ARCS White Beam Vanadium Normalization Data for SNS Cycle 2026A
A data set used to normalize the detector response of the ARCS instrument. See ARCS_336659.md in the data set for more details.
36 MATERIALS SCIENCE↗
SEARCH · Engineering Papers
Results for “Data Set”
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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Statistical characteristics of bursty bulk flow events
Using a common methodology to analyze data from the Active Magnetospheric Particle Tracer Explorer/Ion Release Module (AMPTE/IRM) and International Sun-Earth Explorer 2 (ISEE 2) satellites we report on the statistical properties of bursty bulk flow events (BBFs) in the inner plasma sheet (IPS). A positive correlation between BBFs and the AE index suggests that BBFs are predominantly geomagnetically active time phenomena. Earthward BBFs are more frequent close to midnight and away from Earth, up to a distance of approximately 19 R(sub E). Tailward BBFs are very infrequent in the IRM data set and somewhat less infrequent in the ISEE 2 data set in the region of the satellites' spatial overlap, possibly due to the more active conditions prevailing during the ISEE 2 mission in that region. However, in both data sets the ratio of tailward to earthward BBFs increases with distance from Earth; more than 20% of all BBFs are anti-sunward tailward of X = -19 R(sub E) in the ISEE 2 data set. BBFs are responsible for 60-100% of the measured earthward transport of mass, energy and magnetic flux past the satellite in the regions of maximum occurrence rate, even though they last approximately 10-15% of the IPS observation time there. Thus BBFs represent the primary transport mechanism at those regions. The one-to-one correspondence between BBFs and substorm phase, as well as the relative contribution of BBFs to the total transport observed during substorms are questions that await further investigation based on multi instrument studies of individual events.
Angelopoulos, V.↗
QuantumScents: Quantum-Mechanical Properties for 3.5k Olfactory Molecules
Quantitative structure–odor relationships are critically important for studies related to the function of olfaction. Current literature data sets contain expert-labeled molecules but lack feature data. This paper introduces QuantumScents, a quantum mechanics augmented derivative of the Leffingwell data set. QuantumScents contains 3.5k structurally and chemically diverse molecules ranging from 2 to 30 heavy atoms (CNOS) and their corresponding 3D coordinates, total PBE0 energy, molecular dipole moment, and per-atom Hirshfeld charges, dipoles, and ratios. The authors demonstrate that Hirshfeld charges and ratios contain sufficient information to perform molecular classification by training a Message Passing Neural Network with chemprop (Heid, E.; et al. ChemRxiv, 2023, DOI: 10.26434/chemrxiv-2023-3zcfl) to predict scent labels. Finally, the QuantumScents data set is freely available on Zenodo along with the authors’ code, example models, and data set generation workflow (https://zenodo.org/doi/10.5281/zenodo.8239853).
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Sea ice-atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates
The application of multi-spectral satellite data to estimate polar surface energy fluxes is addressed. To what accuracy and over which geographic areas large scale energy budgets can be estimated are investigated based upon a combination of available remote sensing and climatological data sets. The general approach was to: (1) formulate parameterization schemes for the appropriate sea ice energy budget terms based upon the remotely sensed and/or in-situ data sets; (2) conduct sensitivity analyses using as input both natural variability (observed data in regional case studies) and theoretical variability based upon energy flux model concepts; (3) assess the applicability of these parameterization schemes to both regional and basin wide energy balance estimates using remote sensing data sets; and (4) assemble multi-spectral, multi-sensor data sets for at least two regions of the Arctic Basin and possibly one region of the Antarctic. The type of data needed for a basin-wide assessment is described and the temporal coverage of these data sets are determined by data availability and need as defined by parameterization scheme. The titles of the subjects are as follows: (1) Heat flux calculations from SSM/I and LANDSAT data in the Bering Sea; (2) Energy flux estimation using passive microwave data; (3) Fetch and stability sensitivity estimates of turbulent heat flux; and (4) Surface temperature algorithm.
