Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data visualization and analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA Giovanni: Analyze, Compare, and Visualize 2000+ Earth Satellite and Model Variables Without Downloading Data and Software

Over vast oceans and remote continents, observations are often scarce and discontinuous. Satellite and model data play a critical role in research and applications. However, finding and accessing satellite and model data can be a daunting task for many, especially those outside the community. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA Science Mission Directorate Data Centers, provides Earth science data, information, and services to everyone such as researchers, application users, educators, and students. GES DISC archives and supports datasets applicable to several NASA Earth Science Focus Areas including Atmospheric Composition, Water & Energy Cycles, Carbon Cycle & Ecosystem, and Climate Variability. To facilitate data discovery, evaluation, and exploration, GES DISC has developed the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), an online tool to analyze and visualize NASA remote sensing and model data without downloading data and software. As of this writing, over 2000 Earth satellite and model variables are available in Giovanni, including several wellknown NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP). Giovanni provides twenty-two plots that can be used to analyze, compare, and explore Earth data across different disciplines. Results can be shared with colleagues and downloaded for further analysis. Over the years, Giovanni has helped publish over 3000 referral papers. In this presentation, we will showcase key variables and plot types in Giovanni with examples. In particular, we will present several popular precipitation products from GPM and CPCP for evaluation and comparison.

data analysis↗
NASA NTRS1993

The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

Kruse, F. A.↗
DOE OSTI2021

GNPS Dashboard: collaborative exploration of mass spectrometry data in the web browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗
DOE OSTI2022

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Real Data and Rapid Results: Ocean Color Data Analysis with Giovanni (GES DISC Interactive Online Visualization and ANalysis Infrastructure)

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.

Acker, J. G.↗
NASA NTRS1982

Water tunnel flow visualization and wind tunnel data analysis of the F/A-18

Six degree of freedom studies were utilized to extract a band of yawing and rolling moment coefficients from the F/A-18 aircraft flight records. These were compared with 0.06 scale model data obtained in a 16T wind tunnel facility. The results, indicate the flight test yawing moment data exhibit an improvement over the wind tunnel data to near neutral stability and a significant reduction in lateral stability (again to anear neutral level). These data are consistent with the flight test results since the motion was characterized by a relatively slo departure. Flight tests repeated the slow yaw departure at M 0.3. Only 0.16 scale model wind tunnel data showed levels of lateral stability similar to the flight test results. Accordingly, geometric modifications were investigated on the 0.16 scale model in the 30x60 foot wind tunnel to improve high angle of attack lateral stability.

Erickson, G. E.↗
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↗
DOE OSTI2017

BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI

Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. Here, we demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Synchrophasor Visual Integration and Event Evaluation for Utilities (SynchroVIEEU) with High Penetration of Renewables

The Hawaiian Electric Companies (Companies), operating independent grid systems on the islands of Oahu, Hawaii, Maui, Molokai and Lanai, have been contending with some of the highest levels of renewable generation from both utility-scale and distributed generation resources. Renewable penetration levels exceeding 50% of generation are some of the highest in the nation and are changing the system characteristics and requiring operational changes to grid management. The Companies have aggressively leveraged federal, multi-state and industry grant funding to demonstrate new technologies for monitoring, visualization, forecasting and analysis to inform transformation needs and to improve operational awareness, especially at the distribution level. As part of the American Recovery and Reinvestment Act (ARRA) supported efforts to facilitate integration of renewables, synchrophasor devices were deployed on the islands of Hawaii and Maui in 2010. The devices have potential to provide Hawaii Electric Light Company (HELCO) on Hawaii Island and Maui Electric Company (MECO) with additional information to support post-event analysis and to help quantify observed area-wide changes in voltage and frequency on the systems. Consistent with the U.S. Department of Energy’s (DOE) Office of Electricity Delivery and Energy Reliability (OE) FOA objectives, the goal of this project was to accelerate the integration of synchrophasor information into production grade data visualization and analysis platforms/models to inform timely, efficient and reliable operations of a modern grid in light of high penetrations of renewable resources. The project comprised a two-year development effort to investigate the integration and visualization of synchrophasor field data into production-ready transmission and distribution proactive modeling efforts and a common data analysis platform application currently being used by the Companies. Conceptually, the project was about getting the right data to the right people at the right time in an easily accessible and timely environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2018

