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The low-frequency data can generally be obtained by applying smoothing techniques or a low-pass filter.
Data are presented as log-transformed; log transformation of metabolite data can generally be taken as normally distributed [33], justifying the use of parametric analysis.
Similarly, specific immunological data can generally be used to make a definitive diagnosis.
On the other hand, precision (i.e. variability) of data can generally be improved by increasing the number of measurements.
This makes obtaining individual informed consent impossible in some cases, although permission to use the data can generally be obtained, along with information dissemination using methods outlined above [ 27].
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Underlying meanings in different data sources can generally be discovered through crystallization as crystallization allows the interweaving and blending of data resulting in a more descriptive understanding of the research.
Dates and ages are another type of numeric data that can generally be removed successfully.
The currently developed integrative methods for analysis of multiple genomic data of the same patients can generally be classified into three groups [ 7, 8].
Insofar as data-analysis is concerned, case-crossover designs can generally be regarded as matched case-control studies and consequently conditional logistic regression can be applied.
Prospecting can generally be broken down into buying lists, scraping data, mining data from B2B databases and manual prospecting, ranked from worst quality/highest volume to best quality/lowest volume.
Mathematical approaches to modeling complex behavior can generally be categorized as either data-driven or knowledge-driven.
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CEO of Professional Science Editing for Scientists @ prosciediting.com