This dataset contains results represented in the work titled 'A Robust, Over-the-Air Test Bed for Radio-Frequency Fingerprinting of Cellular Devices', whose abstract sample is below. We present a characterized test bed and algorithms for non-destructive, over-the-air fingerprinting of commercial cellular user equipment (UE). This test bed is designed to repeatably collect radiated fields from cellular devices in a 4G long term evolution (LTE) network configuration. We describe a straightforward classification algorithm to determine the model of each cellular device that allows for a direct correlation between input data from test cellular phones and identification efficacy. Additionally, by controlling the radio channel conditions, we provide a framework for transparently studying dominant uncertainties and sensitivities in data-driven cellular device fingerprinting. The algorithm performs classification with either the error vector magnitude, a quantity derived from demodulated data, or the out-of-band frequency ___domain response of the cellular devices. We have investigated the robustness over time of this fingerprinting method and show over 95% accuracy in identifying UE models from different manufacturers and gaining insight into parameters that can cause a reduction in this level of accuracy and in data-driven approaches in general. This work is part of a larger effort to identify and create a database of genuine off-the-shelf cellular devices to help mitigate counterfeiting and hardware security tampering using RF fingerprinting. As such, the raw data are text files in comma separated value (CSV) format. The text files have varying numbers of columns depending on the figure it is attributed to.