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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Aquifer Injection Modeling (AIM) Toolbox User Guide

The Aquifer Injection Modeling Toolbox (“AIM Toolbox”) software was developed by the Pacific Northwest National Laboratory (PNNL) for the U.S. Environmental Protection Agency’s (EPA) to provide a collection of analytical solutions suitable for evaluating the potential extent of the area impacted by subsurface injection operations. Subsurface injection operations are regulated under the EPA’s Underground Injection Control (UIC) program and are typically related to oil/gas development, waste disposal, or subsurface mining or storage. The analytical algorithms provided in the AIM Toolbox each have different approaches/assumptions/focus with respect to the nature and processes in subsurface and the nature of the injection operations. Collectively the set of analysis algorithms provides a broader evaluation of the area that can potentially be impacted by an injection operation. By providing estimates for the injectate plume extent, the software supports technical aspects of planning, evaluation, and overseeing injection activities. That is, the results help assess when further regulatory controls (e.g., monitoring, reporting) may be required and, in the case of disposal into an underground source of drinking water, the extent of the impacted area would require exemption from protection under the Safe Drinking Water Act. The AIM Toolbox software is available as a single-page web application, providing an interface to provide the necessary inputs, and both chart and map panes for visualizing the results. This User Guide describes the AIM Toolbox software, information required, user interactions, and the underlying basis of the calculations.

58 GEOSCIENCES↗
DOE OSTI2025

DeepDiagnostics: A Software Package for Streamlined Posterior Evaluation

Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.

Voetberg, Maggie [Fermilab]↗
DOE OSTI2021

Aquifer Injection Modeling (AIM) Toolbox User Guide

The Aquifer Injection Modeling Toolbox (“AIM Toolbox”) software was developed by the Pacific Northwest National Laboratory (PNNL) for the U.S. Environmental Protection Agency’s (EPA) to provide a collection of analytical solutions suitable for evaluating the potential extent of the area impacted by subsurface injection operations. Subsurface injection operations are regulated under the EPA’s Underground Injection Control (UIC) program and are typically related to oil/gas development, waste disposal, or subsurface mining or storage. The analytical algorithms provided in the AIM Toolbox each have different approaches/assumptions/focus with respect to the nature and processes in subsurface and the nature of the injection operations. Collectively the set of analysis algorithms provides a broader evaluation of the area that can potentially be impacted by an injection operation. By providing estimates for the injectate plume extent, the software supports technical aspects of planning, evaluation, and overseeing injection activities. That is, the results help assess when further regulatory controls (e.g., monitoring, reporting) may be required and, in the case of disposal into an underground source of drinking water, the extent of the impacted area would require exemption from protection under the Safe Drinking Water Act. The AIM Toolbox software is available as a single-page web application, providing an interface to provide the necessary inputs, and both chart and map panes for visualizing the results. This User Guide describes the AIM Toolbox software, information required, user interactions, and the underlying basis of the calculations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Molecular radiotherapy: The NUKFIT software for calculating the time-integrated activity coefficient

Purpose: Calculation of the time-integrated activity coefficient (residence time) is a crucial step in dosimetry for molecular radiotherapy. However, available software is deficient in that it is either not tailored for the use in molecular radiotherapy and/or does not include all required estimation methods. The aim of this work was therefore the development and programming of an algorithm which allows for an objective and reproducible determination of the time-integrated activity coefficient and its standard error.Methods: The algorithm includes the selection of a set of fitting functions from predefined sums of exponentials and the choice of an error model for the used data. To estimate the values of the adjustable parameters an objective function, depending on the data, the parameters of the error model, the fitting function and (if required and available) Bayesian information, is minimized. To increase reproducibility and user-friendliness the starting values are automatically determined using a combination of curve stripping and random search. Visual inspection, the coefficient of determination, the standard error of the fitted parameters, and the correlation matrix are provided to evaluate the quality of the fit. The functions which are most supported by the data are determined using the corrected Akaike information criterion. The time-integrated activity coefficient is estimated by analytically integrating the fitted functions. Its standard error is determined assuming Gaussian error propagation. The software was implemented using MATLAB.Results: To validate the proper implementation of the objective function and the fit functions, the results of NUKFIT and SAAM numerical, a commercially available software tool, were compared. The automatic search for starting values was successfully tested for reproducibility. The quality criteria applied in conjunction with the Akaike information criterion allowed the selection of suitable functions. Function fit parameters and their standard error estimated by using SAAM numerical and NUKFIT showed differences of <1%. The differences for the time-integrated activity coefficients were also <1% (standard error between 0.4% and 3%). In general, the application of the software is user-friendly and the results are mathematically correct and reproducible. An application of NUKFIT is presented for three different clinical examples.Conclusions: The software tool with its underlying methodology can be employed to objectively and reproducibly estimate the time integrated activity coefficient and its standard error for most time activity data in molecular radiotherapy.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗
DOE OSTI2021

