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All datasets were presented in the main paper.
All article datasets were presented in the main paper and included in the additional supporting file.
The SNP discordant rates between two datasets were presented in Additional file 1: Table S1.
The best supported annotations, that agreed across two or more datasets, were presented along with supporting experimental and protein sequence evidence.
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Experimental results on two RGB-D person re-identification datasets are presented to show the efficiency of our proposed approach.
In this paper, a dimensionality reduction method designed to enable effective Support Vector Machine Recursive Feature Elimination (SVM-RFE) on NIR/MIR datasets is presented.
A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented.
The average 3 × 10 cross-fold validation results obtained from these datasets are presented and compared with the results of certain classification algorithms reported in the literature.
Details of these datasets are presented in Table 1.
Descriptive statistics for used datasets are presented in Table 14.
Once the user is wearing the virtual reality headset inside the induction machine,, datasets are presented in various shapes and formats, to make it easier to understand them.
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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