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Abstract

The information world is full of labeled quantitative data, in which a number of qualitative categories are to be compared based on a quantitative variable. Their graphical representations are various and serve different audiences and purposes. Based on a simple data set and its different visualizations, we will play with the data and their visual representation. We will use well-known charts, such as a regular table, a bar plot, and a word cloud; less-know, such as Cleveland’s dot plot, a fan plot, and a text-table; and new ones, constructed for the very aim of this essay, such as a labeled rectangle plot and a ruler-like graph. Our discussion will not aim to choose the best graph but rather to show the different faces of visualizing labeled quantitative data. I hope to convince the readers that it is always worth spending a minute on pondering how to present their data.

production by MAO as indicated by the increase of peak of absorbance at 492 nm respect to no-tyramine condition ( Fig. 1A and S1A ). Figure 1 Agmatine does not inhibit liver MAO activity. A) Representative experiment showing the absorption spectra without (gray trace) and in presence of tyramine (black trace) or following co-application of 1mM of agmatine (red trace). MAO activity was monitored using the peroxidase-coupling assay (750 μM , pH 7.4 and 30 min). Inset: bar plot showing the mean result of six experiments with tyramine in presence of either 1 mM agmatine

academic traces. Figure 2 shows the values of academic traces T 1 (solid blue line with squares), T 2 (solid orange line with triangles), and ST (solid green line with circles); this figure also shows the typical academic indicators of average citations per paper ( C / P , brown dashed line with x) and the h -index (gray bar plot) of the top 30 computer science universities in the ARWU 2014 subject ranking. From left to right, the universities are listed in descending order according to total citations. Academic traces T 1 , T 2 , and ST share the same scale

1 purinergic receptor P2X, ligand-gated ion channel, 1 -6.48 <0.01 In the next part of analysis, we focused on the z-scores, which tell us whether the biological process is more likely to be decreased (negative value) or increased (positive value). The z-scores were presented as bar plot ( Fig. 2 ) and as segments of inner circles in the figure 3 . As can be seen from the figures, only “positive regulation of inflammatory response” GO BP term is upregulated while the other biological processes are downregulated. Figure 2 The bar plot presenting z-scores of

each sample is represented by an individual trace on the plot. Fig.3 Frequency Response from 10 samples of superflab from 10 kHz to 1 MHz The mean and standard deviation of the resistance component over the frequency range were calculated, as shown in Figure 4 . The x-axis denotes the frequency and the y-axis denotes the mean resistance (bar plot) and standard deviation (line plot) for 10 samples at a 50 different frequencies. Fig. 4 Mean and standard deviation for Superflab from 10 kHz to 1 MHz The frequency response for Sample 2 is shown in Figure 5 . Ten samples

Gerais 4 Discussion To streamline the funding of research to respond to the Zika crisis, worldwide research funding agencies approved a new fast-track research funding policy for emergencies and the acceleration of research and development processes. The bar plot in Figure 2 shows that the number of collaborations among Zika researchers during the public health emergency increased. This could demonstrate researchers’ concern for the emergency as well as their response to increased research funding. This funding policy also gave rise to diverse national scientific

. These models are very basic and are not capable to capture the features like the semantics, word co-occurrences. These models can only be used to find out the decisions on simple tokens generated after pre-processing and feature extraction. From the results, we can easily infer that random forest classifier gives the highest accuracy because it uses multiple decision trees (i.e. 1000 in our case) to take decisions and predicts the label for being sarcastic or non-sarcastic in nature. It can also be observed from the bar-plot that Naïve Bayes classifier has given the

fungal OTUs were minimally observed. Rhodobiaceae, a bacterial genus, was the most significant community in cultivated TL soil. The functional communities in the UT treatment were higher than those of the TL treatment ( Figure 5B ). Figure 2 Microbial community bar plot with cluster tree. Relative abundances of bacterial (A) and fungal (B) phyla in soils under no-tillage and tillage treatments. Figure 3 Bacterial community heatmap analysis at the genus level. Figure 4 Fungal community heatmap analysis at the genus level. Figure 5 OTU Venn (A) and LEfSe (B) analyses of

between different volunteers, with ABEXYOZ 4 dominating among volunteers of group 1 and ABEXYOZ 1 being dominant in those of group 2 (see bar plots in Fig. 7 ). Given that the volunteers in each group were completely different, these results are expected because specific physiological and cardiodynamic events related to the duration of the opening and closure of the aortic valves vary between people [ 13 ]. Variation in the ICG signal between subjects has been previously observed and reported [ 29 ]. Therefore, five different ABEXYOZ types were observed in most of the