Auditory Analytics is a methodological framework that includes data sonification and analytical listening as an integral part of an iterative cycle of data analysis for scientific insight and discovery.
No single data set, visualization, or sonification can ever provide a complete map of a phenomenon. Instead, scientific facts are established progressively through a cascade of evidence—observations, derived data, multiple data sonifications and visualizations, analysis, model building—each stage of which contributes to transforming raw data into a cohesive, cumulative argument.
In Auditory Analytics, data sonification and analytical listening form an indispensable part of that cascade of evidence.
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“Auditory Analytics for Pattern Discovery in Protein Folding Dynamics”
Journal of Chemometrics (2026) Carla Scaletti, Kurt J. Hebel, Martin Gruebele
DOI: 10.1002/cem.70097
Here’s a three-minute introduction to Auditory Analytics:
And a 7-min Video Abstract that includes the Case Study:
This is the pre-peer reviewed version of the following article: [“Auditory Analytics for pattern discovery in protein folding dynamics,” accepted by the Journal of Chemometrics | CONTRIBUTORS: Carla Scaletti*; Kurt J. Hebel; Martin Gruebele*], which has been published in final form at [https://doi.org/10.1002/cem.70097]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
We introduce Auditory Analytics, a methodological framework that utilizes data sonification for scientific discovery. Auditory Analytics describes a cycle of collecting and deriving datasets, mapping data to audible signals (sonification), analytical listening, hypothesis formulation, and tool building, where human insights from any stage of the cycle can feed back into further iterations of the cycle in the form of new datasets, alternative mappings and new models of the original phenomenon. In Auditory Analytics, the remarkable capacities of the human auditory system to monitor complex soundscapes, track multiple sources, and extract meaningful information across multiple timescales are repurposed for exploring, interpreting, and analyzing data. To demonstrate its potential for uncovering relationships and dynamics in physical systems, we apply the Auditory Analytics methodology to the domain of protein-folding, investigating state transitions in a molecular dynamics simulation of the GTT WW domain protein. Auditory Analytics led to the identification of distinct hydrogen bonding patterns that occur as the protein transits between folded and unfolded states and thus to a deeper understanding of the process of protein folding. A single, isolated data mapping — whether visual, auditory, haptic, mathematical, or verbal — provides an incomplete picture of reality; by adding the Auditory Analytics cycle to our portfolio of data interpretation tools, we can build a more complete picture of physical phenomena.