Sentence examples for making data comparison from inspiring English sources

Results vary from study to study, making data comparison and interpretation difficult and possibly inadequate.

The absence of a generic management system for the quantitative data generated in cell migration assays results in each dataset being treated in isolation, making data comparison across experiments difficult.

The methodologies for assessing StO2 during a VOT, however, are very inconsistent in the literature and, consequently, results vary from study to study, making data comparison difficult and potentially inadequate.

To date, only a small number of lysine acetylation marks have been identified to specifically interact with individual BRDs, and the often weak affinities reported for BRD interactions with their potential target sites have been determined by a variety of different techniques making data comparison difficult (Muller et al., 2011).

While fragment analysis methods such as pulsed-field gel electrophoresis (PFGE) are used to trace the sources of contamination for this bacterium, some drawbacks have been identified, namely the complexity of the methods and the difficulty of making data comparisons.

Another challenge is the need to be cognizant of variation in cultural and scientific sensitivities and representativeness that exists among the three MECIDS partner countries when making data comparisons at the regional level.

There are differences in how the financial costs of deaths and serious injuries are calculated in different countries, which makes data comparison very difficult.

This makes data comparison and integration difficult across proteomics experiments conducted independently by different research groups.

However, variations in study design, patient populations, dose titration schedules, and outcome measures among available studies make data comparisons between clinical trials difficult.

More opportunities for students to make data comparisons, to create and interpret data representations and recognise their core distributional features and to read critically 'beyond the data' (cf. Curcio, 2010) are also in need of research and curriculum development.

Where no surveillance feedback bulletins were available to make data comparisons with existing systems at week 26, comparisons were made by using data from the next available bulletin so that data could be compared across all systems.