Sentence examples for table are defined from inspiring English sources

All position angles recorded in the Source Observations Table are defined relative to the tangent plane projection of the individual observation.

The expected frequencies under the null hypothesis in the 2 × 2 × 2 contingency table are defined as follows: μ ^ x y z = μ + + z μ x y + n ; x, y, z ∈ { 0, 1 }.

The final column details the frequency of assignment of individual genes to specific STEM profiles identified by corresponding letters in Figure 2. Functional categories in this table are defined according to the Database of Arabidopsis Transcription Factors [81].

The final column details the frequency of assignment of individual genes to specific STEM profiles identified by corresponding letters in Figure 2. Functional categories in this table are defined according to the following sources: glycolysis, TCA, electron transport and PPP [23]; all other groups: [25].

The final column details the frequency of assignment of individual genes to specific STEM profiles identified by corresponding letters in Figure 2. Functional categories in this table are defined according to the following sources: kinases and phosphatases [62]; hormone biosynthesis [23]; all others: [25, 37].

The final column details the frequency of assignment of individual genes to specific STEM profiles identified by corresponding letters in Figure 2. Functional categories in this table are defined according to the following sources: ribosomes [104] ; PPIs [55]; peptidases [59]; 19S proteasome [24]; E3 RING [105]; HSPs, SKP1s, E3 Ubox [25].

When a table is defined, each column is assigned a data type that provides a broad domain.

In fuzzy-based routing protocols in literature, the fuzzy rule base table is defined manually, which is not optimal for all applications.

Here, 'quality.txt' is read as a column-based ASCII table; the second column of that table is defined as the quality array for data set 1. The set_quality function is used to actually apply the quality flags to data set 1, using the array which is returned by the get_quality function.

Here, 'grouping.txt' is read as a column-based ASCII table; the second column of that table is defined as the group array for data set 1. The set_grouping function is used to actually apply the grouping scheme to data set 1, using the array which is returned by the get_grouping function.

Here, 'filter.txt' is read as a column-based ASCII table; two columns are read by default, and the second column of that table is defined as the filter array for data set 1. The get_filter function returns the x-axis elements of the data array with the assigned filter applied - i.e., missing the elements which were flagged as 'bad' by the filter array (set to less than 1).