UC Berkeley

Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. We introduce the Radiological Anomaly Detection and Identification (RADAI) dataset, a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and it provides list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. We publicly release three complementary datasets for this purpose (training, developer, and testing) together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning (ML). By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.