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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Estimated Darcy Velocities Using Temperature Time Series for Meander A of East River, Colorado

Heat was used as a tracer to quantify seasonal changes in vertical Darcy velocity through the streambed at three different locations about Meander A of East River, Colorado. The file Raw_Temp.csv represents raw temperature data (in degrees Celsius) collected from the three locations, A, B, and C, at depths of 5, 10, 20, and 35 cm below the sediment-water interface. Darcy velocity estimates were calculated from these data using 1DTempPro and are shown in 1DTempPro_estimates.csv file. Positive values represent upwelling conditions, while negative values indicate downwelling conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Comparison of Satellite Observations of Aerosol Optical Depth to Surface Monitor Fine Particle Concentration

Under NASA's Earth Science Applications Program, the Infusing satellite Data into Environmental Applications (IDEA) project examined the relationship between satellite observations and surface monitors of air pollutants to facilitate a more capable and integrated observing network. This report provides a comparison of satellite aerosol optical depth to surface monitor fine particle concentration observations for the month of September 2003 at more than 300 individual locations in the continental US. During September 2003, IDEA provided prototype, near real-time data-fusion products to the Environmental Protection Agency (EPA) directed toward improving the accuracy of EPA s next-day Air Quality Index (AQI) forecasts. Researchers from NASA Langley Research Center and EPA used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument combined with EPA ground network data to create a NASA-data-enhanced Forecast Tool. Air quality forecasters used this tool to prepare their forecasts of particle pollution, or particulate matter less than 2.5 microns in diameter (PM2.5), for the next-day AQI. The archived data provide a rich resource for further studies and analysis. The IDEA project uses data sets and models developed for tropospheric chemistry research to assist federal, state, and local agencies in making decisions concerning air quality management to protect public health.

Kleb, Mary M.↗
DOE OSTI2018

Methods and Models for Analyzing Methane Sorption Capacity of Coal Based on Its Physicochemical Characteristics

The authors study the influence of physicochemical parameters on methane adsorption capacity of coal and offer the analytical method for the methane adsorption capacity for three-phased condition of methane. It is found that in the depth interval to 300 m below surface, methane adsorption capacity measured in lab can exceed natural gas content of coal obtained from geological exploration data by 30%, and the change in the thermodynamic condition of coal–methane system brings irreversible physicochemical consequences in terms of the altered ratios of physical states of the main components. There is no linear connection between natural gas content of a coal bed and its methane adsorption capacity with respect to occurrence depth. The application of Big Data in treatment and interpretation of large data flows is described. The theoretical data predicted using the proposed method and the experimental data on methane content of Kuzbass coal agree.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2023

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Planck intermediate results: XLVI. Reduction of large-scale systematic effects in HFI polarization maps and estimation of the reionization optical depth

This study describes the identification, modelling, and removal of previously unexplained systematic effects in the polarization data of the Planck High Frequency Instrument (HFI) on large angular scales, including new mapmaking and calibration procedures, new and more complete end-to-end simulations, and a set of robust internal consistency checks on the resulting maps. These maps, at 100, 143, 217, and 353 GHz, are early versions of those that will be released in final form later in 2016. The improvements allow us to determine the cosmic reionization optical depth τ using, for the first time, the low-multipole EE data from HFI, reducing significantly the central value and uncertainty, and hence the upper limit. Two different likelihood procedures are used to constrain τ from two estimators of the CMB E- and B-mode angular power spectra at 100 and 143 GHz, after debiasing the spectra from a small remaining systematic contamination. These all give fully consistent results. A further consistency test is performed using cross-correlations derived from the Low Frequency Instrument maps of the Planck 2015 data release and the new HFI data. For this purpose, end-to-end analyses of systematic effects from the two instruments are used to demonstrate the near independence of their dominant systematic error residuals. The tightest result comes from the HFI-based τ posterior distribution using the maximum likelihood power spectrum estimator from EE data only, giving a value 0.055 ± 0.009. Finally, in a companion paper these results are discussed in the context of the best-fit PlanckΛCDM cosmological model and recent models of reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. In this work, we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m 3 /m 3 . It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Untargeted, tandem mass spectrometry (LC/MS-MS) metaproteomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.

