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

准备SARS-CoV-2测试数据以供重用需要使数据在语法和语义上等效。 术语的标准化和通用数据模型实现了前者，而后者是 通过理解数据并使其在RADx-rad获奖者之间具有可比性， 以已知的黄金标准为基准。样品的标准化与标准化同样重要 数据，特别是在高度创新的RADx-rad计划中，将开发新技术， 针对各种设置进行了优化。高度积极的RADx-rad获奖者将获得关于如何 他们的诊断与FDA批准的诊断进行比较，相互比较，他们的诊断在独立的 测试，以及如何确保测试在真实的世界设置中可用。与德克萨斯大学休斯顿健康科学中心合作，加州圣地亚哥大学的信息学/数据科学和传染病研究人员在领先的大型财团中拥有丰富的经验，他们设计了一个独特的RADx-rad财团数据和协调中心（radCDCC）。该中心基于三个支柱：（1）获奖者，NIH和其他计划之间的有效管理和协调;（2）创新方法和工具 收集数据和元数据并使之标准化，以促进数据的可查找性、可获取性、互操作性和重复使用 （3）原则性地制备具有已知数量病毒的标准化样品， 测试新诊断程序的标准化程序，以便在测试和校准之间进行比较 of new新technologies技术.以先进的符合HIPAA标准的云服务、用户友好的Web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NIH的其他RADx项目和其他以COVID-19为重点的项目合作，以确保获奖者的一致性，NIH 和公众在追求有效的，负担得起的，可部署的新技术进行测试

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