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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DataShare Recon Assistant Tool (DSRAT) v.1.0.x

SAND2023-06798O DataShare Recon Assistant Tool (DSRAT) software is used for capturing recon data and information in an emergency response situation on an Android device. Users can capture images, video, item description and spectra information in DSRAT and upload it to DataShare, which is a multi-server application that shares data in real-time to all nodes on the network. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Gilbert, Christina↗
DOE OSTI2024

Mnemosyne

SAND2024-08570O Mnemosyne is an interactive tool for finding, retrieving, and exploring information about U.S. nuclear tests documented in the National Nuclear Security Administration’s NV-209 report. It also acts as an information architecture and codebase for integrating additional information and computational tools related to these tests at the unclassified and classified levels. Users can search by any number of nuclear test attributes—name, yield range, altitude ranges, purpose of a test—and find all matching tests. Users can also retrieve specific test information published in NV-209. The tool displays geospatial and topological data about test location, and it provides an information architecture for storing additional contextual material, such as photographs. Mnemosyne provides a capability of interfacing with HYCHEM, Sandia's nuclear detonation optical waveform tool. The software is designed for use by government, academia, military, and research institutions. The software will likely be advanced to integrate seismic data and the nuclear detonation optical signal simulation code radCTH. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fisher, Dustin↗
DOE OSTI2020

srlife

Solar receiver structural life estimation tool. This software estimates the life of a tubular concentrating solar power receiver given user input describing the working fluid pressures and temperatures. sf-20-068

Messner, Mark↗
DOE OSTI2025

SOHIP Abel Transform and Onion Peeling Model Module

This software provides tools for analyzing and modeling physical systems using mathematical transforms and layered models. It includes (1) functions for performing the Abel transform, which is used to relate measurements of bending angles to properties such as refractive index and radius in a medium. The code can compute bending angles from input profiles and also reconstruct these profiles from observed data; (2) the functions for modeling systems with multiple layers using an onion-peeling approach, allowing users to simulate and analyze the behavior of layered materials or structures. These capabilities are useful for researchers and engineers working in fields such as optics, atmospheric science, and materials analysis, enabling them to interpret and model data from experiments or simulations relates to refraction in spherical symmetric medium.

Xu, Shuang [Lawrence Livermore National Laboratory↗
DOE OSTI2020

AlphaBeta: computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants

Stochastic changes in DNA methylation (i.e., spontaneous epimutations) contribute to methylome diversity in plants. Here, we describe AlphaBeta, a computational method for estimating the precise rate of such stochastic events using pedigree-based DNA methylation data as input. We demonstrate how AlphaBeta can be employed to study transgenerationally heritable epimutations in clonal or sexually derived mutation accumulation lines, as well as somatic epimutations in long-lived perennials. Application of our method to published and new data reveals that spontaneous epimutations accumulate neutrally at the genome-wide scale, originate mainly during somatic development and that they can be used as a molecular clock for age-dating trees.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The Resource Identification Initiative: A cultural shift in publishing

A central tenet in support of research reproducibility is the ability to uniquely identify research resources, i.e., reagents, tools, and materials that are used to perform experiments. However, current reporting practices for research resources are insufficient to allow humans and algorithms to identify the exact resources that are reported or answer basic questions such as “What other studies used resource X?” To address this issue, the Resource Identification Initiative was launched as a pilot project to improve the reporting standards for research resources in the methods sections of papers and thereby improve identifiability and reproducibility. The pilot engaged over 25 biomedical journal editors from most major publishers, as well as scientists and funding officials. Authors were asked to include Research Resource Identifiers (RRIDs) in their manuscripts prior to publication for three resource types: antibodies, model organisms, and tools (including software and databases). RRIDs represent accession numbers assigned by an authoritative database, e.g., the model organism databases, for each type of resource. To make it easier for authors to obtain RRIDs, resources were aggregated from the appropriate databases and their RRIDs made available in a central web portal (www.scicrunch.org/resources). RRIDs meet three key criteria: they are machine readable, free to generate and access, and are consistent across publishers and journals. The pilot was launched in February of 2014 and over 300 papers have appeared that report RRIDs. The number of journals participating has expanded from the original 25 to more than 40. Here, we present an overview of the pilot project and its outcomes to date. We show that authors are generally accurate in performing the task of identifying resources and supportive of the goals of the project. We also show that identifiability of the resources pre- and post-pilot showed a dramatic improvement for all three resource types, suggesting that the project has had a significant impact on reproducibility relating to research resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The Resource Identification Initiative: A cultural shift in publishing

