Data visualization transformed Big Data analysis by providing an easy way for non-data scientists to process information. Patterns are easier to discern when projected in colorful line graphs or scatter plots. Data stories are embedded in documents and presentations. Entire news segments rely on visual data and the viewing public is increasingly becoming better at interpreting visual data imagery.
But for those with vision impairments, or for those tasked with the tedious job of sifting through raw data, the ability to process data by listening is an intriguing development. Here’s how it works.
From Gravitational Waves to Proteins to Climate
Astronomers first discovered that certain frequencies created noise. These patterns and sequences of “chirps” led them to the discovery of gravitational waves. This development has led to audio analysis in other scientific fields.
Biochemists are using data-backed music to analyze the folding patterns of proteins. This analysis can be used to better understand the mechanisms behind diseases like Alzheimer’s. The researchers analyzed “three proteins, including their chemistry, affinity to water, and structure. Then, using melody generation software, they strung together the different notes to create melodies that could be played on a piano.”
Scientists who listen to the music are able to reach similar conclusions as those who use visual data imagery.
In the field of climatology, researchers at the University of Washington are able to condense over half a century’s worth of carbon dioxide data into roughly one minute of sound. This development is good news for scientists but it also has wider ranging applications.
Taking it Further
Musician Erik Ian Walker of the Climate Music Project uses climate data to construct a musical experience that encourages listeners to appreciate the intensity of climate change. By assigning each data metric with a musical counterpart, listeners are able to hear the relationship between different pieces of data. For example, CO2 levels might be experienced as tempo while temperature changes are experienced as changes in volume. The purpose of the resulting piece of music is to show listeners that, over time, the music breaks down into an uncontrollable cacophony.
As always, data is open to a variety of interpretations. Now, researchers and data scientists can discover and share interpretations through a variety of sensations.