Sentence examples for dataset were selected from inspiring English sources
To confirm the assignation and explore the genetic diversity of Mamiellophyceae, ASVs from Bathycoccus, Micromonas, Ostreococcus and Mantoniella represented by more than 200 reads in either the LW or LGC dataset were selected.
As case of study, the most common 26 of 48 species from the Snapshot Serengeti (SSe) dataset were selected and the potential of the Very Deep Convolutional neural networks framework for the species identification task was analyzed.
First, 500 ligatures of the dataset were selected for conducting these experiments.
The 1640 proteins in this dataset were selected from PDB based on the following criteria: (1). the structures are obtained by X-ray experiment, (2). the resolution should be lower than 2 Å, (3) the proteins do not contain any small molecule ligands, (4). the proteins are asymmetric, and (5) the proteins do not contain any modified residue.
Besides a few interactions with a known and well-defined motif (PDB ids: 1SSH, 1W9E, 1Z90 and 2P1K), the interactions in this dataset were selected randomly.
Example(s) from the dataset were selected that best illustrate the category.
The 20-spot dataset was selected on the basis of the non-parametric Wilcoxon test, since the distribution of spot volumes deviates from normality and intra-group variance values are dissimilar.
To compute the uncertainty component for any routine gauging, the four most similar gaugings among the reference stream-gaugings dataset are selected using an analog approach, where analogy includes both riverbed shape and flow distribution complexity.
The dataset is selected in the first wizard step (see Figure 2).
The endpoint value of the COX-2 dataset is selected, showing the activity space (or landscape).