Belowground Biogeochemistry Science Focus Area↗
DOE OSTI2015

SU-E-T-676: Reproducibility and Consistency of Two SunNuclear 3D Scanning Tanks

Purpose: To determine if two Sun Nuclear 3D Scanning (SNC 3DS) tanks collect reproducible and consistent data and test the precision of the SNC Dosimetry auto-setup. Methods: Percent depth doses (PDDs) and profiles were collected on two SNC 3DS tanks with a Varian TrueBeam linear accelerator. SNC Dosimetry auto-setup application was used with CC13 ionization chambers. After auto-setup, collimator light field was checked manually against the position of the chamber. Comparing measured data for repeated measurements with tank 1 allowed evaluation of SNC-3DS auto-setup and tank reproducibility. Comparing measured data between tanks 1 and 2 allowed evaluation of consistency between tanks. Results: Preliminary results showed reproducibility of depth of maximum dose (Dmax) of 0.38mm for a 10cmx10cm field and 0.67mm for 30cmx30cm on a single tank. PDD values at 5cm, 10cm, and 20cm depths were reproducible within 0.26%. Consistency of Dmax between tanks was 0.17mm for a 10cmx10cm field and 0.44mm for 30cmx30cm. PDD values at 5cm, 10cm, and 20cmwere consistent within 0.06%. Profiles showed reproducibility in field width within 0.4mm for a 10cmx10cm field and 0.7mm for a 30cmx30cm field. Profiles showed consistency in field width within 0.2mm for 10cmx10cm and 30cmx30cm field sizes. Penumbra width was reproducible and consistent to under 0.5mm except for 30cmx30cm field size at 30cm depth where the reproducibility was 2.2mm and the consistency was 2.6mm. Conclusion: In conclusion, the SNC 3DS tank shows good reproducibility in measured data. Since the tank to tank variation in measured data is within the uncertainty of repeated single tank measurements the tanks also perform consistently.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS2017

Arc Jet Test and Analysis of Asbestos Free Solid Rocket Motor Nozzle Dome Ablative Materials

Asbestos free solid motor internal insulation samples were recently tested at the MSFC Hyperthermal Arc Jet Facility. Objectives of the test were to gather data for solid rocket motor analog characterization of ablative and in-depth thermal performance of rubber materials subject to high enthalpy/pressure flow conditions. Tests were conducted over a range of convective heat fluxes for both inert and chemically reactive sub-sonic free stream gas flow. Active instrumentation included use of total calorimeters, in-depth thermocouples, and a surface pyrometer for in-situ surface temperature measurement. Post-test sample forensics involved determination of eroded depth, charred depth, total sample weight loss, and documentation of the general condition of the eroded profile. A complete Charring Material Ablator (CMA) style aero thermal analysis was conducted for the test matrix and results compared to the measured data. In general, comparisons were possible for a number of the cases and the results show a limited predictive ability to model accurately both the ablative response and the in-depth temperature profiles. Lessons learned and modeling recommendations are made regarding future testing and modeling improvements that will increase understanding of the basic chemistry/physics associated with the complicated material ablation process of rubber materials.

Clayton, J. Louie↗
NASA NTRS1975

Effects of inlet treatment location and treatment cavity depth on compressor noise

The ability of acoustic liners to reduce compressor noise inside and in front of an inlet was studied. An axial flow research compressor and a specially designed inlet were used inside an anechoic chamber. Acoustic and performance data were obtained for a range of inlet treatment locations and cavity depths to determine their effects on inlet noise over a range of blade passing frequencies. The greatest noise reductions in front of the inlet were obtained with acoustic treatment located close to the compressor and backed with the deepest cavities tested. Inside the inlet the maximum noise level reductions were obtained in the area of the treatment regardless of treatment location. No appreciable losses in compressor performance were measured.

Clark, L. R.↗
NASA NTRS2014

Assimilation of MODIS Snow Cover Through the Data Assimilation Research Testbed and the Community Land Model Version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (2345N). Only minimal modifications are made in the higher-middle (4566N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snowmove poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

data assimilation↗
NASA NTRS1997

Evaluation of Exposure From a Low Energy X-Ray Device Using Thermoluminescent Dosimeters

The exposure from an electron beam welding device was evaluated using thermoluminescent dosimeters (TLDs). The device generated low energy X-rays which the current dose equivalent conversion algorithm was not designed to evaluate making it necessary to obtain additional information relating to TLD operation at the photon energies encountered with the device. This was accomplished by performing irradiations at the National Institute of Standards and Technology (NIST) using low energy X-ray techniques. The resulting data was used to determine TLD badge response for low energy X-rays and to establish the relationship between TLD element response and the dose equivalent at specific depths in tissue for these photon energies. The new energy/dose equivalent calibration data was used to calculate the shallow and eye dose equivalent of badges exposed to the device.

