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\citet{Ellehoj_2010} analyzed 151 sols worth of time-series data, including pressures, temperatures, wind speeds, and images, from instruments on-board the Phoenix Lander \citep{Smith_2008}. To identify dust devil passages in the barometric data, they compared the average pressure in a 20-s window to the average pressure in 20-s windows to either side of the former window. Average pressures in the middle window different by more than 0.1 Pa from the average on either side were identified as possible dust devil passages. Then for every pressure event found, they analyzed the surrounding pressure and temperature values, and non-significant and false events, e.g., from data transfer gaps, were removed by hand (the precise criteria used to exclude an event are not given). In this way, \citet{Ellehoj_2010} identified 197 vortices with a pressure drops larger than 0.5 Pa.  Figure \ref{fig:Ellehoj_data} shows a scatter plot of their reported detections, along with colored contours and histograms. As with most single-barometer surveys, wind velocities were not recorded for the dust devil encounters, so \citet{Ellehoj_2010} reported the observed pressure profile full-width/half-maxes in time $\Gamma_{\rm obs}^\prime$. As above, we assume all observed devils were advected past the barometer at the same velocity so we can convert directly between profile durations and widths, widths ($\Gamma_{\rm obs}^\prime \rightarrow \Gamma_{\rm obs}$),  but velocities recorded for a handful of devil encounters range from 1.4 to 9.3 m/s \citep{Ellehoj_2010}.For the analysis here, we will assume a uniform velocity.  In principle, we could apply a more sophisticated density estimation scheme than the histogram of $\Gamma_{\rm obs}^\prime$- and $P_{\rm obs}$-values, such as kernel density methods (\url{https://ned.ipac.caltech.edu/level5/March02/Silverman/Silver_contents.html}). However, the dataset here is too sparse to merit such an approach.