Sentence examples for the data were identified from inspiring English sources

RSNs and structured artifacts in the data were identified using MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components [55], FSL's implementation of ICA (Independent Component Analysis [56]).

Patterns in the data were identified and deviant cases were explored.

A core category and four categories central to the data were identified.

Four factors which explained about 64.5% of total variance of the data were identified (Table 13).

Biological pathways enriched in the data were identified using IPA software (Ingenuity Systems, Redwood City, CA).

Qualitative comments were typed out and repeated themes in the data were identified.

The interquartile range of the data is identified by the box with the median shown as a line within the box.

The first component is a feature selection and extraction stage where critical informational features about the data are identified for classification purposes.

Since raw pixel intensity is the main way by which phases in both the phantom and the data are identified, a source of input from the intensity is required to form a complete analysis.

Mapping landcover change delineates landscapes under recent disturbance pressure, and these measures are more effective as performance indicators for broad-scale evaluation of natural heritage policies and habitat restoration initiatives when error in the data is identified and accounted for.

This feature of the data is identified automatically using PCA.