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