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---
language:
- en
license: mit
task_categories:
- image-text-to-text
arxiv: 2505.20310
dataset_info:
features:
- name: field
dtype: string
- name: paper_idx
dtype: string
- name: doi
dtype: string
- name: type
dtype: string
- name: table_or_image
dtype: image
- name: text_or_caption
dtype: string
splits:
- name: atmosphere
num_bytes: 202134712.5
num_examples: 1196
- name: agriculture
num_bytes: 446617002
num_examples: 4336
- name: environment
num_bytes: 165016111.375
num_examples: 1125
download_size: 779035060
dataset_size: 813767825.875
configs:
- config_name: default
data_files:
- split: atmosphere
path: data/atmosphere-*
- split: agriculture
path: data/agriculture-*
- split: environment
path: data/environment-*
---
# Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System
[**Project Page**](https://black-yt.github.io/meta-analysis-page/) | [**Paper**](https://huggingface.co/papers/2505.20310) | [**GitHub**](https://github.com/black-yt/Manalyzer)
## Overview
Meta-analysis is a systematic research methodology that synthesizes data from multiple existing studies to derive comprehensive conclusions. Traditional meta-analysis involves a complex multi-stage pipeline including literature retrieval, paper screening, and data extraction, which demands substantial human effort and time.
**Manalyzer** is a multi-agent system that achieves end-to-end automated meta-analysis through tool calls. This repository contains the benchmark constructed to evaluate meta-analysis performance, comprising 729 papers across 3 domains (Atmosphere, Agriculture, and Environment), encompassing text, image, and table modalities, with over 10,000 data points.
## Dataset Structure
The benchmark consists of 729 papers across 3 scientific domains:
- **Atmosphere**: 1,196 examples
- **Agriculture**: 4,336 examples
- **Environment**: 1,125 examples
### Data Fields
Each example in the dataset contains:
- `field`: The scientific domain (Atmosphere, Agriculture, or Environment).
- `paper_idx`: Unique index of the source paper.
- `doi`: Digital Object Identifier of the source paper.
- `type`: Category of the data point.
- `table_or_image`: Visual modality (extracted image of a table or figure).
- `text_or_caption`: Associated text or caption providing context for the visual content.
## Citation
If you find this dataset or the Manalyzer system useful in your research, please cite:
```bibtex
@article{xu2025manalyzer,
title={Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System},
author={Xu, Wanghan and Zhang, Wenlong and Ling, Fenghua and Fei, Ben and Hu, Yusong and Ren, Fangxuan and Lin, Jintai and Ouyang, Wanli and Bai, Lei},
journal={arXiv preprint arXiv:2505.20310},
year={2025}
}
``` |