A central tenet in support of research reproducibility is the ability to uniquely identify research resources, i.e., reagents, tools, and materials that are used to perform experiments. However, current reporting practices for research resources are insufficient to allow humans and algorithms to identify the exact resources that are reported or answer basic questions such as “What other studies used resource X?” To address this issue, the Resource Identification Initiative was launched as a pilot project to improve the reporting standards for research resources in the methods sections of papers and thereby improve identifiability and reproducibility. The pilot engaged over 25 biomedical journal editors from most major publishers, as well as scientists and funding officials. Authors were asked to include Research Resource Identifiers (RRIDs) in their manuscripts prior to publication for three resource types: antibodies, model organisms, and tools (including software and databases). RRIDs represent accession numbers assigned by an authoritative database, e.g., the model organism databases, for each type of resource. To make it easier for authors to obtain RRIDs, resources were aggregated from the appropriate databases and their RRIDs made available in a central web portal (www.scicrunch.org/resources). RRIDs meet three key criteria: they are machine readable, free to generate and access, and are consistent across publishers and journals. The pilot was launched in February of 2014 and over 300 papers have appeared that report RRIDs. The number of journals participating has expanded from the original 25 to more than 40. Here, we present an overview of the pilot project and its outcomes to date. We show that authors are generally accurate in performing the task of identifying resources and supportive of the goals of the project. We also show that identifiability of the resources pre- and post-pilot showed a dramatic improvement for all three resource types, suggesting that the project has had a significant impact on reproducibility relating to research resources.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A meta-evaluation of the quality of reporting and execution in ecological meta-analyses

Quantitatively summarizing results from a collection of primary studies with meta-analysis can help answer ecological questions and identify knowledge gaps. The accuracy of the answers depends on the quality of the meta-analysis. We reviewed the literature assessing the quality of ecological meta-analyses to evaluate current practices and highlight areas that need improvement. From each of the 18 review papers that evaluated the quality of meta-analyses, we calculated the percentage of meta-analyses that met criteria related to specific steps taken in the meta-analysis process (i.e., execution) and the clarity with which those steps were articulated (i.e., reporting). We also re-evaluated all the meta-analyses available from Pappalardo et al. to extract new information on ten additional criteria and to assess how the meta-analyses recognized and addressed non-independence. In general, we observed better performance for criteria related to reporting than for criteria related to execution; however, there was a wide variation among criteria and meta-analyses. Meta-analyses had low compliance with regard to correcting for phylogenetic non-independence, exploring temporal trends in effect sizes, and conducting a multifactorial analysis of moderators (i.e., explanatory variables). In addition, although most meta-analyses included multiple effect sizes per study, only 66% acknowledged some type of non-independence. The types of non-independence reported were most often related to the design of the original experiment (e.g., the use of a shared control) than to other sources (e.g., phylogeny). We suggest that providing specific training and encouraging authors to follow the PRISMA EcoEvo checklist recently developed by O’Dea et al. can improve the quality of ecological meta-analyses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

DeepBench: A simulation package for physical benchmarking data

We introduce **DeepBench**, a python library that generates simple simulated image data from first principles, such as basic geometric shapes and astronomical objects. These data are highly valuable for developing (calibration, testing, and benchmarking) statistical and machine learning models because they make it possible to connect the final data product to physically interpretable inputs. This software includes tools to curate and store the datasets to maximize reproducibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Modeling and Evaluation of Geophysical Methods for Monitoring and Tracking CO2 Migration

