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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
NASA NTRSDate not supplied

Improving Data Discovery, Analysis, and Visualizations With Cloud-Based User Services

The Global Hydrometeorology Resource Center (GHRC) Distributed Active Archive Center (DAAC) is one of 12 DAACs managed by the United States National Aeronautics and Space Administration (NASA) Earth Science Data and Information System (ESDIS) project [1]. GHRC and the other DAACs are designed to process, archive, document, and distribute NASA Earth-observing data, ranging from satellite missions to field campaigns [2]. A major goal of the DAACs is to enable science with these data. Science enabling can be difficult as datasets can be very large, use multiple formats, come from numerous platforms, and require three-dimensional visualization. GHRC is using its expertise with cloud-based technologies to develop open source and open science tools to empower users to explore, coincidentally visualize, and analyze multiple datasets. Being open source, the user community can develop visualizations for their own datasets. This presentation will expand on this objective and highlight the capabilities available to the international community now.

GHRC↗
DOE OSTI2022

More Tools for Visualization and Analysis of Small-Angle Neutron Scattering Data: Descriptions and Examples

With the adoption of drtsans as the data reduction software for the GP-SANS, Bio-SANS and EQ-SANS instruments at ORNL, tools for data visualization and analysis that can be integrated into drtsans scripts are needed to further improve the user experience. New tools that do not need to be incorporated directly into data reduction scripts can also positively impact users during their experiments. In this report, a new set of tools is presented that complements the previous set released. The set includes tools for both fitting data and for visualizing data.

42 ENGINEERING↗
DOE OSTI2026

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

TPSAS-NF1676L-17867-DND

In response to the exponential growth in science data analysis and visualization capabilities, data centers have been developing new processes to package and deliver large volumes of aggregated subsets of archived data. New standards are evolving to help data providers and application programmers manage the growing needs of the science community. These standards evolve from the best practices gleaned from new products and capabilities. The NASA Atmospheric Sciences Data Center (ASDC) has developed and deployed production provider-specific search and subset web applications for the CALIPSO, CERES, TES, and MOPITT missions. This presentation explores a CERES CCCM (CALIPSO, CloudSat, CERES, MODIS) data validation use case that leverages aggregated subset results from CERES CCCM (Level2), CERES SSF (Level2), and CALIPSO LIDAR (Level) datasets. Additionally, it examines the standards and formats that ASDC developers have applied to the delivered files as well as the implementation strategies for subsetting and processing the aggregated products.

Walter E Baskin↗
NASA NTRS2011

Bridging the Gap between NASA Hydrological Data and the Geospatial Community

There is a vast and ever increasing amount of data on the Earth interconnected energy and hydrological systems, available from NASA remote sensing and modeling systems, and yet, one challenge persists: increasing the usefulness of these data for, and thus their use by, the geospatial communities. The Hydrology Data and Information Services Center (HDISC), part of the Goddard Earth Sciences DISC, has continually worked to better understand the hydrological data needs of the geospatial end users, to thus better able to bridge the gap between NASA data and the geospatial communities. This paper will cover some of the hydrological data sets available from HDISC, and the various tools and services developed for data searching, data subletting ; format conversion. online visualization and analysis; interoperable access; etc.; to facilitate the integration of NASA hydrological data by end users. The NASA Goddard data analysis and visualization system, Giovanni, is described. Two case examples of user-customized data services are given, involving the EPA BASINS (Better Assessment Science Integrating point & Non-point Sources) project and the CUAHSI Hydrologic Information System, with the common requirement of on-the-fly retrieval of long duration time series for a geographical point

Rui, Hualan↗
NASA NTRS1995

Scientific visualization tools for the ISTP project: Mission planning, data analysis and model interpretation

Visualization tools are being developed to meet the challenges of mission planning and data analysis presented by the International Solar-Terrestrial Physics (ISTP) program. ISTP encompasses a large number of spacecraft, multiple ground-based observatories, and several theoretical investigations, with the goal of understanding the global behavior of the solar wind/magnetosphere/ionosphere system. The tools include three-dimensional displays of key boundaries in geospace along with spacecraft trajectories, which can be animated and synchronized to universal time. Magnetic field models and MHD simulation results can be invoked to reveal the magnetic topology or to identify magnetic conjunctions between spacecraft and/or ground-based facilities. Simultaneous displays of satellite trajectories, spacecraft-borne observations, and model predictions are available to facilitate data processing and interpretation efforts. The current status of these tools is described, and their implementation at the ISTP Science Planning and Operations Facility and distribution to the entire ISTP community are discussed.

