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Next-generation mobile communication systems are planned to support millimeter Wave (mmWave) transmission in scenarios with high-mobility, such as in private industrial networks. To cope with propagation environments with unprecedented challenges, data-driven methodologies such as Machine Learning (ML) are expected to act as a fundamental tool for decision support in future mobile systems. However, high-quality measurement datasets need to be made available to the research community in order to develop and benchmark ML-based methodologies for next-generation wireless networks. We present a reliable testbed for collecting channel measurements at sub-6 GHz and mmWave frequencies. Further, we describe a rich dataset collected using the presented testbed. Our public dataset enables the development and testing of innovative ML-based channel simulators for both sub-6GHz and mmWave …