication (SHA-256 + SimHash), language, link checks. |
| - **Export**: Semantic chunking with `cl100k_base` tokenizer at 512 tokens / 64 overlap. |
|
|
| ## Pipeline config |
|
|
| | Parameter | Value | |
| |---|---| |
| | Chunk size (tokens) | 512 | |
| | Chunk overlap (tokens) | 64 | |
| | Tokenizer | cl100k_base | |
| | Classifier threshold | 0.6 | |
| | Dedup method | simhash | |
| | Dedup threshold | 0.95 | |
| |
| ## Loading the dataset |
| |
| ```python |
| from datasets import load_dataset |
|
|
| # Documents only |
| docs = load_dataset("RuthvikBandari/Zebrafish_Countries_data", split="documents") |
| |
| # RAG chunks |
| chunks = load_dataset("RuthvikBandari/Zebrafish_Countries_data", split="chunks") |
|
|
| # Or stream a single country |
| import pandas as pd |
| azerbaijan = pd.read_json( |
| "hf://datasets/RuthvikBandari/Zebrafish_Countries_data/by_country/Azerbaijan/documents.jsonl", |
| lines=True, |
| ) |
| ``` |
| |
| ## Limitations |
|
|
| - Coverage breadth is uneven: Tier 1 authorities (FDA, EMA, etc.) dominate document count; many small/island/least-developed nations rely on connection-based gap-fill content rather than direct scrapes. |
| - Gap-fill records are clearly marked: their URLs use the `urn:zebrafish:peer-reference:<ISO>`, `urn:zebrafish:who-profile:<ISO>`, `urn:zebrafish:ich-adoption:<ISO>`, or `urn:zebrafish:gap-fill:<ISO>` URN namespace. Real scrapes have `https://` URLs. |
| - Content currency: documents reflect the state of authority websites at the scrape timestamps recorded per document. Regulatory information evolves; verify against the authority website before relying on a specific provision. |
| - Languages: the dataset is primarily in English. Some non-English source pages were scraped in their original language and tagged accordingly. |
|
|
| ## License |
|
|
| Document content is sourced from public regulatory authority websites and international organisation publications. Each document retains its source URL for attribution; users are responsible for compliance with the source authority's terms of use. |
|
|
| Pipeline code, gap-fill content, and dataset assembly are provided under the project's existing license terms. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{zebrafish_rag_2026, |
| title = {Zebrafish Global Drug Regulatory RAG Dataset}, |
| author = {Ruthvik Bandari}, |
| year = {2026}, |
| url = {https://huggingface.co/datasets/RuthvikBandari/Zebrafish_Countries_data} |
| } |
| ``` |
|
|
