PMCGrab

From PMCID to clean, section-aware JSON for your AI pipelines.

Get started pip install pmcgrab

Home

• 5-minute setup Install PMCGrab and run a multi-paper demo in minutes.

• User Guide Comprehensive guides covering every feature.

• API Reference Auto-generated docs for every function and class.

• Examples Real-world usage and advanced patterns.

Example Usage

from pmcgrab.application.processing import process_single_pmc

# Process a PMC article
data = process_single_pmc("7114487")

if data:
    print(f"Title: {data['title']}")
    print(f"Authors: {len(data['authors'])}")
    print(f"Sections: {list(data['body'].keys())}")

Example Output

{
  "pmc_id": "7114487",
  "title": "Machine learning approaches in cancer research",
  "abstract": "Recent advances in machine learning have revolutionized...",
  "authors": [
    {
      "First_Name": "John",
      "Last_Name": "Doe",
      "Affiliation": "Cancer Research Institute"
    }
  ],
  "body": {
    "Introduction": "Cancer research has evolved significantly...",
    "Methods": "We implemented a deep learning framework...",
    "Results": "Our model achieved 94.2% accuracy...",
    "Discussion": "These findings demonstrate the potential..."
  },
  "journal": "Nature Medicine",
  "figures": [...],
  "tables": [...],
  "references": [...]
}