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tokens: DM measurement (see below). To our knowledge, this is the first time frequency-dependent profile evolution (not related to scattering) has been observed in an FRB. Figure 3. Burst time series plotted in four sub-bands for bursts 8, 10, and 13. The frequency range used for each sub-band is indicated above each time series. Frequency-dependent profile evolution is evident (see Section 3 ). 3.2. Temporal and Flux Properties For each burst we measured the peak flux, fluence, and burst width (FWHM). Before measuring these properties we normalized each burst time series using the radiometer equation where the noise level is given bywhere T sys is the system temperature of the receiver, 20 K for GBT 2 GHz observations and 30 K for Arecibo L-wide; G is the gain of the telescope, 2 K/Jy for GBT and 10 K/Jy for Arecibo; B is the observing bandwidth; and t int is the width of a time-series bin. The peak flux is the highest 1.3 ms-wide bin in this normalized time series. The fluence is the sum of this normalized time series. To measure the width we fit the burst with a Gaussian model. The peak time is the best-fit mean in the Gaussian model. In Table 3 we show the above measured properties for each burst. The GUPPI bursts have peak flux densities in the range 0.02–0.09 Jy at 2 GHz assuming the bursts are detected on axis. These are similar to the range of peak flux densities found for the ALFA-detected bursts presented by Spitler et al. ( 2016 ), 0.02–0.3 Jy. The PUPPI-detected burst had a peak flux density of 0.03 Jy, again assuming a perfectly on-axis detection, similar to the faintest bursts seen by Spitler et al. ( 2016 ). Table 3 Burst Properties Note. Bursts 12–16 were detected at 2 GHz at GBT and burst 17 was detected at 1.4 GHz at Arecibo. The errors quoted on DM are, in order, statistical and systematic. Burst peak times are corrected to the solar system barycenter and referenced to infinite frequency. 3.3. Dispersion Properties We measured the DM of each burst except burst 15, which was detected over too narrow a frequency range to make a reliable DM measurement. For each burst, time series were generated in sub-bands by averaging over blocks of frequency channels. The number of sub-bands depended on the S/N of the burst, and sub-bands with too little signal (S/N 2) or too contaminated by RFI were excluded. Times of arrival (TOAs) for each frequency sub-band were calculated using a Gaussian template. The width of the Gaussian template was chosen to match the burst width measured from the whole band, as described above. TEMPO2 19 19 http://www.atnf.csiro.au/research/pulsar/tempo2/ (Hobbs et al. 2006 ) was used to find the best-fit DM from the TOAs. The results are given in Table 3 . Several of these bursts (13 and 14) have formal DM measurements that are larger than the mean by a statistically significant margin. We argue that this reflects unmodeled frequency-dependent profile evolution (see Figure 3 ) and not a measurement of time-variable DM.