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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Community Fabric: Visualizing communities and structure in dynamic networks

We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Visualizing Microbial Community Dynamics via a Controllable Soil Environment

Visualizing microbial community dynamics within soil environments remains challenging. Here, we developed a custom-built SoilBox system that facilitates probing various changes occurring in microbial community development and molecular processes when exposed to environmental perturbations (e.g., temperature changes, drought, nutrient availability). Using confocal microscopy along with mass spectrometry imaging, we demonstrate the power of this system by correlating microbial growth and spatial metabolic information from soil communities under different moisture regimes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The State of the Art in Visualizing Dynamic Multivariate Networks

Abstract Most real‐world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real‐world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.

Kale, Bharat↗
DOE OSTI2023

MFA-DVR: direct volume rendering of MFA models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. Here, in this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The moving target of visualization software for an increasingly complex world

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. Furthermore, we summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hardware-Accelerated Ray Tracing of CAD-Based Geometry for Monte Carlo Radiation Transport

Monte Carlo radiation transport (MCRT) methods have been used to simulate radiation environments for many decades by tracking individual particles through a model to accumulate statistical information. MCRT geometry is historically formed using the constructive solid geometry (CSG). Recently, significant work has been performed to support simulations using computer-aided design (CAD)-based tessellated surfaces to support highly complex geometries. Ray tracing acceleration data structures from the rendering and visualization community are applied to accelerate particle tracking in CAD-based models. Despite these efforts, CSG representations provide the superior performance in surface intersection operations during particle flight. Concurrently, pseudo Monte Carlo methods have become prevalent in rendering applications to support more realistic models for scattering media, motivating innovations that are advantageous for MCRT simulations. Finally, the authors’ work extends these innovations by employing Intel’s Embree ray tracing kernel within a geometry toolkit for Monte Carlo to improve the simulation performance using CAD-based models by factors of 1.5 to 2.

42 ENGINEERING↗
DOE OSTI2022

The Exploitation of Data Reduction for Visualization

The disparity between the computational speed and storage bandwidth, as demonstrated in Figure 1, is a well known problem that grows with each successive generation. The visualization community is principally responding to this issue by using in situ to reduce which data must be written to storage. However, other communities are taking different, possibly complementary approaches. In particular, data compression is a common general approach to reduce storage demands. Data compression technologies are typically not designed with post processing in mind. The principal metrics measured are compression ratio, the improved bandwidth to storage, and the error introduced. It is assumed that data is inflated to its full size before any post processing can happen. Although when talking about bandwidth disparities, HPC’s dirty little secret is that no part of the memory nor interconnect hardware is increasing at the rate of computation. For example, the Summit supercomputer has a peak computation rate almost 10 times its predecessor, Titan, but only about 4 times the memory, less than twice the aggregate memory bandwidth, and almost no improvement in the interconnect bisection bandwidth. Naively inflating data for post processing does not help with limitations in the memory and interconnect systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

UrbanScaping: Community Spatial Data Visualization & Analytics

Evaluating the electrification potential of buildings through retrofitting is crucial for reducing carbon emissions and the carbon footprint of built environments. This study leverages the Automatic Building Energy Modeling (AutoBEM) software, integrating the Model America database to create an urban context-based spatial analysis platform for community engagement and development. We selected Camp Hill Borough, PA, as a case study to analyze building-specific energy performance and evaluate the electrification potential of each building by switching to different Heating, Ventilation, and Air Conditioning (HVAC) systems and measurement components. The simulation results generated by the workflow provide retrofitting suggestions to help mitigate the carbon footprint as well as energy saving statistics of buildings. Additionally, the developed web-based interface serves as a community engagement platform, allowing residents to provide feedback and further develop interactive communication protocols. The outcomes of this project offer a baseline for community electrification planning and contribute to the design of low-carbon communities.

