Sentence examples for differences in datasets from inspiring English sources

Our QC pipeline was able to overcome significant biases in the LRR data, correcting for intensity differences in datasets originating from separate collections and sample types that differed systematically between phenotypes.

ANOSIM results identified no significant reef treatment effects and also suffered from design constraints and an inability to generate enough permutations to test for significant differences in datasets with relatively small sample sizes.

Such cases include those where the coordinate differences in datasets are significant, where the shapes of the same objects are dissimilar, or even where the shapes are too similar for different objects.

Despite the differences in datasets and classifiers, the two methods largely agree on the taxonomic affiliations of the sequence data.

Interestingly, the influence of differences in dataset composition resulting from various ways of selection of molecules forming a set of inactives has never been thoroughly investigated.

The causes of the discrepancies between the plots for toy datasets and real-life datasets are based on the differences in dataset characteristics between toy datasets and real-life datasets.

In addition to differences in dataset sizes and composition, methodological differences between the family-centric and the PDM-based approach are likely to be responsible for the differences observed and the additional relevant families that were included in the PDMs.

The mean-centering method described in this study was used in a recent meta-analysis whilst our manuscript was under review [ 38], although no attempt was made to account for differences in dataset composition.

To avoid bias arising from differences in dataset size, proteins were randomly removed from the redundancy-reduced sets of fungal proteins such that each organelle was represented by the same number of proteins (n = 99).

This method could be affected by differences in dataset size between species, i.e. small P-values in one species may lead to subsequent small P-values in the cross-species combined distribution, as was detected for the non-normalized data (data not shown).

Designs, data collection and data management procedures are relatively standard for the same type of survey (DHS and MICS); however, there are differences in dataset structures between DHS and MICS and within each of these types of surveys (e.g. diverse variables names for the same variable and/or different coding of the same variables).