Steffen, K.↗
Application of two creep fatigue life models for the prediction of elevated temperature crack initiation of a nickel base alloy
Cyclic Damage Accumulation (CDA) and Total Strain-Strain Range Partitioning (TS-SRP) models for predicting the creep-fatigue crack initiation life of high temperature alloys are presented. The models differ in their fundamental assumptions regarding the controlling parameters for fatigue crack initiation and in the amount of data required to determine model constants. The CDA model represents a ductility exhaustion approach and uses stress quantities to calculate the cyclic fatigue damage. The TS-SRP model is based on the use of total mechanical strain and earlier concepts of the Strain Range Partitioning Method. Both models were applied to a well controlled fatigue data set at a high temperature nickel base alloy, B1900 + Hf, tested at 1600 F and 1800 F. The tests were divided into a baseline data set required to determine model constants and a verification data set for evaluation of the predictive capability of the models. Both models correlated the baseline data set to within factors of two in life, and predicted the verification data set to within a factor of three or better. In addition, sample calculations to demonstrate the application of each model and discusions of the predictive capabilities and areas requiring further development are presented.
Moreno, V.↗
NSSDC data listing
The purpose here is to identify, in a highly summarized way, data available from the National Space Science Data Center (NSSDC). Most data are maintained as offline data sets gathered from individual instruments carried on spacecraft; these comprise the Satellite Data Listing. Descriptive names, time spans, data form, and quality of these data sets are identified in the listing, which is sorted alphabetically, first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets not associated with individual spaceflight instruments are identified in separate listings following the Satellite Data Listing. These include composite spacecraft data sets, ground based data, models, and computer routines. NSSDC also offers data via special services and systems in a number of areas, including the Astronomical Data Center, Coordinated Data Analysis Workshops, NASA Climate Data System, Pilot Land Data System, and Crustal Dynamics Data Information System.
Horowitz, Richard↗
Managing Large Datasets for Atmospheric Research
Since the mid-1980s, airborne and ground measurements have been widely used to provide comprehensive characterization of atmospheric composition and processes. Field campaigns have generated a wealth of insitu data and have grown considerably over the years in terms of both the number of measured parameters and the data volume. This can largely be attributed to the rapid advances in instrument development and computing power. The users of field data may face a number of challenges spanning data access, understanding, and proper use in scientific analysis. This tutorial is designed to provide an introduction to using data sets, with a focus on airborne measurements, for atmospheric research. The first part of the tutorial provides an overview of airborne measurements and data discovery. This will be followed by a discussion on the understanding of airborne data files. An actual data file will be used to illustrate how data are reported, including the use of data flags to indicate missing data and limits of detection. Retrieving information from the file header will be discussed, which is essential to properly interpreting the data. Field measurements are typically reported as a function of sampling time, but different instruments often have different sampling intervals. To create a combined data set, the data merge process (interpolation of all data to a common time base) will be discussed in terms of the algorithm, data merge products available from airborne studies, and their application in research. Statistical treatment of missing data and data flagged for limit of detection will also be covered in this section. These basic data processing techniques are applicable to both airborne and ground-based observational data sets. Finally, the recently developed Toolsets for Airborne Data (TAD) will be introduced. TAD (tad.larc.nasa.gov) is an airborne data portal offering tools to create user defined merged data products with the capability to provide descriptive statistics and the option to treat measurement uncertainty.
Chen, Gao↗
ARM Aerial Facility (AAF) Merged Value-Added Product Report for Historical G-1 Field Campaigns
For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Grumman Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center (ADC) and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated data set was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated data set includes data collected from 236 flights (766.4 hours). Four of the seven field campaigns were based in the U.S. One campaign collected data from the wildfires in the U.S. Pacific Northwest and agricultural burns in the lower Mississippi River valley as part of the Biomass Burning Observation Project (BBOP) in 2013. In 2015, the ARM Cloud Aerosol Precipitation Experiment provided data on atmospheric rivers and associated aerosol-cloud interactions that produce heavy precipitation on the U.S. west coast during the early spring. Research data from Airborne Carbon Measurements-V (ACME-V), collected during the summer of 2015, gave scientists insight into trends and variability of trace gases in the atmosphere over the North Slope of Alaska to improve arctic climate models. In the early summer and autumn of 2016, the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign provided an extensive data set geared toward coupled processes that affect the life cycle of shallow clouds through the interaction among aerosol, cloud, land surface, and ecosystems. In 2014 (March and October), the airborne sampling moved outside of the U.S. to the city of Manaus in central Amazonia, Brazil, where residential and industrial emissions were extensively characterized by flights of the G-1. The GoAmazon2014/15 aircraft campaign data are being integrated with aquatic and terrestrial ecosystem measurements to quantify anthropogenic perturbations to a usually pristine tropical environment. Another international airborne mission was carried out in the Eastern North Atlantic region. The Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign saw the G-1 aircraft fly from Terceira Island in the Azores during the summer of 2017 and the winter of 2018. The campaign studied both seasons to measure key aerosol and cloud processes under various meteorological and cloud conditions with different aerosol sources. Then the G-1 deployed to the Sierras de Córdoba range in central Argentina from October to November 2018 for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign to study orographic convective cloud interactions with their surrounding environment. These comprehensive datastreams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states, and properties for addressing essential science questions in Earth system process studies.
