As Research and Engineering (R&E) networks have evolved to support an array of data-driven science, they have become highly converged and inter-dependent with the wider Internet. This convergence has resulted in remarkable efficiencies, data and resource sharing, and distributed collaborative research, but has also imparted network complexity and dependencies. Because R&E networks carry critical science traffic, provide critical services, and enable production workflows, hidden dependencies can result in correlated failures and security vulnerabilities. The “Cyberinfrastructure Robustness Understanding through data eXtraction” (CRUX) project takes a data-driven approach to better understand the robustness of R&E networks. CRUX performs continuous, network measurement-based data collection and curation to enable studies of the robustness of modern converged R&E networks. The two primary thrusts of CRUX are: 1) innovative active and passive measurement techniques to map the R&E ecosystem and gather currently unavailable data; and 2) dissemination and active engagement to help the community utilize the datasets. Our collection methodology includes new techniques for discovering and cataloging R&E organizational resources, state-of-the-art active measurement techniques for rapid and rich connectivity mapping, passive measurements for attack and misconfiguration detection and characterization, and diverse meta-data. These disparate measurements are fused and augmented into