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At least 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

NCAR/EOL ISFS Data for LASSO-CACTI Overview Paper

5 minute averages of surface meteorology and flux data collected by the NCAR/EOL Integrated Surface Flux System (ISFS) at 15 sites during the RELAMPAGO field campaign. These data have been quality-controlled and are available in NetCDF format. Winds reported by the sonic anemometers have been tilt corrected and rotated into geographic coordinates. Data providence, citation, and acknowledgement This ARM data set is a copy of v2.0 of the NCAR data set obtained on 6-Jun-2024 from https://doi.org/10.26023/ZPHJ-JW9W-2B0Y. The citation for the original data source is: NCAR/EOL In-situ Sensing Facility, Oncley, S. 2021. NCAR/EOL ISFS Surface Meteorology and Flux Products, 5-minute. Version 2.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/ZPHJ-JW9W-2B0Y Accessed 06 Jun 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: "Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/"

atmosphere: surface↗
NASA NTRS2004

Metadata Authoring with Versatility and Extensibility

NASA's Global Change Master Directory (GCMD) assists the scientific community in the discovery of and linkage to Earth science data sets and related services. The GCMD holds over 13,800 data set descriptions in Directory Interchange Format (DIF) and 700 data service descriptions in Service Entry Resource Format (SERF), encompassing the disciplines of geology, hydrology, oceanography, meteorology, and ecology. Data descriptions also contain geographic coverage information and direct links to the data, thus allowing researchers to discover data pertaining to a geographic location of interest, then quickly acquire those data. The GCMD strives to be the preferred data locator for world-wide directory-level metadata. In this vein, scientists and data providers must have access to intuitive and efficient metadata authoring tools. Existing GCMD tools are attracting widespread usage; however, a need for tools that are portable, customizable and versatile still exists. With tool usage directly influencing metadata population, it has become apparent that new tools are needed to fill these voids. As a result, the GCMD has released a new authoring tool allowing for both web-based and stand-alone authoring of descriptions. Furthermore, this tool incorporates the ability to plug-and-play the metadata format of choice, offering users options of DIF, SERF, FGDC, ISO or any other defined standard. Allowing data holders to work with their preferred format, as well as an option of a stand-alone application or web-based environment, docBUlLDER will assist the scientific community in efficiently creating quality data and services metadata.

Pollack, Janine↗
NASA NTRS2021

The Effect of Missing Groups in the Calculation of the Solar Irradiance Deficit: Analysis of the Sunspot Areas from the SOON Network

Sunspot areas are one of the most important indices of solar activity. To obtain an extended time series covering multiple solar cycles one must combine data from different observatories after a proper comparison and calibration of the individual data sets. We compare the daily and group values of sunspot areas provided by the different stations from the Solar Optical Observing Network, SOON, which are determined using similar instruments and techniques. We investigate if there are systematic differences among the stations and whether the differences in the daily values can be attributed to missing groups in the records or errors in the measurements. We find significant differences among the stations of the SOON network in terms of sizes (average daily and group values), quality of observations and coverage (considering number of missing groups and data gaps). Our results indicate that calibration factors for daily values can be used with confidence to combine data sets from different stations. However, for some applications which require the location of the sunspot groups the same correction factors should not be used. We estimate the irradiance deficit due to sunspot through the Photometric Sunspot Index and compare the output from similar data sets to quantify the effect of missing groups. We find differences as high as 150 ppm during the maximum of solar cycle. The effect increases for sunspot groups near the center of the disk accounting for about 80% of the observed differences.

Luis Leuzzi↗
DOE OSTI2024

The S-PLUS Fornax Project (S+FP): A first 12-band glimpse of the Fornax galaxy cluster

