Common Scientific Visualization Mistakes (And How to Fix Them Without Losing Your Mind)

Last month, I read 50 scientific papers. The science was excellent, but the figures? Not so much. I saw the same mistakes over and over again. The truth is, most researchers have never been taught how to make good figures—so I'm sharing these fixes to help you avoid rejection.

It's not your fault - you're a scientist, not a graphic designer. But bad figures can kill good science. Here are the most common mistakes I see, and how to fix them.

Mistake #1: The Legend From Hell

The Problem: Legends with 15 different colors, 8 different line styles, and 12 different symbols. It looks like a rainbow threw up on your figure.

The Fix:

• Group Data: If you have more than 5-6 items, use small multiples (subplots) instead of one chaotic plot.
• Direct Labeling: Place labels directly next to the lines instead of using a separate legend box.
• Simplify: Do you really need to show all 20 samples? Show the average and the range.

Mistake #2: The Scale That Lies

The Problem: Different subplots with different scales, making it impossible to compare data across panels. Or worse, bar charts that don't start at zero.

The Fix:

• Sync Axes: Use the same scale for all panels that show the same type of data.
• Zero Baseline: Bar charts must always start at zero. For line charts, you can zoom in, but label the break clearly.

Mistake #3: The Font That's Too Small

The Problem: Text that's so small you need a magnifying glass to read it. This usually happens when you resize a large figure to fit a small column.

The Fix:

• Design for Final Size: If the figure will be 8cm wide, design it at 8cm wide.
• Minimum Font Size: Never go below 8pt. Aim for 10-12pt for axis labels.

Mistake #4: The Raster Export Disaster

The Problem: Figures exported as PNG or JPEG that look pixelated when printed or viewed at high resolution.

The Fix:

• Vector Always: Use SVG or PDF for manuscripts. These formats scale infinitely without losing quality.
• High DPI: If you must use raster (e.g., for heatmaps), use at least 300 DPI (preferably 600 DPI).

Mistake #5: The Colorblind Nightmare

The Problem: Using red and green together to distinguish data. About 8% of men are colorblind and cannot see the difference.

The Fix:

• Safe Palettes: Use Viridis, Cividis, or other perceptually uniform, colorblind-safe colormaps.
• Double Encoding: Use different line styles (dashed vs. solid) or symbols in addition to colors.

The Automated Solution

You could memorize all these rules and check every figure manually. Or, you could use a tool that handles it for you.

Plotivy automatically:

• Selects colorblind-safe palettes.
• Sets correct font sizes for publication.
• Exports in high-resolution vector formats.
• Aligns axes and legends perfectly.