Data Analysis Approach for Large Data Volumes in a Connected Community

Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems are providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, and in-memory processing of large volumes of streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive and predictive data analysis for understanding live operation of the neighborhood, fault identification, and future opportunities for further optimization of load curves. We then demonstrate how we achieve live situational awareness of the connected neighborhood through a suite of visualization components. Finally, we discuss a few analytic dashboards that address questions such as peak load reductions obtained due to optimization, customer preference for automatic control of appliances (do they override the automatic control of HVAC?, etc.). 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Chinthavali, Supriya↗
DOE OSTI2014

Web-Based Geographic Information System Tool for Accessing Hanford Site Environmental Data

Data volume, complexity, and access issues pose severe challenges for analysts, regulators and stakeholders attempting to efficiently use legacy data to support decision making at the U.S. Department of Energy’s (DOE) Hanford Site. DOE has partnered with the Pacific Northwest National Laboratory (PNNL) on the PHOENIX (PNNL-Hanford Online Environmental Information System) project, which seeks to address data access, transparency, and integration challenges at Hanford to provide effective decision support. PHOENIX is a family of spatially-enabled web applications providing quick access to decades of valuable scientific data and insight through intuitive query, visualization, and analysis tools. PHOENIX realizes broad, public accessibility by relying only on ubiquitous web-browsers, eliminating the need for specialized software. It accommodates a wide range of users with intuitive user interfaces that require little or no training to quickly obtain and visualize data. Currently, PHOENIX is actively hosting three applications focused on groundwater monitoring, groundwater clean-up performance reporting, and in-tank monitoring. PHOENIX-based applications are being used to streamline investigative and analytical processes at Hanford, saving time and money. But more importantly, by integrating previously isolated datasets and developing relevant visualization and analysis tools, PHOENIX applications are enabling DOE to discover new correlations hidden in legacy data, allowing them to more effectively address complex issues at Hanford.

decision support↗
DOE OSTI2024

TEAMER - Field Demonstration of MarineSitu’s Marine Energy Monitoring Tools - CRADA 664 (Abstract)

In order to effectively monitor for marine life around marine energy devices and thus minimize the risk of collision, multiple sensors working in coordination and augmented with around-the-clock automated monitoring algorithms need to be installed in challenging high-energy tidal and wave environments. Such systems are often too expensive for widespread adoption, or lack sufficient sensors or smarts to enable around-the-clock, real-time monitoring without human involvement. MarineSitu has been working to tackle this problem by developing a low-cost, combined sonar and stereo camera sensor array with connected real-time AI-based algorithms for automatically detecting marine life in these marine energy suitable environments. In this TEAMER project with Pacific Northwest National Lab (PNNL), MarineSitu will be testing this novel sensor system for the first time in the high-energy tidal channel environment at PNNL’s Marine and Coastal Research Lab. Throughout this deployment, MarineSitu will be monitoring their system and running analytics on the sensor’s data in real-time. Meanwhile, PNNL Data Scientists and Ocean Engineers, will be evaluating the system’s effectiveness and ease of use both as a tool for plug-and-play environmental monitoring and novel environmental monitoring research. In doing so, the team will improve MarineSitu’s system and software, produce insightful data products, and develop novel visualizations and AI algorithms for combining and analyzing the data produced by systems like MarineSitu’s.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

TEAMER – Field Demonstration of MarineSitu’s Marine Energy Monitoring (Abstract)