ABSTRACT The Fornax galaxy cluster is the richest nearby (D ∼ 20 Mpc) galaxy association in the southern sky. As such, it provides a wealth of opportunities to elucidate on the processes where environment holds a key role in transforming galaxies. Although it has been the focus of many studies, Fornax has never been explored with contiguous homogeneous wide-field imaging in 12 photometric narrow and broad bands like those provided by the Southern Photometric Local Universe Survey (S-PLUS). In this paper, we present the S-PLUS Fornax Project (S+FP) that aims to comprehensively analyse the galaxy content of the Fornax cluster using S-PLUS. Our data set consists of 106 S-PLUS wide-field frames (FoV∼1.4 × 1.4 deg2) observed in five Sloan Digital Sky Survey-like ugriz broad bands and seven narrow bands covering specific spectroscopic features like [O ii], Ca ii H+K, Hδ, G band, Mg b triplet, Hα, and the Ca ii triplet. Based on S-PLUS specific automated photometry, aimed at correctly detecting Fornax galaxies and globular clusters in S-PLUS images, our data set provides the community with catalogues containing homogeneous 12-band photometry for ∼3 × 106 resolved and unresolved objects within a region extending over ∼208 deg2 (∼5 Rvir in RA) around Fornax’ central galaxy, NGC 1399. We further explore the eagle and IllustrisTNG cosmological simulations to identify 45 Fornax-like clusters and generate mock images on all 12 S-PLUS bands of these structures down to galaxies with M⋆ ≥ 108 M⊙. The S+FP data set we put forward in this first paper of a series will enable a variety of studies some of which are briefly presented.

Astronomy & Astrophysics↗
NASA NTRS2000

BOREAS AES MARSII Surface Meteorological Data

Canadian AES personnel collected several data sets related to surface and atmospheric meteorological conditions over the BOREAS region. This data set contains 15-minute meteorological data from six MARSII meteorology stations in the BOREAS region in Canada. Parameters include site, time, temperature, dewpoint, visibility, wind speed, wind gust, wind direction, two cloud groups, precipitation, and station pressure. Temporally, the data cover the period of May to September 1994. Geo-graphically, the stations are spread across the provinces of Saskatchewan and Manitoba. The data are provided in tabular ASCII files, and are classified as AFM-Staff data.

Atkinson, G. Barrie↗
NASA NTRS1986

Comparison of solar hard X-ray and UV line and continuum bursts with high time resolution

The close temporal correlation between the ultraviolet line emission from OV (lambda 1371A) and hard X-rays above 30 keV has been previously established from a comparison of data sets from the Ultraviolet Spectrometer and Polarimeter (UVSP) and Hard X-Ray Burst Spectrometer (HXRBS) instruments on Solar Maximum Mission (SMM). Corresponding features were found to be coincident to within 1 s. Following the SMM repair, similar measurements were made for a flare occurring on May 20, 1984, at 0259 UT, this time with UVSP observing in the continuum near 1600A with much higher time resolution (75ms). The simultaneity of several features in this flare were established to better than 0.1 s, thus reinforcing the close relationship between hard X-ray and UV emission. Following the repair of the UVSP wavelength drive, similar measurements were repeated for a flare occurring at 0148 UT on April 24, 1985. This event was observed simultaneously in the OV line, the UV continuum and in hard X-rays by the same SMM instruments. Temporal comparisons of the three data sets show that, for a few selected features, simultaneity of the hard X-ray and UV emissions does exist. However, cross-correlation analyses of the entire data sets indicate that there is a trend for the fastest OV emission features to be delayed with respect to the hard X-rays by approx. 260 ms. On the other hand the fast OV and UV continuum features are simultaneous to within the instrumental uncertainty. The combined results have established the close relationship of the impulsive phase hard X-ray and UV continuum and OV line emissions. They lend support to the argument that both the hard X-ray and UV radiations originate low in the solar atmosphere.

Orwig, L. E.↗
DOE OSTI2024

Observational constraints on early dark energy

In this paper, we review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 and PR4 cosmic microwave background (CMB) data and large-scale structure (LSS) data sets including galaxy clustering and weak lensing data from the Dark Energy Survey, Subaru Hyper Suprime-Cam and KiDS+VIKING-450, as well as BOSS/eBOSS galaxy clustering and Lyman-[Formula: see text] forest data. We detail the fit to CMB data, and perform the first analyses of EDE using the CAMSPEC and Hillipop likelihoods for Planck CMB data, rather than Plik, both of which yield a tighter upper bound on the allowed EDE fraction than that found with Plik. We then supplement CMB data with LSS data in a series of new analyses. All these analyses are concordant in their Bayesian preference for [Formula: see text]CDM over EDE, as indicated by marginalized posterior distributions. We perform a series of tests of the impact of priors in these results, and compare with frequentist analyses based on the profile likelihood, finding qualitative agreement with the Bayesian results. All these tests suggest prior volume effects are not a determining factor in analyses of EDE. This work provides both a review of existing constraints and several new analyses.

