Figure 1: Breakdown of the dashboard used to perform exploratory analysis on student information.This figure shows students below a incoming math course cutoff and students at or above that same math course cutoff, with those below the cutoff seeing fewer favorable outcomes.

Many undergraduates who pursue a major in Computer Science (CS) at rural universities identify themselves as first-generation students. There is a large body of research that confirms first-generation students are more likely to switch from CS to a different major, or leave their university completely within the first year. In response, this study explored the challenges that first-generation students from rural backgrounds face, and factors that might increase their chances of retention and success within the major. Additionally, the study identified thematic differences between first-generation students and non-first-generation students that highlight potential setbacks, such as their previous exposure and preparation for CS concepts, and their confidence in their ability to succeed in the courses and major. The poster provides details of the survey and other evaluative protocols as well as the analysis results and recommendations for program interventions.

At our university, women are a minority in all computing and engineering majors. This situation is mirrored at other universities nationwide, as well as in technical industries. Stereotype threat, the risk of confirming a negative stereotype about one's social group, can result in women not performing as well as their male counterparts in computing, mathematics, and engineering courses. The aftermath of these experiences cause women to fear they do not belong or are not "smart enough", resulting in women switching to a different major. Even high-performing women can be subject to these pressures. We present a Scholar model for increasing gender diversity in technology fields and an evaluation of its impact on the retention and success of women in computing and engineering majors. We compare the performance of Scholars to that of non-Scholars with similar demographic and academic backgrounds who entered into the College of Engineering and Information Technology at the University of Maryland, Baltimore County.

Abstract In macromolecules, an allosteric effect is said to occur when a change at one site of a molecule affects a distant site. Understanding these allosteric effects can be important for understanding how the functions of complex molecules such as proteins are regulated. One potential application of this knowledge is the development of small molecules that alter the function of proteins involved in diseases. Studying motional correlation can help researchers to discover how a change at a source site affects the target site and thus how allosteric ligands that could serve as drugs are able to exert their therapeutic effects. By improving our ability to analyze these correlated relationships, it may be possible to develop new medications to combat deadly diseases such as Hepatitis C. We present four visual techniques which represent motional correlation on rendered three‐dimensional molecular models, providing new ways to view clusters of correlated residues and paths of allosteric interactions. These techniques give us a new way of investigating the presence of motional correlations in complex molecules. We compare each of these techniques to determine which are the most useful for representing motional correlations.

While intuitive time-series visualizations exist for common datasets, student course history data is difficult to represent using traditional visualization techniques due its concurrent nature. A visual composition process is developed and applied to reveal trends across various groupings. By working closely with educators, analytic strategies and techniques are developed to leverage the visualization composition to reveal unknown trends in the data. Furthermore, clustering algorithms are developed to group common course-grade histories for further analysis. Lastly, variations of the composition process are implemented to reveal subtle differences in the underlying data. These analytic tools and techniques enabled educators to confirm expected trends and to discover new ones.

Abstract As collections of 2D/3D images continue to grow, interest in effective ways to visualize and explore the statistical morphological properties of a group of images has surged. Recently, deformation models have emerged as simple methods to capture the variability and statistical properties of a collection of images. Such models have proven to be effective in tasks such as image classification, generation, registration, segmentation, and analysis of modes of variation. A crucial element missing from most statistical models has been an effective way to summarize and visualize the statistical morphological properties of a group of images. This paper evaluates different visualization techniques that can be extended and used to illustrate the information captured by such statistical models. First, four illustration techniques are described as methods to summarize the statistical morphological properties as captured by deformation models. Second, results of a user study conducted to compare the effectiveness of each visualization technique are presented. After comparing the performance of 40 subjects, we found that statistical annotation techniques present significant benefits when analyzing the structural properties of a group of images.

