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

准备SARS-CoV-2检测数据以供重复使用需要使数据在语法和语义上等价。 术语标准化和通用数据模型实现了前者，而后者是 通过了解数据并使其在RADX-RAD获奖者中具有可比性来实现 对照已知的黄金标准进行标杆。样品的标准化与标准化同等重要 这些数据，特别是在高度创新的RADx-RAD方案中，将开发或 针对各种设置中的部署进行了优化。积极进取的RADx-rad奖获得者将收到关于如何 他们的诊断与FDA批准的诊断相互比较，他们的诊断是如何独立执行的 测试，以及如何确保测试在实际环境中可用。加州大学圣地亚哥分校与德克萨斯大学休斯顿健康科学中心合作，在信息学/数据科学和传染病领域的研究人员在领先的大型财团方面拥有丰富的经验，设计了一个独特的RADX-RAD联盟数据和协调中心(RadCDCC)。该中心以三大支柱为基础：(1)获奖者、国家卫生研究院和其他项目之间的有效管理和协调；(2)创新的方法和工具 收集和标准化数据和元数据，以促进可查找性、可访问性、互操作性和重复使用 (公平)数据共享；和(3)有原则地准备具有已知病毒数量的标准化样本 负载，以及测试新诊断的标准化程序，以允许在测试和校准之间进行比较 新技术的发展。以复杂的符合HIPAA标准的云服务、用户友好的网络工具和 在加州大学圣地亚哥分校计算和临床研究设施的广泛支持下，radCDCC将与 与美国国立卫生研究院的其他RADx计划和其他以新冠肺炎为重点的计划一起，以确保获奖者的一致性， 以及公众对有效、负担得起和可部署的测试新技术的追求

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