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NASA NTRS2012

Exploratory Climate Data Visualization and Analysis Using DV3D and UVCDAT

Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Advanced tools for accessing, analyzing, and visualizing very large and complex climate data are required to maintain rapid progress in Earth system research. To meet this need, NASA, in collaboration with the Ultra-scale Visualization Climate Data Analysis Tools (UVCOAT) consortium, is developing exploratory climate data analysis and visualization tools which provide data analysis capabilities for the Earth System Grid (ESG). This paper describes DV3D, a UV-COAT package that enables exploratory analysis of climate simulation and observation datasets. OV3D provides user-friendly interfaces for visualization and analysis of climate data at a level appropriate for scientists. It features workflow inte rfaces, interactive 40 data exploration, hyperwall and stereo visualization, automated provenance generation, and parallel task execution. DV30's integration with CDAT's climate data management system (COMS) and other climate data analysis tools provides a wide range of high performance climate data analysis operations. DV3D expands the scientists' toolbox by incorporating a suite of rich new exploratory visualization and analysis methods for addressing the complexity of climate datasets.

Maxwell, Thomas↗

NASA NTRS2011

Static and Motion-Based Visual Features Used by Airport Tower Controllers: Some Implications for the Design of Remote or Virtual Towers

Visual motion and other visual cues are used by tower controllers to provide important support for their control tasks at and near airports. These cues are particularly important for anticipated separation. Some of them, which we call visual features, have been identified from structured interviews and discussions with 24 active air traffic controllers or supervisors. The visual information that these features provide has been analyzed with respect to possible ways it could be presented at a remote tower that does not allow a direct view of the airport. Two types of remote towers are possible. One could be based on a plan-view, map-like computer-generated display of the airport and its immediate surroundings. An alternative would present a composite perspective view of the airport and its surroundings, possibly provided by an array of radially mounted cameras positioned at the airport in lieu of a tower. An initial more detailed analyses of one of the specific landing cues identified by the controllers, landing deceleration, is provided as a basis for evaluating how controllers might detect and use it. Understanding other such cues will help identify the information that may be degraded or lost in a remote or virtual tower not located at the airport. Some initial suggestions how some of the lost visual information may be presented in displays are mentioned. Many of the cues considered involve visual motion, though some important static cues are also discussed.

Ellis, Stephen R.↗

NASA NTRS2016

Dynamic Visual Acuity and Landing Sickness in Crewmembers Returning from Long-Duration Spaceflight

Long-term exposure to microgravity causes sensorimotor adaptations that result in functional deficits upon returning to a gravitational environment. At landing the vestibular system and the central nervous system, responsible for coordinating head and eye movements, are adapted to microgravity and must re-adapt to the gravitational environment. This re-adaptation causes decrements in gaze control and dynamic visual acuity, with astronauts reporting oscillopsia and blurred vision. Dynamic visual acuity (DVA) is assessed using an oscillating chair developed in the Neuroscience Laboratory at JSC. This chair is lightweight and easily portable for quick deployment in the field. The base of the chair is spring-loaded and allows for manual oscillation of the subject. Using a metronome, the chair is vertically oscillated plus or minus 2 cm at 2 Hz by an operator, to simulate walking. While the subject is being oscillated, they are asked to discern the direction of Landolt-C optotypes of varying sizes and record their direction using a gamepad. The visual acuity thresholds are determined using an algorithm that alters the size of the optotype based on the previous response of the subject using a forced-choice best parameter estimation that is able to rapidly converge on the threshold value. Visual acuity thresholds were determined both for static (seated) and dynamic (oscillating) conditions. Dynamic visual acuity is defined as the difference between the dynamic and static conditions. Dynamic visual acuity measures will be taken prior to flight (typically L-180, L-90, and L-60) and up to eight times after landing, including up to 3 times on R plus 0. Follow up measurements will be taken at R plus 1 (approximately 36 hours after landing). Long-duration International Space Station crewmembers will be tested once at the refueling stop in Europe and once again upon return to Johnson Space Center. In addition to DVA, subjective ratings of motion sickness will be recorded throughout the testing. Using the chair as a portable and reliable way to test DVA, we aim to test returning astronauts to assess the amount of retinal slip that they experience. By comparing these measurements to their motion sickness scores (using a scale of 1 to 20 where 20 is vomiting), we will correlate the amount of retinal slip to the level of motion sickness experienced. In addition to testing this in returning astronauts, we will perform ground-based studies to determine the effectiveness of stroboscopic goggles in reducing retinal slip and improving DVA. Finally, we will employ stroboscopic goggles in the field to astronauts experiencing high levels of motion sickness to minimize retinal slip and reduce their symptoms.