The underrepresentation of women in technical fields is a widely acknowledged national problem, limiting both the raw size of the talent pool and the diversity of experiences and perspectives of those who will design solutions to key problems facing society. Empowering women to succeed in these fields is clearly one important component of any solution. Because the population in those fields will likely continue to be overwhelmingly male for some time to come, men must also be a key component of the solution. Specifically, since the attitudes of the majority group are a strong determinant of climate, it is almost equally important to foster a population of men supportive of increasing the representation of women. As at most universities and technical companies, women are a minority in all majors in the University of Maryland Baltimore County (UMBC) College of Engineering and Information Technology (COEIT). In most majors, they are a small minority. The UMBC Center for Women and Information Technology (CWIT) is dedicated to increasing the representation of women in engineering and information technology fields. At the heart of these efforts is the CWIT Scholar Program, a selective merit scholarship program for undergraduates majoring in engineering and IT who are committed to increasing the representation of women in those fields. In addition to financial support, the CWIT Scholar Program provides a supportive community, academic and professional development programming, networking opportunities, and a residential Living Learning Community. Almost from the beginning, some CWIT scholars have been men. 28 percent of current CWIT scholars are men. Perhaps not surprisingly, female CWIT scholars have dramatically higher retention and completion rates than other women majoring in these fields. In this article, we look at a second effect of the CWIT Scholar Program, that of changing awareness and attitudes of the men who participate. We discuss programs and present results of a survey of attitudes regarding technical, academic, and gender issues in engineering and IT education. We disaggregate survey participants to show differences between the attitudes of women, scholar men, and men not participating in a diversity program.

The devastating power of hurricanes was evident during the 2005 hurricane season, the most active season on record. This has prompted increased efforts by researchers to understand the physical processes that underlie the genesis, intensification, and tracks of hurricanes. This research aims at facilitating an improved understanding into the structure of hurricanes with the aid of visualization techniques. Our approach was developed by a mixed team of visualization and domain experts. To better understand these systems, and to explore their representation in NWP models, we use a variety of illustration-inspired techniques to visualize their structure and time evolution. Illustration-inspired techniques aid in the identification of the amount of vertical wind shear in a hurricane, which can help meteorologists predict dissipation. Illustration-style visualization, in combination with standard visualization techniques, helped explore the vortex rollup phenomena and the mesovortices contained within. We evaluated the effectiveness of our visualization with the help of six hurricane experts. The expert evaluation showed that the illustration-inspired techniques were preferred over existing tools. Visualization of the evolution of structural features is a prelude to a deeper visual analysis of the underlying dynamics.

This paper presents a volume representation format called BT Volumes, along with a technique to interactively render them and two methods to create useful data in BT Volume format, including high quality reconstruction filtering. Medical applications rely heavily on isosurface data to visualize anatomy, but current real-time iso-surface rendering techniques such as Marching Cubes are limited in flexibility and provide only low-order linear reconstruction filtering. As an alternative to creating triangular geometry to represent the surface, we ray trace an exact isosurface directly inside a pixel shader. We construct a set of Bézier Tetrahedra to approximate any reconstruction filter with arbitrary footprint. We then precompute the volume convolved with this filter as a tetrahedral grid with Bézier weights that can be ray traced in graphics hardware. Our technique is fast, renders any isosurface level without additional work, and performs high quality reconstruction filtering with arbitrary footprints and reconstruction kernels.

Abstract Illustration‐inspired techniques have provided alternative ways to visualize time‐varying data. Techniques such as speedlines, flow ribbons, strobe silhouettes and opacity‐based techniques provide temporal context to the current timestep being visualized. We evaluated the effectiveness of these illustrative techniques by conducting a user study. We compared the ability of subjects to visually track features using snapshots, snapshots augmented by illustration techniques, animations, and animations augmented by illustration techniques. User accuracy, time required to perform a task, and user confidence were used as measures to evaluate the techniques. The results indicate that the use of illustration‐inspired techniques provides a significant improvement in user accuracy and the time required to complete the task. Subjects performed significantly better on each metric when using augmented animations as compared to augmented snapshots.

Visualization of volumetric data faces the difficult task of finding effective parameters for the transfer functions. Those parameters can determine the effectiveness and accuracy of the visualization. Frequently, volumetric data includes multiple structures and features that need to be differentiated. However, if those features have the same intensity and gradient values, existing transfer functions are limited at effectively illustrating those similar features with different rendering properties. We introduce texture-based transfer functions for direct volume rendering. In our approach, the voxel's resulting opacity and color are based on local textural properties rather than individual intensity values. For example, if the intensity values of the vessels are similar to those on the boundary of the lungs, our texture-based transfer function will analyze the textural properties in those regions and color them differently even though they have the same intensity values in the volume. The use of texture-based transfer functions has several benefits. First, structures and features with the same intensity and gradient values can be automatically visualized with different rendering properties. Second, segmentation or prior knowledge of the specific features within the volume is not required for classifying these features differently. Third, textural metrics can be combined and/or maximized to capture and better differentiate similar structures. We demonstrate our texture-based transfer function for direct volume rendering with synthetic and real-world medical data to show the strength of our technique.

