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Both datasets were generated in a similar manner except for two notable differences.
Nine coffee bean transcriptome datasets were generated in this study, including green, yellow and red stages, all in triplicate (Fig. 1a).
In the present investigation, the authors compared the results of 2D and 3D gamma analysis (where both datasets were generated in the same manner) for clinical treatment plans.Fifty IMRT plans were selected from the authors' clinical database, and recalculated using Monte Carlo.
Array datasets were generated in duplicate for liver and kidney of all control and treated animals.
Rarefaction curves of various datasets were generated in MOTHUR using a re-sampling without replacement approach.
Two different datasets were generated: in order to analyze the position of the clades within Musaceae with respect to other phylogenetic groups of Zingiberales, dataset 1 comprised ITS1-ITS2 concatenated regions of all diploid Musaceae accessions as well as the known ITS1-ITS2 sequences of closely related families Strelitziaceae, Lowiaceae, Heliconiaceae and Costaceae [75], [76] (Table S4).
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The datasets are generated in the same way as in Scharl et al. (2010).
Two sub-datasets were generated in order to examine the performance of the software packages when dealing with logistic random effects regression models on a smaller data set.
Results obtained for the smaller dataset were generated in 4-cross-validation process.
Normalization of imputed dataset: imputed dataset was normalized using quantile normalization in R (Bolstad et al., 2003 ) and logged to base 2. Array outlier detection: dissimilarity matrices of the normalized dataset were generated in AVADIS-Pride (Gwadry et al., 2005 ) to determine outlier arrays within the dataset.
The dataset was generated in such a way that only two predictors were related to the response.
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