用AI+人工协作，快速构建材料科学元数据词汇表。
Human-in-the-Loop and AI: Crowdsourcing Metadata Vocabulary for Materials Science
- 结合AI生成与人工反馈，迭代优化术语定义。
- 6人参与，生成19条有效定义，验证方法可行性。
- 适合需要快速标准化的跨学科科研团队使用。
元数据词汇表对推动开放科学和数据可发现、可访问、可互操作、可重用（FAIR）原则至关重要，但其发展受限于人力不足和标准不统一。本文提出MatSci-YAMZ平台，融合人工智能与人类在环（HILT）机制，包括众包方式，支持元数据词汇开发。通过一个概念验证案例，在高度跨学科的材料科学领域中，六位隶属于美国国家科学基金会数据驱动动态设计研究所（ID4）的研究人员持续数周参与平台工作，贡献术语定义并提供示例以促进AI生成定义的优化。共成功生成19条AI定义，通过多次反馈循环验证了AI-HILT方法的可行性。研究结果表明该模型具备：1）成功的概念验证；2）符合FAIR与开放科学原则；3）可推广的研究规程；4）跨领域扩展潜力。总体而言，MatSci-YAMZ所依赖的模型能够提升语义透明度，缩短共识达成与元数据词汇开发时间。
原文摘要 · Abstract (English)
Metadata vocabularies are essential for advancing FAIR and FARR data principles, but their development constrained by limited human resources and inconsistent standardization practices. This paper introduces MatSci-YAMZ, a platform that integrates artificial intelligence (AI) and human-in-the-loop (HILT), including crowdsourcing, to support metadata vocabulary development. The paper reports on a proof-of-concept use case evaluating the AI-HILT model in materials science, a highly interdisciplinary domain Six (6) participants affiliated with the NSF Institute for Data-Driven Dynamical Design (ID4) engaged with the MatSci-YAMZ plaform over several weeks, contributing term definitions and providing examples to prompt the AI-definitions refinement. Nineteen (19) AI-generated definitions were successfully created, with iterative feedback loops demonstrating the feasibility of AI-HILT refinement. Findings confirm the feasibility AI-HILT model highlighting 1) a successful proof of concept, 2) alignment with FAIR and open-science principles, 3) a research protocol to guide future studies, and 4) the potential for scalability across domains. Overall, MatSci-YAMZ's underlying model has the capacity to enhance semantic transparency and reduce time required for consensus building and metadata vocabulary development.
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