Student retention is an important topic in Computer Science departments across the country. Keeping strong students and helping struggling students perform better are two fundamental components of improving retention. Isolating the cause(s) of students leaving the major is an important area of research. We endeavor to explore this problem using a visualization tool to probe student data within the beginning course sequence in Computer Science. We would like to see what patterns exist amongst students, focusing on success, failure, and repetition patterns throughout the first three courses. Identifying these patterns can help isolate some of the causes of decreased retention within the department, allowing us to address individual projects, courses, or exams that may be causing students exceptional difficulty or loss of interest. Due to the large amount of data and the variety of students' paths through their courses, it is essential that a visualization be developed to represent the data. Using graph layouts, parallel coordinates, color-mapping, and interactive selection, users can explore and query the data. Users can discover patterns within the data by selecting subgroups of students and examining the event sequences to find patterns of success, failure, and repetition amongst those students. Departments can use this information to isolate profiles of students for retention, remediation, and recruitment efforts as well as identify areas of the curriculum or instruction that can be improved.

Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.

Many real-world domains resist analysis because they are best characterized by a variety of data types, including relational, spatial, and temporal components. Examples of such domains include disease outbreaks, criminal networks, and the World-Wide Web. We present two types of visualizations based on physical metaphors that facilitate fusion, analysis, and deep understanding of relational, spatio-temporal data. The first visualization is based on the metaphor of fluid flow through elastic pipes, and the second on wave propagation. We discuss both types of visualizations in the context of fusing information about the activities of scientists over time with the goal of constructing career histories

In most graphics and visualization applications, the effects of the mapping parameters on the output domain are multidimensional, non-linear and discontinuous. The complexity of such mapping often makes it difficult for a user to manually explore and manipulate the design parameter space to produce the desired output. Computer assistance is therefore useful in setting the mapping parameter values to generate desired outputs. Existing systems rely on exploring the entire input parameter space, which can be time and resource-intensive, particularly if the number of input parameters is large. We introduce a new approach to handling a large number of mapping parameters more efficiently. The basis for our approach is the identification of a small and effective set of highlevel parameters that can be associated directly with the characteristics of the outputs. Users will have a better understanding of this small set of high-level parameters and can easily modify their values interactively to produce the desired outputs. We demonstrate this technique in manipulating mapping parameters for a non-photorealistic volume rendering application.

Traditionally, time-varying data has been visualized using snapshots of the individual time steps or an animation of the snapshots shown in a sequential manner. For larger datasets with many time-varying features, animation can be limited in its use, as an observer can only track a limited number of features over the last few frames. Visually inspecting each snapshot is not practical either for a large number of time-steps. We propose new techniques inspired from the illustration literature to convey change over time more effectively in a time-varying dataset. Speedlines are used extensively by cartoonists to convey motion, speed, or change over different panels. Flow ribbons are another technique used by cartoonists to depict motion in a single frame. Strobe silhouettes are used to depict previous positions of an object to convey the previous positions of the object to the user. These illustration-inspired techniques can be used in conjunction with animation to convey change over time.

When performing packet-level analysis in intrusion detection, analysts often lose sight of the big picture while examining these low-level details. In order to prevent this loss of context and augment the available tools for intrusion detection analysis tasks, we developed an information visualization tool, the time-based network traffic visualizer (TNV). TNV is grounded in an understanding of the work practices of intrusion detection analysts, particularly foregrounding the overarching importance of context and time in the process of intrusion detection analysis. The main visual component of TNV is a matrix showing network activity of hosts over time, with connections between hosts superimposed on the matrix, complemented by multiple, linked views showing port activity and the details of the raw packets. Providing low-level textual data in the context of a high-level, aggregated graphical display enables analysts to examine packet-level details within the larger context of activity. This combination has the potential to facilitate the intrusion detection analysis tasks and help novice analysts learn what constitutes normal on a particular network.

This paper presents the Intrusion Detection toolkit (IDtk), an information Visualization tool for intrusion detection (ID). IDtk was developed through a user-centered design process, in which we identi- fied design guidelines to support ID users. ID analysts protect their networks by searching for evidence of attacks in ID system output, firewall and system logs, and other complex, textual data sources. Monitoring and analyzing these sources incurs a heavy cognitive load for analysts. The use of information visualization techniques offers a valuable addition to the toolkit of the ID analyst. Several visualization techniques for ID have been developed, but few usability or field studies have been completed to assess the needs of ID analysts and the usability and usefulness of these tools. We intended to fill this gap by applying a user-centered design process in the development and evaluation of IDtk, a 3D, glyph-based visualization tool that gives the user maximum flexibility in setting up how the visualization display represents ID data. The user can also customize whether the display is a simple, high-level overview to support monitoring, or a more complex 3D view allowing for viewing the data from multiple angles and thus supporting analysis and diagnosis. This flexibility was found crucial in our usability evaluation.

