Datasets:

jmhb
/

pubmed_bioasq_2022

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PubMed BioASQ 2022 Corpus

This dataset contains the PubMed abstracts corpus from the BioASQ 2022 challenge, comprising approximately 23 million biomedical documents with MeSH (Medical Subject Headings) annotations.

Purpose

This is a convenience collection of the PubMed corpus from the BioASQ 2022 Challenge, reformatted for easier use in retrieval and QA systems. The original source is the BioASQ challenge data. We created this processed version with multiple formats (JSON, Parquet, JSONL) to facilitate different use cases in our PaperSearchQA work.

IMPORTANT: The underlying PubMed abstracts and MeSH annotations are from the BioASQ 2022 challenge release. We have reformatted this data for convenience but all content originates from BioASQ.

Dataset Description

The corpus includes PubMed abstracts covering publications up to 2022, originally sourced from the BioASQ Challenge. This is the foundational corpus used for the PaperSearchQA benchmark for evaluating retrieval and question-answering systems in the biomedical domain.

Key Statistics

Total Documents: ~23 million PubMed abstracts
Coverage: Publications up to 2022
Annotations: MeSH terms (Medical Subject Headings)
Size: ~63GB total (multiple formats)
Languages: Primarily English

Data Formats

The dataset is provided in three formats for different use cases:

data/allMeSH_2022.json (27GB) - Original BioASQ format
- Complete JSON with all metadata
- Contains: PMID, title, abstract, journal, publication year, MeSH terms
data/allMeSH_2022.parquet (13GB) - Optimized structured format
- Apache Parquet for efficient analytics
- Same fields as JSON, optimized for queries and filtering
- Recommended for data analysis workflows
data/corpus/pubmed.jsonl (23GB) - Retrieval-optimized format
- One document per line (JSONL)
- Format: {"_id": "PMID", "title": "...", "text": "...", "metadata": {...}}
- Optimized for information retrieval systems (BM25, dense retrievers)
- Compatible with Pyserini, Elasticsearch, and other IR tools

Auxiliary Files

data/indices/allMeSH_2022_pmid_index.pkl (243MB) - PMID lookup index
data/indices/allMeSH_2022_mesh_index.pkl (981MB) - MeSH term index

Usage

Loading the Parquet Format (Recommended for Analysis)

import pandas as pd
from huggingface_hub import hf_hub_download

# Download the parquet file
file_path = hf_hub_download(
    repo_id="jmhb/pubmed_bioasq_2022",
    filename="data/allMeSH_2022.parquet",
    repo_type="dataset"
)

# Load into pandas
df = pd.read_parquet(file_path)

# Example: Filter by year
recent_papers = df[df['year'] >= 2020]

# Example: Search by MeSH term
covid_papers = df[df['meshMajor'].apply(lambda x: 'COVID-19' in str(x))]

Loading the JSONL Format (for Retrieval)

import json
from huggingface_hub import hf_hub_download

# Download the JSONL corpus
file_path = hf_hub_download(
    repo_id="jmhb/pubmed_bioasq_2022",
    filename="data/corpus/pubmed.jsonl",
    repo_type="dataset"
)

# Read documents
documents = []
with open(file_path, 'r') as f:
    for line in f:
        doc = json.loads(line)
        documents.append(doc)

# Example document structure:
# {
#   "_id": "12345678",
#   "title": "Example paper title",
#   "text": "Abstract text...",
#   "metadata": {
#     "journal": "Nature",
#     "year": 2020,
#     "meshMajor": ["COVID-19", "SARS-CoV-2"]
#   }
# }

Using with Pyserini (BM25 Retrieval)

from pyserini.search import SimpleSearcher
from huggingface_hub import hf_hub_download
import os

# Download corpus
corpus_path = hf_hub_download(
    repo_id="jmhb/pubmed_bioasq_2022",
    filename="data/corpus/pubmed.jsonl",
    repo_type="dataset"
)

# Index with Pyserini (run once)
os.system(f"python -m pyserini.index -collection JsonCollection \
    -generator DefaultLuceneDocumentGenerator \
    -threads 8 \
    -input {os.path.dirname(corpus_path)} \
    -index ./pubmed_index \
    -storePositions -storeDocvectors -storeRaw")

# Search
searcher = SimpleSearcher('./pubmed_index')
hits = searcher.search('COVID-19 treatments', k=10)

for hit in hits:
    print(f"PMID: {hit.docid}")
    print(f"Score: {hit.score:.4f}")
    print(f"Title: {json.loads(hit.raw)['title']}\n")

Loading Original JSON Format

import json
from huggingface_hub import hf_hub_download

file_path = hf_hub_download(
    repo_id="jmhb/pubmed_bioasq_2022",
    filename="data/allMeSH_2022.json",
    repo_type="dataset"
)

with open(file_path, 'r') as f:
    data = json.load(f)

# Access documents
for article in data['articles'][:5]:
    print(f"PMID: {article['pmid']}")
    print(f"Title: {article['title']}")
    print(f"Year: {article['year']}")
    print(f"MeSH: {article.get('meshMajor', [])}\n")

Data Fields

Common Fields Across Formats

pmid (string): PubMed unique identifier
title (string): Article title
abstractText (string): Article abstract
journal (string): Journal name
year (integer): Publication year
meshMajor (list): Major MeSH descriptor terms
meshMinor (list): Minor MeSH descriptor terms (if available)

JSONL-Specific Fields

_id (string): Document ID (same as PMID)
text (string): Combined abstract text
metadata (dict): Contains journal, year, MeSH terms

Conversion Scripts

The repository includes scripts to regenerate the different formats:

scripts/allMesh_to_parquet.py: Convert JSON to Parquet
scripts/make_pubmed_corpus.py: Generate JSONL corpus from JSON

See the scripts for usage details.

Source and Attribution

Original Data Source: BioASQ 2022 Challenge

PubMed Data

PubMed abstracts are in the public domain and may be used freely. However, proper attribution is required:

Source: U.S. National Library of Medicine (NLM)
Database: PubMed / MEDLINE
Required Attribution:
- Cite the National Center for Biotechnology Information (NCBI) as the source
- Provide a link to the original PubMed record when possible
- Example: "Data from PubMed (NCBI, NLM, NIH)"

Citation

The corpus structure follows the BioASQ 2022 challenge format. If using this dataset for research, you must cite the original BioASQ papers (the first two below). If you found this processed version valuable, please also consider citing PaperSearchQA:

@article{krithara2023bioasq,
  title={BioASQ-QA: A manually curated corpus for Biomedical Question Answering},
  author={Krithara, Anastasia and Nentidis, Anastasios and Bougiatiotis, Konstantinos and Paliouras, Georgios},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={170},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{tsatsaronis2015overview,
  title={An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition},
  author={Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and Polychronopoulos, Dimitris and others},
  journal={BMC bioinformatics},
  volume={16},
  number={1},
  pages={138},
  year={2015},
  publisher={Springer}
}

@misc{burgess2026papersearchqalearningsearchreason,
      title={PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR},
      author={James Burgess and Jan N. Hansen and Duo Peng and Yuhui Zhang and Alejandro Lozano and Min Woo Sun and Emma Lundberg and Serena Yeung-Levy},
      year={2026},
      eprint={2601.18207},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2601.18207},
}