RADx-Rad Discoveries & Data: Consortium Coordination Center Program Organization

RADx-Rad 发现

基本信息

批准号：
10321007
负责人：
Eliah S Aronoff-Spencer
金额：
$ 584.89万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-21 至 2022-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10321007
关键词：
Agreement Antibodies Area Award Back Benchmarking Biological Assay COVID-19 COVID-19 diagnostic COVID-19 pandemic COVID-19 testing Calibration California Clinical Pathology Clinical Research Cloud Computing Cloud Service Collaborations Communicable Diseases Communication Communities Computer software Consultations Data Data Collection Data Coordinating Center Data Discovery Data Science Data Scientist Data Sources Development Diagnostic Diagnostic tests Ensure Evaluation FAIR principles FDA approved Funding Goals Gold Health Insurance Portability and Accountability Act Health Sciences Informatics Internet Knowledge Laboratories Logistics Metadata Methods Online Systems Persons Positioning Attribute Preparation Privacy Procedures Process Protocols documentation Quality Control RADx RADx Radical Reporting Research Personnel Resources Risk Role SARS-CoV-2 infection Sampling Science Secure Security Semantics Signal Transduction Specialist Specimen Standardization Structure Technology Terminology Testing Texas Time United States National Institutes of Health Universities Update Validation Viral Viral Load result Virus Work base dashboard data access data centers data exchange data management data modeling data reuse data sharing data standards design diagnostic technologies distributed data expectation experience help-seeking behavior indexing innovation meetings member new technology news novel diagnostics programs quality assurance repository search engine syntax testing services tool transmission process user centered design user-friendly virology

项目摘要

ABSTRACT Preparing SARS-CoV-2 testing data for reuse requires making the data syntactically and semantically equivalent. Standardization of terminologies and a common data model accomplish the former, while the latter is accomplished through understanding the data and making it comparable across RADx-rad awardees by benchmarking against known gold standards. The standardization of samples is as important as standardizing the data, particularly in the highly innovative RADx-rad program, where new technologies will be developed or optimized for deployment in various settings. Highly motivated RADx-rad awardees will receive advice on how their diagnostics compare to FDA-approved ones, with each other, how their diagnostic performs in independent testing, as well as how to ensure the tests are usable in real world settings. In collaboration with University of Texas Health Science Center at Houston, University of California San Diego researchers in informatics/data science and infectious diseases with ample experience in leading large consortia have designed a unique RADx- rad Consortium Data and Coordination Center (radCDCC). This center is based on three pillars: (1) effective administration and coordination among awardees, NIH, and other programs;; (2) innovative approaches and tools to collect and standardize data and metadata to promote findability, accessibility, interoperability and reuse (FAIR) for data sharing;; and (3) principled preparation of standardized samples with known quantities of viral loads, and standardized procedures for testing new diagnostics to allow comparison across tests and calibration of new technologies. Backed by sophisticated HIPAA-compliant cloud services, user friendly web-tools, and extensive support from UCSD’s facilities for computation and for clinical research, the radCDCC will interface with other RADx programs and other COVID-19 focused programs at NIH to ensure alignment of awardees, NIH and the public in the pursuit of effective, affordable, and deployable new technologies for testing.

抽象的准备 SARS-CoV-2 测试数据以供重复使用需要使数据在语法和语义上等效。术语标准化和通用数据模型实现了前者，而后者则是通过理解数据并使其在 RADx-rad 获奖者之间进行比较来完成根据已知的黄金标准进行基准测试与标准化一样重要。数据，特别是高度创新的 RADx-rad 计划，其中将开发新技术或针对各种环境中的部署进行了优化，积极主动的 RADx-rad 奖项将收到有关如何进行的建议。他们的诊断与 FDA 批准的诊断进行比较，以及他们的诊断如何独立进行。测试，以及如何确保测试在现实世界中可用。休斯顿德克萨斯健康科学中心、加州大学圣地亚哥分校信息学/数据研究人员科学和传染病领域拥有丰富的经验，领导大型财团设计了独特的 RADx- rad 联盟数据和协调中心 (radCDCC) 该中心基于三个支柱：(1) 有效。获奖者、NIH 和其他项目之间的管理和协调；；(2) 创新方法和工具收集和标准化数据和元数据，以促进可查找性、可访问性、互操作性和重用 (FAIR) 用于数据共享；以及 (3) 已知病毒数量的标准化样品的原则性制备负载和用于测试新诊断的标准化程序，以便在测试和校准之间进行比较由先进的符合 HIPAA 的云服务、用户友好的网络工具和在 UCSD 计算和临床研究设施的广泛支持下，radCDCC 将与与 NIH 的其他 RADx 项目和其他以 COVID-19 为重点的项目合作，以确保奖项的一致性，NIH 以及公众追求有效、负担得起且可部署的新技术进行测试。