The phrase "analysis of such datasets" is correct and usable in written English.
It can be used when discussing the examination or evaluation of specific sets of data in a research or analytical context.
Example: "The analysis of such datasets is crucial for understanding the trends in consumer behavior."
Alternatives: "examination of these datasets" or "evaluation of such data".
A key challenge is the processing and analysis of increasingly larger datasets to continue to provide meaningful and useful results, as the time required for manual analysis of such datasets will soon become impractical.
Consequently, the analysis of such datasets is very time consuming.
There is a myriad of bioinformatics solutions that facilitate download and analysis of such datasets.
Poretools provides two different strategies for facilitating the analysis of such datasets.
Analysis of such datasets will enable the identification of large proportions of the QTN for quantitative traits.
Minor points: 1) In the first paragraph of Results and Discussion, the comment "no reference computational framework is currently available to facilitate the analysis of such datasets" is not entirely correct, since software tools, such as Max Quant Perseus, or X-PRESIDENT can handle HLA-peptidome data without effort (for example: see the second reference cited, Bassani-Sternberg et al. 2015).
Many population genetic programs, such as IMa (Hey and Nielsen, 2007), Structure (Pritchard et al., 2000) or Compute (Thornton, 2003) use multiple genes for analysis, however, preparation of such datasets, even with sequences in hand, is far from straightforward.
The multi-omic profiling required for full analysis would generate a very large, complex dataset, and biologically meaningful interpretation of such datasets requires use of powerful systems biology techniques for integrating multidimensional information into networks [ 4].
Open standards, a defined terminology, and reproducible results allows people to have trust in publicly available datasets and reconstruction of such datasets, and hence improves the reliability of the subsequent statistical analysis.
The companies sharing their data do not always know how much personal information can be inferred from the analysis of such large datasets, and this has led, so far in other cases than mobile phone data, to a few scandals in the recent years [187, 188].
Hence, new methods and tools will be required to support the systematic analysis of such combined datasets.
