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Constructing a complex network of the big textual data, the global properties of the network reveals the nature of dataset in general.
Several interesting findings about the system and the dataset, in general, were observed.
While we are able to compute the optimal τ threshold for a test dataset, in general we would not have access to τ *.
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GeoDCAT-AP is a metadata profile which is a combination of the INSPIRE metadata profile for spatial data and the W3C's DCAT application profile for public sector datasets in general.
It provides the first comprehensive evaluation of ML estimation of k for highly overdispersed datasets (i.e. those with k<1); it reports the coverage accuracy of CIs derived from those estimates; and it examines potential biases in estimation due to methods and errors of data collection, with application to epidemiological datasets in particular and biological datasets in general.
Together, these increase the relevance of our findings to cancer datasets in general, and in particular to data which have been collected in times prior to the introduction of more sophisticated modes of data entry.
Even when the same mutant pairs are considered, the technical variation can lead to some disagreement in the detection results and to relatively large inconsistency between the datasets in general [ 8, 11].
*raw dataset ~proteins purified #using LPIT > 300 ^medium confidence experimental reports Based on our success in identifying valid ING interacting proteins from the unfiltered dataset in Krogan et al., we strongly agree with Hart et al. that the research community would be much better served by the release of raw interaction datasets in general for comparison and consolidation.
21 The first dataset is actually already sorted, but the second dataset is, in general, not sorted.
Results of NC - AD/CA comparisons in our dataset are, in general, in good concordance with such comparisons performed on datasets provided by Galamb et al. (GSE4183) [15] and Sabates-Bellver et al. (GSE8671) [14].
We found that the networks representing the coevolutionary residue connections within our dataset are in general of 'small-world' type as they have clustering coefficient values higher than random networks and also show smaller mean shortest path lengths similar and/or lower than random and regular networks.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com