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On the second randomization method (R2), we also remove the visitation frequencies by generating for each user a new random trajectory with the same number of displacements, and the same number of distinct visited locations.

Following Morris & Doak [43] we simulated population growth by generating, for each time step (year), a set of random demographic rates (of those with annual estimates) that were correlated according to the empirical correlation matrix (Table 2).

The resulting Riptide model is then evaluated on a set of test PSMs, generating for each PSM a feature vector of probabilities.

A common DNA polymerase characteristic is to sometimes add an Adenine at the ends of PCR products generating (for each allele) a mix of products differing in lengths by one nucleotide ('−A' and '+A' products).

This randomization method samples (with repetitions) uniformly and independently multiple alignment columns from the set of corresponding synonymous codons, thus generating for each amino acid the same 'vertical' codon frequencies as in the original alignment matrix (but in a different order).

In Dependency Models, genotypes are illustrated as D, and phenotypes could be illustrated as different numbers of D and (or) C. Discrete and dependent data sets were generated by selecting the number of different discrete values possible, then raising that value to the power equal to the number of data sets to be generated, and generating for each of those values in a probability of occurrence.

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Stimuli generators were automatically generated for each type of constituent.

179 descriptors were generated for each dataset.

One trajectory is generated for each partial.

RMSE values are generated for each cycle.

Paired samples were generated for each patient.

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