Efficient and informative visualization of surfaces with uncertainties is an important topic with many applications in science and engineering. In these applications, the correct course of action may depend not only on the location of a boundary, but on the precision with which that location is known. Examples include environmental pollution borderline detection, oil basin edge characterization, or discrimination between cancerous and healthy tissue in medicine. This paper presents a method for producing visualizations of surfaces with uncertainties using points as display primitives. Our approach is to render the surface as a collection of points and to displace each point from its original location along the surface normal by an amount proportional to the uncertainty at that point. This approaoh can be used in combination with other techniques such as pseudocoloring to produce efficient and revealing visualizations. The basic approach is sufficiently flexible to allow natural extensions; we show incorporation of expressive modulation of opacity, change of the stroke primitive, and addition of an underlying polygonal model. The method is used to visualize real and simulated tumor formations with uncertainty of tumor boundaries. The point-based technique is compared to pseudocoloring for a position estimation task in a preliminary user study.

Simulating hand-drawn illustration can succinctly express information in a manner that is communicative and informative. We present a framework for an interactive direct stipple rendering of volume and surface-based objects. By combining the principles of artistic and scientific illustration, we explore several feature enhancement techniques to create effective, interactive visualizations of scientific and medical data sets. We also introduce a rendering mechanism that generates appropriate point lists at all resolutions during an automatic preprocess and modifies rendering styles through different combinations of these feature enhancements. The new system is an effective way to interactively preview large, complex volume and surface data sets in a concise, meaningful, and illustrative manner. Stippling is effective for many applications and provides a quick and efficient method to investigate both volume and surface models.

Efficient and informative visualization of surfaces with uncertainties is an important topic with many applications in science and engineering. Examples include environmental pollution borderline identification, identification of the limits of an oil basin, or discrimination between contaminated and healthy tissue in medicine. This paper presents an approach for such visualization using points as display primitives. The approach is to render each polygon as a collection of points and to displace each point from the surface in the direction of the surface normal by an amount proportional to some random number multiplied by the uncertainty level at that point. This approach can be used in combination with other techniques such as pseudo-coloring and shading to give rise to efficient and revealing visualizations. The method is used to visualize real and simulated tumor formations with uncertainty of tumor boundaries.

The Gauss map projects surface normals to a unit sphere, providing a powerful visualization of the geometry of a graphical object. it can be used to predict visual events caused by changes in lighting, shading, and camera control. We present an interactive technique for portraying the Gauss map of polygonal models, mapping surface normals and the magnitudes of surface curvature using a spherical projection. Unlike other visualizations of surface curvature, we create our Gauss map directly from polygonal meshes without requiring any complex intermediate calculations of differential geometry. For anything other than simple shapes, surface information is densely mapped into the Gaussian normal image, inviting the use of visualization techniques to amplify and emphasize details hidden within the wealth of data. We present the use of interactive visualization tools such as brushing and linking to explore the surface properties of solid shapes. The Gauss map is shown to be simple to compute, easy to view dynamically, and effective at portraying important features of polygonal models.

Simulating hand-drawn illustration techniques can succinctly express information in a manner that is communicative and informative. We present a framework for an interactive direct volume illustration system that simulates traditional stipple drawing. By combining the principles of artistic and scientific illustration, we explore several feature enhancement techniques to create effective, interactive visualizations of scientific and medical datasets. We also introduce a rendering mechanism that generates appropriate point lists at all resolutions during an automatic preprocess, and modifies rendering styles through different combinations of these feature enhancements. The new system is an effective way to interactively preview large, complex volume datasets in a concise, meaningful, and illustrative manner. Volume stippling is effective for many applications and provides a quick and efficient method to investigate volume models.

Photographic volumes present a unique, interesting challenge for volume rendering. In photographic volumes, the voxel color is pre-determined, making color selection through transfer functions unnecessary. However, photographic data does not contain a clear mapping from the multi-valued color values to a scalar density or opacity, making projection and compositing much more difficult than with traditional volumes. Moreover, because of the nonlinear nature of color spaces, there is no meaningful norm for the multi-valued voxels. Thus, the individual color channels of photographic data must be treated as incomparable data tuples rather than as vector values. Traditional differential geometric tools, such as intensity gradients, density and Laplacians, are distorted by the nonlinear non-orthonormal color spaces that are the domain of the voxel values. We have developed different techniques for managing these issues while directly rendering volumes from photographic data. We present and justify the normalization of color values by mapping RGB values to the CIE L*u*v* color space. We explore and compare different opacity transfer functions that map three-channel color values to opacity. We apply these many-to-one mappings to the original RGB values as well as to the voxels after conversion to L*u*v* space. Direct rendering using transfer functions allows us to explore photographic volumes without having to commit to an a-priori segmentation that might mask fine variations of interest. We empirically compare the combined effects of each of the two color spaces with our opacity transfer functions using source data from the Visible Human project.

Describes algebraic methods for creating implicit surfaces using linear combinations of radial basis interpolants to form complex models from scattered surface points. Shapes with arbitrary topology are easily represented without the usual interpolation or aliasing errors arising from discrete sampling. These methods were first applied to implicit surfaces by V.V. Savchenko, et al. (1995) and later developed independently by G. Turk and J.F. O'Brien (1998) as a means of performing shape interpolation. Earlier approaches were limited as a modeling mechanism because of the order of the computational complexity involved. We explore and extend these implicit interpolating methods to make them suitable for systems of large numbers of scattered surface points by using compactly supported radial basis interpolants. The use of compactly supported elements generates a sparse solution space, reducing the computational complexity and making the technique practical for large models. The local nature of compactly supported radial basis functions permits the use of computational techniques and data structures such as k-d trees for spatial subdivision, promoting fast solvers and methods to divide and conquer many of the subproblems associated with these methods. Moreover, the representation of complex models permits the exploration of diverse surface geometry. This reduction in computational complexity enables the application of these methods to the study of the shape properties of large, complex shapes.

Accurately and automatically conveying the structure of a volume model is a problem which has not been fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images which may match the appearance of translucent materials in nature but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance but generally require substantial hand-tuning for each new data set in order to be effective. We introduce the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques. Since the features to be enhanced are defined on the basis of local volume characteristics rather than volume sample values, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing the structural perception of volume models through the amplification of features and the addition of illumination effects.

In many applications of scientific visualization, a large quantity of data is being processed and displayed in order to enable a viewer to make informed and effective decisions. Since little data is perfect, there is almost always some degree of associated uncertainty. This uncertainty is an important part of the data and should be taken into consideration when interpreting the data. Uncertainty, however, should not overshadow the data values. Many methods that address the problem of visualizing data with uncertainty can distort the data and emphasize areas with uncertain values. We have developed a method for showing the uncertainty information together with data with minimal distraction. This method uses procedurally generated annotations which are deformed according to the uncertainty information. As another possible technique we propose distorting glyphs according to the uncertainty information.

Accurately and automatically conveying the structure of a volume model is a problem that has not been fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images which may match the appearance of translucent materials in nature but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance but generally require substantial hand-tuning for each new data set in order to be effective. We introduce the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques. Since the features to be enhanced are defined on the basis of local volume characteristics rather than volume sample values, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features and the addition of illumination effects.

Using inductive learning techniques to construct classification models from large, high-dimensional data sets is a useful way to make predictions in complex domains. However, these models can be difficult for users to understand. We have developed a set of visualization methods that help users to understand and analyze the behavior of learned models, including techniques for high-dimensional data space projection, display of probabilistic predictions, variable/class correlation, and instance mapping. We show the results of applying these techniques to models constructed from a benchmark data set of census data, and draw conclusions about the utility of these methods for model understanding.

We present the design of a simulator for a prototype interventional magnetic resonance imaging scanner. This MRI scanner is integrated with an operating theater, enabling new techniques in minimally invasive surgery. The simulator is designed with a threefold purpose: to provide a rehearsal apparatus for practicing and modifying conventional procedures for use in the magnetic environment; to serve as a visualization workstation for procedure planning and previewing as well as a post-operative review; and to form the foundation of a laboratory workbench for the development of new surgical tools and procedures for minimally invasive surgery. The simulator incorporates pre-operative data, either MRI or CT exams, as well as data from commercial surgical planning systems. Dynamic control of the simulation and interactive display of pre-operative data in lieu of intra-operative data is handled via an opto-electronic tracking system. The resulting system is contributing insights into how best to perform visualization for this new surgical environment.

