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Previous studies have reported data only for one step of the turn and expressed the data in terms of a global reference frame making it difficult to understand how the forces act on the body to cause a change in heading and orientation during a turn.
We expressed the data as the mean number of positive cells±standard error of the mean (SEM).
Since the absolute level of proliferation greatly differed between the two T cell types, we also expressed the data as the percent of the maximum response (Fig. 4C).
We expressed the data collected in an individual session as the proportion of trials on which the central element was judged to be rightward (Vertical alignment or Horizontal Bisection) or upward (Horizontal alignment or Vertical Bisection) of the reference elements.
We expressed the data as the proportion of trials on which subjects judged the central element to be rightward (Vertical alignment or Horizontal Bisection) or upward (Horizontal alignment or Vertical Bisection).
We expressed the data as means ± SDs.
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We describe here, the different microarray technologies, their use and the different ways to express the data as genes expression patterns.
We write the deformed curve as (overline {gamma }), and we also express the data associated with (overline {gamma }) by putting —.
PCA expresses the data as a linear combination of the most significant features of a given posture.
Principal component analysis (PCA) is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
Smith (2002) comments that PCA is a way of identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com