Sentence examples for null datasets from inspiring English sources

Based on a fixed-effects group analysis of null datasets (8 participants scanned while at rest), we show that the magnitude of the problem can be very substantial.

Using this in connection with our techniques allows us to detect activations in clumps of a few (even one) voxel in periodic designs, yet produce essentially no false positive detections at any voxels in null datasets.

To do this, we select randomly 20 datasets from each of 40 models and remove the 2 disease-causal SNPs, which create a total of 800 null datasets.

To do this, we generate 100 datasets from each of the 40 models, which is a total of 4000 null datasets.

To estimate the type I error rate, the null datasets were generated under the same scheme as used for the detection power analysis except that there was no causal pair intended.

When we tried to select randomly 100 datasets from each of 40 models, which is a total of 4000 null datasets, the type I error varied from 0.014 and 0.040, which is also consistently smaller than 0.05.

In the case of the null dataset, we expect that there would not be significant coordination between the clusters.

When evaluating the population dynamics of the different datasets, it was observed that the circadian dataset showed a population distribution very similar to that of the null dataset.

For the null dataset (Figure 6) we see that the maximum deviation from the baseline occurs when a single cluster has been added.

However, when performing the greedy selection on the null dataset (Figure 6) and the circadian dataset (Figure 1), we can see a clear difference between the progression of the transcriptional state of the null dataset and that of the circadian dataset.

Therefore, despite the fact that the population dynamics between the circadian dataset and the null dataset appear similar, this does not discount the fact that significant coordination between the different clusters exist.