Robust localization is crucial for numerous applications including autonomous navigation, emergency response, and location-based services, particularly in environments where traditional positioning systems face limitations. While signals of opportunity (SoO) from multiple systems offer promising solutions, the lack of comprehensive multimodal datasets and unified fusion frameworks hinders the development of reliable hybrid localization. This paper addresses these challenges by presenting two main contributions: First, we introduce an open dataset comprising synchronized measurements from GNSS, WiFi, Ultra-Wideband (UWB), and Bluetooth Low Energy (BLE) ranging systems. The data were collected via self-surveying in an industrial facility characterized by indoor-outdoor transitions. Second, we demonstrate the practical application of this dataset by adapting a grid filter for multi-system fusion, providing a proof-of-concept state estimation solution. Our experimental results highlight how different positioning systems can effectively compensate for each other's weaknesses, particularly during challenging indoor-outdoor transitions.