Sentence examples for the data were identified using 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]).

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

Following sequencing, the data were identified using a basic local alignment search tool BLAST-n (http://www.ncbi.nlm.nih.gov/BLAST) or RDP database [ 21].

Natural groupings in the data were identified using unsupervised hierarchical agglomerative cluster analysis with average linkage on the 16 potential biomarkers.

Preliminary codes emerging from the data were identified, using the interview guides and the evaluation questions to keep the context of the data in mind.

Themes and patterns in the data are identified using constant comparative techniques and data are organised into codes and categories to enable theory to be developed at a substantive level.

In brief, the fastICA algorithm was applied after the optimal number of sources explaining the variance in the data was identified using PCA with MDL criterion (Li et al., 2007).

The base case costing scenario assumed that in the IPTi study cohort all suspected cases of malaria received an RDT at a unit cost of US$0.75 [10] In the absence of data, it is assumed that 50% of all malaria diagnoses recorded in the HMIS data were identified using microscopy at a unit cost of USD0.27 [10] and the other 50% were treated presumptively.

HUs for the core data were identified using three popular correlations such as Kozeny Carmen (KC) equation, Nooruddin and Hossain equation, and the power law flow unit equation.

Corrected LST and NDVI, which indicate a strong relationship with the land-cover data, were identified using scattergrams.

Tandem repeats in the genome sequence data were identified using Tandem Repeats Finder [ 43].