SEARCH · Engineering Papers

Results for “data diversity”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,243 records · Page 69

All sources NASA NTRS DOE OSTI

DOE OSTI2023

SeeQ: A Programming Model for Portable Data-Driven Building Applications

This paper introduces SeeQ, a programming model and an abstraction framework that facilitates the development of portable data- driven building applications. Data-driven approaches can provide insights into building operations and guide decision-making to achieve operational objectives. Yet the configuration of such applications per building requires extensive effort and tacit knowledge. In SeeQ, we propose a portable programming model and build a software system that enables self-configuration and execution across diverse buildings. The configuration of each building is captured in a unified data model - in this paper, we work with the Brick ontology without loss of generality. SeeQ focuses on the distinction between the application logic and the configuration of an application against building-specific data inputs and systems. We test the proposed approach by configuring and deploying a diverse range of applications across five heterogeneous real-world buildings. The analysis shows the potential of SeeQ to significantly reduce the efforts associated with the delivery of building analytics.

analytics↗

DOE OSTI2023

Tokenized Data for FORGE Foundation Models

This dataset comprises a vast corpus of 257 billion tokens, accompanied by the corresponding vocabulary file employed in the pre-training of FORGE foundation models. The primary data source for this corpus is scientific documents derived from diverse origins, and they have been tokenized using the Hugging Face BPE tokenizer. Further details about this research can be found in the publication titled FORGE: Pre-Training Open Foundation Models for Science authored by Junqi Yin, Sajal Dash, Feiyi Wang, and Mallikarjun (Arjun) Shankar, presented at SC'23. The data tokenization pipeline and resulting artifacts use CORE data [Ref: Knoth, P., and Zdrahal, Z. (2012). CORE: three access levels to underpin open access. D-Lib Magazine, 18(11/12)]. For use of these data sets for any purpose, please follow the guidelines provided in https://core.ac.uk/terms .

Yin, Junqi↗

DOE OSTI2020

Density Functionals for Hydrogen Storage: Defining the H2Bind275 Test Set with Ab Initio Benchmarks and Assessment of 55 Functionals

Efficient and high-capacity storage materials are indispensable for a hydrogen-based economy. In silico tools can accelerate the process of discovery of new adsorbent materials with optimal hydrogen adsorption enthalpies. Density functional theory is well-poised to become a very useful tool for enabling high-throughput screening of potential materials. In this work, we have identified density functional approximations that provide good performance for hydrogen binding applications following a two-pronged approach. First, we have compiled a data set (H2Bind275) that comprehensively represents the hydrogen binding problem capturing the chemical and mechanistic diversity in the binding sites encountered in hydrogen storage materials. We have also computed reference interaction energies for this data set using coupled-cluster theory. Second, we have assessed the performance of 55 density functional approximations for predicting H 2 interaction energies and have identified two hybrid density functionals (ωB97X-V and ωB97M-V), two double hybrid density functionals (DSD-PBEPBE-D3(BJ) and PBE0-DH), and one semilocal density functional (B97M-V) as the best performing ones. We have recommended the addition of empirical dispersion corrections to systematically underbinding density functionals such as revPBE, BLYP, and B3LYP for improvements in performance at negligible additional cost. We have also recommended the usage of the def2-TZVPP basis set as it represents a good compromise between accuracy and cost, limiting the finite basis set errors to less than 1 kJ/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1980

Remote sensing data of SP mountain and SP lava flow in north-central Arizona

Multifrequency airborne radar image data of SP Mountain and SP flow in north-central Arizona were obtained in diverse viewing directions and direct and cross-polarization and compared with surface and aerial photography, Landsat multispectral scanner data, airborne thermal infrared imagery, surface geology, and surface roughness statistics. The extremely blocky, basaltic andesite of SP flow is brighter on direct-polarization K-band images than on cross-polarized images taken simultaneously. This effect is explained by multiple scattering and the strong wavelength dependence of polarization effects caused by the rectilinear basaltic andesite scatters. Two distinct types of surface relief on SP flow, one extremely blocky, the other subdued, are clearly discriminated on the visible and thermal wavelength images but are separable only on the longer wavelength L-band radar image data.