In order to effectively monitor for marine life around marine energy devices and thus minimize the risk of collision, multiple sensors working in coordination and augmented with around-the-clock automated monitoring algorithms need to be installed in challenging high-energy tidal and wave environments. Such systems are often too expensive for widespread adoption, or lack sufficient sensors or smarts to enable around-the-clock, real-time monitoring without human involvement. MarineSitu has been working to tackle this problem by developing a low-cost, combined sonar and stereo camera sensor array with connected real-time AI-based algorithms for automatically detecting marine life in these marine energy suitable environments. In this TEAMER project with Pacific Northwest National Lab (PNNL), MarineSitu will be testing this novel sensor system for the first time in the high-energy tidal channel environment at PNNL’s Marine and Coastal Research Lab. Throughout this deployment, MarineSitu will be monitoring their system and running analytics on the sensor’s data in real-time. Meanwhile, PNNL Data Scientists and Ocean Engineers, will be evaluating the system’s effectiveness and ease of use both as a tool for plug-and-play environmental monitoring and novel environmental monitoring research. In doing so, the team will improve MarineSitu’s system and software, produce insightful data products, and develop novel visualizations and AI algorithms for combining and analyzing the data produced by systems like MarineSitu’s.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Development of Ambient Mass Spectrometry Rapid Analysis Methods for Trace Inorganics

Ambient mass spectrometry is being investigated as a new method for rapidly characterizing trace inorganic species, including nuclear materials. SRNL is currently developing electrospray ionization (ESI) and paper spray ionization (PSI) methods for mass spectrometry for the detection of trace inorganic compounds JEOL AccuTOF™-DART ® LP 4G mass spectrometer. Sample analysis generates a complex three-dimensional data set with 10 2 -10 3 individual mass spectra spread temporally. To improve the quality and ease of data extraction from this amount of information, a simple data analysis pipeline written in R/R Studio has been improved and expanded upon. Specifically, the software now has header inputs to set base input/output filenames, isotopes of interest, and a list of concentrations analyzed on the instrument. Using list-based structures to enable batch processing of many concentrations at once, it then loads each file, extracts the relevant intensities for masses of interest from each spectrum, and iterates through each concentration to generate graphs of internal standard & analyte correlations, measured isotope ratios, and total ion chromatograms. It then filters the data to remove points at background levels or at the detector saturation limit. Using this filtered data, the pipeline re-creates the graphs to reveal high-quality information from a low-quality raw dataset. All graphs of the R software then output to PowerPoint format. Additionally, faster 3D visualization functionality was added to the pipeline for viewing the entire data file produced by the instrument as an intensity map. With these data analysis tools ready, samples of strontium nitrate/yttrium nitrate as an analyte/internal standard combination were measured on the AccuTOF. From the filtered data, calibration curves were prepared and limits of detection (LOD) of 1 ppb for ESI-MS and 20 ppb for PSI-MS were obtained. The quality available from the calibration curves and the low limits of detection demonstrate the value of the instrument/analysis pipeline system in characterizing trace inorganics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

A Design Document: Custom Analytical Tools and Automation for the Industrial Hygienist

This report outlines an approach for developing a modular and scalable web-based application suite, Internal Data Access and Visualization (I-DAV), that would be developed to meet the specific analytic requirements for Industrial Hygienist (IH) analysts and tank vapors Subject Matter Expert’s (SME) at the Hanford site. Through interviews and requirements elicitation with IH analyst, and on-site in depth review of workflow patterns and expected outcomes, PNNL has developed a framework for proposed collection of interdependent goal oriented software applications. Commercial off the shelf applications, which require only minimal custom code, were deployed this year as a mechanism of rapid prototyping and providing proof of concept. This report describes these efforts and accomplishments to date, as well as concepts for future applications and general enhancement of the DAV and core concepts for the I-DAV. Additional content was integrated into this report as of March 2019. Updated content was added to reflect the inclusion of I-DAV concepts and ideas, and refined versions of original concepts set forth as the work commenced and continued through the first half of FY19. New and refined concepts have been the product of ongoing weekly meetings, outreach, and elicitation with WRPS.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Design Document: Custom Analytical Tools and Automation for the Industrial Hygienist

