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During these phases, raw data is condensed, rearranged, and finally, to enable analysis and interpretation, mapped into a thematic framework that is developed specifically for each individual study [ 17].
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These data are condensed to a single result, usually a ranking of the available management options according to the management goal.
Qualitative data are condensed in frequency tables.
The data were condensed and normalized (Table 1).
Categorical data were condensed by absolute and relative values.
This data was condensed using algorithms into quantified metrics to obtain kinetic proliferation curves.
The coded data was condensed and categorised into broader themes [ 15].
Raw data were condensed into text components, the smallest size being sentences and the largest complete sections of text, interview by interview.
Therefore, we introduced Minimal Net Clustering in this paper so that the data was condensed and the computational cost could be reduced by a purely numerical method without biological bias.
As the transit process involves only the oesophageal cranio-caudal dimension (lateral motion is of no concern), the dynamic image data was condensed into a single image with one spatial dimension and one temporal dimension (vertical and horizontal respectively), allowing an easy calculation of the time-activity curves.
The data were condensed as follows: 1) pain ratings of the week before T1 were arithmetically averaged and served as baseline; 2) since the gaps of time between interventions differed among the subjects, pain ratings between two sessions or between session 5 and T2 were averaged, resulting in five post-intervention measures.
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