Skip to content

Dataset Card for PubMed

A set of permissively licensed papers collected from tthe PubMed Central.

PubMed Central is an open-access archive of biomedical and life sciences research papers maintained by the U.S. National Institutes of Health’s National Library of Medicine. We collected papers from PubMed whose metadata indicated that the publishing journal had designated a CC BY, CC BY-SA, or CC0 license. PubMed stores the text content of each article as a single nXML file, which we convert to markdown using pandoc. Per-document license information is available in the license entry of the metadata field of each example. Code for collecting, processing, and preparing this dataset is available in the common-pile GitHub repo.

Dataset Description

  • Number of samples: 3.79M
  • Number of tokens (Llama 3): 35.32B
  • Average document length in tokens (min, max): 9.33K (6, 1.13M)

Dataset Structure

An entry in the dataset consists of the following fields:

  • id (str): An unique identifier for each document.
  • text(str): The content of the document.
  • source (str): The source of the document.
  • added (str): An date for when the document was added to this collection.
  • created (str): An date range for when the document was originally created.
  • token_count (int): The number of tokens in the sample computed using the Llama 8B tokenizer

Additional Processing

Dataset Statistics

Additional Information

License Information

While we aim to produce datasets with completely accurate licensing information, license laundering and inaccurate metadata can cause us to erroneously assign the incorrect license to some documents (for further discussion of this limitation, please see our paper). If you believe you have found an instance of incorrect licensing in this dataset, please start a discussion on this repository.

Citation Information

If you use this dataset, please cite: 

@article{kandpal2025common,
  title={{The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text}},
  author={Nikhil Kandpal and Brian Lester and Colin Raffel and Sebastian Majstorovic and Stella Biderman and Baber Abbasi and Luca Soldaini and Enrico Shippole and A. Feder Cooper and Aviya Skowron and Shayne Longpre and Lintang Sutawika and Alon Albalak and Zhenlin Xu and Guilherme Penedo and Loubna Ben  and Elie Bakouch and John David  and Honglu Fan and Dashiell Stander and Guangyu Song and Aaron Gokaslan and John Kirchenbauer and Tom Goldstein and Brian R and Bhavya Kailkhura and Tyler Murray},
  journal={arXiv preprint},
  year={2025}
}