Rosenberg, M.J.F↗

NASA NTRS2014

Effect of Control System Augmentation on Handling Qualities and Task Performance in Good and Degraded Visual Environments

In June 2013, NASA and the U.S. Army jointly conducted a simulation experiment in the NASA-Ames Vertical Motion Simulator that examined and quantified the effects of limited-authority control system augmentation on handling qualities and task performance in both good and degraded visual environments (DVEs). The vehicle model used for the experiment was the OH-58D with similar size, weight and performance, and the same 4-blade rotor system as the Bell 407 civilian helicopter that is commonly used for medical evacuation and emergency medical services. The control systems investigated as part of this study included the baseline aircraft Rate Command system, a short-term Attitude Command/Attitude Hold system that uses lagged-rate feedback to provide a short-term attitude response, Modernized Control Laws that provide an Attitude Command/Attitude Hold control response type, and Modernized Control Laws with an additional Position Hold function. Evaluation tasks included the ADS-33 Hover, Sidestep, Acceleration/Deceleration, and Pirouette Mission Task Elements, as well as a new proposed Emergency Medical Services task that includes an approach and landing at a minimally prepared remote landing site. Degraded visual environments were simulated with night vision goggles and an unaided night scene. A total of nine experimental test pilots participated in the four-week simulation experiment. Data recorded during the evaluation included Cooper-Harper handling qualities ratings, Bedford Workload scale ratings, and task performance. The Usable Cue Environment (UCE) was measured for this simulation experiment, and found to be UCE equals 1 in good visual environments and UCE equals 2 in degraded visual environments with night vision goggles. Results showed that handling qualities ratings were improved with a control system providing short-term attitude response over a rate command system, although the improvements were not sufficient to produce Level 1 handling qualities in degraded visual environments. Results for an Attitude Command/Attitude Hold control system showed that borderline Level 1 handling qualities could be achieved in degraded visual environments, and the 10 percent authority stability augmentation system was adequate to obtain these handling qualities ratings.

Handling Qualities↗

NASA NTRS2018

NASA GIBS and Worldview: Visualizing NASA's Earth Science Data for All to Explore

For more than 20 years, the NASA Earth Observing System (EOS) has operated dozens of remote sensing satellites collecting nearly 15 Petabytes of data that span thousands of science parameters. Within these observations are keys the Earth Scientists have used to unlock many discoveries that we now understand about our planet. Also contained within these observations are a myriad of opportunities for learning and education. The challenge is making them accessible to educators and students in intuitive and simple ways so that effort can be spent on lesson enrichment and not overcoming technical hurdles. The NASA Global Imagery Browse Services (GIBS) system and NASA Worldview website provide a unique view into EOS data through daily full resolution visualizations of hundreds of earth science parameters. For many of these parameters, visualizations are available within hours of acquisition from the satellite. For others, visualizations are available for the entire mission of the satellite. Accompanying the visualizations are visual aids such as color legends, place names, and orbit tracks. By using these visualizations, educators and students can observe natural phenomena that enrich a scientific education.

Cechini, Matthew↗

NASA NTRS2018

Challenges in Development of Online Visualization and Analysis Tools for Satellite Data

