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Baseline and end-of-treatment characteristics have been summarized using the usual descriptive statistics: by count and percentage for qualitative variables and by mean and standard deviation for quantitative variables.
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The statistics were summarized using the Nash-Sutcliffe coefficient.
The results were summarized using the final efficiencies obtained in each test (Fig. 7).
Agent learning can generally be summarized using the concept of stigmergy (Fig. 2).
The industry innovation requirements (IIRs) are summarized using the Delphi method.
Continuous variables were summarized using the number of observations (n), mean, standard deviation (SD), median, minimum, and maximum.
Data were summarized using the mean and standard deviation (SD), or median and interquartile range (IQR) or frequency and percentages.
Relevance was summarized using the following categories: C1 = Relevant without restrictions, C2 = Relevant with restrictions, and C3 = Not relevant.
Ice resistance tests were summarized using the volumetric coefficient, CV (=∇/L3), instead of L/B and CB variations.
Data were summarized using the mean and standard deviation (SD) for normally distributed variables and the median and inter-quartile range (IQR) for non-normally distributed variables.
Further functional annotation was performed using PLAZA (version 3.0) comparative genomics and GO annotation was summarized using the Plant GO slim category.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com