Climatological data about thunderstorms is traditionally collected by balloons or planes traveling through the storm along straight tracts. Such data lends itself to simple 2D representations. The data described in this paper was gathered by a sail plane spiraling in an updraft within a thundercloud. The more complex organization of data samples demands more complex representation methods. This paper describes a system developed using the Visualization Toolkit (VTK) to explore such data. The data consists of several scalar values and a set of vector values associated with positional data on the measuring devices. The goal of this visualization is to explore the location of point charges suggested by the electromagnetic field vectors and determine if any correlation exists between the point charge location and standard cloud microstructure scalar measurements such as temperature. There are several problems associated with visualizing this rather unique set of data. They stem from the fact that the data is a sparse spiraling sample of scalars and vectors. The system allows the track of the plane to be displayed as a line, a tube or a ribbon; scalar values can be displayed as transparent isosurfaces; and the vector data as an arrow plot along that track, given a color that is constant, based on orientation or related to the value of a scalar. Any combination of methods can be used to display the data. A single primitive can be overloaded in many ways, or several different variables can all be displayed simultaneously.

Color is widely and reliably used to display the value of a single scalar variable. It is more rarely, and far less reliably, used to display multivariate data. Dynamic control over the parameters of the color mapping results in a more effective environment for the exploration of multivariate spatial distributions. The paper describes an empirical study comparing the effectiveness of static versus dynamic representations for the exploration of qualitative aspects of bivariate distributions. In this experiment, subjects made judgments about the correspondence of the shape, location, and magnitude of two patterns under conditions with varying amounts of random noise. Subjects made significantly more correct judgements (p<0.001) about feature shape and relative positions using the dynamic representation, on average forty-five percent more. The differences between static and dynamic representations were greater in the presence of noise.

Many scientific and medical visualization techniques produce irregular surfaces whose shape and structure need to be understood. Examples include tissue and tumor boundaries in medical imaging, molecular surfaces and force thresholds in chemical and pharmaceutical applications, and isosurfaces in a wide range of 3D domains. The 3D shape of such surfaces can be particularly difficult to interpret because of the unfamiliar, irregular shapes, the potential concavities and bulges, and the lack of parallel lines and right angles to provide perspective depth cues. Attempts to display multiple irregular surfaces by making some or all of them transparent further complicates the problem. Texture can provide valuable cues to aid in the interpretation of irregular surfaces. Opacity-modulating textures offer a mechanism for the display of multiple surfaces without the extreme loss of clarity of multiple transparent surfaces. This paper presents a method for creating simple repeating textures and mapping them onto irregular surfaces.

Outputs from a physiologically based toxicokinetic (PB-TK) model for fish were visualized by mapping time series data for specific tissues onto a three dimensional representation of a rainbow trout. The trout representation was generated in stepwise fashion: cross sectional images were obtained from an anesthetized fish using a magnetic resonance imaging (MRI) system; images were processed to classify tissue types; images were stacked and processed to create a three dimensional representation of the fish, encapsulating five volumes corresponding to the liver, kidney, muscle, gastrointestinal tract, and fat. Kinetic data for the disposition of pentachloroethane in trout were generated using a PB-TK model. Model outputs were mapped onto corresponding tissue volumes, representing chemical concentration as color intensity. The visualization was then animated, to show the accumulation of pentachloroethane in each tissue during a continuous branchial (gill) exposure.

Calico, a dynamic tool for the creation and manipulation of color mappings for the exploration of multivariate, quantitative data, was used to study the effects of user control and smooth change on user preference, accuracy, and confidence. The results of the study, as well as other user experiences with Calico, support the hypothesis that dynamic manipulation of color mappings is a useful feature of systems for the exploration of quantitative data using color. The main effect observed is a clear user preference for representations providing control over the mapping, a small but significant increase in accuracy, and greater confidence in information gleaned from manipulable displays. A smaller and less consistent effect showed greater user preference for an confidence in representations which provided smooth change between images.>

Reports from five research centers involved with atmospheric and environmental visualization issues are presented in this case study. Visualization with heterogeneous computer architectures is highlighted in the US EPA Scientific Visualization Center discussion. The NASA Marshall Space Flight Center effort to develop the multidimensional analysis of sensor systems (MASS) environment is presented. Florida State University's building of a new scientific visualization package, Sci An, is reported. This is followed by a discussion of the design and implementation of VIS-AD, an experimental laboratory for developing scientific algorithms, at the University of Wisconsin-Madison. The visualization of global atmospheric data at IBM Thomas J. Watson Research Center is highlighted.>

No abstract available.