Datasets:

opendatalab
/

Sci-Base

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 **SciVerse** is a comprehensive, multi-layered scientific data foundation designed to provide the ultimate data infrastructure for the AI for Science (AI4S) community. As scientific research becomes increasingly data-driven, SciVerse supplies the essential, high-quality data resources required to build robust scientific knowledge systems and accelerate research.
 SciVerse consists of three core data pillars:
 * **Sci-Base (The Foundation Layer):** The massive-scale, purely objective scientific knowledge base. Comprising over 25 million deeply cleaned and parsed Open Access documents, it provides the comprehensive, purely factual scientific corpus that serves as the universal foundation for all downstream scientific applications.
 * **Sci-Align (The Alignment Data Layer):** A highly curated, structured dataset mapping direct scientific relationships and precise factual alignments. It focuses on well-defined entity interactions—such as mapping specific chemical reaction pathways (e.g., via SMILES strings), condition-to-result pairings, and standardized structural descriptions. This layer provides the structured factual alignment needed for models to accurately connect and ground foundational scientific concepts.
 * **Sci-Evo (The Evaluation Data Layer):** 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.

 **SciVerse** is a comprehensive, multi-layered scientific data foundation designed to provide the ultimate data infrastructure for the AI for Science (AI4S) community. As scientific research becomes increasingly data-driven, SciVerse supplies the essential, high-quality data resources required to build robust scientific knowledge systems and accelerate research.
 SciVerse consists of three core data pillars:
 * **Sci-Base (The Foundation Layer):** The massive-scale, purely objective scientific knowledge base. Comprising over 25 million deeply cleaned and parsed Open Access documents, it provides the comprehensive, purely factual scientific corpus that serves as the universal foundation for all downstream scientific applications.
 * **Sci-Align (The Alignment Data Layer):** A highly curated, structured dataset mapping direct scientific relationships and precise factual alignments. It focuses on well-defined entity interactions—such as mapping specific chemical reaction pathways (e.g., via SMILES strings), condition-to-result pairings, and standardized structural descriptions. This layer provides the structured factual alignment needed for models to accurately connect and ground foundational scientific concepts.
 * **Sci-Evo (The Evaluation Data Layer):** 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.