Earthdata User Interface (EUI) Library

As more remote sensing data moves to the cloud, the design tools we use to build user experiences and visualizations need to change and adapt to make the best use this new data reality. EUI 2.0, the next major iteration of the Earthdata User Interface Library, aims to make creating rich user experiences around NASA's Earth Observation System Data and Information System (EOSDIS) data easy and user-friendly. Building on a solid framework of design components, EUI 2.0 will be open source and have off-the-shelf integration with the Common Metadata Repository (CMR), basic mapping and visualization tools, a refreshed design toolkit for building apps and websites that fit the Earthdata design theme and guidelines. EUI 2.0 will allow for the rapid development of websites and applications based on EOSDIS tools and data holdings. It will also make use of cloud data availability to enable data visualization and analysis without the need to download and sync data.

Siarto, Jeff↗
NASA NTRSDate not supplied

NASA’s Moon Trek Portal: New Capabilities Supporting Mission Planning and Engagement

Introduction: NASA’s Moon Trek (https://trek.nasa.gov/moon/) is one of a growing number of interactive, browser-based, online portals for planetary data visualization and analysis produced by NASA’s Solar System Treks Project (SSTP). Moon Trek continues to be enhanced with new data and new capabilities enabling it to facilitate the planning and conducting of upcoming lunar missions by NASA, its commercial partners, and its international partners, as well as advancing its role as a valuable outreach tool. A Comprehensive Online Web Portal: Developed at NASA’s Jet Propulsion Laboratory (JPL) and managed as a project of NASA’s Solar System Exploration Research Virtual Institute (SSERVI) at NASA Ames Research Center, Moon Trek is a browser-based web portal. The portal provides easy-to-use tools for browsing, data layering, data product blending, and feature search among thousands of data products covering topography, mineralogy, elemental abundance, geology, and much more. Visualizations are provided in var-ious map projections, interactive 3D viewing, and in virtual reality. Using an in-house stereo workflow, SSTP is able to produce new NAC-based high-resolution mosaics and DEMs. Diverse Applications for Lunar Exploration: Baseline analytic tools available to all users include dis-tance measurement, elevation profiling, sun angle calculation, and 3D print file generation. More advanced account-level tools allow users to perform more computationally intensive analyses. These include ray-traced lighting analysis for user-specified areas over user-specified time/date ranges and time intervals, electro-static surface potential analysis, subsetting of large data products, slope analysis, and Lunar Laser Ranging geometry calculation. Artificial intelligence (AI) and ma-chine learning (ML) based tools have been implemented for crater detection and hazard analysis, boulder detection and hazard analysis, and rockfall detection. New Tools Facilitating Exploration: Additional, new tools have recently been added and others are in development, offering even greater functionality in con-ducting analyses of potential landing sites and areas of surface operations. The new Line-of-Sight tool facilitates communications planning between locations on the lunar surface, between any given site on the lunar surface and a specified ground station on the Earth, and between a site on the lunar surface and a relay asset in lunar orbit, all taking into account local lunar topography. The new Data Plotter tool provides both tabular and graphical representations of pixel values along a user specified path for a growing number of data products. The new NAC Finder tool will identify and pro-vide access to NAC images that intersect a user-defined path or bounded area. The SSTP development team is looking to leverage the capabilities of its existing AI and ML crater, boulder, and rockfall detection and analysis tools, and extend that technology to a generalized feature detector that can be trained on instances of specific types of landforms and then search the lunar surface for more examples of such features. New traverse planning tools are being developed with use cases in generalized concept studies and specific mission planning in mind. These will facilitate finding optimal traverse paths based on constraints such as slope, lighting, hazard avoidance, and communications. These will be complemented by new traverse visualization capabilities. Users will be able to interactively ride along with a rover, examining 3D views of the terrain while adjusting camera height and viewing angle along with selecting different data layer overlays to drape across the terrain. Engaging the Public: The capabilities being developed for mission planning are being leveraged to further enhance Moon Trek’s proven utility as a valuable public outreach resource. This includes providing multiple lev-els of engagement with different points of entry. At its simplest level, promoting understanding through visualization, media and the public will be able to easily visualize and conduct their own exploration of lunar sites targeted by NASA and its partners. For a more in-depth experience, we are working with our stakeholders to promote understanding through interaction by extend-ing our current landing site and traverse analysis capabilities, making simplified access to these tools available to those who want to explore more deeply key factors in planning a mission through interactive and possibly even gamified experiences. The highest degree of public outreach, focusing on engagement through scientific participation, could be achieved through our work with missions and the NASA Office of the Chief Scientist on Moon Trek’s extension as a tool with specialized capabilities for facilitating citizen science. In such scenarios, participants become members of extended mis-sion science teams, using dedicated and integrated interfaces to analyze mission data to help answer questions key to lunar science and exploration. We are work-ing with NASA’s Office of Communications, museums, planetariums, and the media to help them easily integrate accurate, detailed visualizations of NASA’s lunar destinations and exploration into their content/productions and to engage diverse audiences in diverse venues.

