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The data was partitioned in blocks containing approximately an equal number of slot values using an algorithm outlined in [18].
The first method that was used was a 'random split', herein 10% of the data was partitioned using semi-stratified random partitioning with a fixed random seed as implemented in PP and set apart for future reference.
The original data was partitioned to sets by the comparison stimulus luminance and the answer.
The data was partitioned into subgroups according to gender and ethnicity.
The data was partitioned into training and test data.
For further analysis, the data was partitioned into 6 tissue-array sets.
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Diurnal heat-flux data were partitioned into daytime and nighttime, and positive and negative fractions.
These data were partitioned randomly into two sets, for training and testing.
The rows of data are partitioned based on their primary key.
In fact, these data are partitioned into RSUM j = r j1 + r j2 + … + r jk parts.
When using both OW and RF links concurrently, data is partitioned between the links.
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