Over the years, various online visualization and analysis tools have been developed to facilitate satellite data access and help scientific users around the world to conduct research and develop applications (e.g., data product evaluation, what-if questions, etc.). For those who are new to satellite data products, using them can be a daunting task due to many obstacles in data processing such as data formats, complex data structures, special software packages, unfamiliar terminology, etc., especially when one is not sure whether a dataset is suitable for his/er research project. Even for experienced users, developing software for data processing and analysis can be a costly and time-consuming task. Online visualization tools can overcome many of these difficulties and allow users to focus on scientific questions. For example, Giovanni (the Geospatial Interactive Online Visualization and Analysis Infrastructure, https://giovanni.gsfc.nasa.gov), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), allows access over 1900 satellite and model variables in 82 measurement groups of 8 disciplines without downloading data and software. Main features include basic functions for data analysis and visualization, data provenance, output data in different formats (ASCII, NetCDF, GeoTIFF), and more. Over the years, ~1700 peer-reviewed publications in different disciplines have been benefited from Giovanni in research activities (e.g. initial investigation, what-if questions, product evaluation). Despite the success of online visualization and analysis tools, challenges and new opportunities still exist and more can be done with new requirements and technology. Examples are: a) how to increase the efficiency of dataset search by enhancing intuitive aspects; b) how to facilitate interdisciplinary research; c) how to provide data quality information; d) how to engage users to participate in data quality assessment; and more. NASA Earth Observing System Data and Information System (EOSDIS) satellite-based data products are processed at various levels ranging from Level 0 to Level 4. While most users use data products at higher levels (Level-3 and 4), products at lower levels are still important for case studies, algorithm development, ground validation, etc. In this presentation, we will use Giovanni as an example to present and discuss challenges and near-future opportunities for satellite data online visualization and analysis tools.

Liu, Zhong↗

NASA NTRS2019

Bringing Analysis Closer to Data: Developing a Visualization Tool for L2 Earth Science Satellite Data

Earth Science satellite missions provide a unique opportunity for scientists to visualize complex and multifaceted observations projected geospatially across maps of the Earth. While visualization tools can help scientists comprehend, analyze, and share data, visualizing Level-2 Earth Sciences data poses its own specific set of challenges. Since the geospatial information in Level-2 data files is stored as independent variables, the plotting process involves matching dimensional information from latitude and longitude with a desired variable. Variables are stored in different ways across various Earth Science data file formats, which complicates the process of extracting data and plotting variables from a given file without requiring extensive user input and prerequisite familiarity with the file type variable structure. In coordination with NASA’s Goddard Earth Sciences Data Information Services Center (GES DISC), the team developed a Level-2 Earth Science data visualization tool that aims to address some of the complexities associated with plotting Level-2 data. This tool offers command-line and user interface support for file and variable selection to accommodate varying use cases and degrees of user familiarity with the structure of a given file. The visualization tool is written in Python 3 and utilizes a modular approach to facilitate continued expansion and reuse. In addressing some common complications involved in plotting Level-2 Earth Sciences data, the tool aims to help to link the process of analysis more directly with data acquisition and visualization, bringing analysis closer to data across levels of processing.

Li, Angela W.↗

NASA NTRS2018

Do You See What I See? Interactive Visualization of Mission Design and Navigation

Mission Design and Navigation (MDNav) is an intensive process requiring advanced computational resources, expert human intuition, and many successive human-in-the-loop iterations to converge on acceptable trajectory designs or navigation solutions. The current bottleneck in MDNav is not the underlying computational algorithms but the human cognitive capacity to prune through a multitude of simulated results to select high-value candidates. One approach to alleviate this burden is through the judicious application of visualizations that allow humans to interactively ﬁlter data in multiple dimensions to reveal salient patterns and highlight divergences. When designed eﬃciently, such interactive visualizations should aid human operators to get familiar with data faster, visually observe correlations, and communicate ﬁndings more eﬀortlessly. In this work, we present three visualization case studies that have the potential to increase human operator eﬃciency in MDNav. While identifying the most critical “pain points” that operators face, and also working on potential solutions, we followed a human-centered design approach. We started with a series of interviews with potential users, and then rapidly created prototypes for alternative solutions, validated outcomes with feedback from users through out development of these proof of concept visualizations. With this survey of our current eﬀorts, we demonstrate the transformative capability of interactive data visualizations for improving mission development and operations, enabling operators to grow intuition, and communicating key concepts across diverse mission teams.

