README.md

---
license: other
license_name: bioasq-research-license
license_link: https://bioasq.org/participate
task_categories:
- question-answering
- summarization
language:
- en
tags:
- biomedical
- scientific-qa
- biology
- medicine
- pubmed
size_categories:
- 1K<n<10K
---

# BioASQ - All Question Types

A comprehensive collection of BioASQ challenge questions organized by question type as separate splits.

## Purpose

This dataset is a **convenience collection** of BioASQ questions reformatted for easier use. The original source data is from the [BioASQ Challenge](http://bioasq.org/). We created this reorganized version (with question types as splits) to facilitate evaluation in our PaperSearchQA work.

**IMPORTANT**: This is not the original BioASQ dataset. We have simply reorganized the BioASQ data into splits by question type. All underlying question and answer data is from BioASQ.

## Dataset Description

This dataset contains **5,404 biomedical questions** from the BioASQ challenge, distributed across four question types:

- **factoid** (1,609 samples): Questions with short factual answers
- **yesno** (1,464 samples): Yes/No questions
- **summary** (1,283 samples): Questions requiring summarization
- **list** (1,048 samples): Questions with list-based answers

Each question type is provided as a separate split for convenient filtering and evaluation.

## Source and Attribution

**Original Data Source**: [BioASQ Challenge](http://bioasq.org/)

BioASQ is a series of challenges on large-scale biomedical semantic indexing and question answering.

**IMPORTANT**: To use BioASQ data, you must:
1. Register at: https://bioasq.org/
2. Review and comply with BioASQ terms of use
3. Cite the BioASQ papers (see below)
4. Acknowledge the BioASQ dataset in any publications

## Dataset Structure

### Data Fields

Each sample contains:

- `id`: Unique question identifier
- `body`: The question text
- `type`: Question type (factoid, yesno, summary, or list)
- `ideal_answer`: Reference answer(s)
- `exact_answer`: Structured answer (for factoid/list questions)
- `documents`: URLs of relevant PubMed documents
- `snippets`: Relevant text snippets from the documents

### Data Splits

The dataset is organized into 4 splits by question type:

| Split | Examples |
|-------|----------|
| factoid | 1,609 |
| yesno | 1,464 |
| summary | 1,283 |
| list | 1,048 |
| **Total** | **5,404** |

## Usage

```python
from datasets import load_dataset

# Load all question types
dataset = load_dataset("jmhb/BioASQ")

# Load only factoid questions
factoid = load_dataset("jmhb/BioASQ", split="factoid")

# Load specific question types
dataset = load_dataset("jmhb/BioASQ", split=["factoid", "yesno"])
```

## Citation

If you use this dataset, you **must** cite the original BioASQ papers (the first two below). If you found this processed version valuable, please also consider citing PaperSearchQA:

```bibtex
@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},
}
```

## License and Terms of Use

### BioASQ License

The original BioASQ data is available for **research and educational purposes** under specific terms:

- **Non-commercial use**: Freely available for academic research and education
- **Commercial use**: Requires written permission from BioASQ organizers
- **Attribution**: Must cite BioASQ papers and acknowledge the dataset
- **Registration**: Users must register at https://bioasq.org/

For full license terms, see: https://bioasq.org/participate

### This Dataset

This reorganized version is provided for research convenience. All terms and conditions of the original BioASQ license apply.

## Links

- **BioASQ Challenge**: [http://bioasq.org/](http://bioasq.org/)
- **BioASQ Participants Area**: [https://participants-area.bioasq.org/](https://participants-area.bioasq.org/)
- **PaperSearchQA Project**: [https://jmhb0.github.io/PaperSearchQA](https://jmhb0.github.io/PaperSearchQA)
- **Code Repository**: [GitHub](https://github.com/jmhb0/PaperSearchQA)

## Acknowledgments

This dataset is made available through the BioASQ Challenge organizers. We thank them for creating and maintaining this valuable resource for the biomedical NLP community.