Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial IoT. The technology is mature and the scientific literature contains multiple datasets and methods for localization based on fixed UWB nodes. At the same time, research in UWB-based relative localization and infrastructure-free localization is gaining traction, further domains. tools and datasets in this domain are scarce. Therefore, we introduce in this paper a novel dataset for benchmarking infrastructure-free relative localization targeting the domain of multi-robot systems. Compared to previous datasets, we analyze the performance of different relative localization approaches for a much wider variety of scenarios with varying numbers of fixed and mobile nodes. A motion capture system provides ground truth data, are multi-modal and include inertial or odometry measurements for benchmarking sensor fusion methods. Additionally, the dataset contains measurements of ranging accuracy based on the relative orientation of antennas and a comprehensive set of measurements for ranging between a single pair of nodes. Our experimental analysis shows that high accuracy can be localization, but the variability of the ranging error is significant across different settings and setups.