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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Radial Star Formation Histories in 32 Nearby Galaxies

The spatially resolved star formation histories are studied for 32 normal star-forming galaxies drawn from the Spitzer Extended Disk Galaxy Exploration Science survey. At surface brightness sensitivities fainter than 28 mag arcsec{sup −2}, the new optical photometry is deep enough to complement archival ultraviolet and infrared imaging and to explore the properties of the emission well beyond the traditional optical extents of these nearby galaxies. Fits to the spectral energy distributions using a delayed star formation history model indicate a subtle but interesting average radial trend for the spiral galaxies: the inner stellar systems decrease in age with increasing radius, consistent with inside-out disk formation, but the trend reverses in the outermost regions with the stellar age nearly as old as the innermost stars. These results suggest an old stellar outer disk population formed through radial migration and/or the cumulative history of minor mergers and accretions of satellite dwarf galaxies. The subset of S0 galaxies studied here show the opposite trend compared to what is inferred for spirals: characteristic stellar ages that are increasingly older with radius for the inner portions of the galaxies, and increasingly younger stellar ages for the outer portions. This result suggests that either S0 galaxies are not well modeled by a delayed-τ model, and/or that S0 galaxies have a more complicated formation history than spiral galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Patch-level CO2 and CH4 fluxes and porewater concentrations in experimental wetlands, 5 and 10 PPT saltwater intrusion simulations, Louisiana 2023-2024

This dataset containes carbon dioxide (CO2) and methane (CH4) flux measurements collected from wetland vegetation patches dominated by Typha domingensis and Panicum hemitomon to assess greenhouse gas flux responses to experimental saltwater intrusion (SWI) pulses. Measurements were conducted before, during, and after simulated SWI events at target salinities of approximately 5 parts per thousand (ppt) with durations of 6, 10, and 17 days and 10 ppt with a duration of 48 days, alongside a control wetland (with no salinity added, flood manipulation only). These data were generated to evaluate how the magnitude and duration of SWI alter wetland carbon exchange and related biogeochemical and plant responses. This data package includes flux measurements from the wetland surface (i.e, soil/water surface and enclosed vegetation) and from the soil/water surface only; porewater and surface water concentrations of CO2 and CH4; salinity, pH, electrical conductivity collected in porewater (at 5, 10, and 20 cm soil depths) and in surface water; soil redox potential; leaf spectral indices, leaf vapor pressure deficit, stomatal conductance; water level, salinity, and photosynthetically active radiation; and aboveground biomass.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL RESPIRA↗
DOE OSTI2024

Patch-level CO2 and CH4 fluxes and porewater concentrations in experimental wetlands, 2 PPT saltwater intrusion simulations, Aug-Oct 2022: Louisiana

This dataset contains carbon dioxide (CO2) and methane (CH4) flux measurements from patches of wetland vegetation dominated by Typha domingensis and Panicum hemitomon, which were conducted to assess flux responses to acute saltwater intrusion. The measurements occurred before, during, and after simulated acute saltwater intrusion events of low concentrations of ~ 2 PPT. The measurements comprise gas fluxes from the wetland surface (i.e., soil-water column and vegetation) and fluxes from the soil-water column exclusively. These two sets of fluxes are separated into two files and are complemented with four more files containing porewater concentrations of CO2 and CH4 collected at 0-5 cm, 10-15 cm, and 20-25 cm depth increments, spectral indices measurements, biomass, and sediment elevation table measurements. The files can be opened with regular text editors or spreadsheet programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Storm and Annual Time Scale Hydrological Data for the Russian River Watershed 1996-2022

