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We introduce the UZH-FPV Drone Racing dataset, which is the most aggressive visual-inertial odometry dataset to date. Large accelerations, rotations, and apparent motion in vision sensors make aggressive trajectories difficult for state estimation. These sequences were recorded with a first-person-view (FPV) drone racing quadrotor fitted with sensors and flown aggressively by an expert pilot. The trajectories include fast laps around a racetrack with drone racing gates, as well as free-form trajectories around obstacles, both indoor and out. We present the camera images and IMU data from a Qualcomm Snapdragon Flight board, ground truth from a Leica Nova MS60 laser tracker, as well as event data from an mDAVIS 346 event camera, and high-resolution RGB images from the pilot's FPV camera. With this dataset, our goal is to help advance the state of the art in high speed state estimation.