AI助手帮科学家从海量数据中快速提取知识并生成代码。
From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data
- 设计专用AI代理系统，整合科研环境与工具链。
- 用户实测验证其在知识检索与代码生成上的有效性。
- 适合需要高效处理复杂数据的科研团队使用。
欧洲X射线自由电子激光设施（European XFEL）的科学家面临海量复杂数据的分析挑战，需结合领域知识与特定设施和软件的分散信息。为此，我们采用设计科学方法，通过文献综述、16款AI工具的系统评估、多轮访谈、焦点小组及专家用户研究，开发并评估了两个原型系统。研究识别出科学数据分析中的关键知识障碍，提炼出支持知识检索与源代码生成的AI代理需求，并提出可适应不断演进的AI工具生态的系统设计建议。这些发现为高度专业化的科研环境中构建可持续的AI支持提供了指导。
原文摘要 · Abstract (English)
Scientists at European XFEL conduct experiments that generate very large and complex datasets. The subsequent data analysis is challenging as scientists must combine their domain expertise with facility- and software-specific knowledge scattered across documentation, tools, and support channels. To address this problem, we designed and evaluated an agentic AI system tailored to the scientists' needs and integrated with the high-performance computing environment of European XFEL. Using a design science research approach, we conducted a rapid literature review, a systematic evaluation of 16 AI tools, multiple interviews, a focus group, and a user study with experts at European XFEL to develop and evaluate two prototypes. Our study identifies key knowledge challenges in scientific data analysis, derives requirements for an AI agent that supports knowledge retrieval and source code generation, and proposes design recommendations for a specialized system adaptable to the evolving AI tool landscape. These findings provide guidance for developing maintainable AI support in highly specialized scientific environments.
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