Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Raw Data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Full spectrum (350-2500 nm) canopy spectral reflectance, Seward Peninsula, Alaska, 2018

Full-range (350-2500 nm) canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council, as well as the supplemental Kougarok Mile 80 site within the larger Seward Peninsula, Alaska region. The data were collected in July 2018 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). Species included in the spectra and functional trait dataset include Alnus viridis, Betula nana, Betula glandulosa, Arctous alpina, and Salix pulchra. This data package includes 35 files in a variety of formats including processed canopy spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA208_flmd.csv.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 OSTI2020

Mesoscale Science Data Analytics

This is software that will be used to do data analytics in experimental workflows for x-ray mesoscale science. This tool set will provide a mechanism for supporting experiments in many ways, from collecting calibration information and raw data, to managing and viewing data to extracting crystallographic and physical parameters. It will eventually include development of a fully automated workflow that will include statistical information and prediction capabilities to support the scientists in decision-making and replanning their experiments when necessary.

Sweeney, Christine↗
DOE OSTI2025

WFIP3 - SHIP site - NREL Profiling Lidar (Windcube v2.1) / Reviewed data

This dataset contains reviewed data from the profiling lidar (Windcube v2.1) deployed on WFIP3's SHIP. The reviewed files herein are based on the lidar's RTD files (i.e., the real-time raw data files at near 1 Hz resolution). The data have been corrected for the motion of the ship.

17 WIND ENERGY↗
DOE OSTI2025

WFIP3 - BARG site - NREL Profiling Lidar (Windcube v2.1) / Reviewed Data

This dataset contains reviewed data from the profiling lidar (Windcube v2.1) deployed on WFIP3's barge. The reviewed files herein are based on the lidar's RTD files (i.e., the real-time raw data files at near 1 Hz resolution). The data have been corrected for the motion of the ship.

17 WIND ENERGY↗
DOE OSTI2024

Data and scripts associated with the manuscript "A Hydrogeophysical Framework to Assess Infiltration During a Simulated Ecosystem-Scale Flooding Experiment”

This package contains the data and scripts used in "A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment" (Adebayo et al., 2023). The Geophysics folder contains two subfolders containing Ground Penetrating Radar (GPR) and Electrical Resistivity Imaging (ERI) datasets acquired between August 23 and August 26, 2021. The GPR subfolder contains raw data (Tempest_Project.GPZ) and processed results using the Sensors and Software EKKO Project software. The ERI subfolder contains two additional subfolders named Static and Timelapse. The Static subfolder contains background resistivity datasets acquired before infiltration, both raw and inverted results using the ResIpy code. The Timelapse subfolder contains repeated resistivity datasets acquired during infiltration on two profiles, along with raw and inverted results using the ResIpy code. The Flow model folder contains ATS model simulation files. The Geophysics-informed subfolder contains simulation datasets for two profiles generated using information from the geophysics data. The National_database-informed subfolder contains simulation datasets for two profiles generated using information from a national soil database. Each subfolder contains initial simulation results, multiple ensembles, and final simulation files. The Sensitivity_analysis folder contains the observation files exported from the flow model, notebooks used in running the code, and sensitivity analysis results showing the sensitivity of different parameters over a 48-hour period. We used mutual information-based global sensitivity analysis for the sensitivity analysis, following Jiang et al., 2022. This study shows a novel approach that integrates geophysical data with hydrological modeling to understand subsurface flow during coastal flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Supporting Data for "On the magnetic contribution of itinerant electrons to neutron diffraction in the topological antiferromagnet CeAlGe"

Contents of this DOI are data for the research paper "On the magnetic contribution of itinerant electrons to neutron diffraction in the topological antiferromagnet CeAlGe" by the authors: V. Pomjakushin et al. The dataset contains raw data from neutron scattering experiments of CeAlGe powder performed at the CNCS spectrometer at Spallation Neutron Source at ORNL.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Full Spectrum, 350 - 2500 nm, Leaf and Canopy Spectral Reflectance, Seward Peninsula, Alaska, 2017

Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council within the larger Seward Peninsula, Alaska region. The data were collected in the months of July and August of 2017 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). This data package includes 26 files in a variety of formats including processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA110_flmd.csv.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 OSTI2025

