The phrase "a much larger dataset" is correct and usable in written English.
It can be used when comparing the size of datasets, particularly in contexts related to data analysis, research, or machine learning.
Example: "In our study, we found that the results were more reliable when we analyzed a much larger dataset."
Alternatives: "a significantly larger dataset" or "a considerably larger dataset."
However, thorough validation awaits a much larger dataset and testing of the model under a variety of on-farm situations.
Our analyses confirm some previously detected trends relating sequence divergence to structural variation but for a much larger dataset and in some superfamilies the new data reveal exceptional structural variation.
But some corroboration comes from eminent author and political analyst Sean Trende, who found the same strong relationship among counties – that is, counties (which provide a much larger dataset) with higher levels of white evangelicals are voting in a lower percentage for Romney.
In this study we experiment with a much larger dataset.
Next, it was applied to a much larger dataset of traumatized children (replication study).
Secure procedures hAnothern proposed topicrform the dimensional redundern and cell lookup.
The purpose of the present study was to further assess the diagnostic performance of Inno-Lia based algorithms for incident HIV-1 infection and to see whether previous estimates for sensitivity and specificity would be confirmed in a new, much larger dataset of newly diagnosed patients with no overlap to our first study [ 16].
The emergence of a robust local maximum of the ACC t) is also demonstrated on an additional much larger dataset extracted from the ChEMBL 20 database [42] (downloaded on 04/24/2015).
Thus, we were not able to replicate Schubert's findings on an external and much larger dataset.
A more stringent threshold was used with the transcriptomics to limit the influence of the much larger dataset and retain a focus on the proteomics data.
RAPIDS also includes support for multi-node, multi-GPU deployments, vastly accelerating processing over CPUs and allowing training on much larger dataset sizes for verticals across many industries.
