Sentence examples for in data requirements from inspiring English sources
Six updating methods have been defined, varying in data requirements and in the smallest spatial unit that can be updated.
Thus, depending on the type of analysis required, an ex-post econometric model may be preferred by policy makers to an ex-post unit commitment model given the significant reduction in data requirements and computational time involved.
Because of the large differences in data requirements across the different frameworks and type of data (e.g. QSARs, non-standardised tests), we recommend considering all available information in a weight of evidence (WoE) approach - as suggested in REACH (Annex XI 1.2 of [1]).
The ARM approach is capable of generating relational databases using archetypes and templates for archetype-based EHR systems, thus successfully adapting to changes in data requirements.
Finally, DES may induce over-specification, whereby possible patient pathways become more complex than necessary, thus implying an increase in data requirements.
Since the granularities of archetypes and data requirements are not always identical, archetypes composed by archetype slots with a single occurrence represent one concept, and can be embedded together in data requirements.
Our results speak both to general issues in phylogenetic data requirements and specific progress in the phylogeny of emydid turtles.
Greater emphasis on the potential contribution of TK in guiding study design and interpretation should be incorporated in regulatory data requirements and associated guidance.
We conclude by proposing improvements in the data requirements and related aspects of the REACH system.
This results in greater realism in describing a patient's evolution through the healthcare system and offers more flexibility in the data requirements needed to feed the model.
Not only is BBDR modeling of low-dose risk subject to the same problems as empirical modeling of apical responses, but these problems are also exacerbated by uncertainties inherent in complex models and in the added data requirements and heterogeneity inherent in those data.