Arora, Nitin↗

DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Scalable Comparative Visualization of Ensembles of Call Graphs

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey , a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. Finally, we demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2001

Quantifying the performance limits of human saccadic targeting during visual search

In previous studies of saccadic targeting, the issue how visually guided saccades to unambiguous targets are programmed and executed has been examined. These studies have found different degrees of guidance for saccades depending on the task and task difficulty. In this study, we use ideal-observer analysis to estimate the visual information used for the first saccade during a search for a target disk in noise. We quantitatively compare the performance of the first saccadic decision to that of the ideal observer (ie absolute efficiency of the first saccade) and to that of the associated final perceptual decision at the end of the search (ie relative efficiency of the first saccade). Our results show, first, that at all levels of salience tested, the first saccade is based on visual information from the stimulus display, and its highest absolute efficiency is approximately 20%. Second, the efficiency of the first saccade is lower than that of the final perceptual decision after active search (with eye movements) and has a minimum relative efficiency of 19% at the lowest level of saliency investigated. Third, we found that requiring observers to maintain central fixation (no saccades allowed) decreased the absolute efficiency of their perceptual decision by up to a factor of two, but that the magnitude of this effect depended on target salience. Our results demonstrate that ideal-observer analysis can be extended to measure the visual information mediating saccadic target-selection decisions during visual search, which enables direct comparison of saccadic and perceptual efficiencies.

Non-NASA Center↗

DOE OSTI2022

Heterogeneous Visualization: A Call for Empirically Driven Technology Advancement and Adoption; Preprint

Our ability to generate data from simulations and experimental platforms is generally outpacing our ability to analyze those data efficiently and effectively. There are a growing number and variety of possible visualization display modalities, from commodity head-mounted displays to large-scale immersive environments, from desktop and touch displays to high-resolution display walls. Many of these visualization technologies and visual environments are rapidly evolving and could revolutionize analysis for certain classes of complex scientific and engineering data. However, the choice of what technology or combination of technologies to use has historically been largely ad hoc and lacks a carefully validated understanding of how to best utilize different visualization technologies to advance the science. To maximize the impact of our data and analyses, it is imperative that we use the most effective visualization environments for the analysis task at hand.

heterogeneous visualization↗

DOE OSTI2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models: Preprint

Uncertainty visualization is a key component in translating important insights from ensemble data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models for computationally expensive simulations, users can interact with more aspects of data spaces than ever before. However, the integration of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.

ensemble visualization↗

DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Integrative visualization of the molecular structure of a cellular microdomain

Abstract An integrative approach to visualization is used to create a visual snapshot of the structural biology of the polar microdomain of Caulobacter crescentus . The visualization is based on the current state of molecular and cellular knowledge of the microdomain and its cellular context. The collaborative process of researching and executing the visualization has identified aspects that are well determined and areas that require further study. The visualization is useful for dissemination, education, and outreach, and the study lays the groundwork for future 3D modeling and simulation of this well‐studied example of a cellular condensate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Simulation of Helium Flow Visualization Apparatus for Studies of Blanket Cooling in Fusion Reactors

Flow visualization is essential to understanding helium cooling performance. This article investigates helium flow visualization in an apparatus to support the design of the blanket first wall for a fusion reactor. Helium’s safety advantages make it an attractive coolant, but effective cooling and flow visualization remain challenging. Sophisticated simulations are conducted in different test sections to address three key areas in the design of a test apparatus and cooling channel enhancements: the influence of viewing glass windows on flow patterns, the impact of increased heating, and the effectiveness of cooling structures like baffles. Viewing glass windows introduce flow recirculation region bifurcation and flow asymmetry, affecting flow patterns and necessitating careful analysis in future experiments. Increased heating results in helium flow detachment from the heated surface, leading to hot spots. Cooling structures, particularly baffles, prove effective in maintaining consistent attachment to the heated surface, improving the heat transfer performance. Further, this study also examines a tunable parameter in the turbulence model, highlighting the importance of accurate model tuning for future fusion reactor cooling designs. With an imminent helium flow visualization facility, these simulations will be used to optimize cooling structures on the heated wall to improve flow attachment and heat transfer efficiency. This work serves as a first investigation of the helium flow visualization apparatus for blanket cooling enhancement in fusion reactor design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models

Uncertainty visualization is a key component in translating important insights from ensemble simulation data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models trained on ensemble data, we can substitute computationally expensive simulations, which allows users to interact with more aspects of data spaces than ever before. However, the use of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.

ensemble data↗