Edwards, David L.↗
DOE OSTI2018

The Dark Energy Survey Data Release I

We describe the first public data release of the Dark Energy Survey, DES DR1, consisting of reduced single-epoch images, co-added images, co-added source catalogs, and associated products and services assembled over the first 3 yr of DES science operations. DES DR1 is based on optical/near-infrared imaging from 345 distinct nights (2013 August to 2016 February) by the Dark Energy Camera mounted on the 4 m Blanco telescope at the Cerro Tololo InterAmerican Observatory in Chile. We release data from the DES wide-area survey covering similar to 5000 deg(2) of the southern Galactic cap in five broad photometric bands, grizY. DES DR1 has a median delivered point-spread function of g = 1.12, r = 0.96, i = 0.88, z = 0.84, and Y = 0.'' 90 FWHM, a photometric precision of <1% in all bands, and an astrometric precision of 151 mas. The median co-added catalog depth for a 1.'' 95 diameter aperture at signal-to-noise ratio (S/N) = 10 is g = 24.33, r = 24.08, i = 23.44, z = 22.69, and Y = 21.44 mag. DES DR1 includes nearly 400 million distinct astronomical objects detected in similar to 10,000 co-add tiles of size 0.534 deg(2) produced from similar to 39,000 individual exposures. Benchmark galaxy and stellar samples contain similar to 310 million and similar to 80 million objects, respectively, following a basic object quality selection. These data are accessible through a range of interfaces, including query web clients, image cutout servers, jupyter notebooks, and an interactive co-add image visualization tool. DES DR1 constitutes the largest photometric data set to date at the achieved depth and photometric precision.

Abbott, T. M. C.↗
DOE OSTI2023

New method for level-lifetime measurements with thick scintillators

Level lifetimes provide key insight into the structure of atomic nuclei and serve as stringent tests of theoretical descriptions. Though several methods for determining level lifetimes exist for both reaction measurements and decay studies, here the focus is on techniques involving the direct measurement of time differences between population and subsequent depopulation of excited states. The techniques presented herein are broadly applicable across multiple timing ranges, but the approach is specifically described for the βγ timing method. A multi-step, amplitude-dependent time walk correction was employed to address the data analysis complications that arise from using thick scintillators for electron detection. Further, additional corrections for the depth of the interaction must also be performed when the parent isotope has been implanted into the detector. Techniques are presented for performing these time walk and depth of interaction corrections. Subsequently, a new Monte Carlo method utilizing measured detector responses obtained from the data, coupled with chi-square minimization, is presented for extracting excited state lifetimes ≳ 100 picoseconds. The framework of this Monte Carlo method is developed for the decay of a state in 68 Zn with a known 1.6 ps half life, which is considered prompt given the detection sensitivity of the technique, and then benchmarked using two other excited states in neutron-rich Ni isotopes with 120(34) ps and 1.05(3) ns half lives. Using this new method which takes into account the thick scintillator used, these same half lives were measured to be 135(10) ps and 1.04(24) ns, respectively. The overall good agreement demonstrates the validity of the technique.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1975

Remote sensing of Mississippi River characteristics

A data collection program initiated by the Vicksburg District, to develop a workable knowledge of the principles relating to the transport of water and sediment to gain a better understanding of the Mississippi River characteristics is introduced. Aerial photographs and thermal infrared imagery were collected over two test reaches at low and high water stages. Qualitative interpretation of the data relates to the river characteristics such as flow patterns, relative velocities, sediment concentration distribution, water-depth effects, and effects of man made structures. Ground truth information is correlated with the remotely sensed data.

Ruff, J. F.↗
NASA NTRS1996

A Comparison of Cloud Microphysical and Optical Properties during TOGA-COARE

The impact of cirrus clouds on climate is an issue of research interest currently. Whether cirrus clouds heat or cool the Earth-atmosphere system depends on the cloud shortwave albedo and infrared reflectance and absorptance. These in turn are determined by the size distribution, phase, and composition of particles in the clouds. The TOGA-COARE campaign presented an excellent opportunity to study cirrus clouds and their influence on climate. In this campaign, a microphysics instrument package was flown aboard the DC-8 aircraft at medium altitudes in cirrus clouds. This package included a 2D Greyscale Cloud Particle Probe, a Forward Scattering Spectrometer Aerosol Probe, and an ice crystal replicator. At the same time the ER-2 equipped with a radiation measurement system flew coordinated flight tracks above the DC-8 at very high altitude. The radiation measurement made were short and long wave fluxes, as well as narrowband fluxes, both upwelling and downwelling. In addition LIDAR data is available. The existence of these data sets allows for a the comparison of radiation measurement with microphysical measurements. For example, the optical depth and effective radius retrieved from the ER-2 radiation measurements can be compared to the microphysical data. Conversely, the optical properties and fluxes produced by the clouds can be calculated from the microphysical measurements and compared to those measured aboard the ER-2. The assumptions required to make these comparisons are discussed. Typical microphysical results show a prevalence of micron-sized particles, in addition to the cloud particles that exceed 100 mm. The large number of small particles or "haze" cause the effective cloud radii to shift to smaller sizes, leading to changes in optical parameters.

Strawa, A. W.↗
NASA NTRS2018

Snowex 2017 Community Snow Depth Measurements: A Quality-Controlled, Georeferenced Product

Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas.

Brucker, L.↗