Sentence examples for datasets is often from inspiring English sources

The accuracy of these datasets is often unknown and is non-uniform within each dataset.

However, accurate information on the benefits and deficits of these datasets is often lacking, especially in the West African subcontinent.

At the same time, they have also introduced many analytical difficulties that cannot be properly addressed with current statistical procedures, because the number of genomic covariates in these datasets is often larger than the sample size.

The validity of 3D primitives in 3D GIS datasets is often a prerequisite for using them in simulation and decision-making software, e.g. visibility analysis, noise pollution assessment, and energy estimation.

Within the UK itself, the analysis of bulk communications data or bulk personal datasets is often the only way for the security and intelligence agencies to progress investigations and identify terrorists from very limited lead intelligence, or when their communications have been deliberately concealed.

As highlighted by Biljecki et al. [5], in practice the quality of available 3D datasets is often poor: they contain geometric and topological errors, e.g. duplicate vertices, missing surfaces, self-intersecting volumes, etc. Often these errors are not visible at the scale the datasets are visualised [18], and, as a consequence, practitioners are not aware of the problem.

Third, existing datasets are often static and not frequently updated.

Such datasets are often high dimensional and heterogeneous in nature, with complex geospatial relations.

Datasets are often distributed and their size and privacy considerations warrant distributed techniques.

Chronologically complete datasets are often used in climate variability studies by analyzing data in multiple temporal windows.

Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels.