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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multi-Species Complex and Standard Metabolomic Samples with Verified Truth Annotations Dataset

This dataset contains 4523251 (~6.35 GB) metabolite-spectra matches following identification with CoreMS. Data were manually curated as true positives, true negatives, or unknowns. Calculations for spectral similarity scores were carried out with two methods for a total of ~12.7 GB (6.35 * 2) of data. They are all .tsv files, though can easily be changed to .txt. The file types are: * human cerebrospinal fluid (CSF), human blood plasma human urine: already published here https://www.nature.com/articles/s41597-021-00894-y, • purchased FAMES standards • fungi species (A. niger, A. nidulans, T. reesei) • soil crust

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Electricity and Gas IDS Dataset

The following dataset was collected from a set of cybersecurity experiments conducted in an Electricity and Natural Gas environment. The architecture was instantiated within the powerNET testbed at Pacific Northwest National Laboratory, and is comprised of both simulated components and hardware-in-the-loop devices. The test environment consisted of a substation and control center network representative of electrical systems. In addition, it also contained a compressor station, and an odorizer and pressure regulation station representative of oil and natural gas systems. The various devices on the electrical and gas systems were organized into multiple networks to mimic real-world deployments. There were 14 testing scenarios overall that covered a wide variety of cybersecurity and infrastructure events.

Industrial Control↗
DOE OSTI2021

Electricity and Gas IDS Dataset

The following dataset was collected from a set of cybersecurity experiments conducted in an Electricity and Natural Gas environment. The architecture was instantiated within the powerNET testbed at Pacific Northwest National Laboratory, and is comprised of both simulated components and hardware-in-the-loop devices. The test environment consisted of a substation and control center network representative of electrical systems. In addition, it also contained a compressor station, and an odorizer and pressure regulation station representative of oil and natural gas systems. The various devices on the electrical and gas systems were organized into multiple networks to mimic real-world deployments. There were 14 testing scenarios overall that covered a wide variety of cybersecurity and infrastructure events.

Industrial Control↗
DOE OSTI2024

Predictive Phenomics Initiative Project Dataset Catalog Collection

The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems. Research projects within this initiative are divided into three Thrust Areas (TAs): TA1) Enhancing Multi-Scale Phenomics Measurements, TA2) Identifying Molecular Patterns of Biological Function, and TA3) Computational Methods - Phenotypic Signatures. In efforts to enable discovery, reproducibility, and reuse of PPI-funded digital research data generated or used through the course of the proposed research-funded lifecycles, all corresponding digital data assets conducted under the Laboratory Directed Research and Development Program at PNNL are linked to this PPI dataset catalog collection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predictive Phenomics Initiative Project Dataset Catalog Collection

The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems. Research projects within this initiative are divided into three Thrust Areas (TAs): TA1) Enhancing Multi-Scale Phenomics Measurements, TA2) Identifying Molecular Patterns of Biological Function, and TA3) Computational Methods - Phenotypic Signatures. In efforts to enable discovery, reproducibility, and reuse of PPI-funded digital research data generated or used through the course of the proposed research-funded lifecycles, all corresponding digital data assets conducted under the Laboratory Directed Research and Development Program at PNNL are linked to this PPI dataset catalog collection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dataset_for_Molecular_Motion_Below_the_Glass_Transition_A_Solid-State_NMR_Study_of_Siloxane_Polymer_Dynamics Study

