The Metadata Powerwash - Integrated tools to make biomedical data FAIR

Metadata Powerwash - 使生物医学数据公平的集成工具

• 批准号: 10093841
• 负责人: Mark A Musen
• 金额: $ 33.48万
• 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/10093841
• 关键词: Age; Biological Specimen Banks; Categories; Collection; Common Data Element; Communities; Computers; Data; Data Science; Data Set; Disease; FAIR principles; Funding Agency; Goals; Gold; Information Technology; Knowledge; Libraries; Link; Manuals; Metadata; Methods; Names; Natural Language Processing; Numerical value; Ontology; Pharmaceutical Preparations; Problem Solving; Process; Records; Reporting; Reproducibility; Research; Research Personnel; Resources; Retrieval; Sampling; Science; Scientist; Specific qualifier value; Speed; Standardization; Structure; Technology; Testing; Time; Variant; Work; base; biomedical scientist; data archive; data repository; data reuse; experimental study; improved; indexing; information organization; interoperability; metadata standards; public repository; repository; sample collection; search engine; secondary analysis; tool

Project Summary The metadata that describe scientific data are fundamental resources to enable (1) the discovery and reuse of the data and (2) the reproducibility of the experiments that generated the data in the first place. Metadata are essential for scientists to understand the associated data and to reuse them, as well as for information technology to index the data, to make the data available, and to provide filters for scientists to search for the corresponding datasets. Currently, the scientific metadata hosted in public repositories suffer from multiple quality issues that limit scientists’ ability to find and reuse the experimental datasets to which they refer. It can take many weeks of a scientist’s time to identify a collection of datasets that fulfill specific criteria when the data are so poorly described—and the majority of the process is necessarily manual. We propose to develop an end-to-end solution to standardize biomedical metadata with the help of ontologies—data structures that define the terms in an application domain and the relationships among them. There are hundreds of ontologies that provide standard terms for use in biomedicine, and they are essential resources to make biomedical metadata interoperable and reusable. Our approach also will build on the technology created by the Center for Expanded Data Annotation and Retrieval (CEDAR), which offers a library of building blocks and common data elements for defining computer-based metadata templates based on community standards. Our plan involves three specific aims. First, we will develop a method and tool to standardize the multiple, ad hoc metadata field names that may appear in metadata to represent the same type of information by replacing those field names with the field names used in standard metadata templates or, if no appropriate template match is available, with terms from a relevant ontology. Second, we will develop methods and tools to standardize different types of metadata field values, for example, categorical values such as drugs or diseases, and numerical values such as age, or sample collection date. Third, we will evaluate the speed, precision, and recall of our metadata transformation pipeline—built out of the methods and tools to standardize field names and values—on a large corpus of metadata that we will manually curate based on existing public metadata. We will also carry out experiments to test the effect of the standardized metadata when biomedical scientists perform dataset search in the context of their work.

项目概要 描述科学数据的元数据是实现 (1) 数据的发现和重用以及（2）生成数据的实验的可重复性 首先，元数据对于科学家理解相关数据至关重要。 并重用它们，以及信息技术来索引数据，使数据 可用，并为科学家提供过滤器来搜索相应的数据集。 目前，公共存储库中托管的科学元数据存在多种质量问题 这限制了科学家查找和重复使用他们所参考的实验数据集的能力。 科学家需要花费数周的时间来识别满足特定要求的数据集集合 当数据描述如此糟糕时的标准——并且大部分过程必然是 手动的。 我们建议开发一个端到端的解决方案来标准化生物医学元数据 本体的帮助——定义应用程序域中的术语和 它们之间的关系有数百个本体，为它们提供了标准术语。 用于生物医学，它们是制作生物医学元数据的重要资源 我们的方法也将建立在互操作性和可重用性的技术之上。 扩展数据注释和检索中心 (CEDAR)，提供建筑图书馆 用于定义基于计算机的元数据模板的块和通用数据元素 社区标准。 我们的计划涉及三个具体目标，首先，我们将开发一种标准化方法和工具。 可能出现在元数据中表示相同内容的多个临时元数据字段名称 通过将这些字段名称替换为标准中使用的字段名称来确定信息类型 元数据模板，或者，如果没有合适的模板匹配可用，则使用相关术语 其次，我们将开发标准化不同类型元数据的方法和工具。 字段值，例如药物或疾病等分类值以及数值 例如年龄或样本采集日期第三，我们将评估速度、精确度和召回率。 我们的元数据转换管道 - 由标准化领域的方法和工具构建 名称和值——基于我们将手动管理的大量元数据 现有的公共元数据我们也会进行实验来测试效果。 当生物医学科学家在他们的背景下进行数据集搜索时标准化元数据 工作。