<story><title>Why scientists need to be better at data visualization</title><url>https://www.knowablemagazine.org/article/mind/2019/science-data-visualization</url></story><parent_chain></parent_chain><comment><author>stared</author><text>When I was a Ph.D. student I was surprised that in academia (my field was: quantum information theory) the majority of researchers put zero effort, and thought, in presenting data in a clean and clear way. (As a reaction I devoted a section in my thesis to say what is data vis.)
One my day &amp;quot;but it is for other specialists&amp;quot;. Nope, it is not the problem. Of course, every data vis should be tailored for a specific audience. But there is typically zero though put their either. See section 3.1.2 from &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;1412.6796&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;1412.6796&lt;/a&gt;.&lt;p&gt;From a more recent one (&amp;quot;Simple diagrams of convoluted neural networks&amp;quot;, &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;medium.com&amp;#x2F;inbrowserai&amp;#x2F;simple-diagrams-of-convoluted-neural-networks-39c097d2925b&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;medium.com&amp;#x2F;inbrowserai&amp;#x2F;simple-diagrams-of-convoluted...&lt;/a&gt;):&lt;p&gt;&amp;quot;[In academic] research, visualization is a mere afterthought (with a few notable exceptions, including the Distill journal, &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;distill.pub&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;distill.pub&amp;#x2F;&lt;/a&gt;).
One may argue that developing new algorithms and tuning hyperparameters are Real Science&amp;#x2F;Engineering™, while the visual presentation is the domain of art and has no value. I couldn’t disagree more!
Sure, for computers running a program it does not matter if your code is without indentations and has obscurely named variables. But for people — it does. Academic papers are not a means of discovery — they are a means of communication.&amp;quot;</text></comment>
<story><title>Why scientists need to be better at data visualization</title><url>https://www.knowablemagazine.org/article/mind/2019/science-data-visualization</url></story><parent_chain></parent_chain><comment><author>rflrob</author><text>While I absolutely agree that scientists need more formal training in data visualization, the claim that &amp;quot;few scientists take the same amount of care with visuals as they do with generating data or writing about it&amp;quot; doesn&amp;#x27;t ring true to me about the scientists in my field (genetics&amp;#x2F;genomics).  There is a widespread recognition that effective figures are the strongest way to communicate your message, the question is what is the the best way to create those effective figures.  Part of the problem is that as datasets get bigger, it&amp;#x27;s rarely sustainable to put a lot of care into each and every version of a plot, but automating creation of figures is really hard.&lt;p&gt;If I were going to start designing a course in creating scientific figures, I think I&amp;#x27;d have a roughly even split between the psychophysics of visual perception (e.g. distinguishing between similar quantities of lengths&amp;#x2F;angles&amp;#x2F;colors&amp;#x2F;etc; designing for color-blind readers; ) and hands-on work in a real programming environment turning data to figures.</text></comment>