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- # U-RxnDiagram-15k Dataset
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  ## 🌌 The SciVerse Data Foundation
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  * **Sci-Evo (Scientific Evolution Data):** A multi-layered, high-density reasoning dataset designed for complex problem-solving and deep scientific evaluation. Going beyond basic facts, this layer captures deep, causal descriptions—detailing not just the 'what', but the underlying reasoning for specific experimental designs, multi-step mathematical derivations, and the complex logic of how modifying specific conditions alters outcomes. It is constructed to rigorously measure a model's advanced scientific reasoning accuracy and logical depth.
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- ## U-RxnDiagram-15k Dataset Overview
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  U-RxnDiagram-15k Dataset is a large-scale dataset specifically designed for chemical reaction diagram parsing, containing chemical reaction images extracted from scientific literature PDFs along with detailed annotations. This dataset aims to support research in cheminformatics, document analysis, and computer vision fields.
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  For questions or suggestions, please contact songjiahe@pjlab.org.cn
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- *Last updated: 2025*
 
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+ # U-RxnDiagram-15k Dataset (Sci-Align)
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  ## 🌌 The SciVerse Data Foundation
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  * **Sci-Evo (Scientific Evolution Data):** A multi-layered, high-density reasoning dataset designed for complex problem-solving and deep scientific evaluation. Going beyond basic facts, this layer captures deep, causal descriptions—detailing not just the 'what', but the underlying reasoning for specific experimental designs, multi-step mathematical derivations, and the complex logic of how modifying specific conditions alters outcomes. It is constructed to rigorously measure a model's advanced scientific reasoning accuracy and logical depth.
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  ---
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+ ## U-RxnDiagram-15k Dataset Overview (Sci-Align)
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  U-RxnDiagram-15k Dataset is a large-scale dataset specifically designed for chemical reaction diagram parsing, containing chemical reaction images extracted from scientific literature PDFs along with detailed annotations. This dataset aims to support research in cheminformatics, document analysis, and computer vision fields.
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  For questions or suggestions, please contact songjiahe@pjlab.org.cn
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