These files contains observed and simulated hydrological data (discharge, precipitation) that have been aggregated to the storm-event scale and to the annual time scale for the Russian River Watershed (RRW), California. Observed data are obtained from 12 different USGS hydrological stations located throughout the watershed, and discharge is simulated using the GR5H hourly hydrological model. Precipitation data are obtained from the NASA NLDAS NOAH Community Land Surface Model. All simulated and observed discharge are aggregated to the storm and annual time scales. We investigated changes in watershed hydrological conditions in the Russian River Watershed, a Mediterranean, drought prone, wildfire-adapted ecosystem, following eleven wildfires that occurred from 2017-2020. We ask two research questions: 1) How do interacting wildfire events, drought, and atmospheric rivers impact hydrological conditions of the watershed and in particular streamflow?, and 2) What percentage of new and/or cumulative wildfire disturbance is required to initiate hydrological change? We hypothesize that sub-watersheds of the RRW Mediterranean ecoregion have not burned beyond an intrinsic, and still unknown, threshold required to initiate change. Using a series of paired burned/unburned catchments nested within the larger watershed, we examined temporal and spatial patterns of pre-and-post wildfire water hydrology using a rainfall runoff hydrological model compared with data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CROCUS Weather Data at Northeastern Illinois University Rooftop

This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CROCUS Weather Data at Chicago State University Prairie Site

Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at Chicago State University (CSU) at a prarie site on campus in Chicago, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (CSU), data level (raw, a1), date (year, month, day), and hour (0000).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CROCUS Weather Data at Argonne National Laboratory Prairie Site

Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CROCUS Weather Data at University of Illinois - Chicago Tower

Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the University of Illinois in Chicago, Illinois, on the meteorological tower near the greenhouse on campus. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. File naming convention includes the project (CROCUS), location (UIC), data level (raw, a1), date (year, month, day), and hour (0000).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CROCUS Low Cost All-in-One Weather Station AMB-001 Data Argonne National Laboratory Prairie Site

The Ambient Weather WS-2902D (AMB) is a low cost weather station that has become very useful for filling data gaps in harder to deploy locations. These low cost weather stations collect 13 second data, which is averaged to a five minute data output available to users through an API key. The data files contain measurements for precipitation, temperature, wind chill/heat index, relative humidity, dew point, UV index, solar radiation, wind speed, wind direction, wind gust, and with an external particulate matter 2.5 (PM 2.5) sensor. Having all of these measurements in one condense system allows for fast deploying and dense network capabilities. Three of the AMB weather stations were deployed at the Argonne Testbed for Multiscale Observational Science (ATMOS), a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. The instruments are denoted by their three digit identifier (CMS-AMB-xxx) format. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (atmos), instrument name (CMS-AMB-001), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or ACT-DOE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CROCUS Low Cost All-in-One Weather Station AMB-004 Data Argonne National Laboratory Prairie Site

The Ambient Weather WS-2902D (AMB) is a low cost weather station that has become very useful for filling data gaps in harder to deploy locations. These low cost weather stations collect 13 second data, which is averaged to a five minute data output available to users through an Application Programming Interface (API) key. The data files contain measurements for precipitation, temperature, wind chill/heat index, relative humidity, dew point, UV index, solar radiation, wind speed, wind direction, wind gust, and with an external particulate matter 2.5 (PM 2.5) sensor. Having all of these measurements in one condense system allows for fast deploying and dense network capabilities. Three of the AMB weather stations were deployed at the Argonne Testbed for Multiscale Observational Science (ATMOS), a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. The instruments are denoted by their three digit identifier (CMS-AMB-xxx) format. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (atmos), instrument name (CMS-AMB-004), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or ACT-DOE.

EARTH SCIENCE > ATMOSPHERE > AEROSOLS > PARTICULAT↗
DOE OSTI2023

Observation of flat and weakly dispersing bands in the van der Waals semiconductor Nb 3 Br 8 with breathing kagome lattice

Niobium halides, Nb 3 ⁢X 8 (X=Cl,Br,I), which are predicted two-dimensional magnets, have recently gotten attention due to their breathing kagome geometry. Here we have studied the electronic structure of Nb 3⁢ Br 8 by using angle-resolved photoemission spectroscopy (ARPES) and first-principles calculations. ARPES results depict the presence of multiple flat and weakly dispersing bands. These bands are well explained by the theoretical calculations, which show they have Nbd character indicating their origination from the Nb atoms forming the breathing kagome plane. This van der Waals material can be easily thinned down via mechanical exfoliation to the ultrathin limit and such ultrathin samples are stable as depicted from the time-dependent Raman spectroscopy measurements at room temperature. Finally, these results demonstrate that Nb 3 ⁢Br 8 is an excellent material not only for studying breathing kagome induced flat band physics and its connection with magnetism but also for heterostructure fabrication and for practical application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2025