Geological sequestration has been proposed as a viable option for mitigating the vast amount of CO 2 being released into the atmosphere daily. Test sites for CO 2 injection have been appearing across the world to ascertain the feasibility of capturing and sequestering carbon dioxide. A major concern with full scale implementation is monitoring and verifying the permanence of injected CO 2 . Geophysical methods, an exploration industry standard, are non-invasive imaging techniques that can be implemented to address that concern. Geophysical methods, seismic and electromagnetic, play a crucial role in monitoring the subsurface pre- and post-injection. Seismic techniques have been the most popular but electromagnetic methods are gaining interest. The primary goal of this project was to develop a new geophysical tool, a software program called GphyzCO2, to investigate the implementation of geophysical monitoring for detecting injected CO 2 at test sites. The GphyzCO2 software consists of interconnected programs that encompass well logging, seismic, and electromagnetic methods. The software enables users to design and execute 3D surface-to-surface (conventional surface seismic) and borehole-to-borehole (cross-hole seismic and electromagnetic methods) numerical modeling surveys. The generalized flow of the program begins with building a complex 3D subsurface geological model, assigning properties to the models that mimic a potential CO 2 injection site, numerically forward model a geophysical survey, and analyze the results. A test site located in Warren County, Ohio was selected as the test site for the full implementation of GphyzCO2. Specific interest was placed on a potential reservoir target, the Mount Simon Sandstone, and cap rock, the Eau Claire Formation. Analysis of the test site included well log data, physical property measurements (porosity), core sample resistivity measurements, calculating electrical permittivity values, seismic data collection, and seismic interpretation. The data was input into GphyzCO2 to demonstrate a full implementation of the software capabilities. Part of the implementation investigated the limits of using geophysical methods to monitor CO 2 injection sites. The results show that cross-hole EM numerical surveys are limited to under 100 meter borehole separation. Those results were utilized in executing numerical EM surveys that contain hypothetical CO 2 injections. The outcome of the forward modeling shows that EM methods can detect the presence of CO 2 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

MOGO: Model-Oriented Global Optimization of Petascale Applications

The MOGO project was initiated under in 2008 under the DOE Program Announcement for Software Development Tools for Improved Ease-of-Use on Petascale systems (LAB 08-19). The MOGO team consisted of Oak Ridge National Lab, Argonne National Lab, and the University of Oregon. The overall goal of MOGO was to attack petascale performance analysis by developing a general framework where empirical performance data could be efficiently and accurately compared with performance expectations at various levels of abstraction. This information could then be used to automatically identify and remediate performance problems. MOGO was be based on performance models derived from application knowledge, performance experiments, and symbolic analysis. MOGO was able to make reasonable impact on existing DOE applications and systems. New tools and techniques were developed, which, in turn, were used on important DOE applications on DOE LCF systems to show significant performance improvements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Incorporating Cold Cap Behavior in a Joule-heated Waste Glass Melter Model

In this paper, an overview of Joule-heated waste glass melters used in the vitrification of high level waste (HLW) is presented, with a focus on the cold cap region. This region, in which feed-to-glass conversion reactions occur, is critical in determining the melting properties of any given glass melter. An existing 1D computer model of the cold cap, implemented in MATLAB, is described in detail. This model is a standalone model that calculates cold cap properties based on boundary conditions at the top and bottom of the cold cap. Efforts to couple this cold cap model with a 3D STAR-CCM+ model of a Joule-heated melter are then described. The coupling is being implemented in ModelCenter, a software integration tool. The ultimate goal of this model is to guide the specification of melter parameters that optimize glass quality and production rate.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Nano-Antenna Electromagnetic Energy Gathering Film

RedWave Energy Inc. entered into a CRADA with BEA to leverage and license nanoantenna technology. The scope of work involved the systematic development and advancement of software modeling/simulation tools, material analysis methods, and fabrication procedures applicable to nanoantennas. The strategy is composed of three elements: nano-antenna technology from the Department of Energy’s Idaho National Laboratories (INL), High Speed Diodes (Metal Insulator Metal) from the University of Colorado, roll-to-roll fabrication from MicroContinuum, Inc.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

NREL Energy Models Examine the Potential for Wind and Solar Grid Integration (Fact Sheet)

As renewable energy generating sources, such as wind turbines and solar power systems, reach high levels of penetration in parts of the United States, the National Renewable Energy Laboratory (NREL) is helping the utility industry to peer into the future. Using software modeling tools that the lab developed, NREL is examining the future operation of the electrical grid as renewable energy continues to grow.

14 SOLAR ENERGY↗