[earthscienceman]

Not that you're wrong but researchers are also deeply imperfect. They're rushed, they're given no time to actually improve their work, and the emphasis is entirely on 'good-enough' publications. The number of times I've been involved in a paper where the mentality wasn't "get it out the door, now" is.... zero times.

Plots often fail to clarify for the precise reason that clarification takes time and effort and those things are lacking in academia in spades. Are people intentionally hiding ugly details, definitely on occasion? But I don't think it's the primary source of such bad figures.

[defrost]

All that you said with a heavy dash of good data visualisation is more of a skill and an artform than many people realise.

I've had four decades of crunching numbers in a variety of Engineering, Geophysics, and science applications with a hefty amount of public consulting on a variety of applications and of the large population of those good at gathering and recording data perhaps only 20% had that extra talent for good visualisation to convey meaning without distortion.

[ctxc]

Are there resources or good examples you would recommend?

[yummypaint]

There is a book called. "how to lie with statistics" by Huff that should probably be required reading for everyone. It's not very technical and a pretty quick read

[frederikb]

I really enjoyed Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic. The author really breaks down the individual elements of good/bad visualizations using case studies with lots of actionable advice. Highly recommended.

[armagon]

I liked "Signal: Understanding What Matters in a World of Noise" by Stephen Few.

[mif]

https://www.dataviz-inspiration.com/