Enterprise AI Analysis
SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models
Authors: Chuan Qin, Xin Chen, Chengrui Wang, Pengmin Wu, Xi Chen, Yihang Cheng, Jingyi Zhao, Meng Xiao, Xiangchao Dong, Qingqing Long, Boya Pan, Han Wu, Chengzan Li, Yuanchun Zhou, Hui Xiong and Hengshu Zhu
Publication Date: August 3, 2025
In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions-Quality, FAIRness, Explainability, and Compliance-which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for Earth, Life, and Materials Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators-Knowledge, Understanding, Reasoning, Multimodality, and Values-spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences.
Executive Impact: The SciHorizon Imperative
The SciHorizon framework directly addresses critical challenges in AI-for-Science, delivering immediate and quantifiable impact across key enterprise metrics.
Leading LLM Performance (Gemini-2.5-pro-preview)
Scientific Datasets Analyzed
LLMs Benchmarked
Core LLM Competency Dimensions
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Integrated Assessment for AI4Science Readiness
The SciHorizon framework integrates a comprehensive assessment of AI4Science readiness, addressing both scientific data and Large Language Models (LLMs). It encompasses two main components: scientific data assessment and LLM assessment. This holistic approach ensures that AI applications in scientific discovery are supported by high-quality, AI-ready data and robust, capable models. The framework is designed to be generalizable, making it applicable across various scientific disciplines and research contexts.
By providing a unified and systematic approach, SciHorizon helps researchers and developers identify strengths, pinpoint areas for improvement, and accelerate the development and deployment of AI-driven scientific solutions. This integrated perspective is crucial for advancing AI4Science, ensuring that models are not only powerful but also reliable and aligned with scientific research values.
Assessing AI-Ready Scientific Data
Our framework for scientific data assessment is built upon four principal dimensions: Quality, FAIRness, Explainability, and Compliance. These dimensions are further subdivided into 15 specific sub-dimensions, providing a granular and comprehensive evaluation of data readiness for AI applications in science.
Quality ensures data accuracy, completeness, consistency, and timeliness. FAIRness (Findable, Accessible, Interoperable, Reusable) principles are operationalized through recommended identifiers, vocabularies, formats, and standards. Explainability focuses on diversity, unbias, domain applicability, and task applicability, crucial for model transparency and interpretability. Compliance addresses provenance, ethics, safety, and trustworthiness, ensuring responsible AI deployment. This rigorous assessment helps identify high-potential datasets that can drive significant advancements in AI-driven scientific discovery.
Evaluating LLM Capabilities in Science
The LLM assessment component of SciHorizon evaluates models across five core competencies: Knowledge, Understanding, Reasoning, Multimodality, and Values. These are broken down into 16 specific sub-dimensions to provide a fine-grained analysis of LLM capabilities in scientific contexts.
Knowledge evaluates factual accuracy, robustness, externalization, and helpfulness. Understanding assesses comprehension of scientific facts and concepts. Reasoning measures numerical and deductive problem-solving. Multimodality focuses on interpreting scientific charts and multimodal content. Finally, Values assesses adherence to ethical guidelines, academic integrity, and responsible AI principles, ensuring LLMs operate within established scientific norms. This comprehensive evaluation helps identify the most suitable LLMs for various AI4Science applications.
SciHorizon LLM Performance (Gemini-2.5-pro-preview Overall Score)
Case Study: DeepSeek-R1's Strong Performance
The Chinese open-source model DeepSeek-R1 ranks third overall (71.68%), showcasing consistent and competitive results across disciplines. Notably, it secures second place in Chemistry (74.96%) and Earth Sciences (75.40%). DeepSeek-R1 also excels in Reasoning and Values, indicating robust logical inference and ethical alignment. This highlights its potential as a strong domestic alternative with broad general-purpose scientific capabilities, particularly for applications requiring high performance in specific scientific domains.
| LLM Model | Key Strengths in AI4Science (based on SciHorizon) |
|---|---|
| Gemini-2.5-pro-preview |
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| Gemini-2.5-flash-preview-thinking |
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| DeepSeek-R1 |
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| Claude-3.7-sonnet-thinking |
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Gemini-2.5-pro-preview Reasoning Score
Enterprise Process Flow
Identify Key Scientific Challenges
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Assess Data Readiness with SciHorizon
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Benchmark LLMs for Task Suitability
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Tailored AI Model Deployment
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Continuous Monitoring & Optimization
Case Study: Advancing Earth Sciences with SciHorizon Data Recommendations
SciHorizon identified reusable scientific data products in Earth Science, like the China Meteorological Forcing Dataset (CMFD), as foundational for AI applications. These datasets integrate multi-source data, offering long-term temporal sequences, extensive spatial coverage, diverse features, and rich semantic content, highly compatible with AI models. CMFD's strong FAIRness score (4.66) and domain applicability (4.38) ensure its usability and potential for advanced AI applications, demonstrating how SciHorizon's data assessment directly guides impactful scientific advancements.
Gemini-2.5-Pro-Preview Multimodality Score
Case Study: Value Alignment in Claude-3.7-sonnet-thinking
Claude-3.7-sonnet-thinking achieved the highest overall Values score (69.90%) in our benchmark, leading in Mathematics (71.05%) and Physics (69.67%). This demonstrates its strong alignment with ethical guidelines and responsible AI principles across scientific disciplines. In contrast to other high-performing models like Gemini-2.5-pro-preview which had a lower value score, Claude-3.7-sonnet-thinking exemplifies an LLM that not only generates accurate outputs but also upholds integrity, fairness, and social responsibility in its scientific applications, making it suitable for sensitive research areas.
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Your AI Implementation Timeline
Our proven methodology ensures a smooth, efficient, and impactful AI integration. Here's what you can expect:
Phase 01: Initial Assessment & Strategy Alignment
Conduct a deep dive into your existing data infrastructure and current AI models. Align on strategic objectives for AI4Science integration and customize SciHorizon benchmarks to your specific domain needs.
Phase 02: Data Readiness Audit & Enhancement
Execute a comprehensive audit of scientific datasets using SciHorizon's Quality, FAIRness, Explainability, and Compliance dimensions. Identify gaps and implement best practices for data curation, preparation, and AI-ready formatting.
Phase 03: LLM Benchmarking & Selection
Benchmark leading open-source and closed-source LLMs against your scientific tasks across SciHorizon's Knowledge, Understanding, Reasoning, Multimodality, and Values dimensions. Select optimal models for your specific AI4Science applications.
Phase 04: Pilot Implementation & Iteration
Deploy selected AI models with AI-ready data in a pilot environment. Collect feedback, refine model performance, and iteratively enhance data pipelines and model capabilities based on real-world scientific workflows.
Phase 05: Full-Scale Integration & Monitoring
Roll out SciHorizon-driven AI solutions across your enterprise. Establish continuous monitoring systems for data quality, model performance, and ethical compliance. Provide ongoing support and optimization to ensure sustained impact.
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