54 ENVIRONMENTAL SCIENCES↗
ARM Aerial Facility (AAF) Merged Value-Added Product Report for Historical G-1 Field Campaigns
For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Grumman Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center (ADC) and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated data set was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated data set includes data collected from 236 flights (766.4 hours). Four of the seven field campaigns were based in the U.S. One campaign collected data from the wildfires in the U.S. Pacific Northwest and agricultural burns in the lower Mississippi River valley as part of the Biomass Burning Observation Project (BBOP) in 2013. In 2015, the ARM Cloud Aerosol Precipitation Experiment provided data on atmospheric rivers and associated aerosol-cloud interactions that produce heavy precipitation on the U.S. west coast during the early spring. Research data from Airborne Carbon Measurements-V (ACME-V), collected during the summer of 2015, gave scientists insight into trends and variability of trace gases in the atmosphere over the North Slope of Alaska to improve arctic climate models. In the early summer and autumn of 2016, the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign provided an extensive data set geared toward coupled processes that affect the life cycle of shallow clouds through the interaction among aerosol, cloud, land surface, and ecosystems. In 2014 (March and October), the airborne sampling moved outside of the U.S. to the city of Manaus in central Amazonia, Brazil, where residential and industrial emissions were extensively characterized by flights of the G-1. The GoAmazon2014/15 aircraft campaign data are being integrated with aquatic and terrestrial ecosystem measurements to quantify anthropogenic perturbations to a usually pristine tropical environment. Another international airborne mission was carried out in the Eastern North Atlantic region. The Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign saw the G-1 aircraft fly from Terceira Island in the Azores during the summer of 2017 and the winter of 2018. The campaign studied both seasons to measure key aerosol and cloud processes under various meteorological and cloud conditions with different aerosol sources. Then the G-1 deployed to the Sierras de Córdoba range in central Argentina from October to November 2018 for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign to study orographic convective cloud interactions with their surrounding environment. These comprehensive datastreams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states, and properties for addressing essential science questions in Earth system process studies.
54 ENVIRONMENTAL SCIENCES↗
Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.
Roberts, Yolanda, L.↗
Modeled soft X-ray solar irradiances
Solar soft X-rays have historically been inaccurately modeled in both relative variations and absolute magnitudes by empirical solar extreme ultraviolet (EUV) irradiance models. This is a result of the use of a limited number of rocket data sets which were primarily associated with the calibration of the AE-E satellite EUV data set. In this work, the EUV91 solar EUV irradiance model has been upgraded to improve the accuracy of the 3.0 to 5.0 nm relative irradiance variations. The absolute magnitude estimate of the flux in this wavelength range has also been revised upwards. The upgrade was accomplished by first digitizing the SOLRAD 11 satellite 4.4 to 6.0 nm measured energy flux data set, then extracting and extrapolating a derived 3.0 to 5.0 nm photon flux from these data, and finally by performing a correlation between these derived data and the daily and 81-day mean 10.7 cm radio flux emission using a multiple linear regression technique. A correlation coefficient of greater than 0.9 was obtained between the dependent and independent data sets. The derived and modeled 3.0 to 5.0 nm flux varies by more than an order of magnitude over a solar cycle, ranging from a flux below 1 x 10(exp 8) to a flux greater than 1 x 10(exp 9) photons per sq cm per sec. Solar rotational (27-day) variations in the flux magnitude are a factor of 2. The derived and modeled irradiance absolute values are an order of magnitude greater than previous values from rocket data sets related to the calibration of the AE-E satellite.
Tobiska, W. Kent↗
Serial crystallography with multi-stage merging of thousands of images
KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.
36 MATERIALS SCIENCE↗
Linear and nonlinear trending and prediction for AVHRR time series data
The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.
