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As an example, this data set is used to estimate a set of regional extensive margins by AEZ according to the regional classifications used by Hertel et al. In general, the regional/AEZ extensive margins obtained from this data set fall in the range between 0.42 and 1.
For example this data point could be added to the first part of Figure 2 (all genes) by indicating the promoter types of the central and subtelomeric var genes (central: UpsC, UpsB and Ups A; subtelomeric: Ups B and UpsA).
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To improve our analysis, we are testing methods to integrate disparate classes of data; for example, this genomic data covers a range including binary for mutation, categorical for copy number, and continuous for mRNA and methylation.
Mobile phones can be used to monitor eye movements and body pose, for example, and this data could be used to tailor individual educational programmes based on the teaching methods that elicit the greatest positive emotional response.
Figure 3d shows a typical example of this data.
An example of this data from one return is given in Fig. 1.
For example, with this data, models found by SA_geom after only 60 s of run time have an average, cross-validated CC of 0.97.
In this approach, the data are set up in a long form so that each observation represents the number of events in each arm of each study (see the Additional file 1: Appendix for an example of this data structure).
For example, this sample data set has a continuous variable called 'Age' that represents the patient's age.
Examples of this data set are shown in Figures 5 and 6. Figure 5 Results for the city road view.
In this example, the data flow comes from the outer area to the sink.
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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