S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918485585559552 |
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| author | Wang, He Guo, Longteng Huo, Pengkang Lin, Xuanxu Yuan, Yichen Jiang, Jie Liu, Jing |
| author_facet | Wang, He Guo, Longteng Huo, Pengkang Lin, Xuanxu Yuan, Yichen Jiang, Jie Liu, Jing |
| contents | Multimodal learning has revolutionized general domain tasks, yet its application in scientific discovery is hindered by the profound semantic gap between complex scientific imagery and sparse textual descriptions. We present S1-MMAlign, a large-scale, multi-disciplinary multimodal dataset comprising over 15.5 million high-quality image-text pairs derived from 2.5 million open-access scientific papers. Spanning disciplines from physics and biology to engineering, the dataset captures diverse visual modalities including experimental setups, heatmaps, and microscopic imagery. To address the pervasive issue of weak alignment in raw scientific captions, we introduce an AI-ready semantic enhancement pipeline that leverages advanced multimodal large language models to recaption images, by synthesizing comprehensive context from paper abstracts and the citation contexts of corresponding figures. Technical validation confirms that our enhancement pipeline markedly improves data quality via reduced SciBERT pseudo-perplexity and enhanced CLIP image-text alignment, while also significantly boosting multimodal large language models performance in zero-shot scientific captioning, multi-domain scientific reasoning, and visual instruction tuning. S1-MMAlign provides a pivotal foundational resource for cross-modal scientific understanding in the AI for Science era, supporting the development of scientific foundation models and a wide range of downstream scientific intelligence applications. The dataset is publicly available at https://huggingface.co/datasets/ScienceOne-AI/S1-MMAlign. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_00264 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding Wang, He Guo, Longteng Huo, Pengkang Lin, Xuanxu Yuan, Yichen Jiang, Jie Liu, Jing Computer Vision and Pattern Recognition Multimodal learning has revolutionized general domain tasks, yet its application in scientific discovery is hindered by the profound semantic gap between complex scientific imagery and sparse textual descriptions. We present S1-MMAlign, a large-scale, multi-disciplinary multimodal dataset comprising over 15.5 million high-quality image-text pairs derived from 2.5 million open-access scientific papers. Spanning disciplines from physics and biology to engineering, the dataset captures diverse visual modalities including experimental setups, heatmaps, and microscopic imagery. To address the pervasive issue of weak alignment in raw scientific captions, we introduce an AI-ready semantic enhancement pipeline that leverages advanced multimodal large language models to recaption images, by synthesizing comprehensive context from paper abstracts and the citation contexts of corresponding figures. Technical validation confirms that our enhancement pipeline markedly improves data quality via reduced SciBERT pseudo-perplexity and enhanced CLIP image-text alignment, while also significantly boosting multimodal large language models performance in zero-shot scientific captioning, multi-domain scientific reasoning, and visual instruction tuning. S1-MMAlign provides a pivotal foundational resource for cross-modal scientific understanding in the AI for Science era, supporting the development of scientific foundation models and a wide range of downstream scientific intelligence applications. The dataset is publicly available at https://huggingface.co/datasets/ScienceOne-AI/S1-MMAlign. |
| title | S1-MMAlign: A Large-Scale, Multi-Disciplinary Dataset for Scientific Figure-Text Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.00264 |