Science Behind the Dance – Autobiography by Wayne McGregor

Auto-Bio-Graphy = Self-Life-Writing or how your body and life look as told through choreography.  This is what Wayne McGregor imagined as he began working on Autobiography with the McGregor Company Dancers.  The Science Writing and Communication club (SWAC) and Carolina Performing Arts recently sat down with the dancers to discuss how science and dance intersect.

SWAC learned that McGregor has been collaborating with scientists for many years regarding different facets of dance.  For example, his dancers have worked with Professor David Kirsh at the University of California, San Diego, where he studied creative cognition with the dancers and how dance content is created and remembered between dancers and choreographer.

Photo by Andrej Uspenski

Company Wayne McGregor dancers perform Autobiography. Photo by Andrej Uspenski.

When McGregor began working on Autobiography, inspired by having his DNA sequenced, he gave his dancers ideas, or what they call tasks, to demonstrate different concepts with their bodies through dance.  For example, at the beginning of creating Autobiography, the dancers were paired into groups of 4 and given the letters A,T,G, or C.  He then asked them to create choreography together.  Then McGregor and the dancers visited The Wellcome Sanger Institute in Cambridge UK, a world leader in genome research, and learned what the letters A,T,G,C meant biologically as the building blocks of DNA. After visiting the sequencing facility, McGregor asked the dancers to repeat their choreography tasks.  During our conversation, the dancers said that with their new understanding of DNA biology, the choreography tasks took on a new meaning.

The dance Autobiography is broken down into 23 different choreographed segments that are assigned an order randomly by an algorithm to mimic the randomness of DNA recombination.  This aspect of the choreography is complicated for the dancers, who don’t know the order of the segments until a couple days before the performance.  This meant that sometimes they would be dancing for long periods of time, whereas other times their performances would be broken up into smaller segments throughout the night.  Sometimes the dance segments flow into the next piece of choreography seamlessly, and sometimes they end quite abruptly.  In our conversation, the dancers said they envisioned this re-ordering and occasional abrupt stopping as being very similar to the chaos of life.

An interesting moment from our conversation evolved as both dancers and scientists alike realized that we both strive to achieve communication through our bodies.  It is easy to imagine how dancers do this, but not as easy to imagine how scientists communicate with body movement.  As scientists, we realized that we attempt to communicate concepts visually through use of our bodies, whether it be through gesturing with our hands to emphasize points during a presentation, or through mimicking with our bodies what we believe is happening, invisible to us, inside a cell during DNA damage, repair, or replication.  Another moment in our conversation where science connected easily to dance was when the scientists and dancers discussed how both published scientific findings and performed choreography are both put into the ether for others to interpret using their personal lenses.  We all interpret data differently based on our own experiences, and as scientists and as dancers we hope that people find use in our work and can apply it to their own lives.

Peer edited by Adrienne Cox.

Follow us on social media and never miss an article:


A picture is worth a thousand words…or more

“Use a picture. It’s worth a thousand words.” This timeless expression first appeared in a 1911 Syracuse Post Standard newspaper article. If you ask Mohamad Elgendi, he’ll say it’s more like 10000 words, based on how fast our mind processes words vs. images. Although true for almost everything, this phrase is becoming even more important in the sciences where data visualization is a necessity for clearly communicating complex and large data sets.

The concept of data visualization is simple: it is the representation of data in a graphical or pictorial format. Creating effective data visualizations, however, is quite difficult. Scientists are often tasked with this challenge every day, whether by presenting their work to peers or to the general public through written and oral forms of communication. Data visualizations play a huge role in all of these outputs, so scientists should be pretty good at it, right?

Most scientists would probably say they are decent at preparing figures and graphics for someone that is within their field of study. Beyond just the typical representations of data like bar plots, scatter plots, pie charts, and line graphs, different fields within the life sciences have created various types of plots for representing certain data. For example, protein sequence conservation is sometimes depicted in “sequence logo plots”. But these field specific data representations may not be appropriate for all audiences and branching out to create something that is both visually appealing and effective at conveying the proper message to the right audience is tough.

There are multiple possible explanations for the gap in scientists’ ability to make effective data visualizations. The first is that we simply are not trained in art or graphic design. Additionally, scientists do not always have access to someone, such as a graphic designer, to collaborate with for making figures. Although there are efforts being made, such as this one at the University of Washington, that work to forge collaborations between science and design students. Another factor that introduces a hurdle to scientists making good data visualizations is time. First, a good figure requires a complete and thorough understanding of the data which can take a tremendous amount of time, particularly in the days of big data, where data sets are extremely vast and complex. Finally, it also takes time to create a figure. Creating a beautiful data visualization requires hours of training and working with unfamiliar software, such as Adobe Illustrator, that takes patience and persistence to master.

So scientists need to improve their data visualization skills but it is often difficult to find the time to practice some of these skills. Some helpful beginners tips for data visualization are shown below because the goal is always the same.

Goal of data visualization: To create a story from a set of data in a clear manner

How to get there:

  1. out your narrative, or the story that you want to tell with the data. This requires a comprehensive understanding of the dataset you aim to represent along with the an understanding of your audience.
  2.   Determine the best way to represent the data. This sounds easier than it actually is and could take some time making and comparing multiple different types of figures. Again remember the story and the audience.
  3.   Learn a little bit about how the brain perceives images, color, and depth. Learning the core principles of design, such as color choice, negative space, and typography, can have an immediate impact on
    the visual appearance of the graphic. This
    document highlights data visualization specifically for the life sciences and Nature has compiled a collection of articles related to design.
  4.   Get feedback from everybody. Before finalizing a data visualization make sure to get feedback from multiple people with different backgrounds. Ensure they all interpret the data as you aimed to present it. And, as most things are not perfect the first time, refine and remake until you create your ideal data visualization.

Nearly every scientist hopes to turn the ideas in their head into a beautiful work of art, similar to this process of going from sketch to infographic. It takes time, patience, and practice to develop these skills. If you are a scientist looking to enhance your data visualization skills consider taking an online course, reading up on data visualization, practice making figures from some largely accessible datasets or for your colleagues, entering a contest such as the NSF Vizzies Challenge, or attending a conference or workshop.

Peer edited by Alex Mullins.

Follow us on social media and never miss an article: