Science at the Speed of Thought 

 Judith E. Devaney, Steven G. Satterfield, John G. Hagedorn 

 National Institute of Standards and Technology 

 100 Bureau Dr., MS 8914 

 Gaithersburg MD 20899-8911 

{judith.devaney,steven.satterfield,john.hagedorn}@nist.gov i http://math.nist.gov/mcsd/savg

ABSTRACT

In this paper we describe a flexible environment that

combines scientific data mining with parallel computing in 

an immersive visualization environment. The goal is to 

minimize the time between the generation of a scientific

hypothesis and the test of that idea, or science at the speed 

of thought. 

Author Keywords

Discovery Science, Scientific Datamining, Immersive 

Visualization. 

ACM Classification Keywords

H5 Information interfaces and presentation; I2 Artificial 

Intelligence; I3 Computer graphics 

INTRODUCTION 

Science advances through iterations of theory, experiment, 

and analysis. But the methods that scientists use to go about

their work are changing[10]. Increasingly experiment 

means computational experiment as computers increase in 

speed and memory. Parallel computing assists in this by 

providing access to more processors and more memory.

Consequently more complex models that run in feasible

times become possible. Laboratory experiments as well are

becoming parallel as combinatorial experiments become

more common. Both types of experiment can produce large 

quantities of data where the time to analyze data can exceed 

the time to generate it. The generated data may also be non 

intuitive. For example nanoscience studies materials whose 

properties may change greatly as their size is reduced[1]. 

Thus analysis may benefit from new ways to examine and 

interact with data. But scientists also need access to their 

traditional analysis tools while taking advantage of new

methods and technologies. Immersion is being actively 

studied as a tool for scientific visualization[11]. 

THE IMMERSIVE VISUALIZATION ENVIRONMENT 

In our immersive visualization environment, we do not

distinguish between information visualization[3] and 

scientific visualization, but rather seek to combine both to

provide the maximum feedback to the scientist. We also

provide an access path directly to the computing 

environment where the scientist can execute commands and 

have the result returned to the immersive environment. We 

use freely available data mining software as well as our own 

equation discovery software[4]. We have created a Glyph

ToolBox to facilitate creating three dimensional glyphs that

includes color and opacity parameters as well as location 

information. Additionally, we have created a suite of tools 

that allow us to display our data with differing views in 

space as well as time. Our environment has some aspects of 

a museum where there are different rooms that have 

multiple types of display such as wall hangings, rugs, 

sculptures, animations, and interaction. (In a virtual

museum, you can even change the architecture (rooms) on 

the fly). It also has some aspects of a supermarket where 

variations on the same thing are presented in multiple ways

and similar things are grouped together. We illustrate some 

some of these ideas with three data sets. 

EXAMPLES 

Figure 1 shows part of our visualization of the Iris data set

[2]. (The full visualization contains multiple rooms with an 

alternate visualization of the same data set in each room, 

enabling a scientist to visit each of the rooms.) On the near 

side of the left wall is a parallel coordinate plot[6] of the 

cluster identified with the transparent envelope. On the far 

side of the left wall is a plot of the probability density 

distribution of each of the attributes in the data set. The right 

wall shows how the attributes rank with Information

Gain[12]. In the foreground is a set of statistics that have

been computed on the fly in response to a user command.

The points of the data set are represented as glyphs where 

the attributes have been mapped to glyph attributes. The 

points are plotted in the central cube. A user can also

interact with this visualization by turning the transparent 

envelops of the clusters on and off individually, and the 

Figure 1. Part of an immersive visualization of the Iris

data set. The full version contains multiple rooms. 

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parallel coordinate plots with them. 

Figure 2 shows a combined surface volume visualization of

Optical Coherence Tomography of a scaffolding used to

grow cells on. The light gray is the scaffolding and the dark

gray is the interface between the scaffolding and the pore 

space. The scaffolding can be viewed by itself or the 

segmentation (surface) can be viewed alone as well. The 

user clicks through the individual views to see each one. 

Separate views can also show a time sequence of the cells

growing on the scaffolding; thus giving scientists insight

into the growth process. 

Figure 2. A combined surface volume visualization of

Optical Coherence Tomography of a scaffolding used to 

grow cells on. 

According to Louis Pasteur “In the field of observation, 

chance favors only the prepared mind” [8]. Scientists are

prepared, but can we increase chance? We believe that with

the tools of immersive visualization, data mining,

interaction, and multiple views, it is possible to accelerate 

concept development. Figure 3 shows a visualization of a 

shake gel[9]. Because of this visualization, scientists were

able to determine that it is the water's oxygen atoms, instead 

of the hydrogen atoms as previously thought, that attach to 

the clay. When a scientist can get access to their data at the 

Figure 3. Visualization of a Shake Gel.

scale of the data, where access means multiple types of 

visualizations in space and time as well as the ability to 

compute and recompute values on the fly, the possibility of

discovery increases as in the case of the shake gel. 

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