S1-MMAlign: 15.5 Million Scientific Image-Text Pairs from 2.5M Papers

S1-MMAlign contains 15.5 million image-text pairs extracted from 2.5 million open-access scientific papers across biology, chemistry, and physics. Developed by ScienceOne-AI and released in 2026, it provides a large-scale resource for aligning complex scientific imagery with textual descriptions. The dataset is designed to bridge the semantic gap in scientific multimodal learning using peer-reviewed literature.

Use Cases

• Visual Question Answering (VQA) using scientific imagery and associated text to interpret diagrams
• Image-to-text generation for automated captioning of complex scientific figures in biology and chemistry
• Feature extraction for training domain-specific vision-language models using 15.5 million multimodal records

Strengths

• 15.5 million image-text pairs
• Multi-disciplinary coverage including biology, chemistry, and physics
• Sourced from 2.5 million peer-reviewed open-access papers

Limitations

• Potential for label noise from automated extraction of paper captions
• Restricted to open-access literature which may introduce domain bias
• Non-commercial license limits industrial application

Provenance

Source

ScienceOne-AI, derived from 2.5 million open-access scientific papers

Collection Method

Automated extraction and alignment from open-access scientific publications

Freshness

Last updated March 2026.

Geography

Global scientific literature