Cyber-Physical Systems are distributed, heterogeneous, decentralized and loosely coupled networks in which individual systems measure physical processes, exchange information, and influence processes. Sensors measure these physical processes, while aggregators process them and actuators perform resulting actions. Decisions are often based on sensor data collected by other systems. Furthermore, the aggregators also interchange information and use them to derive own decisions. Decisions must be comprehensible. However, this is only the case if all data dependencies are known. Due to the size of these networks, their loose coupling and their dynamic behavior, decisions made by a system are not always easy to understand. If an error occurs in the system, the error source must be identified. It must be known on which data a decision was based. However, since the decision can be based on information from other nodes, the search for the error source is not a trivial task. Keep in mind, that dependent nodes can have dependencies themselves as well. We present the Information Flow Monitor (IFM) that collects information about semantic data dependencies in dynamic networks. The collected dependency information is provided at a central network location. Subsequently, semantic dependencies between information can be visualized.