Sentence examples for prioritization of data from inspiring English sources

The strong prioritization of data classes in the standard is the main reason for the low performance of the low priority data classes.

Bhumkar said that while other apps have treated "beauty and speed as a zero sum game,BeamItIt can offer both, thanks to dataflow and data binding technology, allowing for "prioritization of data and pixels" as well as "pushing the relevant data on demand".

The DSS approach permits model users to change the dependency networks, change the prioritization of data, and add or remove indicators and measures thus, it is flexible and dynamic.

The DSS model selected a data source for each metric, watershed by watershed, based on availability of data and a prioritization of data sources for each metric so that at each watershed the best data for that particular metric was used.

First, as described above, central research decisions were made to guide database design and development (grey boxes in Fig.  5; and described in detail in Additional file 2. As there were more datasets to integrate into LAGOS than there were funds or time available (a common problem in science), prioritization of data was critical to ensure that our research goals were met.

Research has demonstrated that further enhancement of the curation process for CTD, as well as for many manually curated biomedical resources, would be achieved by improving: (i) the triage and prioritization of data-rich relevant articles and (ii) the identification of curatable content within these articles (8).

More important, the process of model construction has forced us to highlight both the level of sufficiency of published data and the prioritization of future data collection.

If this is not the case, users may wish to explore phenotype-based (Robinson et al., 2014) or genomic data fusion (Sifrim et al., 2013) prioritization of exome data, or if possible sequence additional family samples to enable linkage filtering (Rödelsperger et al., 2011; Smith et al., 2011), or sequence additional unrelated individuals for intersection-based (Robinson et al., 2011) approaches.

This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models.

In this study the Insubria PBT Index in QSARINS is applied to the screening and prioritization of various data sets, containing a large variety of chemicals of heterogeneous molecular structure, previously screened by various authors by different methods, for their potential PBT behavior.

Sensitivity analyses can evaluate sources of uncertainty in model estimates to allow prioritization of additional data collection (such as spatiotemporal variability in indicators and pathogens).