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This experiment did, however, suggest: (i) it is not necessary to expect a specific category in detection; (ii) dataset difference impacts performance; (iii) a system relying on such an approach may obtain worse results.
Multiple imputation of non-respondents to question about recent dengue cases reconciled 10 datasets (1000 iterations each) to generate almost identical data to those from the original dataset (difference −0.012, 95% confidence interval −0.020 to −0.004).
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In the Twenty-07 Study (1990 dataset), differences between the baseline, age 18-19 responders and the age 18-19 weightedatasetet were all small and non-significant.
To evaluate the impact of the SNP dataset differences on genome-wide scans for signatures of natural selection, we performed (iii) sliding-window analyses (100 kb windows, 25 kb step size) to identify signals of putative selective sweeps based on population differentiation.
In addition to the raw data correlations, we performed correlations using the % year-to-year change for each of the datasets (difference model [4]), a total of 307 comparisons.
In all three datasets, differences between the scores of first authors from different regions were statistically significant, even after application of the Bonferroni correction for multiple hypothesis testing (see Table 2).
In analyses of individual datasets, differences were observed by tumor histology.
For the unemployment dataset, the difference was not significant.
At the R52 dataset the difference of mean error is significant in favor of confidence.
Compared to the registry dataset, the difference amounts to 9.51 fold.
For the best8 dataset, the difference between Raw and Linear is less than 1%.
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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