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Interactive Jupyter Notebook Tutorial

Want hands-on experience with PMCGrab? Our interactive Jupyter notebook provides a complete walkthrough from installation to building AI-ready datasets.

What's Inside

The notebook covers:

  • Single Paper Processing: Start with one paper and explore the output
  • Batch Processing: Build a multi-paper dataset
  • AI/ML Preparation: Structure data for RAG, vector databases, and LLM training
  • Data Export: Save everything in organized formats
  • Analysis: Visualize and understand your dataset

Quick Start

  1. Download the notebook:
  2. View on GitHub (renders properly)

  3. Direct download (notebook file)

  4. Install dependencies:

# Update uv first (or install if you don't have it)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install PMCGrab and Jupyter
uv add pmcgrab jupyter pandas matplotlib seaborn
  1. Launch Jupyter:
uv run jupyter notebook
  1. Open the notebook and start processing papers!

Prerequisites

  • Python 3.10+
  • Internet connection (to fetch papers from PMC)
  • Basic Python knowledge (helpful but not required)

What You'll Build

By the end of the notebook, you'll have:

  • Individual Paper JSONs: Clean, structured data for each paper
  • RAG Chunks: Ready for vector database ingestion
  • Training Examples: Structured for LLM fine-tuning
  • Dataset Analysis: Statistical overview of your collection

Perfect For

  • First-time users wanting interactive exploration
  • Data scientists building biomedical datasets
  • AI researchers preparing training data
  • Students learning about scientific text processing

Troubleshooting

Common Issues:

"Failed to process PMC..."

  • Network connectivity issue
  • Paper may not be open access
  • Try a different PMC ID

"ModuleNotFoundError"

  • Make sure you're using uv run jupyter notebook
  • Verify installation: uv run python -c "import pmcgrab; print('OK')"

Notebook won't start

  • Check Python version: python --version (need 3.10+)
  • Try: uv add jupyter then uv run jupyter notebook

Advanced Usage

Once you're comfortable with the basics:

  • Scale up: Process 100s or 1000s of papers using the CLI
  • Integrate: Connect to your vector database or ML pipeline
  • Customize: Modify the notebook for your specific use case

Next Steps

After completing the notebook:

Need Help?

Ready to turn scientific literature into AI-ready data?