She blinded me with science! But data visualization is here to help

Chelsea Wegner ·
7 March 2018
Science Communication |     11 comments

Chelsea Wegner

Data is powerful. Data is beautiful. Science can be… complicated. So how do we prevent scaring, confusing or boring people with our results? How do we convey complex processes in a way that is easy to digest? How do we make our science aesthetically pleasing? There are a variety of tools available that can really help to show our results in captivating ways.

Reaching a wide general audience and accurately conveying your message is the pinnacle of data visualization and science communication. The general public may not always be your target, though. Knowing your audience is always key to effectively communicating your work. The graphic below was created by Ed Hawkins, who also created a climate spiral data graphic that went viral in 2016 and was even used at the 2016 Rio Summer Olympics opening ceremony. This is another dataset that he presented in a similar way. It shows how atmospheric CO2 has been increasing since measurements began in 1958, better known as the Keeling Curve. It leaves any viewer with a clear message – atmospheric CO2 is on the rise. It is also a really compelling image that sticks with you.

The spiral of CO2 data based on observations from the Mauna Loa Observatory, Hawaii.
The spiral of CO2 data based on observations from the Mauna Loa Observatory, Hawaii.¹

Not all data visualization needs to be presented in a super catchy GIF. The image below presents the same data in a static format but still clearly conveys the same message.

Monthly mean atmospheric carbon dioxide at Mauna Loa Observatory, Hawaii. The carbon dioxide data (red curve) is measured as the mole fraction in dry air. The black curve represents the seasonally corrected data.
Monthly mean atmospheric carbon dioxide at Mauna Loa Observatory, Hawaii. The carbon dioxide data (red curve) is measured as the mole fraction in dry air. The black curve represents the seasonally corrected data.²

This week’s class took on our “before” figures from last week, generally created in programs such as Excel, Matlab, or R, and used Adobe Illustrator to develop our “after” figures following the feedback from our classmates and tips outlined in the previous blog. The outcome from this process was truly impressive. We saw each student transform their data into clean, less ambiguous, and effective messaging.

Here is my before and after example from class. This is data from my research where I have analyzed stable isotopes of carbon and nitrogen (δ13C and δ15N), total organic carbon (TOC) and carbon to nitrogen ratios (C/N) from a sediment core that was collected in the Chukchi Sea. This information helps to inform sources of carbon (terrestrial versus marine), paleoproductivity, organic matter diagenesis, and nitrogen utilization. Further data analysis is required to address this, but my goal was to highlight the variability over time and distinguish possible environmental shifts, also to generally clean the figure up for clarity. To do this, I incorporated changes to the x-axis scales to emphasize variability, highlighted the shifts in the TOC and C/N with a shaded box, added a title, adjusted colors, and rearranged my combined datasets. I also added a small conceptual diagram to help visualize these measurements within the context of the sediment core.

My "before" (top) and "after" (bottom) data visualizations with improvements based on class feedback.

As with each of our products in class thus far, there is always room for improvement and you should continue to refine your work. I certainly have more work to do but I feel confident our process is moving things in the right direction. It helps to continuously share your data visuals with others, get feedback and adjust accordingly.

There were a few recurring themes that we identified as areas for further improvements:

  • Active titles – these are extremely helpful in guiding the viewer to understand what they are looking at. What is the relationship? Just tell us. Context is important and an active title may not be as important in a publication. However, it is a good idea to have one available and make this an adaptable graphic to include in presentations, posters or as a standalone figure. This may also help you better define your message.
  • Defining the key message – Make sure you give this some thought. What story do you want to tell? Does your data visual do this on its own? There may be opportunities to better frame your data by pairing them with maps or images. More to come in future classes! So check back.
  • Signal to noise ratio – Look for patterns in your data that might be overshadowed by noise. Use colors, shaded boxes, or some other means to draw the viewer’s eye to these patterns. However, make sure you have the statistics to support it!
  • Colors – This can be a really strong tool for conveying your message, and could even convey a misleading message. Remember how powerful red is? We discussed considering color density or shades if the variables are the same but consist of different concentrations, abundances, etc.
  • Font size – Don’t make your readers squint! A good rule of thumb is to drop your graphic in powerpoint to see how it translates. Better yet, print it out and you should be able to read the smallest text while holding at arm’s length.
  • Acronyms – This is really important and a common mistake. You should never assume everyone knows what your acronym means, even if it is glarlingly obvious to you or those in your field. You may be excluding some of your audience. We discussed the use of “DON” in class. DON may mean Dissolved Organic Nitrogen to the environmental world, but a hodgepodge of other things to everyone else (Double or Nothing?). Don’t forget to define those acronyms.
  • Legends and labeling – We reviewed a variety of options to display legends and label our axes to improve the overall look and feel of the data visual. There isn’t one way to do this. Perhaps you could use a symbol or maybe no legend at all is needed. Play around with your options but don’t leave your viewer guessing what something represents.

Here is another great example from class where some of these suggestions were applied. This student was able to effectively highlight trends, improve the use of colors and define his key message with an active title.

