Results for “web visualization” — Page 21

NASA NTRS2016

Managing and Servicing Physical Oceanographic Data at a NASA Distributed Active Archive Center

The NASA Earth Science Data Information Systems Project funds and operates 12 Distributed Active Archive Center(s) (DAAC) throughout the United States. Of these 12 centers, the Physical Oceanography DAAC (PO.DAAC) is committed to providing long term archival, distribution and stewardship for NASA physical oceanographic data, primarily derived from space-born satellite systems, but also including a growing set of recent and future in situ observations from the SPURS-1 and SPURS-2 campaigns. Notable NASA missions supported include: Seasat, TOPEX/Poseidon, NSCAT, QuikSCAT, ISS-RapidScat, Jason-1, Jason-2/OSTM, GRACE, Aquarius, GHRSST, and MODIS. The following interagency and international missions are also supported by PO.DAAC: AVHRR, Coriolis, DMSP, MetOp-A, MetOp-B, Oceansat-2. The PO.DAAC currently holds 525 datasets in public distribution, spanning the following observational parameters: sea surface temperature, sea surface salinity, ocean color, ocean surface currents, ocean surface wind speed, ocean surface wind direction, sea surface height, significant wave height, ocean water mass/thickness, and sea ice age. A hundred of these datasets are available in near-real-time. Datasets are distributed through a variety of open-source access protocols including FTP, OPeNDAP, and THREDDS. FTP will soon be phased out in favor of a recently introduced HTTPS PO.DAAC Drive interface that supports WebDAV and interoperable machine-to-machine communication. OPeNDAP supports remote data/metadata query, subset, and download. THREDDS provides the features of OPeNDAP with the additional feature of temporal aggregation. PO.DAAC also offers proprietary tools and services to further enhance the data discovery, visualization and analysis experience, including but not limited to: State of the Ocean, Web Services (data/metadata discovery and extraction), HiTIDE Level-2 subsetter, Live Access Server (LAS), Webification (w10nsci), and Rich Site Summary (RSS) Datacasting. To assist with provenance of datasets, PO.DAAC has implemented DOIs for the data it distributes so that they can be properly cited. There is a user forum and helpdesk that contains data recipes and via which users can get guidance. In summary, this presentation aims to provide a general overview of PO.DAAC’s web portal and data holdings along with a set of illustrative examples leading prospective data users into the practical utility of its tools and services.

Moroni, David F.↗

NASA NTRS2019

NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research

The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these- gaps. Established in the mid-1980's, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, hydrology, including well-known NASA satellite missions (e.g. TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies and discuss future plans.

Liu, Zhong↗

DOE OSTI2017

RE Data Explorer: Informing Variable Renewable Energy Grid Integration for Low Emission Development

The RE Data Explorer, developed by the National Renewable Energy Laboratory, is an innovative web-based analysis tool that utilizes geospatial and spatiotemporal renewable energy data to visualize, execute, and support analysis of renewable energy potential under various user-defined scenarios. This analysis can inform high-level prospecting, integrated planning, and policy making to enable low emission development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2014

Enabling a systems biology knowledgebase with gaggle and firegoose

The overall goal of this project was to extend the existing Gaggle and Firegoose systems to develop an open-source technology that runs over the web and links desktop applications with many databases and software applications. This technology would enable researchers to incorporate workflows for data analysis that can be executed from this interface to other online applications. The four specific aims were to (1) provide one-click mapping of genes, proteins, and complexes across databases and species; (2) enable multiple simultaneous workflows; (3) expand sophisticated data analysis for online resources; and enhance open-source development of the Gaggle-Firegoose infrastructure. Gaggle is an open-source Java software system that integrates existing bioinformatics programs and data sources into a user-friendly, extensible environment to allow interactive exploration, visualization, and analysis of systems biology data. Firegoose is an extension to the Mozilla Firefox web browser that enables data transfer between websites and desktop tools including Gaggle. In the last phase of this funding period, we have made substantial progress on development and application of the Gaggle integration framework. We implemented the workspace to the Network Portal. Users can capture data from Firegoose and save them to the workspace. Users can create workflows to start multiple software components programmatically and pass data between them. Results of analysis can be saved to the cloud so that they can be easily restored on any machine. We also developed the Gaggle Chrome Goose, a plugin for the Google Chrome browser in tandem with an opencpu server in the Amazon EC2 cloud. This allows users to interactively perform data analysis on a single web page using the R packages deployed on the opencpu server. The cloud-based framework facilitates collaboration between researchers from multiple organizations. We have made a number of enhancements to the cmonkey2 application to enable and improve the integration within different environments, and we have created a new tools pipeline for generating EGRIN2 models in a largely automated way.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Visualizing temperature-dependent phase stability in high entropy alloys

High entropy alloys (HEAs) contain near equimolar amounts of five or more elements and are a compelling space for materials design. In the design of HEAs, great emphasis is placed on identifying thermodynamic conditions for single-phase and multi-phase stability regions, but this process is hindered by the difficulty of navigating stability relationships in high-component spaces. Traditional phase diagrams use barycentric coordinates to represent composition axes, which require ( N – 1) spatial dimensions to represent an N -component system, meaning that HEA systems with N > 4 components cannot be readily visualized. Here, we propose forgoing barycentric composition axes in favor of two energy axes: a formation-energy axis and a ‘reaction energy’ axis. These Inverse Hull Webs offer an information-dense 2D representation that successfully captures complex phase stability relationships in N ≥ 5 component systems. We use our proposed diagrams to visualize the transition of HEA solid-solutions from high-temperature stability to metastability upon quenching, and identify important thermodynamic features that are correlated with the persistence or decomposition of metastable HEAs.

Chemistry↗

DOE OSTI2018

The Gene Ontology Resource: 20 years and still GOing strong

The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the 'GO ribbon' widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Hohman, Frederick M.↗

DOE OSTI2019

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).

human lung, sorted cells, integrated omics, Transc↗

DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

DOE OSTI2021

Enhancing the Useability of the IAEA's Physical Model: An Analysis of User Feedback

This study presents the results of Physical Model user interviews conducted with 12 people who have International Atomic Energy Agency (IAEA) safeguards experience. The goal of this report is to provide additional context for policy makers when prioritizing the needs of the IAEA regarding support for enhancements to the Physical Model. The interviews covered the general usability of the Physical Model, its effectiveness as a technical reference, and the scope and utility of the Physical Model's indicators and content. The key conclusions from these interviews are as follows: 1) The Physical Model is difficult to find and not well advertised. 2) The Physical Model can be difficult to use due to its format, which makes searching for information difficult, and lack of visual content. Therefore, the Physical Model should be supported in multiple formats (print, PDF, web) to maximize its usability for different stakeholders. 3) The scope of the Physical Model's content and indicators is likely sufficient, and translating indicators into other languages is unlikely to be worth the effort. 4) Despite its inefficiencies and other issues, the Physical Model in its current form is a vital (albeit under-utilized) resource for key stakeholders. 5) Despite its broad use case the Physical Model is not a catch-all tool and force-fitting the Physical Model into functions for which it has not been designed for would likely diminish its value. 6) It can be difficult to disentangle challenges related to the Physical Model and its usability from broader challenges at the Agency, such as general issues with communication and coordination. Based on these conclusions and considering the results of past studies on the Physical Model funded by NA-241, the analysis team developed a list of recommendations and associated actions for IAEA consideration. These recommendations are prioritized based on the assessed level of difficulty for the IAEA to implement a recommendation, emphasizing low hanging fruit then building to more ambitious changes to the Physical Model's format and content.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Offshore Geologic Carbon Storage Data Collection and International Project Inventory

We present an interactive data collection to aggregate, understand, and disseminate the data that are publicly available to support offshore GCS which can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments. The Offshore Geologic Carbon Storage Data Collection is an Experience Builder web application of multiple web mapping applications, aggregated into a single tool for each data type for access, visualization, and exploration. We also present a spatial inventory of global offshore GCS efforts to visualize the scale and locations of actualized and potential offshore GCS. It includes project location, project type and stage, CO2 storage resource potential, injection rate, reservoir and seal geology, and key literature references. Quantitative and qualitative comparisons of the distribution and magnitude of projects by their attributes lends spatial insight into the status of global GCS operations and storage resource potential, thereby enabling comparative assessments and cross-cutting knowledge transfer for projects in development. These datasets illuminate trends in ongoing offshore projects and can be leveraged by stakeholders to estimate storage resources, identify subsurface analogs, review regulations, and address challenges to offshore GCS. Additionally, opportunities for concurrent decarbonization strategies can be identified.

