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The representative scaffolds in each dataset and the difference of the scaffold diversity between the studied datasets were highlighted by the Tree Maps.
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Tree Maps for the studied datasets; Figure S2.
The unified structures of the datasets also aided discovering shared mechanisms between the studied pathogens.
In the present study, datasets of multiply injured patients entered into the TraumaRegister DGU® between 2002 and 2010 were analyzed.
However, significant differences were found for all parameters between the Study group after treatment dataset versus the Control group and the Study group before treatment datasets, with the exception of the comparison between pre-treatment Study group versus post-treatment Study group for overbite.
Potential reasons for differences in risk factors between the models include the size of the dataset available for model development, the availability of each risk factor, differences in the time interval of data collection, difference in the populations studied between development datasets and differences in risk factor definitions.
Finally, we performed additional analyses aimed at illustrating the heterogeneity between the studies included in our individual patient dataset.
The datasets were registered to minimize differences between the studies.
An early description of the association between elevated RHR and mortality in hypertension was described in the Framingham Study dataset.
A comparison of the Gene Ontology of the other datasets to ours revealed an astonishing similarity between the studies.
The biggest variation was observed between the datasets of study 1 (GSE3141 (Bild et al., 2006)) and study 5 (GSE29013 (Xie et al., 2011)), which showed more than a 32 fold-difference in expression levels.
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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