Sentence examples for datasets applicable to from inspiring English sources

Using temporal linear averaging (TLA), we produced satellite-imagery datasets applicable to temporal composites for the summer seasons of three years.

Additionally by generating two types of four locus datasets - datasets with minimized three-way accuracies and datasets with unconstrained three-way accuracies - we give researchers the flexibility to pick datasets applicable to their specific study.

Therefore, this dataset is applicable to test the performance of object labeling.

Although a major concern in the validation of a microarray-based prediction model is model transferability i.e. the usefulness of a transferred model outside of its intended use, it is also desirable that the signature developed from the internal dataset is applicable to predict future samples.

The advantage of using existing datasets is especially applicable to decision makers, because of the ease in elucidating some of the information inherent in multivariate classification analysis, and eventually one is able to extrapolate results from small areas to wider regions such as prefectures or to the nationwide level if similar profiles exist.

Although the distributions derived from these particular datasets are only applicable to the particular microarray platforms used in this study (HG-U133 Plus 2.0, HG-133A and MoGene-1_0-st-v1), it is straightforward to produce similar distributions for the corresponding probesets of reference in other microarray platforms (actually, more platforms are available in the implementation of the method).

The standard applies to all geographic data–– it is applicable to datasets in series, datasets, individual geographic features, and their attributes.

The general material recognition dataset is not applicable to FOD material recognition.

The cointegration test is applicable to datasets that are stationary at the same level (either first difference or second difference).

PCA is a multivariate eigenanalysis technique that is applicable to datasets that are approximately normally-distributed with variables that are linearly related [37].

We consider that framework to be broadly applicable to datasets describing relationships between entities from different classes, where we want to be able to simultaneously visualize the different classes such that visual distance can be associated to a dissimilarity measure, both within class and between classes.