README.md

metadata

license: cc0-1.0
language:
  - en
tags:
  - pubmed
pretty_name: PubMedAbstractSubset

PubMed Abstracts Subset

This dataset contains a probabilistic sample of publicly available PubMed metadata sourced from the National Library of Medicine (NLM).

If you're looking for the precomputed embedding vectors (MedCPT) used in our work Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation, they are available in a separate dataset: slinusc/PubMedAbstractsSubsetEmbedded.

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📄 Description

Each entry in the dataset includes:

• title: Title of the publication
• abstract: Abstract text
• PMID: PubMed identifier

The dataset is split into 24 .jsonl files, each containing approximately 100,000 entries, for a total of ~2.39 million samples.

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🔍 How to Access

▶️ Option 1: Load using Hugging Face datasets (streaming)

from datasets import load_dataset

dataset = load_dataset("slinusc/PubMedAbstractsSubset", streaming=True)

for doc in dataset:
    print(doc["title"], doc["abstract"])
    break

Streaming is recommended for large-scale processing and avoids loading the entire dataset into memory.

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💾 Option 2: Clone using Git and Git LFS

git lfs install
git clone https://huggingface.co/datasets/slinusc/PubMedAbstractsSubset
cd PubMedAbstractsSubset

After cloning, run git lfs pull if needed to retrieve the full data files.

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📦 Format

Each file is in .jsonl (JSON Lines) format, where each line is a valid JSON object:

{
  "title": "...",
  "abstract": "...",
  "PMID": 36464820
}

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📚 Source and Licensing

This dataset is derived from public domain PubMed metadata (titles and abstracts), redistributed in accordance with NLM data usage policies.

• Used in:
  Stuhlmann et al. (2025), Efficient and Reproducible Biomedical QA using RAG, arXiv:2505.07917

  https://github.com/slinusc/medical_RAG_system

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🏷️ Version

• v1.0 – Initial release (2.39M entries, 24 JSONL files)

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📬 Contact

Maintained by @slinusc.
For questions or issues, please open a discussion or pull request on the Hugging Face dataset page.