Smid, J.↗
A Collection of More than 900 Gas Mixture Adsorption Experiments in Porous Materials from Literature Meta-Analysis
Information on mixture adsorption equilibrium is vital in developing adsorption-based separation processes. Because measuring mixture adsorption is more difficult than measuring single-component adsorption, far more data of the latter kind are available. Previous efforts to compile experimental mixture adsorption data for gases have given data sets with at most a few dozen examples. Here, we report the results of systematic literature meta-analysis that produced a data set of more than 900 gas mixture adsorption experiments. This collection includes data from 125 different binary mixtures including 60 different molecular species and information from 333 different adsorbents. We refer to this data set as the Binary adsorption ISOtherm ExperimeNtal 2020 (BISON-20) Database. Because the BISON-20 data set enormously expands the number and variety of experimental results for binary gas adsorption that are readily available, it will be a useful resource for future efforts in developing new materials or processes for gas separations. As initial applications of the BISON-20 data set, we show how identifying replicate measurements can be used to assess the reliability of binary adsorption data, how the accuracy of Ideal Adsorbed Solution Theory (IAST) can be systematically tested using experimental data, and how trends in selectivity for gas separations across many materials can be examined.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Testing the Physical Mechanisms of Gamma-Ray Bursts with Multi-Instrument Time-Resolved Spectroscopy
We have continued the project of time-resolved spectral analyses of gamma-ray bursts observed jointly by the BATSE and the Wide-Field Camera on board BeppoSAX. We are making progress understanding the systematic differences between the two data sets. These data comprise the most important joint analysis set for our project. In several meetings, we have reported on metal efforts to understand the blackbody portion of the time series of spectra from GRB970111. Clearly, a fading thermal component can provide a 'seed' spectrum for Compton upscattering. It is very likely the X-ray excess that has been observed previously in BATSE data alone continues into the X-ray band observed by the WFC. We have also made progress in joint fitting of BATSE Large Area Detector and Spectroscopy Detector data with that of the Total Absorption Scintillation Calorimeter (TASC) of the EGRET experiment on CGRO. The TASC data are important to understanding the high-energy response of the BATSE data. We have produced time-sequences of spectra for two important GRB with data from both instruments. The Summer workshop on GRBs at the Aspen Center for Physics provided an opportunity for in-depth discussion of our on-going work. To aid our effort, we continue to make improvements in our spectral analysis software, RMFIT (rewritten from WINGSPAN).
Briggs, Michael S.↗
NASA Ocean Altimeter Pathfinder Project: Data Processing Handbook - Report 1
The NOAA/NASA Pathfinder program was created by the Earth Observing System (EOS) Program Office to determine how satellite-based data sets can be processed and used to study global change. The data sets are designed to be long time-sedes data processed with stable calibration and community consensus algorithms to better assist the research community. The Ocean Altimeter Pathfinder Project involves the reprocessing of all altimeter observations with a consistent set of improved algorithms, based on the results from TOPEX/POSEIDON (T/P), into easy-to-use data sets for the oceanographic community for climate research. This report describes the processing schemes used to produce a consistent data set and two of the products derived f rom these data. Other reports have been produced that: a) describe the validation of these data sets against tide gauge measurements and b) evaluate the statistical properties of the data that are relevant to climate change. The use of satellite altimetry for earth observations was proposed in the early 1960s. The first successful space based radar altimeter experiment was flown on SkyLab in 1974. The first successful satellite radar altimeter was flown aboard the Geos-3 spacecraft between 1975 and 1978. While a useful data set was collected from this mission for geophysical studies, the noise in the radar measured and incomplete global coverage precluded ft from inclusion in the Ocean Altimeter Pathfinder program. This program initiated its analysis with the Seasat mission, which was the first satellite radar altimeter flown for oceanography.
Koblinsky, C. J.↗
User friendly database for Neptune planetary radio astronomy observations
Planetary Radio Astronomy (PRA) data from the Voyager Neptune encounter were cleaned and reformatted in a variety of formats. Most of these formats are new and have been specifically designed to provide easy access and use of the data without the need to understand esoteric characteristics of the PRA instrument or the Voyager spacecraft. Several data sets were submitted to the Planetary Data System (PDS) and have either appeared already on peer reviewed CDROM's or are in the process of being reviewed for inclusion in forthcoming CD-ROM's. Many of the data sets are also available online electronically through computer networks; it is anticipated that as time permits, the PDS will make all the data sets that were a part of this contract available both online and on CD-ROM's.
Evans, David R.↗
DENSS-Multiple
It takes multiple data sets from small-angle neutron scattering experiment at different contrast conditions to synthetically use DENSS algorithm (https://www.nature.com/articles/nmeth.4581) to combine all the information from each data set. As the multiple data set contains more information than a single data, all information in the multiple data set are collectively contribute to the final single synthetic structure that represents a molecular particle structure.
Qian, Shuo (000000024842828X)↗