language:
- en
license: mit
task_categories:
- image-text-to-text
arxiv: 2505.2031
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 | Paper | GitHub
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:
@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}
}