Moon Trek↗
DOE OSTI2019

Extreme-Scale Distribution-Based Data Analysis

Scientists overview and identify regions of interest by transforming data into compact information descriptors that characterize simulation results and allow detailed analysis on demand. Among many existing feature descriptors, statistical information derived from data samples is a promising approach to taming the big data avalanche because data distributionscomputed from a population can compactly describe the presence and characteristics of salient data features with minimal data movement. The ability to computationally summarize and process data using distributions also provides an efficient and representative capture of information that can adjust to size and resource constraints, with the added benefit that uncertainty associated with results can be quantified and communicated. In this project, we have developed a distribution-based data analysis and visualization framework for optimizing in situ processing and analysis of extreme-scale scientific data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Giovanni: A System for Rapid Access, Visualization and Analysis of Earth Science Data Online

Collecting data and understanding data structures traditionally are the first steps that a user must take, before the core investigation can begin. This is a time-consuming and challenging task, especially when science objectives require users to deal with large multi-sensor data that are usually in different formats and internal structures. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has created the GES DISC Interactive Online Visualization and ANalysis Infrastructure, Giovanni, to enable Web-based visualization and analysis of satellite remotely sensed meteorological, oceanographic, and hydrologic data sets, without users having to download data. The current operational Giovanni interfaces provide the capability to process a number of important satellite measurements, such as (1) ozone and other trace gases from TOMS, OMI, HALOE, and MLS; (2) air temperature, water vapor, and geopotential height from AIRS; (3) aerosols from MODIS TerrdAqua, and GOCART model; (4) precipitation from TRMM and ground measurements; (5) chlorophyll and other ocean color products from SeaWiFS and MODIS Aqua; and (6) sea surface temperature from MODIS Aqua. Depending on the input data structure, the system provides simple statistical analysis and creates time-averaged area plot, area-averaged time series, animations, Hovmoller latitude vs. time and longitude vs. time plots, as well as vertical profiles. The inter-comparison interfaces allow a user to compare observations from different instruments, to conduct anomaly analysis, and to study basic relationships between physical parameters. Giovanni handles data with different temporal and spatial resolutions and, thus, enables both regional and global long-term climate research and short-term special events investigation, as well as data validations and assessments. Because of its simplicity of usage, Giovanni is powerful and versatile, able to assist a wide range of users, from the discipline scientists conducting preliminary research in various fields, to students in the classroom learning about weather, climate, and other natural phenomena. Giovanni can be accessed from: http://disc. esfc.nasa.gov/techlab/giovanni/index.shtml

Shen, S.↗