Empirical Validation of GA4GH's Data Use Ontology with NIH Datasets' Data Use Limitations

GA4GH 数据使用本体与 NIH 数据集数据使用限制的实证验证

• 批准号: 10367297
• 负责人: HEIDI L REHM
• 金额: $ 10.56万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-03 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367297
• 关键词: Address; Administrator; Adopted; Advocacy; Algorithms; Automation; Biomedical Research; Catalogs; Code; Communication; Consent; Data; Data Set; Decision Support Systems; Ensure; Environment; Ethics; Evaluation; Feedback; Funding; Future; Genomics; Goals; Grant; Health; Institutes; Language; Leadership; Legal; Maps; Ontology; Participant; Policies; Positioning Attribute; Publications; Readability; Recommendation; Regulation; Research; Research Personnel; Resources; Science Policy; Stream; System; Testing; Time; Triage; United States National Institutes of Health; Update; Validation; Vocabulary; Work; base; data access; data repository; data sharing; data standards; data submission; database of Genotypes and Phenotypes; design; evidence base; federated computing; genomic data; health data; implementation tool; member; parity; programs; repository; response

Project Summary The goal of this project is to validate the Global Alliance for Genomics and Health (GA4GH) Data Use Ontology (DUO) using >700 NIH datasets to inform its use in scaling data sharing. The DUO was developed to provide a standard vocabulary for describing permitted secondary uses of genomic data. By standardizing data use language globally, the DUO expedites data sharing compliant with participants’ consent and ethical and legal regulations. As the largest public funder of biomedical research in the world NIH’s database of Genotypes and Phenotypes (dbGaP) holds over 7,582 datasets on over 330,000 variables available via dbGaP as of March 2021; making the NIH-funded datasets housed in dbGaP an ideal test case for the validation of the DUO. In this project we will work with NIH data access committees (DACs) to align their datasets’ existing data use limitations (DULs) with data use terms from the DUO. As a result, we will identify which DULs and DUO terms have parity and where gaps exist between the two, and discuss if and how to address the gaps with DACs. We aim to summarize these key findings and recommendations in a formal publication, following on the initial DUO validation of 120 datasets at the Broad Institute, and via a communication to GA4GH Data Use & Research Identities work stream leadership (of which Mr Lawson is a member) for their consideration in evolving the DUO. Thereafter, we will advance discussions with the NIH DACs to ensure all NIH DULs can be mapped to machine-readable term(s) in the DUO. Once completed through either adjustments to the DUO, the NIH DULs, or both, we will tag all datasets with the appropriate DUO terms in the Broad Institute’s Data Use Oversight System (DUOS) Dataset Catalog. The DUOS Dataset Catalog will be upgraded to query datasets’ by their permitted data use (e.g., DULs) so that researchers are able filter out datasets they likely will not be approved to access. The DUOS’ decision-support algorithm will match the DUO terms on access requests to the DUO terms on the dbGaP datasets. DUOS will use this to automatically triage requests and propose a decision to grant access or not that can easily be reviewed by DACs. With the DUL inputs accurately calibrated and their functionality enabled in DUOS, the NIH DACs and Office of Science Policy would be well-positioned to codify their DAR review policy into DUOS’ decision support algorithm. This would enable automated decisions on DARs for typical requests, and significantly reduce the burden on DACs and DAR turnaround time to researchers.

项目摘要 该项目的目标是使用本体论验证全球基因组学与健康联盟(GA4GH)数据 (二)使用&gt；700 NIH数据集通知其在扩展数据共享方面的使用。二人组的开发旨在提供一种 描述允许的基因组数据二次使用的标准词汇。通过标准化数据使用 在全球范围内，两人加快了符合参与者同意以及伦理和法律的数据共享 规章制度。作为世界上最大的生物医学研究公共资助者，美国国立卫生研究院的基因分型和 截至3月，表型(DBGaP)拥有超过7,582个数据集，涉及超过330,000个变量 2021年；使保存在DBGaP中的NIH资助的数据集成为验证这两个人的理想测试用例。在……里面 这个项目我们将与NIH数据访问委员会(DAC)合作，以调整其数据集的现有数据使用 数据限制(DURL)使用二人组的术语。因此，我们将确定哪些DURL和DUO术语 进行奇偶校验以及两者之间存在差距的地方，并讨论是否以及如何通过DAC解决差距。我们 我的目标是在最初的两人组的基础上，在正式出版物中总结这些关键的发现和建议 在布罗德研究所验证了120个数据集，并通过与GA4GH数据使用和研究的通信进行了验证 身份工作流领导力(劳森先生是其中的成员)，供他们在发展 二人组。此后，我们将推进与NIH DAC的讨论，以确保所有NIH DUL都可以映射到 二人组中的机器可读术语(S)。一旦通过对二人组，NIH DUL的任何一种调整完成， 或者两者兼而有之，我们将在布罗德研究所的数据使用监督中使用适当的DUO术语标记所有数据集 系统(DUOS)数据集目录。DUOS数据集目录将升级为查询数据集 允许使用数据(例如，DURL)，以便研究人员能够过滤出他们可能不会获得批准的数据集 才能访问。Duo的决策支持算法将Duo的访问请求术语与Duo匹配 有关DBGaP数据集的术语。Duos将利用这一点自动对请求进行分类并提出决策 授予或不授予DAC可以轻松审查的访问权限。DUL输入经过精确校准，并且其 在DUOS中启用的功能，NIH DAC和科学政策办公室将处于有利地位，可以将 他们的DAR审查政策纳入了Duos的决策支持算法。这将使自动决策成为可能 DAR用于典型请求，并显著减少DAC的负担和DAR周转时间，以 研究人员。