Science visualization 3: Redraw Figures 2-4

by helenajambor

Part 3 on “How to accentuate the figures of a scientific paper”:

Re-drawing of Figures (2-4):

Figure 2

Figure2_notes

Before

  1. Layout: The axis is too fat, it is almost more prominent than the data. Typically, I advocate muting it by showing a thin line in grey, for example. If a legend can be placed within the chart area, most often one can simply label the data lines themselves in the corresponding color! That way it takes even less time to read the entire graph.
  2. Color-scheme: For the entire figure set, I have reserved color exclusively for the data on the hopanoid diplopterol (yellow) while the control experiments are shown in shades of grey.
  3. Gridlines: are in 99/100 cases not necessary to guide the reader through the data. However, here they are used to point to the condensation plot on the right. But this takes some effort to find out! I have solved this by unlinking the axes of the monolayer data and the condensation plot.
  4. Axes: It was not immediately obvious that the condensation plot shares the y-axis with the SM monolayer plot. I have unlined the two plots and added a new axis to the condensation plot. In addition, the error bars are very prominent and in some cases they even hide the data bar.
  5. Bar versus Boxplot: Here the median of several experiments is shown in a bar graph – this would be better shown in a boxplot. Even better, if I had had access, would have been to show the distribution of the actual data (Beyond the bargraph). Or, a more radical solution would be to just state the two numbers! Usually, a plot is not necessary when only two numbers should be compared.
  6. Rotated text: is hard to read, it is almost always worth the space to avoid it!! Here: by having two lines of text! Then one can also remove abbreviations entirely!

 Figure2_redraw_version4

After

 

Figure 3

 

Figure3_notes

Before

 

  1. Color scheme: Here, values from measuring membrane packaging are shown. This just shows valued on a single scale – hence a single color would be sufficient! And be easier to read! And even if this actually was diverging data that critically needed two colors (above/below a threshold for example), one would and should not choose a rainbow color scale. As documented in many, many, many blog posts and opinion pieces, rainbow colors do not faithfully reveal graded distributions (Rainbow color map still considered harmful!).
  2. Label clearly: new abbreviations are used, but not introduced in the figure itself – again, it is almost always worth the extra space to increase readability. And here, we have a lot of space!
  3. Cluttering: the extra line is supposed to separate figure part A from B and C. See Figure 1: if the spacing and grouping of panel and panel parts is done clearly, there is no need for a separating line.
  4. Order of panels: Figures are “read” just like a text, from left to right. Therefore panel C will be read before panel While fixing this is sometimes really tricky, in this case it is easy!
  5. Intersection x/y-axis: as a rule (with few notable exceptions), the x-axis should intersect with the y-axis at zero! Also in this panel, the weight of the axes and lines as well as the color scheme does not match to the other figures (but, in this case I lack original data and therefore could not implement changes)
  6. Interrupted axes: interruptions of any axes should best be avoided or at least motivated by the data. In this case, I think it is not necessary to do at all! The plot shows the mean GP index shown in panel A (and the same value for ordered and disordered areas). I have used grey bars to guide the eye to the mean values and reserved white background for the additional calculations of the mean of sub populations.
Figure3_redraw_version3

After

 

 Figure 4

 

Figure4_notes

Before

 

  1. Labeling of the structures could be slightly improved for clarity, especially since the names are re-used in the figure and paper.
  2. Spacing of panel parts: the spacing of charts in panel B could be improved to increase readability and I have used headers to guide the reader through the individual plots. Also, I have matched Figure 4B to the previous, similar Figure 2A.
  3. Data label/legends: as before, I have again chosen color just for the molecule of interest and mutated and homogenized the control data (here is an article on how not to mix attributes such as color, texture etc). The dotted line was visually more “active” than even the colored line showing the hopanoid data!!!
  4. Spacing: by spacing the parts of C better, the readability of the entire figure is enhanced.
  5. Legend: the legend is placed in between the two parts of C and in addition is not 100% identical to B although they should be!
Figure4_redraw

After

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