This dataset contains solid-state 1H and 13C NMR relaxometry data, differential scanning calorimetry (DSC) data, and size exclusion chromatography (SEC/GPC) data supporting the study of sub-glass-transition (sub-Tg) molecular dynamics in a composition- and sequence-controlled series of diphenyl-substituted polysiloxanes (PDMS, 14Ph, 33Ph, 50Ph, 67Ph, and 100Ph; 0–100% diphenylsiloxane content by mole).All solid-state NMR data were acquired on a 200 MHz Bruker Avance III HD spectrometer using a static 7 mm HX probe or a 4 mm HX probe under 4 kHz magic-angle spinning. Raw Bruker TopSpin experiment folders are included for: (1) variable-temperature 1H lineshape measurements used to determine linewidth (FWHM) as a function of temperature across the glass transition; (2) 1H T1 (saturation recovery with solid-echo detection), probing nanosecond-scale dynamics near the 1H Larmor frequency; (3) 1H T1rho (direct spin-lock, 62.5 kHz), probing microsecond-scale segmental dynamics; (4) 13C-detected Lee–Goldburg cross-polarization 1H T1rho (LGCPH T1rho) for 33Ph and 50Ph, resolving aromatic and aliphatic proton environments; and (5) 13C T1 relaxation for 33Ph and 50Ph. Differential scanning calorimetry data (TA Instruments DSC 25, −150 to +120 °C, up to +300 °C for 100Ph, 10 °C/min) are included for all six compositions and support the glass-transition temperatures in Table 1 and Figure 1. Size exclusion chromatography data (Agilent 1200 Series, PL-Gel 300 mixed-C column, THF mobile phase, polystyrene calibration standards) are included for the three synthesized copolymers (33Ph, 50Ph, 67Ph) and support the number-average molecular weights in Table 1. Processed data include per-composition relaxation-time summaries (Excel), curve-fitting and Bloembergen-Purcell-Pound (BPP) model analysis notebooks (Jupyter/Python), and Igor Pro (.pxp) master files used to generate the manuscript's figures.

Bloembergen-Purcell-Pound theory↗
DOE OSTI2021

Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States

This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.

AI↗
DOE OSTI2023

Data Validation Experiments with a Computer-Generated Imagery Dataset for International Nuclear Safeguards

Computer vision models have great potential as tools for international nuclear safeguards verification activities, but off-the-shelf models require fine-tuning through transfer learning to detect relevant objects. Because open-source examples of safeguards-relevant objects are rare, and to evaluate the potential of synthetic training data for computer vision, we present the Limbo dataset. Limbo includes both real and computer-generated images of uranium hexafluoride containers for training computer vision models. Here, we generated these images iteratively based on results from data validation experiments that are detailed here. The findings from these experiments are applicable both for the safeguards community and the broader community of computer vision research using synthetic data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Testing gravitational physics by combining DESI DR1 and weak lensing datasets using the E_G estimator

The action of gravitational physics across space-time creates observable signatures in the behaviour of light and matter. We perform combined-probe studies using data from the Baryon Oscillation Spectroscopic Survey (BOSS) and Dark Energy Spectroscopic Instrument survey Data Release 1 (DESI-DR1), in combination with three existing weak lensing surveys, the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper Suprime-Cam Survey (HSC), to test and constrain General Relativity (GR) in the context of the standard model of cosmology (LCDM). We focus on measuring the gravitational estimator statistic, E_G, which describes the relative amplitudes of weak gravitational lensing and galaxy velocities induced by a common set of overdensities. By comparing our amplitude measurements with their predicted scale- and redshift-dependence within the GR+LCDM model, we demonstrate that our results are consistent with the predictions of the Planck cosmology. The redshift span of the DESI dataset allows us to perform these E_G measurements at the highest redshifts achieved to date, z ~ 1.

cosmology↗
DOE OSTI2023

Battery data integrity and usability: Navigating datasets and equipment limitations for efficient and accurate research into battery aging

