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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 361 records · Page 20
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DOE OSTI2026

Multi-omics data resource: Data package 24 (Pck024)

The data package consists of isolated pancreatic islets from 3 human donors treated with IL-1β, IFNγ or IL-1β + IFNγ for 6 h and IL-1β, IFNγ, IL-1β + IFNγ, IL-1β + IFNγ + NMMA or NMMA for 18 h and submitted for scRNA-seq. This study examines cytokine-stimulated changes in gene expression in human islets using single-cell RNA sequencing. Data contributors: Jennifer S Stancill & John A Corbett: Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA Data repository: GSE251730 Publication: 10.1093/function/zqae015

Sarkar, Soumyadeep [Pacific Northwest National Lab↗
DOE OSTI2026

Multi-omics data resource: Data package 25 (Pck025)

This data package comprises omics datasets from human pancreatic islets treated with IL-1β + IFNγ or with estrogen (E2) for 18 h. Two RNA-seq datasets are available: the first is a discovery dataset involving human islets treated with or without IL-1β + IFNγ for 18 hours; the second is a validation dataset, where human islets are treated with or without IL-1β + IFNγ or E2 for 18 hours. DIA proteomic analysis was performed on the same validation dataset samples. Data contributors: Kiersten L. Webster, Sarah Tersey & Raghavendra G. Mirmir: Kovler Diabetes Center and Department of Medicine, The University of Chicago, Chicago, IL, 60637, USA. Soumyadeep Sarkar, Raghavendra Mirmira, Ernesto S. Nakayasu: Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA. Data repository: RNA-seq: GSE310965 Proteomics: MSV000101892 Publication: PMID 41279069

Sarkar, Soumyadeep [Pacific Northwest National Lab↗
DOE OSTI2026

Fracture Intersections under Stress: Laboratory Data and Code [Data set]

The connectivity of natural and induced fractures governs the injection and withdrawal of fluids from subsurface reservoirs. Connectivity depends on intersections that control how fluids mix and move through the entire system. Here, we present data sets from 3D X-ray microscopy measurements of simple fracture networks under stress. 3D printing was used to create prismatic blocks that formed fracture networks composed of 2 orthogonal fractures. The network orientation was either "x" or "+" relative to an applied vertical stress. 3D data sets were collected for normal loads of 25, 100 and 200 Newtons for samples with fracture surfaces with either correlated or uncorrelated asperity distributions. The file contains data from the 12 samples analyzed along with an example code used to extract the intersection geometry. Additional experimental details can be found in the manuscript "Geologic Stress Modulates Fluid Mixing at Fracture Intersections" (10.1038/s43247-026-03525-9)and supplemental information to appear in Communications Earth & Environment in 2026.

02 PETROLEUM↗
DOE OSTI2022

Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska: Supporting Data

Remote sensing maps of plant functional type (PFT) fractional cover (FCover), dominant PFT, and FCover uncertainty derived from NASA's Airborne Visible / Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380-2510 nm) was collected as a part of the collaboration between NASA's Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE's Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps of the NGEE-Arctic Council watershed on the Seward Peninsula, Alaska, created using AVIRIS-NG imagery collected on July 9th, 2019. The map data and metadata are provided as GeoTIFF (*.tif), ENVI image (*.dat), and text (*.txt, *hdr) formats. Additional map quicklooks are provided as *.pdf files and GIS *.kml files. These datasets are provided in support of Yang et al., (2023), "Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska".The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Techniques for storing data to enhance recovery and detection of data corruption errors

Often there are errors when reading data from computer memory. To detect and correct these errors, there are multiple types of error correction codes. Disclosed is an error correction architecture that creates a codeword having a data portion and an error correction code portion. Swizzling rearranges the order of bits and distributes the bits among different codewords. Because the data is redistributed, a potential memory error of up to N contiguous bits, where N for example equals 2 times the number of codewords swizzled together, only affects up to, at most, two bits per swizzled codeword. This keeps the error within the error detecting capabilities of the error correction architecture. Furthermore, this can allow improved error correction and detection without requiring a change to error correcting code generators and checkers.

Mills, Peter↗
DOE OSTI2023

Techniques for storing data to enhance recovery and detection of data corruption errors

Often there are errors when reading data from computer memory. To detect and correct these errors, there are multiple types of error correction codes. Disclosed is an error correction architecture that creates a codeword having a data portion and an error correction code portion. Swizzling rearranges the order of bits and distributes the bits among different codewords. Because the data is redistributed, a potential memory error of up to N contiguous bits, where N for example equals 2 times the number of codewords swizzled together, only affects up to, at most, two bits per swizzled codeword. This keeps the error within the error detecting capabilities of the error correction architecture. Furthermore, this can allow improved error correction and detection without requiring a change to error correcting code generators and checkers.