Mulhern, Julia↗

DOE OSTI2024

International Offshore Geologic Carbon Storage Project Inventory and Data Collection

We present an interactive data collection to aggregate, understand, and disseminate the data that are publicly available to support offshore GCS which can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments. The Offshore Geologic Carbon Storage Data Collection is an Experience Builder web application of multiple web mapping applications, aggregated into a single tool for each data type for access, visualization, and exploration. We also present a spatial inventory of global offshore GCS efforts to visualize the scale and locations of actualized and potential offshore GCS. It includes project location, project type and stage, CO2 storage resource potential, injection rate, reservoir and seal geology, and key literature references. Quantitative and qualitative comparisons of the distribution and magnitude of projects by their attributes lends spatial insight into the status of global GCS operations and storage resource potential, thereby enabling comparative assessments and cross-cutting knowledge transfer for projects in development. These datasets illuminate trends in ongoing offshore projects and can be leveraged by stakeholders to estimate storage resources, identify subsurface analogs, review regulations, and address challenges to offshore GCS. Additionally, opportunities for concurrent decarbonization strategies can be identified.

Mulhern, Julia↗

DOE OSTI2024

International Offshore Geologic Carbon Storage Inventory and Data Collection

We present an interactive data collection to aggregate, understand, and disseminate the data that are publicly available to support offshore GCS which can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments. The Offshore Geologic Carbon Storage Data Collection is an Experience Builder web application of multiple web mapping applications, aggregated into a single tool for each data type for access, visualization, and exploration. We also present a spatial inventory of global offshore GCS efforts to visualize the scale and locations of actualized and potential offshore GCS. It includes project location, project type and stage, CO2 storage resource potential, injection rate, reservoir and seal geology, and key literature references. Quantitative and qualitative comparisons of the distribution and magnitude of projects by their attributes lends spatial insight into the status of global GCS operations and storage resource potential, thereby enabling comparative assessments and cross-cutting knowledge transfer for projects in development. These datasets illuminate trends in ongoing offshore projects and can be leveraged by stakeholders to estimate storage resources, identify subsurface analogs, review regulations, and address challenges to offshore GCS. Additionally, opportunities for concurrent decarbonization strategies can be identified.

Mulhern, Julia↗

DOE OSTI2023

Quality Assurance Project Plan for the Salish Sea Model – Continuing Development of New Capabilities and Applications: European Green Crab (EGC) Larval Transport, Coupling to VELMA and Atlantis, Performance Improvements and Diagnostic Applications

This quality assurance project plan (QAPP) is for proposed FY24-FY27 efforts to (a) develop/expand a predictive European Green Crab (EGC) larval dispersal model for the Salish Sea, (b) add linkage between VELMA (Visualizing Ecosystem Land Management Assessments) watershed model and Atlantis model (ecosystem dynamics and food web) through SSM, and (c) continue SSM capability development through the addition of metals, harmful algal blooms (HABs), and FVCOM-LTRANS (Lagrangian Larval Transport) modules.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

What’s in Your Pocket? Trends in Mobile Apps for Biosurveillance and Decision-Making

Mobile applications offer tremendous potential for collecting data for biosurveillance through participatory surveillance and the integration of new and traditional data streams. This panel features epidemiologists, data scientists, and technology partners who will address the full application lifecycle -- from the assessment of the specific demand, resource, and technology specifications, to implementation and evaluation-- of several innovative apps. Audience engagement using a new web app will gather input on other uses of mobile apps for data collection, visualization, and analyses. The panel and audience discussion will be compiled into guidance for future development of mobile apps for biosurveillance purposes to advance population health.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2000

Visualization of Atmospheric Water Vapor Data for SAGE

The goal of this project was to develop visualization tools to study the water vapor dynamics using the Stratospheric Aerosol and Gas Experiment 11 (SAGE 11) water vapor data. During the past years, we completed the development of a visualization tool called EZSAGE, and various Gridded Water Vapor plots, tools deployed on the web to provide users with new insight into the water vapor dynamics. Results and experiences from this project, including papers, tutorials and reviews were published on the main Web page. Additional publishing effort has been initiated to package EZSAGE software for CD production and distribution. There have been some major personnel changes since Fall, 1998. Dr. Mou-Liang Kung, a Professor of Computer Science assumed the PI position vacated by Dr. Waldo Rodriguez who was on leave. However, former PI, Dr. Rodriguez continued to serve as a research adviser to this project to assure smooth transition and project completion. Typically in each semester, five student research assistants were hired and trained. Weekly group meetings were held to discuss problems, progress, new research direction, and activity planning. Other small group meetings were also held regularly for different objectives of this project. All student research assistants were required to submit reports for conference submission.

Kung, Mou-Liang↗

NASA NTRS2016

Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service

The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.

GLOBE↗

NASA NTRS2017

Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service

The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.

GLOBE Data↗