With the potential of high temporal and spatial sampling and the capability of utilizing existing fiber‐optic infrastructure, distributed acoustic sensing (DAS) is in the process of revolutionizing geophysical ground‐motion measurements, especially in remote and urban areas, where conventional seismic networks may be difficult to deploy. Yet, for DAS to become an established method, we must ensure that accurate amplitude and phase information can be obtained. Furthermore, as DAS is spreading into many different application domains, we need to understand the extent to which the instrument response depends on the local environmental properties. Based on recent DAS response research, we present a general workflow to empirically quantify the quality of DAS measurements based on the transfer function between true ground motion and observed DAS waveforms. With a variety of DAS data and reference measurements, we adapt existing instrument‐response workflows typically in the frequency band from 0.01 to 10 Hz to different experiments, with signal frequencies ranging from 1/3000 to 60 Hz. These experiments include earthquake recordings in an underground rock laboratory, hydraulic injection experiments in granite, active seismics in agricultural soil, and icequake recordings in snow on a glacier. The results show that the average standard deviations of both amplitude and phase responses within the analyzed frequency ranges are in the order of 4 dB and 0.167π radians, respectively, among all experiments. Possible explanations for variations in the instrument responses include the violation of the assumption of constant phase velocities within the workflow due to dispersion and incorrect ground‐motion observations from reference measurements. The results encourage further integration of DAS‐based strain measurements into methods that exploit complete waveforms and not merely travel times, such as full‐waveform inversion. Ultimately, our developments are intended to provide a quantitative assessment of site‐ and frequency‐dependent DAS data that may help establish best practices for upcoming DAS surveys.

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