Mills, Peter↗
DOE OSTI2021

Data-Driven Distributed Algorithms for Estimating Eigenvalues and Eigenvectors of Interconnected Dynamical Systems

Here, the paper presents data-driven algorithms to estimate in a distributed manner the eigenvalues, right and left eigenvectors of an unknown linear (or linearized) interconnected dynamic system. In particular, the proposed algorithms do not require the identification of the system model in advance before performing the estimation. As a first step, we consider interconnected dynamical system with distinct eigenvalues. The proposed strategy first estimates the eigenvalues using the well-known Prony method. The right and left eigenvectors are then estimated by solving distributively a set of linear equations. One important feature of the proposed algorithms is that the topology of communication network used to perform the distributed estimation can be chosen arbitrarily, given that it is connected, and is also independent of the structure or sparsity of the system (state) matrix. The proposed distributed algorithms are demonstrated via a numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic Distribution of High-Rate Data Processing from CERN to Remote HPC Data Centers

The prompt reconstruction of the data recorded from the Large Hadron Collider (LHC) detectors has always been addressed by dedicated resources at the CERN Tier-0. Such workloads come in spikes due to the nature of the operation of the accelerator and in special high load occasions experiments have commissioned methods to distribute (spill-over) a fraction of the load to sites outside CERN. The present work demonstrates a new way of supporting the Tier-0 environment by provisioning resources elastically for such spilled-over workflows onto the Piz Daint Supercomputer at CSCS. Furthermore, this is implemented using containers, tuning the existing batch scheduler and reinforcing the scratch file system, while still using standard Grid middleware. ATLAS, CMS and CSCS have jointly run selected prompt data reconstruction on up to several thousand cores on Piz Daint into a shared environment, thereby probing the viability of the CSCS high performance computer site as on demand extension of the CERN Tier-0, which could play a role in addressing the future LHC computing challenges for the high luminosity LHC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Large Deviations Anomaly Detection (LAD) for collection of multivariate time series data: Applications to COVID-19 data

Time series anomaly detection is frequently used to identify extreme behaviors within a single time series. Identifying extreme trends in relation to a collection of other time series, on the other hand, is frequently of significant interest, such as in public health policy, social justice, and pandemic propagation. Using concepts from large deviations theory , we propose an algorithm that can scale to large collections of time series data. This paper expands on the LAD algorithm presented in Guggilam et al. (2022). The proposed algorithm is an online anomaly detection method for identifying anomalies in a collection of multivariate time series that takes advantage of the algorithm’s ability to scale to high-dimensional data. We show how the proposed Large Deviations Anomaly Detection (LAD) algorithm can be used to identify regions with anomalous trends in COVID-19 cases, deaths, biweekly growth rates, vaccinations, and fatality rates. Several of the observed anomalous trends are associated with regions that have demonstrated poor response to the COVID pandemic.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

FEbeam: Cavity and electron emission data conversion, processing, and analysis. A freeware toolkit for rf injectors

FEbeam is an all-in-one field emission data processing interface with the capability to analyze the field emission cathode performance in an rf injector by extracting the field enhancement factor, local field, and effective emission area from the Fowler–Nordheim equations. It also has the capability of processing beam imaging micrographs using its sister software, FEpic. Furthermore, the current version of FEbeam was designed for the Argonne Cathode Test-stand of the Argonne Wakefield Accelerator facility switch yard. With slight modifications, FEbeam could work for many rf field emission injectors. This software is open-source and can be found at GitHub.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

Tayal, Kshitij↗
DOE OSTI2022

Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to data calibration

This work, together with its companion paper, Secco, Samuroff et al., presents the Dark Energy Survey Year 3 cosmic shear measurements and cosmological constraints based on an analysis of over 100 million source galaxies. With the data spanning 4143 deg 2 on the sky, divided into four redshift bins, we produce the highest significance measurement of cosmic shear to date, with a signal-to-noise of 40. We conduct a blind analysis in the context of the ΛCDM model and find a 3% constraint of the clustering amplitude, S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.759$^{+0.025}_{-0.023}$. A ΛCDM-Optimized analysis, which safely includes smaller scale information, yields a 2% precision measurement of S 8 = 0.772$^{+0.018}_{-0.017}$ that is consistent with the fiducial case. The two low-redshift measurements are statistically consistent with the Planck Cosmic Microwave Background result, however, both recovered S 8 values are lower than the high-redshift prediction by 2.3σ and 2.1σ (ρ-values of 0.02 and 0.05), respectively. The measurements are shown to be internally consistent across redshift bins, angular scales and correlation functions. The analysis is demonstrated to be robust to calibration systematics, with the S 8 posterior consistent when varying the choice of redshift calibration sample, the modeling of redshift uncertainty and methodology. Similarly, we find that the corrections included to account for the blending of galaxies shifts our best-fit S 8 by 0.5σ without incurring a substantial increase in uncertainty. We examine the limiting factors for the precision of the cosmological constraints and find observational systematics to be subdominant to the modeling of astrophysics. Specifically, we identify the uncertainties in modeling baryonic effects and intrinsic alignments as the limiting systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Performance Evaluation of Data-Enhanced Hierarchical Control for Grid Operations

This paper presents a hardware-in-the-loop (HIL) simulation to evaluate the performance of voltage regulation of Data-Enhanced Hierarchical Control (DEHC). This DEHC uses an advanced distribution management system (ADMS) for grid operations to control legacy and grid-edge devices and coordinate with distributed energy management systems (DERMS) to manage high penetrations of photovoltaics (PV) on a utility distribution system. The HIL platform provides realistic laboratory testing, including accurate modeling (legacy devices, grid-edge devices, and PV) of the real-world distribution system from a utility partner, a real controller (ADMS), software controller DERMS, hardware grid-edge devices, and standard communications protocols. The test results demonstrate functionalities of the integrated platform and the performance of voltage regulation of the coordinated control systems. Based on laboratory testing, the utility can set up the same grid-automation system to manage DERs, legacy devices, and grid-edge devices to achieve their system-level control and operation objectives (e.g., voltage regulation), thus de-risking potential issues such as instability for field deployment.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

CYPminer: an automated cytochrome P450 identification, classification, and data analysis tool for genome data sets across kingdoms

Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.

59 BASIC BIOLOGICAL SCIENCES↗