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SciHorizon-DataEVA: An Agentic System for AI-Readin... | AI Research

Key Takeaways

  • SciHorizon-DataEVA is an agentic system designed to evaluate the "AI-readiness" of scientific data.
  • 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.
  • Each dimension is decomposed into measurable atomic elements that enable fine-grained and executable assessment.
Paper AbstractExpand

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-criteria level, we introduce the Sci-TQA2 principles, which organize AI-readiness into four complementary dimensions: Governance Trustworthiness, Data Quality, AI Compatibility, and Scientific Adaptability. Each dimension is decomposed into measurable atomic elements that enable fine-grained and executable assessment. To operationalize these principles at scale, we develop Sci-TQA2-Eval, a hierarchical multi-agent evaluation approach orchestrated through a directed, cyclic workflow. Our Sci-TQA2-Eval dynamically constructs dataset-aware evaluation specifications by combining lightweight dataset profiling, applicability-aware metric activation, and knowledge-augmented planning grounded in domain constraints and dataset-paper signals. These specifications are executed through an adaptive, tool-centric evaluation mechanism with built-in verification and self-correction, enabling scalable and reliable assessment across heterogeneous scientific data. Extensive experiments on scientific datasets spanning multiple domains demonstrate the effectiveness and generality of SciHorizon-DataEVA for principled AI-readiness evaluation.

SciHorizon-DataEVA is an agentic system designed to evaluate the "AI-readiness" of scientific data. As machine learning becomes central to scientific discovery, the effectiveness of these models is often limited by the quality and structure of the underlying data. Currently, there is no systematic, scalable way to determine if a dataset is truly suitable for AI tasks. This system addresses that gap by providing a comprehensive, automated framework to assess whether scientific data is prepared for modern AI-driven research.

The Sci-TQA² Principles

To provide a structured evaluation, the researchers introduced the Sci-TQA² principles, which break down AI-readiness into four essential dimensions:

  • Governance Trustworthiness: Ensures data can be safely shared and reused by checking for proper licensing, ethical compliance, and provenance.

  • Data Quality: Assesses technical reliability, such as completeness, accuracy, and consistency, to ensure the data is ready for computational pipelines.

  • AI Compatibility: Evaluates whether the data structure and features are actually suitable for AI models, looking at factors like class balance and feature importance.

  • Scientific Adaptability: Determines if the data supports genuine scientific reasoning and discovery, rather than just statistical pattern matching, by checking for causal variables and coverage of experimental conditions.

A Hierarchical Multi-Agent Approach

The system operationalizes these principles through a process called Sci-TQA²-Eval. This approach uses a multi-agent workflow that functions like an automated expert:

  1. Profiling: A "Data Inspector" scans the dataset to understand its structure and format without needing to load the entire, potentially massive, file. 2. Planning: A "Knowledge-Augmented Planner" selects only the relevant metrics for that specific dataset, ensuring the evaluation is both efficient and domain-appropriate. 3. Execution: An adaptive tool-centric engine performs the actual assessment. If a specific tool is missing, the system can construct new routines to handle the task. 4. Verification: A feedback loop monitors the process, allowing the system to self-correct if it encounters errors or needs to refine its evaluation strategy.

Why This Matters for Science

Existing evaluation tools often struggle with the sheer diversity of scientific data, which ranges from genomic sequences and medical images to complex molecular graphs. Many current methods are static, manual, or limited to specific data types, making them difficult to scale as scientific repositories grow. By using an agentic, automated system, SciHorizon-DataEVA can handle heterogeneous data across different scientific disciplines. This allows researchers to move beyond ad-hoc data selection and ensures that the data fueling AI-for-Science workflows is robust, reliable, and scientifically valid.

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