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.

项目摘要/摘要 该提议提交给补充授予R01 LM013498-01，“元数据元素 - 综合 使生物医学数据公平的工具。 在线数据集中的元数据使相应的数据可访问，可访问，可互操作和可重复使用， 因此“准备就绪。”父母赠款的目标是改变注释实验的元数据 在线数据集以符合正式报告指南的表格，并使用标准的术语 NIH存储库的本体和常见数据元素。该研究取决于称为 Cedar，管理一个与定义的报告指南相对应的元数据模板库 标准元数据描述中的预期属性 - 值对。元数据使用这些 雪松元数据模板建议可能是标准报告指南的元素 科学家在撰写元数据时经常使用的特质条目的意图。雪松 技术虽然已被广泛使用且极其成功，但已经7岁了，需要现代化。 对雪松的增强功能将为父母赠款带来明显的好处。 Cedar使用其元数据模板库来协助科学家在创作新的元数据来描述新的元数据时协助他们 他们的实验产生的数据集。该系统确保新元数据遵守 尽可能适当的标准。雪松计划作为基于云的数据的一部分包括在内 NIH RADX程序的枢纽，该计划支持在诊断测试领域进行广泛研究 新冠肺炎。不幸的是，雪松还没有准备好云。如果雪松是在增强中发挥最佳作用 NIH RADX数据的AI精加工，那么理想情况下是必要的。促进雪松的角色 在创建AI-Ready数据集中 微服务，通过使这些微服务可发现和可观察到，并通过迁移整个 到云的系统，（2）我们将使Cedar成为易于维护和的高度可用系统 进化;我们将利用新方法来简化和增强系统的体系结构，并 首次设计系统时，我们无法使用的组件。结果，雪松将是 更可扩展，可维护和可部署。新架构将推动AI的应用 技术不仅对RADX数据，而且对NIH重要的数据集。