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In order to address this issue we have investigated our dataset and provided the insight in the 'Examination of dataset' paragraph of 'Result' section.
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Each text in the introduction, methods, and results subsets of the paragraph dataset was also compared to the entire paragraph dataset.
And our third dataset consisted of paragraphs with more than 50 words but less than 200 words.
As shown in the previous paragraph, real datasets are useful to test tools in conditions that resemble their everyday usage.
But in Pathway analysis, first paragraph, the 3x dataset is 357 genes.
So, for reasons discussed in the previous paragraph, even a dataset with a nonsignificant LD result can potentially provide useful information about effective population size.
That is, the replicated datasets described in the previous paragraph were used as blocking factors to compare scenarios across a heteroskedastic error gradient.
In the first paragraph, a description of the dataset is given.
Using the filters and parameters described in the preceding paragraphs, we obtain a redundant dataset that we call the PRIMARY PRII) database.
When cleaning the dataset with different problems mentioned in the first paragraph, the unsuitable cleaning sequence might not obtain the expected effect.
It operates by comparing each paragraph with every other paragraph in the dataset looking for the most similar one and each term in a paragraph is given the same weight.
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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