Schaber, G. G.↗

NASA NTRS1992

GammaCore: The Compton Observatory research environment

The Compton Observatory Science Support Center (COSSC) is developing a coherent analysis environment for the analysis of Compton and other gamma-ray astronomy data. This environment, GammaCore, allows the astronomer to access the data analysis systems developed at the Principal Investigator (PI) sites for the four Compton Observatory instruments. In addition users have access to standard astronomical tools such as IRAF, IDL, and XANADU. The user interface of GammaCore is the AGCL (AnswerGarden Command Language), developed at the AXAF Science Center. The parameter interface supported by the AGCL allows GammaCore to access all PI software systems in a uniform fashion. These systems are quite different, having been developed independently on heterogeneous systems without much concern for general portability. The data kibitzer concept, where a window running in a specific PI environment is controlled by the AGCL, has been used extensively. Users can choose to view what is going on in the native environment, to use the window to control PI software directly, or to ignore the PI systems entirely and to work only through the homogeneous AGCL interface. Software developed at the COSSC is also integrated within GammaCore. Extensive facilities for conversions of PI data formats to and from FITS have been developed. Access to the Compton data archive and catalogs will also be completely integrated with the GammaCore. Users can retrieve any publicly available Compton data. This paper examines the issues that have arisen in attempting to meld these widely diverse systems. The advantages and limitations of the parameter interface and the kibitzer are discussed along with issues of data portability, documentation, and the feasibility of multi-instrument analysis. Limited capabilities are now available within GammaCore with significant enhancements planned over the coming year. An implementation including all PI systems will be available within that time. Instructions on how to access GammaCore and how to get more information are given.

Mcglynn, T. A.↗

DOE OSTI2013

Revealing Occupancy Patterns in an Office Building through the Use of Occupancy Sensor Data

Energy simulation programs like DOE-2 and EnergyPlus are tools that have been proven to aid with energy calculations to predict energy use in buildings. Some inputs to energy simulation models are relatively easy to find, including building size, orientation, construction materials, and HVAC system size and type. Others vary with time (e.g. weather and occupancy) and some can be a challenge to estimate in order to create an accurate simulation. In this paper, the analysis of occupancy sensor data for a large commercial, multi-tenant office building is presented. It details occupancy diversity factors for private offices and summarizes the same for open offices, hallways, conference rooms, break rooms, and restrooms in order to better inform energy simulation parameters. Long-term data were collected allowing results to be presented to show variations of occupancy diversity factors in private offices for time of day, day of the week, holidays, and month of the year. The diversity factors presented differ as much as 46% from those currently published in ASHRAE 90.1 2004 energy cost method guidelines, a document referenced by energy modelers regarding occupancy diversity factors for simulations. This may result in misleading simulation results and may introduce inefficiencies in the final equipment and systems design.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

High intraspecific genome diversity in the model arbuscular mycorrhizal symbiont Rhizophagus irregularis

Arbuscular mycorrhizal fungi (AMF) are known to improve plant fitness through the establishment of mycorrhizal symbioses. Genetic and phenotypic variations among closely related AMF isolates can significantly affect plant growth, but the genomic changes underlying this variability are unclear. To address this issue, we improved the genome assembly and gene annotation of the model strain Rhizophagus irregularis DAOM197198, and compared its gene content with five isolates of R. irregularis sampled in the same field. All isolates harbor striking genome variations, with large numbers of isolate-specific genes, gene family expansions, and evidence of interisolate genetic exchange. The observed variability affects all gene ontology terms and PFAM protein domains, as well as putative mycorrhiza-induced small secreted effector-like proteins and other symbiosis differentially expressed genes. High variability is also found in active transposable elements. Overall, these findings indicate a substantial divergence in the functioning capacity of isolates harvested from the same field, and thus their genetic potential for adaptation to biotic and abiotic changes. Our data also provide a first glimpse into the genome diversity that resides within natural populations of these symbionts, and open avenues for future analyses of plant–AMF interactions that link AMF genome variation with plant phenotype and fitness.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

The MOSFIRE Deep Evolution Field (MOSDEF) survey: Rest-frame optical spectroscopy for ∼1500 H-SELECTED GALAXIES AT 1.37⩽z⩽3.8

