  Collective perception has received considerable attention as a promising
approach to overcome occlusions and limited sensing ranges of vehicle-local
perception in autonomous driving. In order to develop and test novel collective
perception technologies, appropriate datasets are required. These datasets must
include not only different environmental conditions, as they strongly influence
the perception capabilities, but also a wide range of scenarios with different
road users as well as realistic sensor models. Therefore, we propose the
Synthetic COllective PErception (SCOPE) dataset. SCOPE is the first synthetic
multi-modal dataset that incorporates realistic camera and LiDAR models as well
as parameterized and physically accurate weather simulations for both sensor
types. The dataset contains 17,600 frames from over 40 diverse scenarios with
up to 24 collaborative agents, infrastructure sensors, and passive traffic,
including cyclists and pedestrians. In addition, recordings from two novel
digital-twin maps from Karlsruhe and T\"ubingen are included. The dataset is
available at https://ekut-es.github.io/scope
