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First in a series of articles on sonification.
Sonification at NCSA: An interview with Robin Bargar









Understanding the Nature of the Data

One necessary step in sonifying data is to understand the dynamics of the researcher's work. This entails examining their models, the dimensions of their numerical simulations, and the changes in the data occurring over time. Bargar adds "if the data isn't time-based, we have to go back and add temporal relationships. We have to first understand the form of the data, then we need to determine how we might interact with it -- do we passively view it as we would a photograph or a movie, or can we engage with the data, push or twist objects representing the data, something more than flying through a space? Then we examine what information the researcher wants to learn from their data. Only then can we create a portrait of what kinds of sounds would fit those characteristics."

Unfortunately, this data doesn't always tell you what it should all sound like. To overcome this, Bargar's team creates some boundaries. "We develop production rules first," he explains. "Then, along with the researcher, we investigate what type of sound would be appropriate, remembering that there may be no precedent. If the data they're studying has a naturally occurring aural relationship in the real world it gives us a good starting point. Or possibly there's an accepted framework of artificial or mechanical sounds. We bring in our own ideas at this point and, together with the researcher, we decide what the data might actually sound like. Then we connect it to some specific sounds."

As an example, Bargar points out some recent work in sonifying numerically modeled storms with NCSA Senior Research Scientist Robert Wilhelmson, who is also a member of the Alliance's environmental hydrology Application Technology Team. "Our group decided to provide what we thought were good information rich and controllable listening experiences based on Bob's data, and then we let him decide what was most informative to his audience." In this way the researcher can perhaps correlate the sound to their previous knowledge and select the best listening experience from their perspective.

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