A tremendous commitment of resources is needed to acquire, understand and apply battery data in terms of performance and aging behavior. There are many state of performance (SOP) and state of health (SOH) metrics that are useful to guide alignment of batteries to end-use, yet how these metrics are measured or extracted can make the difference between usable, valuable datasets versus data that lacks the necessary integrity to meet baseline confidence levels for SOP/SOH quantification. This work will speak to 1) types of data that support SOP and SOH evaluations on mechanistic terms, 2) measurement conditions needed to assure high data integrity, 3) equipment limitations that can compromise data high fidelity, and 4) the impact of cell polarization on data quality. A common goal in battery research and field use is to work from a data platform that supports economical paths of data capture while minimizing down-time for battery diagnostics. An ideal situation would be to utilize data obtained during normal daily use (“pulses or cycles of convenience”) without stopping the daily duty cycles to perform dedicated SOP/SOH diagnostic routines. However, difficulties arise in trying to make use of daily duty cycle data (denoted as cycle-by-cycle, CBC) that underscores the need for standardization of conditions: temperature and duty cycles can vary over the course of a day and throughout a week, month and year; polarization can develop within an immediate cycle and throughout successive cycles as a hysteresis. If CBC data is envisioned as a data source to determine performance and aging trends, it should be recognized that polarization is a frequent consequence of CBC and thus makes it difficult to separate reversible and irreversible components to metrics such as capacity loss and resistance increase over aging. Since CBC conditions can have a major impact on data usability, we will devote part of this paper to CBC data conditioning and management. Differential analyses will also be discussed as a means to detect changing trends in data quality. Our target cell chemistries will be lithium-ion types NMC/graphite and LMO/LTO.

25 ENERGY STORAGE↗
DOE OSTI2025

Applying Deep Learning for Wildfire Identification: Economical and Accessible Solutions Leveraging Small Datasets

Wildfires significantly impact human health, air quality, visibility, weather, and climate change and cause substantial economic losses. While state and county-operated air quality monitors provide critical insights during wildfires, they are not available in all regions. This highlights the need for affordable, accessible tools that allow the general public to assess air quality impacts. In this study, we apply machine learning with deep neural networks to diagnose air quality rapidly from sky images taken at the Pacific Northwest National Laboratory in Richland, WA, USA. Using a convolutional neural network (CNN) framework, we trained a deep learning model to classify air quality indices based on sky images. By leveraging transfer learning, our approach fine-tunes a pre-trained model on a small dataset of sky images, significantly reducing training time while maintaining high accuracy. Our results demonstrate the potential of deep learning to provide rapid air quality diagnostics during wildfire episodes, offering early warnings to the public and enabling timely mitigation strategies, particularly for vulnerable populations. Additionally, we show that lower respiratory infections pose the highest health risk during acute smoke exposures. Reactive oxygen species (ROS) from wildfire particles further exacerbate health risks by triggering inflammation and other adverse effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

Farguell, Angel (ORCID:000000032395220X)↗
DOE OSTI2020

On a Method For Reconstructing Computed Tomography Datasets from an Unstable Source

As work continues in neutron computed tomography, at Los Alamos Neutron Science Center (LANSCE) and other locations, source reliability over the long imaging times is an issue of increasing importance. Moreover, given the time commitment involved in a single neutron image, it is impractical to simply discard a scan and restart in the event of beam instability. In order to mitigate the cost and time associated with these options, strategies are presented in the current work to produce a successful reconstruction of computed tomography data from an unstable source. The present work uses a high energy neutron tomography dataset from a simulated munition collected at LANSCE to demonstrate the method, which is general enough to be of use in conjunction with unstable X-ray computed tomography sources as well.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset

Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Searching for Pairs of Higgs Bosons in the LHC Run 2 Dataset

The discovery of the Higgs boson confirms the existence of a scalar sector of the standard model, responsible for electroweak symmetry breaking, but the nature and properties of the potential at the origin of this mechanism are still unknown. By studying the production of pairs of Higgs bosons (HH), physicists can directly measure the coupling of the Higgs boson to itself and thus determine the shape of this potential, which has far-reaching implications on the origin and evolution of our Universe. Because of this deep connection to the foundations of electroweak symmetry breaking, HH production is also an ideal place to search for manifestations of yet-unknown physics, such as modifications of the strength of the self-coupling and of the interaction between pairs of vector bosons and Higgs bosons. In this review article, we summarize the current searches for HH production at ATLAS and CMS, using the LHC Run 2 dataset, discuss the implications of our current constraints on physics beyond the standard model, and briefly review prospect for future HH searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