README.md

metadata

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

PubMed Abstracts Subset (10%)

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

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📄 Description

Each entry in the dataset includes:

• id: Local unique identifier
• 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:

{
  "id": "pubmed23n1166_0",
  "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.