Peredo, M.↗
DOE OSTI2021

Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. Herein we present a state–of–the–art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time–varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work here focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. In conclusion, our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Federated Giovanni: A Distributed Web Service for Analysis and Visualization of Remote Sensing Data

The Geospatial Interactive Online Visualization and Analysis Interface (Giovanni) is a popular tool for users of the Goddard Earth Sciences Data and Information Services Center (GES DISC) and has been in use for over a decade. It provides a wide variety of algorithms and visualizations to explore large remote sensing datasets without having to download the data and without having to write readers and visualizers for it. Giovanni is now being extended to enable its capabilities at other data centers within the Earth Observing System Data and Information System (EOSDIS). This Federated Giovanni will allow four other data centers to add and maintain their data within Giovanni on behalf of their user community. Those data centers are the Physical Oceanography Distributed Active Archive Center (PO.DAAC), MODIS Adaptive Processing System (MODAPS), Ocean Biology Processing Group (OBPG), and Land Processes Distributed Active Archive Center (LP DAAC). Three tiers are supported: Tier 1 (GES DISC-hosted) gives the remote data center a data management interface to add and maintain data, which are provided through the Giovanni instance at the GES DISC. Tier 2 packages Giovanni up as a virtual machine for distribution to and deployment by the other data centers. Data variables are shared among data centers by sharing documents from the Solr database that underpins Giovanni's data management capabilities. However, each data center maintains their own instance of Giovanni, exposing the variables of most interest to their user community. Tier 3 is a Shared Source model, in which the data centers cooperate to extend the infrastructure by contributing source code.

Giovanni↗
DOE OSTI2020

Data Services for Visualization and Analysis - ASC Level II Milestone (7186)

A new in transit Data Service is presented and compared to the traditional file-based workflow and the newly refactored in situ Catalyst workflow. Each workflow is enabled by the IOSS mesh interface equipped with data management layers for Exodus and CGNS (file-based), Catalyst (in situ), and FAODEL (in transit). FAODEL is a distributed object store that can transmit data across MPI allocations. Catalyst is a Para View-based visualization capability developed as part of the CSSE Data Services effort. The workflows considered here take SPARC data into Catalyst for visualization post-processing. Although still in unoptimized form, we show that the in transit approach is a viable alternative to file-based and in situ workflows and offers several advantages to both simulation and post-processing developers. Since IOSS is a mature interface with wide adoption across Sandia and externally, each workflow can be reconfigured to use different simulations that generate mesh data and post-processing tools that consume it.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗
DOE OSTI2025

G-Mapper: Learning a Cover in the Mapper Construction

The Mapper algorithm is a visualization technique in topological data analysis (TDA) that outputs a graph reflecting the structure of a given dataset. However, the Mapper algorithm requires tuning several parameters in order to generate a “nice” Mapper graph. This paper focuses on selecting the cover parameter. We present an algorithm that optimizes the cover of a Mapper graph by splitting a cover repeatedly according to a statistical test for normality. Our algorithm is based on G-means clustering, which searches for the optimal number of clusters in 𝑘-means by iteratively applying the Anderson–Darling test. Our splitting procedure employs a Gaussian mixture model to carefully choose the cover according to the distribution of the given data. In conclusion, experiments for synthetic and real-world datasets demonstrate that our algorithm generates covers so that the Mapper graphs retain the essence of the datasets, while also running significantly faster than a previous iterative method.

G-means clustering↗
NASA NTRS1995

Envision: An interactive system for the management and visualization of large geophysical data sets

Envision is a software project at the University of Illinois and Texas A&M, funded by NASA's Applied Information Systems Research Project. It provides researchers in the geophysical sciences convenient ways to manage, browse, and visualize large observed or model data sets. Envision integrates data management, analysis, and visualization of geophysical data in an interactive environment. It employs commonly used standards in data formats, operating systems, networking, and graphics. It also attempts, wherever possible, to integrate with existing scientific visualization and analysis software. Envision has an easy-to-use graphical interface, distributed process components, and an extensible design. It is a public domain package, freely available to the scientific community.

Searight, K. R.↗