SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data
arXiv:2604.26645v2 Announce Type: replace-cross Abstract: AI-for-Science (AI4Science) is increasingly transforming scientific discovery by embedding machine learning models into prediction, simulation, and hypothesis generation workflows across domains. However, the effectiveness of these models is fundamentally constrained by the AI-readiness of scientific data, for which no scalable and systematic evaluation mechanism currently exists. In this work, we propose SciHorizon-DataEVA, a novel agentic system to scalable AI-readiness evaluation of heterogeneous scientific data. At the evaluation-cr
The proliferation of AI in scientific research (AI4Science) necessitates robust and scalable methods to evaluate the quality and readiness of scientific data for AI applications.
Ensuring the AI-readiness of scientific data is critical for the effective and reliable deployment of machine learning in scientific discovery and for maximizing the return on investment in AI4Science initiatives.
The introduction of agentic systems like SciHorizon-DataEVA provides a systematic and scalable mechanism for assessing data quality for AI, potentially accelerating scientific breakthroughs and standardizing data practices.
- · AI4Science researchers
- · Data scientists
- · Scientific research institutions
- · Machine learning model developers
- · Researchers with poorly structured data
- · Traditional data validation methods
Improved efficiency and accuracy of AI models applied to scientific problems by ensuring higher quality input data.
Faster scientific discovery cycles due to more reliable AI-driven hypothesis generation and simulation.
The emergence of new data infrastructure and governance standards specifically designed for AI-driven scientific research.
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