Transportation research increasingly depends on heterogeneous data from roadside cameras, UAV imagery, LiDAR, and in-vehicle sensors. Differences in data representations, model interfaces, and evaluation protocols complicate cross-dataset benchmarking and reproducibility. We present Ozone, a unified transportation research platform organized around Hardware, Data, Model, and Evaluation layers, with a Prototype layer supporting deployment-oriented applications. Its data layer links vehicle trajectories, traffic conflicts, reconstructed crashes, and digital-twin maps through a shared trajectory schema. The source inventory spans 19 trajectory datasets recorded at 59 sites in eight countries, with completed converters for 12 sources and adaptation ongoing for seven. The converters align coordinate systems, object classes, oriented bounding boxes, and kinematic fields. A shared implementation of surrogate safety measures supports conflict extraction, while map and motion constraints link crash reports to reconstructed trajectories. CARLA digital-twin maps and SUMO traffic logic connect these records to simulation, model development, and evaluation. Validation on 500 vehicles from four datasets reports mean position, heading, and speed errors below 0.15 m, 2.5°, and 0.3 m/s. A workflow study reports an 85 % reduction in experiment setup time. Case studies demonstrate human-factor experiments and automated-driving safety testing, while the model interfaces support traffic modeling and world-model research. The source code and datasets are publicly available.

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