The effect of dataset size on the frequency of collisions was investigated for populations present in 1000 Genomes Project phase 1 data [ 20].
We investigate the effects of temporal range of sampling and dataset size on the precision and accuracy of parameter estimation.
Metabolites, on the other hand, have a very narrow distribution of scaffolds (only 14.3% scaffolds relative to the dataset size) of which 38.9% recur.
In order to estimate the potential improvement in the error rate when using larger datasets we performed the following analysis: we computed the error rate of the FLP and the FTP as function of the dataset size (% of the original dataset; see Additional file 8).
Although SLiMFinder runtimes do appear to increase exponentially with increasing dataset size, the slope of the curve is very shallow and none of the test datasets took more than an hour to run on a single 3.2GHz processor (Figure S2A).
It is also of interest to ask how the program scales with dataset size in terms of the results returned.
SLiMDisc [8], for example, scales very poorly with dataset size: the number of motifs returned and the scores of returned motifs increases substantially.
To further explore the effect of dataset size, four subsets of the pedigree 1 dataset, with a random 100, 500, and 1000 individuals, and all 2000 individuals, were used.
The reduced costs have made the machines more available which has caused an increase in dataset size, the number of datasets, and hence the number of users [2].
We also evaluate the impact of the dataset size and the precision of the GPS coordinates on the uniqueness of the data.
However, missing values in some feature groups and the requirement of a balanced dataset reduced the size of the training dataset.