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CROCUS Weather Data at Northwestern University Rooftop

This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2025

Plant Characteristics, Porewater, Gas Flux, and Soil Biogeochemistry at Council Road Site Mile Marker 71, Seward Peninsula, Alaska, 2023

Data collected at Council, AK (64°51’35.0”N 163°41’59.1”W) during a summer campaign in 2023. Water data consists of soil porewater collected by centrifuging soil cores and also by field collection with porewater samplers (rhizons). Gas data consists of CO2, CH4 and N2O surface soil fluxes measured with a portable FTIR analyzer. Plant and root data consists of biomass, root length, diameter and mass. Soil data consists of total C and N. Air, water and soil samples span two main locations: a thermokarst wetland and an upland tussock. 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).This dataset was generated to broadly address the following research question: how will climate change (i.e., thawing permafrost, landscape change) alter the ecosystem flux (sink versus source) of important greenhouse gases such as CO2, CH4 and N2O?Description of the contents of this data package: This dataset contains 5 different individual .csv files containing plant, soil, water and gas data. No software is needed to utilize them. PFTCover: Plant functional type ground cover in 1x1 meter plots. SoilCores: Solidphase and porewater phase soil biogeochemical variablesPlantData: Above and belowground plant traits. GasFlux: Surface plant-soil gas measurementsFieldPorewater: Field collected porewater biogeochemical variables

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface localized magnetism in transition metal doped alumina

Alumina is a structural ceramic that finds many uses in a broad range of applications. It is widely employed in the aerospace and biomedical sectors due to its stability at high temperatures and in harsh chemical environments. Here, we show that magnetism can be induced at alumina surfaces by doping with 3d transition metals. We analyze the electronic structure, spin magnetic moments, and spin density of α-Al 2 O 3 as a function of both dopant species (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu) and depth using first principles calculations. Our results show that all dopants, with the exception of Sc, produce magnetic moments that are concentrated to the surface of alumina with varying degrees of delocalization. It is seen that all of the dopants are at least meta-stable on the surface and must overcome an energy barrier of 0.19–1.14 eV in order to diffuse from the surface into the bulk. As a result of judiciously doping with select 3$\textit{d}$ transition metals the surface of alumina can be made magnetic. This could lead to novel applications in data storage, catalysis, and biomedical engineering through an added surface functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2023

National Center for Airborne Laser Mapping (NCALM) LiDAR, Imagery, and DEM data from five NGEE Arctic Sites, Seward Peninsula, Alaska, August 2021

From August 8 through August 16 of 2021, airborne remote sensing data was collected by the National Center for Airborne Laser Mapping (NCALM) in collaboration with NGEE Arctic scientists. Data was collected around five NGEE Arctic study sites on the Seward Peninsula of Alaska: Teller mm 27, Teller mm 47, Kougarok mm 64, Kougarok mm 86, and Council mm 71. A Robinson R44 II helicopter with a RIEGL VQ-580 II airborne laser scanner was used to collect the LiDAR point cloud data for each study site. A Phase One iXM-RS100F camera was integrated with the Riegl sensor to collect RGB imagery. This data package contains LiDAR point clouds (.las), RGB imagery (tif), 1 m or 50 cm Digital Elevation Models (.tif) generated from the LiDAR data, and shapefiles of the .las tiling system for each site (.shp). Two supplemental documents are also included in the package: 1) a report describing data collection details, GNSS corrections, and processing steps and 2) a document describing the LiDAR Classification used (.pdf). This survey was conducted towards the end of the summer on the Seward Peninsula, and can be paired with data collected in April of 2022 during the snow-on campaign "National Center for Airborne Laser Mapping (NCALM) LiDAR and DEM data from two NGEE Arctic Sites, Seward Peninsula, Alaska, Winter 2022" (Singhania et.al, 2023) (NGA314). 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↗