Astronomy & Astrophysics↗
NASA NTRS1984

An initial approach to the utilization of VAS satellite sounding data

The primary objective is the development of methods to utilize satellite soundings. To achieve that, a more precise understanding of the characteristics of satellite derived soundings is required. The initial approach in the utilization of satellite soundings is to find a way to combine rawinsondes and satellite data into a unified data set. If this can be done in a meaningful way, satellite data could be integrated into existing data and interpretation would be enhanced. In this attempt, both rawinsonde and satellite soundings will be decomposed by the use of the Fourier cosine series. Then, each harmonic will be compared to get a better understanding of the representativeness of the satellite sounding data. Harmonics taken from satellite and rawinsonde soundings will be combined to provide a unified data set that can be used both over data sparse ocean areas as well as over land where rawinsonde data are available.

Shin, Kyung-Sup↗
NASA NTRS2017

How Long Do Satellites Need to Overlap? Evaluation of Climate Data Stability from Overlapping Satellite Records

Sensors on satellites provide unprecedented understanding of the Earth's climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellite missions. This paper addresses how the length of overlap affects the ability to identify an offset or a drift in the overlap of data between two sensors. Ozone and temperature data sets are used as examples showing that overlap data can differ by latitude and can change over time. New results are presented for the general case of sensor overlap by using Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) and Solar Stellar Irradiance Comparison Experiment (SOLSTICE) solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments' measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 %/yr uncertainty (0.00008 W/sq m/nm/yr), the overlap for these two satellites would need to be 2.5 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps occur adding uncertainty to both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged data set. Results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations offer additional challenges due to the complexity of the observations, but Earth observations may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.

climate data records↗
DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗
NASA NTRS1992

Data reduction using cubic rational B-splines

A geometric method is proposed for fitting rational cubic B-spline curves to data that represent smooth curves including intersection or silhouette lines. The algorithm is based on the convex hull and the variation diminishing properties of Bezier/B-spline curves. The algorithm has the following structure: it tries to fit one Bezier segment to the entire data set and if it is impossible it subdivides the data set and reconsiders the subset. After accepting the subset the algorithm tries to find the longest run of points within a tolerance and then approximates this set with a Bezier cubic segment. The algorithm uses this procedure repeatedly to the rest of the data points until all points are fitted. It is concluded that the algorithm delivers fitting curves which approximate the data with high accuracy even in cases with large tolerances.

Chou, Jin J.↗
DOE OSTI2022

Colour gradients of low-redshift galaxies in the DESI Legacy Imaging Survey

ABSTRACT Radial colour gradients within galaxies arise from gradients of stellar age, metallicity, and dust reddening. Large samples of colour gradients from wide-area imaging surveys can complement smaller integral-field spectroscopy data sets and can be used to constrain galaxy formation models. Here, we measure colour gradients for low-redshift galaxies (z < 0.1) using photometry from the DESI Legacy Imaging Survey DR9. Our sample comprises ∼93 000 galaxies with spectroscopic redshifts and ∼574 000 galaxies with photometric redshifts. We focus on gradients across a radial range 0.5Reff to Reff, which corresponds to the inner disc of typical late-type systems at low redshift. This region has been the focus of previous statistical studies of colour gradients and has recently been explored by spectroscopic surveys such as MaNGA. We find that the colour gradients of most galaxies in our sample are negative (redder towards the centre), consistent with the literature. We investigate empirical relationships between colour gradient, average g − r and r − z colour, Mr, M⋆, and sSFR. Trends of gradient strength with Mr (M⋆) show an inflection around Mr ∼ −21 ($\log _{10} \, M_\star /\mathrm{M_\odot }\sim 10.5$). Below this mass, colour gradients become steeper with increasing M⋆, whereas colour gradients in more massive galaxies become shallower. We find that positive gradients (bluer stars at smaller radii) are typical for galaxies of $M_{\star }\sim 10^{8}\, \mathrm{M_\odot }$. We compare our results to age and metallicity gradients in two data sets derived from fits of different stellar population libraries to MaNGA spectra, but find no clear consensus explanation for the trends we observe. Both MaNGA data sets seem to imply a significant contribution from dust reddening, in particular, to explain the flatness of colour gradients along the red sequence.

Astronomy & Astrophysics↗
DOE OSTI2020

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data

Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae . Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators ( STE12-TEC1 and RAP1-HAP1-MSN4 ). Conclusion Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

In this work, we investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R 2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Carbon dioxide (CO 2 ) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, in this study, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO 2 in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO 2 solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO 2 solubility (ln(x CO 2 )) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO 2 solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO 2 capture and utilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