This report outlines an approach for developing a modular and scalable web-based application suite, Internal Data Access and Visualization (I-DAV), that would be developed to meet the specific analytic requirements for Industrial Hygienist (IH) analysts and tank vapors Subject Matter Expert’s (SME) at the Hanford site. Through interviews and requirements elicitation with IH analyst, and on-site in depth review of workflow patterns and expected outcomes, PNNL has developed a framework for proposed collection of interdependent goal oriented software applications. Commercial off the shelf applications, which require only minimal custom code, were deployed this year as a mechanism of rapid prototyping and providing proof of concept. This report describes these efforts and accomplishments to date, as well as concepts for future applications and general enhancement of the DAV and core concepts for the I-DAV. Additional content was integrated into this report as of March 2019. Updated content was added to reflect the inclusion of I-DAV concepts and ideas, and refined versions of original concepts set forth as the work commenced and continued through the first half of FY19. New and refined concepts have been the product of ongoing weekly meetings, outreach, and elicitation with WRPS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Materials Informatics ChemVis

A Browser-based visualization of morphology, material, and chemical data extracted from chemical papers. This UI is focused for specifically nanomaterial synthesis papers. The visual analytic environment allows the user to explore a large number of papers quickly, through an interactive, intuitive, and visual way that helps see possible trends in the data. This exploration-oriented systems allows domain experts who may not have an extensive background in writing analysis code or database queries to easily express their idea to experiment on the data and view the results. The visualization software requires the data in JSON format. The data packaged with the software is the result of 35k nanomaterial synthesis papers queried from Elsevier, created through a separate software pipeline. The data contains information such as the title, authors, chemicals, and created material and morphology.

Liu, Shusen↗
DOE OSTI2024

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

Johnson, Andrew E.↗
DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗
DOE OSTI2018

OpenMSI Arrayed Analysis Tools (OMAAT) v2.0

Mass spectrometry (MS) is an analytical technique that enables all chemical species within a complex mixture to be resolved and identified based on their mass:charge ratio. MS is an increasingly important tool in life science research and development because it can resolve or separate small molecules, peptides, lipids, proteins, biopolymers and fragments of all of these structures. Unfortunately, the use of MS has been limited for lack of software tools to visualize, manipulate, store and transfer MS data. Ben Bowen, Oliver Ruebel and colleagues made a huge advance on the resolution of these limitations with the invention of "OpenMSI" (#2013-004), a computational method that makes the handling of these very large files (up to 50 Tb) much easier, especially for scientists with limited background in bioinformatics or sophisticated software applications of the type needed to handle such data files. With the OpenMSI Arrayed Analysis Tool (OMAAT, #2016-188) invention, the authors take MS data analysis one step further by providing tolls for automatic sample position optimization and ion selection. Even though sample deposition technologies have improved greatly, there is always the chance of encountering outliers in a predefined roster, especially in very large sample sets. Manual curation of the data would be extremely time consuming, so the inventors have included an algorithm that automatically finds the outliers. OMAAT is written in Python with accompanying Jupyter (formerly iPython) notebook and is fully integrated with OpenMSI.

Bowen, Benjamin↗
DOE OSTI2020

Towards Interactive, Reproducible Analytics at Scale on HPC Systems

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

containers↗
DOE OSTI2012

Large Scale Computing and Storage Requirements for Nuclear Physics Research

The National Energy Research Scientific Computing Center (NERSC) is the primary computing center for the DOE Office of Science, serving approximately 4,000 users and hosting some 550 projects that involve nearly 700 codes for a wide variety of scientific disciplines. In addition to large-scale computing resources NERSC provides critical staff support and expertise to help scientists make the most efficient use of these resources to advance the scientific mission of the Office of Science. In May 2011, NERSC, DOE’s Office of Advanced Scientific Computing Research (ASCR) and DOE’s Office of Nuclear Physics (NP) held a workshop to characterize HPC requirements for NP research over the next three to five years. The effort is part of NERSC’s continuing involvement in anticipating future user needs and deploying necessary resources to meet these demands. The workshop revealed several key requirements, in addition to achieving its goal of characterizing NP computing. The key requirements include: 1. Larger allocations of computational resources at NERSC; 2. Visualization and analytics support; and 3. Support at NERSC for the unique needs of experimental nuclear physicists. This report expands upon these key points and adds others. The results are based upon representative samples, called “case studies,” of the needs of science teams within NP. The case studies were prepared by NP workshop participants and contain a summary of science goals, methods of solution, current and future computing requirements, and special software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, “multi-core” environment that is expected to dominate HPC architectures over the next few years. The report also includes a section with NERSC responses to the workshop findings. NERSC has many initiatives already underway that address key workshop findings and all of the action items are aligned with NERSC strategic plans.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