Chowdhury, Shovan [ORNL]↗
DOE OSTI2022

Bringing chemical structures to life with augmented reality, machine learning, and quantum chemistry

Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or protein data bank ID and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The NIH Somatic Cell Genome Editing program

The move from reading to writing the human genome offers new opportunities to improve human health. The United States National Institutes of Health (NIH) Somatic Cell Genome Editing (SCGE) Consortium aims to accelerate the development of safer and more-effective methods to edit the genomes of disease-relevant somatic cells in patients, even in tissues that are difficult to reach. Here we discuss the consortium’s plans to develop and benchmark approaches to induce and measure genome modifications, and to define downstream functional consequences of genome editing within human cells. Central to this effort is a rigorous and innovative approach that requires validation of the technology through third-party testing in small and large animals. New genome editors, delivery technologies and methods for tracking edited cells in vivo, as well as newly developed animal models and human biological systems, will be assembled—along with validated datasets—into an SCGE Toolkit, which will be disseminated widely to the biomedical research community. We visualize this toolkit—and the knowledge generated by its applications—as a means to accelerate the clinical development of new therapies for a wide range of conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.

Sensemaking, human-machine teaming, agency, artifi↗
DOE OSTI2024

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations

Abstract Background Microbes and their viruses are hidden engines driving Earth’s ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed. Results Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro - and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity ( π and θ ), and selective pressures (pN/pS and Tajima’s D ) within and fixation indices ( F ST ) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity ( π ) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better “bet hedge” in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences. Conclusions These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro - and microdiversity across microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Microgrid Hardening Design Toolkit User Guide: Software v0.29

Microgrid Hardening Design Toolkit helps microgrid stakeholders assess community-specific natural hazard risks to microgrid components and provides mitigation strategies to reduce those risks. Using census tract location inputs and the Federal Emergency Management Agency (FEMA) Risk Index dataset, the tool provides a ranked list of the highest risk hazards for a selected community and hazard-specific mitigation options for microgrid components. It also provides risk twin communities with similar hazard risk profiles and includes a radar-chart to visually compare risk profiles across communities. The toolkit is expected to help users quickly understand community-specific hazard risks and make decisions for microgrid hardening.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

WormBase in 2022—data, processes, and tools for analyzing Caenorhabditis elegans

WormBase (www.wormbase.org) is the central repository for the genetics and genomics of the nematode Caenorhabditis elegans. We provide the research community with data and tools to facilitate the use of C. elegans and related nematodes as model organisms for studying human health, development, and many aspects of fundamental biology. Throughout our 22-year history, we have continued to evolve to reflect progress and innovation in the science and technologies involved in the study of C. elegans. We strive to incorporate new data types and richer data sets, and to provide integrated displays and services that avail the knowledge generated by the published nematode genetics literature. Here, we provide a broad overview of the current state of WormBase in terms of data type, curation workflows, analysis, and tools, including exciting new advances for analysis of single-cell data, text mining and visualization, and the new community collaboration forum. Concurrently, we continue the integration and harmonization of infrastructure, processes, and tools with the Alliance of Genome Resources, of which WormBase is a founding member.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Optical Test Bench (SYSM 5620 Final Report)

The National Ignition Facility (NIF) is a research facility that focuses on High Energy Density Science and Inertial Confinement Fusion. In the NIF, 192 laser beams are directed into a miniscule Target Capsule to generate an implosion – this is called a NIF Shot. Many NIF Users conduct experiments using different Targets and laser inputs to generate reactions that are of interest to the scientific community. Those reactions are monitored using various Target Diagnostic instruments, including Shot Time Photography – which provides crucial images of the Target as the implosion occurs. A subset of these NIF Users is less interested in the reaction itself and more interested in subjecting materials or devices to the intense environment that these reactions can create. Using the Target as a source of x-rays, neutrons, or blast loading for particular materials has been significant to the shock physics community and to those who work in extreme design spaces. However, the Material and Radiation Effects team – that performs these experiments on behalf of the NIF Users – has not been able to make good use of the existing Shot Time Photography Diagnostics. The STP cameras are focused on the Targets rather than the MRE Customers’ materials, the current system is not oriented to have a good view of the MRE Customers’ materials, and the cameras are limited in their capabilities (black and white, poor resolution, etc.). As MRE experiments increase in quantity and importance to the scientific community, the desire for visual monitoring of the experiments to substantiate models and inform other data is also rising.

42 ENGINEERING↗