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These examples have been chosen because (1) the datasets are good and (2) both type 1 and type 2 phreatic eruptions are illustrated.
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The agreement of measured and predicted datasets was good for both models, as measured using various statistical parameters.
The quantitative agreement between the EI- and PI-datasets is good.
The authors have not addressed this issue at all, and simply assume that their dataset is good enough.
Thus, we conclude that the overall quality of the produced segmentation maps for the rat brain dataset is good.
While the correlation with the Broad dataset was good (r = 0.73), our data correlated less well with SYDH dataset (r = 0.46).
The imputed ratios of the microarray datasets are all less than 50%, it seems to be reasonable to assume the imputed dataset is good enough to analyze the gene regulatory network.
These data indicate that these RNA-seq datasets are of good quality.
As is summarized in Figure 4, the results for both datasets are in good quantitative agreement with the known (or measured) composition of the input data.
Furthermore, the wild-type datasets are in good agreement with our model as shown in Figure 2, where the amplitude h of the mechanical damage in the disease-free mice (wild-type) is one tenth of its counterpart in the mdx mice.
Cross-comparisons between two independent halves of patient dataset revealed that subnetworks are good at distinguishing T-ALL patients from healthy individuals, regardless of the dataset in which they are found.
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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