Data Visualization: Intro to Infographics

Today, science & R&D social media channels have become just as cluttered as consumer social media channels. For academic researchers, trying to get the word out on your research paper has come to parallel digital and online marketing. It’s as if communicating research main points effectively wasn’t hard enough. Now, even trying to stay afloat on Twitter—much less going viral—is a challenge.

This is where data visualizations come in to play. Visualized data, such as charts, infographics, and interactive figures can represent extensive amounts of complicated data more coherently. It's significantly faster to analyze information in graphical format (versus in spreadsheets). Consequently, scientists, government bodies, and businesses are able to spot correlations, patterns, trends, outliers, etc. with greater ease.
Data visualization also makes communication possible, effective, and interesting. Getting over the subject-specific learning curve (e.g. jargon) often makes sharing findings to the general public hard--even with other researchers! Using visually impactful representations of data gets the message across quickly, engages new audiences, encourages sharing and visibility, and opens the floor to new research opportunities. Click here to read about How the Scientific Community Reacts to Newly Submitted Preprints.

According to Buffercontent with visuals get 94% more total views and visual content is more than 40X more likely to get shared on social media than other types of content. In fact, infographics are liked and shared on social media 3X more than other any other type of content. (MassPlanner) So here are a few common types of data visualizations to help the writer to explain and reader to explore large quantities of data.

Common Types of Infographics

Cartograms represent one variable–such as population or GDP– as land area or distance in a 2D illustration. The space is distorted to compare and contrast the variable across many categories. This cartogram by South China Morning Post, represents 23 languages (out of 7,102 known languages alive today) spoken by 4.1 billion (out of total 7.4 billion) in the world.
Choropleths shade in areas of a map to represent the measurement of a statistical variable, such as number of specific Google keyword by state or population density by region. A series of choropleths by Zeit shows shows some useful and interesting insights about Germany.
Scatter Plots displays values for two variables for a set of data as a collection of points. This creative scatter plot reposted by Scientific American shows how much caffeine is in some of the most common beverages we drink for energy (e.g. Starbucks caffe Americano, Snapple Lemon Tea, Monster Energy) and has even conveniently categorized them by drink type (e.g. coffee, tea, energy drink, etc.).
Multilevel pie charts (also known as sunburst/ring charts) visualizes hierarchical data with concentric circles, where inner layers are umbrella categories for outer layers. The Charted Cheese Wheel is helpful for fancy events.
These are just a few popular types of data visualization, but there are many more. See Anna Vital's How to Think Visually infographic for 72 different types of infographics for more inspiration. Don't forget to tag us using #Authorea & #OpenScience on Twitter when sharing your data visualizations!

[Someone else is editing this]

You are editing this file