The phrase "specifics of the datasets" is grammatically correct and commonly used in written English.
It refers to the precise details or characteristics of a collection of data. Example: "Before analyzing the data, we need to understand the specifics of the datasets, such as their sources, sample size, and variables included."
An effective visualization module has to perform a problem-specific abstraction of the dataset, leading to a more compact and hence more efficient visual representation.
Data aggregation affects how the variability and heterogeneity of a phenomenon can be observed, given that the change of scale may show or hide specific properties of the dataset in a change of resolution.
Their drawbacks comprehend their dependence on specific features of the dataset from which they are derived, such as the size of the proteins it contains, and their physical meaning is still a subject of debate.
There are limitations to this study due to specific characteristics of the dataset.
For each performed test, we describe specific information of the dataset used, as the number of sequences and the average and maximum length of the sequences.
Thus, specific features of the dataset (e.g. sequence length, number of taxa), the individual branch (e.g. length, depth of position in the tree), and the overall tree (e.g. variation in branch lengths) all likely impact branch length estimation.
Once we generated the peptide lists of the identified proteins from Sanger or TIGR databases, we compared them in order to determine the presence of peptides or proteins which are specific to one of the datasets.
For these pairs, their putative regulatory interaction called edge strength in the following was set to the number of occurrences of the specific TF gene pair of the datasets CheA, Metacore direct activation, Metacore direct inhibition, Metacore indirect activation and Metacore indirect inhibition.
In sum, Bayesian results from more realistic simulations are generally consistent with simple simulations, but the complexity of the datasets produces specific effects not predicted from simple datasets.
All clones were analyzed by direct amplicon TRFLP to unambiguously assign putative environmental clone taxonomic information directly to peaks in the experimental TRFLP dataset; additional specifics of the TRFLP, cloning, and statistical assignment methods used here may be found in Nelson [28].
