bioc-pmc-api

name	bioc-pmc-api
description	Access PMC Open Access articles in BioC format for text mining
metadata	{"openclaw":{"emoji":"🧬","category":"literature","subcategory":"fulltext","keywords":["bioc","pmc","text mining","biomedical nlp","full text","pubmed central"],"source":"https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/"}}

BioC API for PMC Open Access

Overview

The BioC API provides full-text articles from PubMed Central (PMC) in the BioC format — a simplified XML/JSON structure designed specifically for biomedical text mining. Unlike the standard PMC OAI service (which returns JATS XML), BioC pre-segments text into passages with offset annotations, making it ideal for NLP pipelines, named entity recognition, relation extraction, and other text mining tasks. Free, no authentication required.

API Endpoints

Base URL

https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/{PMCID}/unicode

Retrieve by PMC ID

# JSON format (recommended for programmatic use)
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/unicode"

# XML format
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_xml/PMC6267067/unicode"

# ASCII encoding (strips non-ASCII characters)
curl "https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_json/PMC6267067/ascii"

Retrieve by PubMed ID

# Convert PMID to PMCID first, then query
curl "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=29346600&format=json"
# Returns: {"records": [{"pmid": "29346600", "pmcid": "PMC6267067", ...}]}

BioC JSON Structure

{
  "source": "PMC",
  "date": "2024-01-15",
  "key": "collection.key",
  "documents": [
    {
      "id": "PMC6267067",
      "passages": [
        {
          "infons": {
            "section_type": "TITLE",
            "type": "title"
          },
          "offset": 0,
          "text": "Article Title Here"
        },
        {
          "infons": {
            "section_type": "ABSTRACT",
            "type": "abstract"
          
           
           
        
        
           
             
             
          
           
           
        
      
    
  

Key fields:

• passages[].infons.section_type: TITLE, ABSTRACT, INTRO, METHODS, RESULTS, DISCUSS, CONCL, REF, FIG, TABLE
• passages[].offset: Character offset from document start
• passages[].text: Plain text content of the passage

Python Usage

import requests
import json

def get_bioc_article(pmcid: str, fmt: str = "json") -> dict:
    """Fetch a PMC article in BioC format."""
    url = f"https://www.ncbi.nlm.nih.gov/research/bionlp/RESTful/pmcoa.cgi/BioC_{fmt}/{pmcid}/unicode"
    resp = requests.get(url, timeout=30)
    resp.raise_for_status()
    return resp.json() if fmt == "json" else resp.text

def extract_sections(bioc_doc: dict) -> dict:
    """Extract text organized by section type."""
    sections = {}
    for doc in bioc_doc.get("documents", []):
        for passage in doc.get("passages", []):
            section = passage.get("infons", {}).get("section_type", "OTHER")
            text = passage.get("text", "")
            sections.setdefault(section, []).append(text)
    return {k: "\n".join(v) for k, v in sections.items()}

# Example: fetch and parse
article = get_bioc_article("PMC6267067")
sections = extract_sections(article)
print(f"Title: {sections.get('TITLE', 'N/A')}")
print()
()

Data Coverage

• PMC Open Access Subset: ~4M+ articles with CC licenses
• Author Manuscript Collection: NIH-funded author manuscripts
• Updates: New articles added daily

Rate Limits

• Follow NCBI standard: 3 requests per second
• For bulk access, use the PMC FTP service instead
• Add tool=your_tool_name&email=[e-mail] to requests for priority queue

Citation

When using this API in publications, cite:

Comeau DC, Wei CH, Islamaj Dogan R, Lu Z. PMC text mining subset in BioC: about 3 million full text articles and growing. Bioinformatics, btz070, 2019.

References

• BioC-PMC API Documentation
• BioC Format Specification
• PMC Open Access Subset