--- 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} } ```