Legacy Survey of Space and Time Data Preview 1: raw dataset type

The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets. This dataset is a subset of the full data release consisting of the raw dataset type. These are unprocessed images from the LSST Commissioning Camera. This release contains 16,125 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy

High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hourly temperature and surface water level at the Ikpikpuk delta from 5/2012 to 11/2012

This data package includes the soil temperature and surface water level observations in 2012 at the Ikpikpuk River delta in northern Alaska. The data file consists of the observation table with both the .xlsx and .csv file formats. The files can be accessed through several software tools, like Microsoft Excel, and text editors. This is raw data from the near-surface ground temperature sensors at 15 cm (active layer) and 100 cm (permafrost) soil depth and a surface water sensor at the Ikpikpuk delta site in Alaska, US. The Ikpikpuk River delta is located on the Arctic Coastal Plain of northern Alaska. The Ikpikpuk River drains a land area of 15,330 km2, emptying into Smith Bay, and the delta is fan-shaped with an aerial extent of 106 km2 (Fuchs et al., 2018). This dataset supports the publication entitled "The thermal response of permafrost to coastal floodplain flooding" submitted to Environmental Research Letters (under review), where the observed water level is used as the upper boundary condition for the model simulations and the observed subsurface temperature is compared with the model simulations to answer the question about how coastal flooding affects permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Frost Table Elevation across a Low-Centered and a High-Centered Polygon, Mapped using Ground Penetrating Radar, Utqiagvik (Barrow), Alaska, 2015

Ground Penetrating Radar, active layer thickness point measurements, and ground elevation surveys were conducted on a low-centered and a high-centered polygon in Utqiagvik (Barrow), AK (on October 2nd, 2015) to map frost table topography and evaluate its influence on hydrological flow. Data were collected to improve our understanding of the topography of the permafrost table across polygons which may have a major influence on hydrology. This dataset includes 2 zip files containing the raw data, 2 csv files of processed data, and a pdf user guide.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 OSTI2020

Offsite Data Processing for the GlueX Experiment

The Thomas Jefferson National Accelerator Facility (JLab) 12GeV accelerator upgrade completed in 2015 is now producing data at volumes unprecedented for the lab. The resources required to process this data now exceed the capacity of the onsite farm necessitating the use of offsite computing resources for the first time in the history of JLab. GlueX is now utilizing NERSC and PSC for raw data production. Details of the workflow are presented.

Lawrence, David↗
DOE OSTI2025

Scalable Hybrid Learning Techniques for Scientific Data Compression

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Here, this article presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

ITER↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2025

Arctic shrub and Eriophorum leaf and root decomposition, northern Alaska, 2017-2018

This data package contains litter decomposition data collected from 170 plots of rapidly expanding shrub genera (Alnus, Betula, and Salix) and a widespread sedge (Eriophorum vaginatum) along a latitudinal and temperature gradient in northern Alaska. These data were produced from a litter bag experiment that took place from July 2017 to July 2018 and include mass loss and nitrogen loss decomposition metrics for both leaf and root litters. These raw data support a submitted manuscript that examines the variability in decomposition between shrub and graminoid leaf and root litters across a 1-year experiment across the graminoid-dominated Arctic tundra and reveals how deciduous shrub expansion affects litter decomposition in tundra ecosystems. Data are presented by site (n=5) and patch (shrub or sedge plot) in csv files. The site and plot location data and environmental measurement data are provided in Fraterrigo and Chen (2020). Additional methods regarding plot distribution and environmental measurements are in Chen et al. (2020) and Fraterrigo et al. (2024).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors (Phase-I)

With an ever increasing demand for high precision data from modern detectors for discovery science and precision measurements, all major high energy nuclear and particle experiments, current and future, are facing the challenge on how to deal with the large volume of raw data generated from sophisticated state-of-the-art detectors in high rate collisions. These goals need to be balanced with available hardware and cost limits on DAQ (Data AcQuisition system) bandwidth and offline computing resources to capture, store and process the signal events. Two prototypical examples are the upcoming sPHENIX experiment, the DOE next generation heavy ion physics experiment at the Relativistic Heavy Ion Collider at BNL, and the future EIC experiments that are planned to be online circa 2030.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