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Use of ensemble data and models could attenuate this risk.
The intensive use of ensemble data requires computational resources for data processing and storage, management of long-run simulations and transfer of huge datasets.
Ruginski et al. (2016) compared five different encodings of ensemble data (three summary displays, one display of the mean, and one ensemble display) of hurricane forecast tracks, using a task where participants predicted the extent of damage that would occur at a given location.
Although similar modeling attempts have been made by Corry et al. (2010) and Deplazes et al. (2012) by using distance changes measured from ensemble FRET, we would like to emphasize that all smFRET measurements were used for the simulation while previously only a selected subset of ensemble data were used (as other data were not consistent with the resultant model) (Corry et al., 2010).
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Further, salient features of ensemble displays evoke judgments that are indicative of accurate interpretations of the underlying probability distribution of the ensemble data.
Zhang, S., Harrison, M. J., Rosati, A. & Wittenberg, A. System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies.
While researchers have developed algorithms to reduce visual crowding (e.g., Liu et al., 2016), visual crowding may still occur when all of the ensemble data is plotted.
The three summary displays included a standard cone of uncertainty, which had a mean line, a cone without the mean line, and a cone in which the color saturation corresponded to the probability distribution of the ensemble data.
We compared the transcriptome with CDS with that of human (Ensemble data release 69) and X. tropicalis (Xenbase, Xentr7_2).
As has been discussed in the preceding sections, we found that a combined analysis of observed and model ensemble data is a useful method for analyzing the development of plumes and the distribution of radioactive materials, but neither approach alone is adequate.
The CDSL had functionalities to unify the spatial extent and resolution, projection and calendar system of gridded data for creating ensemble data sets that could be imported into a model of interest using a function call.
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