Improved metadata authoring to enhance AI/ML readiness of associated datasets

改进元数据创作，以增强相关数据集的 AI/ML 准备情况

• 批准号: 10592638
• 负责人: Mark A Musen
• 金额: $ 27.45万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592638
• 关键词: 7 year old; Architecture; Area; Artificial Intelligence; Award; Big Data to Knowledge; COVID diagnostic; COVID-19 testing; Collaborations; Common Data Element; Computer software; Computers; Data; Data Set; Development; Diagnostic tests; Elements; Ensure; FAIR principles; Funding; Goals; Grant; Guidelines; Infrastructure; Institutes; Investigation; Libraries; Metadata; Methods; Modernization; North Carolina; Online Systems; Ontology; Play; Process; RADx; Readiness; Renaissance; Reporting; Research; Research Personnel; Resources; Rest; Role; Scientist; Secure; Standardization; Supplementation; System; Techniques; Technology; United States National Institutes of Health; Universities; Work; cloud based; data archive; data hub; data management; data repository; design; experimental study; improved; metadata standards; novel strategies; online repository; open source; parent grant; programs; repository; spelling; system architecture; tool; web-accessible

PROJECT SUMMARY/ABSTRACT This proposal is submitted to supplement grant R01 LM013498-01, “The Metadata Powerwash—Integrated tools to make biomedical data FAIR.” The parent grant proposes to study AI methods to standardize the metadata in online datasets to make the corresponding data findable, accessible, interoperable, and reusable, and thus “AI-ready.” The goal of the parent grant is to transform the metadata that annotate experimental datasets online to a form that adheres to formal reporting guidelines and that uses terms from standard ontologies and common data elements from NIH repositories. The research depends on technology known as CEDAR, which manages a library of metadata templates that correspond to reporting guidelines that define the expected attribute–value pairs in standard metadata descriptions. The Metadata Powerwash uses these CEDAR metadata templates to suggest what elements from standard reporting guidelines might have been intended by the idiosyncratic entries that scientists often use when they author metadata. The CEDAR technology, while widely used and extremely successful, is already 7 years old and in need of modernization. Enhancements to CEDAR will have obvious benefits to the parent grant. CEDAR uses its library of metadata templates to assist scientists when they author new metadata to describe the datasets that result from their experiments. The system ensures that the new metadata are adherent to appropriate standards whenever possible. CEDAR is slated to be included as part of the cloud-based Data Hub for the NIH RADx program, which supports a wide range of studies in the area of diagnostic testing for COVID-19. Unfortunately, CEDAR is not cloud-ready. Thus, if CEDAR is to play an optimal role in enhancing the AI-readiness of NIH RADx data, then ideally additional work is necessary. To advance the role of CEDAR in the creation of AI-ready datasets, (1) we will make CEDAR cloud-native by containerizing all CEDAR microservices, by making these microservices discoverable and observable, and by migrating the entire system to the cloud, and (2) we will make CEDAR a highly available system that is easy to maintain and evolve; we will simplify and enhance the system’s architecture, taking advantage of new approaches and components that were not available to us when the system was first designed. As a result, CEDAR will be much more scalable, maintainable, and deployable. The new architecture will advance the application of AI techniques not only to RADx data, but also to a wide range of datasets of importance to the NIH.

项目概要/摘要 该提案提交给补充赠款 R01 LM013498-01，“元数据 Powerwash — 集成 使生物医学数据变得公平的工具。”家长资助建议研究人工智能方法来标准化生物医学数据。 在线数据集中的元数据，使相应的数据可查找、可访问、可互操作和可重用， 因此，“AI 就绪”。家长资助的目标是转变注释实验的元数据。 将数据集在线转换为符合正式报告指南并使用标准术语的形式 该研究依赖于来自 NIH 存储库的本体论和通用数据元素。 CEDAR，管理元数据模板库，这些模板对应于定义 标准元数据描述中的预期属性值对元数据 Powerwash 使用这些。 CEDAR 元数据模板，用于建议标准报告指南中的哪些元素可能是 科学家在创作元数据时经常使用的特殊条目。 该技术虽然被广泛使用并且非常成功，但已经有 7 年历史了，需要现代化。 CEDAR 的增强将为家长补助金带来明显的好处。 CEDAR 使用其元数据模板库来帮助科学家创作新的元数据来描述 系统确保新的元数据符合实验结果的数据集。 CEDAR 预计将尽可能包含在基于云的数据中。 NIH RADx 计划的中心，支持诊断测试领域的广泛研究 不幸的是，CEDAR 尚未准备好应对新冠肺炎 (COVID-19) 云，因此，如果 CEDAR 要在增强功能方面发挥最佳作用。 NIH RADx 数据的人工智能准备情况，那么理想情况下需要进行额外的工作来推进 CEDAR 的作用。 在创建 AI 就绪数据集时，(1) 我们将通过容器化所有 CEDAR 来使 CEDAR 云原生 微服务，通过使这些微服务可发现和可观察，并迁移整个微服务 （2）我们将把CEDAR打造成一个易于维护和维护的高可用系统 发展；我们将利用新的方法和技术来简化和增强系统的架构 系统最初设计时我们无法使用的组件因此，CEDAR 将被使用。 新架构更具可扩展性、可维护性和可部署性，将推动人工智能的应用。 技术不仅适用于 RADx 数据，还适用于对 NIH 重要的各种数据集。