Such variability in data volume imposes variable requirements on availability of compute resources used to process given data.
Variability in data storage and core measure definitions is very common.
Therefore, it is fairly common to expect variability in data collected from digestibility trials with fish.
Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample.
Unfortunately, this variability in data dependency makes the modeling stage more complex and slows down the overall compression scheme.
Given the variability in data coverage since 2009, we are most confident in model snapshots that are dependent on data early and late in the time series.
There are also inherent limitations of a retrospective chart review, such as the lack of clinical homogeneity among the different sites, missing clinical data, and variability in data abstraction.
Comparisons across studies are equally challenging due to variability in data coding, definitions, data collection/storage procedures and assessment used in concussion studies and clinical practices coupled with the complexity and heterogeneity of concussion diagnosis (Aukerman et al. 2016; Buckley et al. 2015; Maas 2009).
For normal tissues, interstitial glucose may be modeled, but in tumors with all their heterogeneity and variability, this is likely to remain challenging, and this will likely continue to present a significant source of variability in data that depend on tissue glucose but measure blood glucose.
