Sentence examples for dataset rich from inspiring English sources

We present the Arabic Online Commentary Dataset, a 52M-word monolingual dataset rich in dialectal content, and we describe our long-term annotation effort to identify the dialect level (and dialect itself) in each sentence of the dataset.

The downscaling of historical climate data for application to the BCM to calculate hydrologic response to ongoing changes in climate has provided a dataset rich in regional representation of climatic and hydrologic trends, but spatially detailed enough to provide fine-scale examples of local impacts of climate on the landscape.

Although the NLCA is non-mandatory, we have previously shown that this is a valid and representative dataset (Rich et al, 2011b).

We have used the recently validated National Lung Cancer Audit NLCCA) dataset (Rich et al, 2011b) together with comorbidity data from Hospital Episode Statistics to study the impact of patient features and features of the National Health Service (NHS) hospital Trust on the use of chemotherapy in people with small-cell lung cancer.

Phylogenetic analyses based on datasets rich in both genes and species (phylogenomics) are becoming a standard approach to resolve evolutionary questions.

Thus, our results are consistent with the hypothesis that membrane proteins undergo at least as many interactions as other proteins, since we observed that datasets rich in membrane interactions tend to have a higher normalized degree than the datasets with fewer of these interactions.

Moreover, we investigated this directly by evaluating the distribution of interaction degree normalized by the total number of interactions in datasets rich in membrane interactions (e.g., PF-PCA and SU-2HY, both with more than 65% membrane interactions) or with a poor coverage of membrane interactions (e.g., two hybrid (2HY), (29%) and affinity capture-MS (14%)).

MSCAN focuses on very high P predictions, while Cister is geared towards high values of R. It is noticeable that Stubb's performance is much below the rest, possibly because it uses distinct motif-to-motif transition probabilities, which can only be properly learned without overfitting from datasets richer in scope than the present one.

But the dataset is rich and allows us to control for many variables that would be unobservable in other datasets.

But what makes this dataset particularly rich is the national and subnational data compiled and provided by countries throughout the region.

It minimally organizes and describes a dataset in (rich) detail and goes further to interpret various aspects of the research topic" [ 32].