Another "Before" and "After" example from class highlighting events from stream gauge data including discharge, temperature and dissolved oxygen for the Potomac River in 2015. The "After" figure is very clean and easy to understand.

Keep at it and remember, information is beautiful!

Picture Citations:

  1. Atmospheric carbon dioxide concentration by Ed Hawkins licensed under Creative Commons Attribution-ShareAlike 4.0 International License
  2. Full Mauna Loa CO2 Record by Dr. Pieter Tans, NOAA/ESRL and Dr. Ralph Keeling, Scripps Institution of Oceanography.

Next Post > Scientific synthesis paper shows Chesapeake Bay nutrient diet is working


  • Katie Fitzenreiter 4 years ago

    Great job, Chelsea! You started off strong with a catchy title and the eye-catching GIF, and kept my attention throughout the entire post. I liked the "before" and "after" figures, and your explanations of the changes that were made to improve them. It makes me think about how I can further improve my own graphs.

  • Jamie Currie 4 years ago

    Nice work! I agree the song was an intuitive choice. I particularly liked how you showed that CO2 spiral graphic -- turning a graphic into a GIF is not an intuitive choice, but is definitely something that we could use more of. Moving it to the top of the blog was a great way of drawing the reader in. You also did a nice job of integrating some of the best examples of our class figures.

  • Annie Carew 4 years ago

    This is really well-written, Chelsea. I like the use of specific student examples, and your list of tips was concise and easy to follow. Linking us to previous blogs about conceptual diagrams and data visualization was a great touch. Like Tom, I was mesmerized by the opening figure/gif. It's such a compelling visual and really draws the reader in. Well done!

  • Nicole Basenback 4 years ago

    I love the title! It's so catchy and it makes you want to read the rest. You definitely captured the main talking points from class.

  • Rebecca Wenker 4 years ago

    Great blog Chelsea, you're a talented writer! This blog was very engaging, and I thought your use of images was excellent. I think the message of this blog is beneficial for people from a wide range of backgrounds, academic fields, and education levels.

  • Lexy McCarty 4 years ago

    Nice job! The first paragraph did a great job grabbing my attention and making me interested. I found it really easy to follow your blog, largely due to all the graphics you used. They did a great job providing visuals for your text.

  • Jessie Todd 4 years ago

    The GIF really made your article relatable! I liked that I was able to click the links embedded and go beyond the blog. The blog flowed really well. Overall nice read.

  • Erin 4 years ago

    Good Title Chelsea! Not only did it give Bill an easy song, but addressed one of the key issues that we have with data visualization. I thought you did a really good job of integrating the images into the blog. They weren't just there to have images, but you discussed them in a way that I found helpful!

  • Tom Butler 4 years ago

    I was drawn into this blog by the animation of CO2 levels at the beginning, great hook. I was also impressed by the number of hyperlinks you used, as I still have to learn this process myself. That was a brilliant move to link your name to the UMCES profile. I think your highlighting of concepts from class really helped me solidify my understanding of text fonts and sizes.

  • Natalie Peyronnin 4 years ago

    Great post! I like the use of external links for fun stuff and the writing style was easy to read and engaging.

  • Bill Dennison 4 years ago

    This blog is one of the ones that the Science Visualization students have been producing every week this semester. I have been commenting on these excellent blogs with the lyrics of a song that I have adapted. The students have caught onto this trend and now they are feeding me blog titles that immediately lend themselves to adapted songs. Chelsea Wagner’s wonderful blog about data visualization is in keeping with this new tradition, using as part of the title “She blinded me with science”. Too easy. I adapted the 1982 hit song “She Blinded Me With Science” by Thomas Daley:

    I Blinded You With Science
    7 March 2018
    William C. Dennison

    Science. It’s poetry in motion
    I turned my tender eyes to see
    Data deep as any ocean
    As sweet as any harmony

    Mmm, but I blinded you with science
    I blinded you with science
    And failed you in comprehension
    When I'm giving you ideas to share
    I’m blinding you with science, science

    I can smell the chemicals
    Blinding me with science, science
    Science, science
    Mmm, but it's poetry in motion
    And when I turn my eyes to see
    Data deep as any ocean
    As sweet as any harmony

    Mmm, but I blinded you with science
    And failed to get your attention
    When I’m giving you ideas to share
    Blinding you with science, science
    Science, I can hear the tension
    Blinding you with science, science

    It's poetry in motion
    And now we’re using visualization
    To improve your retention
    With graphics in harmony
    No more blinding you with science
    Blinding you with science
    And hitting you with technology
    Good heavens, data graphics
    They’re beautiful,
    I-I don't believe it
    There we go again
    Data’s tidied up,
    And I can see everything
    All my graphs and figures
    And careful notes
    And antiquated notions
    But, it's poetry in motion
    And when I turned my eyes to see
    Data deep as any ocean
    As sweet as any harmony

    Mmm, but I blinded you with science
    I blinded you with, with science
    I blinded you with science.

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