The phrase "availability of large datasets" is correct and usable in written English.
You can use it when discussing the presence or accessibility of extensive collections of data, particularly in research or data analysis contexts.
Example: "The availability of large datasets has significantly improved the accuracy of machine learning models."
Alternatives: "access to extensive datasets" or "presence of substantial data collections".
To the general public, "big data" and "deep learning" are increasingly becoming synonymous, fueled by the never-ending hype machine of Silicon Valley and the very legitimate advances occurring in deep learning powered largely by the massive availability of large datasets and the unique abilities of those models to make sense of all that data.
Clustering algorithms are recently regaining attention with the availability of large datasets and the rise of parallelized computing architectures.
The availability of large datasets led to significant performance improvements in several vision-based classification tasks.
With the availability of large datasets in the last two decades, learning from data and extracting value from such large quantities of data has become a prominent field, generally known as machine learning.
With the advances of sequencing technology and the availability of large datasets, evolutionary biology needs to employ novel techniques, which are akin to those developed within statistical physics [1], to analyse and understand patterns in population dynamics.
The rapid availability of large datasets makes it necessary to explore statistical methods that can be computationally less intensive and more flexible for data exploration.
Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.
With the availability of larger datasets and the ability to track communities over time, key ecological concepts such as resilience, alternative stable states, predator prey cycling, and bottom-up versus top-down regulation of ecosystems will be increasingly important.
A third development is the availability of on-line storage of large datasets, especially of open data, including GIS coverages and remotely-sensed images.
Experience with analysis of large datasets is a big plus.
The increasing abundance of substantial GWAS datasets and the increasing availability of large eQTL datasets [Fairfax et al., 2012; Fu et al., 2012; Hao et al., 2012], together with our reassurance that it is possible to conduct these tests whilst maintaining type 1 error rates and the availability of software in our R package will facilitate more widespread formal colocalisation testing.