In this paper we present the MOSFIRE Deep Evolution Field (MOSDEF) survey. The MOSDEF survey aims to obtain moderate-resolution (R = 3000–3650) rest-frame optical spectra (∼3700–7000 Å) for ∼1500 galaxies at 1.37⩽z⩽3.80 in three well-studied CANDELS fields: AEGIS, COSMOS, and GOODS-N. Targets are selected in three redshift intervals: 1.37⩽z⩽1.70, 2.09⩽z⩽2.61, and 2.95⩽z⩽3.80, down to fixed H{sub AB} (F160W) magnitudes of 24.0, 24.5, and 25.0, respectively, using the photometric and spectroscopic catalogs from the 3D-HST survey. We target both strong nebular emission lines (e.g., [O ii] λλ3727,3730, Hβ, [O iii] λλ4960,5008, Hα, [N ii] λλ6550,6585, and [S ii] λλ6718,6733) and stellar continuum and absorption features (e.g., Balmer lines, Ca-ii H and K, Mgb, 4000 Å break). Here we present an overview of our survey, the observational strategy, the data reduction and analysis, and the sample characteristics based on spectra obtained during the first 24 nights. To date, we have completed 21 masks, obtaining spectra for 591 galaxies. For ∼80% of the targets we derive a robust redshift from either emission or absorption lines. In addition, we confirm 55 additional galaxies, which were serendipitously detected. The MOSDEF galaxy sample includes unobscured star-forming, dusty star-forming, and quiescent galaxies and spans a wide range in stellar mass (∼10{sup 9}−10{sup 11.5} M{sub ⊙}) and star formation rate (∼10{sup 0}−10{sup 3} M{sub ⊙} yr{sup −1}). The spectroscopically confirmed sample is roughly representative of an H-band limited galaxy sample at these redshifts. With its large sample size, broad diversity in galaxy properties, and wealth of available ancillary data, MOSDEF will transform our understanding of the stellar, gaseous, metal, dust, and black hole content of galaxies during the time when the universe was most active.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2017

VISAGE Visualization for Integrated Satellite, Airborne and Ground-Based Data Exploration

The primary goal of the VISAGE project is to facilitate more efficient Earth Science investigations via a tool that can provide visualization and analytic capabilities for diverse coincident datasets. This proof-of-concept project will be centered around the GPM Ground Validation program, which provides a valuable source of intensive, coincident observations of atmospheric phenomena. The data are from a wide variety of ground-based, airborne and satellite instruments, with a wide diversity in spatial and temporal scales, variables, and formats, which makes these data difficult to use together. VISAGE will focus on "golden cases" where most ground instruments were in operation and multiple research aircraft sampled a significant weather event, ideally while the GPM Core Observatory passed overhead. The resulting tools will support physical process studies as well as satellite and model validation.

Data fusion↗

NASA NTRS2005

A Multi-scale Approach to Urban Thermal Analysis

An environmental consequence of urbanization is the urban heat island effect, a situation where urban areas are warmer than surrounding rural areas. The urban heat island phenomenon results from the replacement of natural landscapes with impervious surfaces such as concrete and asphalt and is linked to adverse economic and environmental impacts. In order to better understand the urban microclimate, a greater understanding of the urban thermal pattern (UTP), including an analysis of the thermal properties of individual land covers, is needed. This study examines the UTP by means of thermal land cover response for the Salt Lake City, Utah, study area at two scales: 1) the community level, and 2) the regional or valleywide level. Airborne ATLAS (Advanced Thermal Land Applications Sensor) data, a high spatial resolution (10-meter) dataset appropriate for an environment containing a concentration of diverse land covers, are used for both land cover and thermal analysis at the community level. The ATLAS data consist of 15 channels covering the visible, near-IR, mid-IR and thermal-IR wavelengths. At the regional level Landsat TM data are used for land cover analysis while the ATLAS channel 13 data are used for the thermal analysis. Results show that a heat island is evident at both the community and the valleywide level where there is an abundance of impervious surfaces. ATLAS data perform well in community level studies in terms of land cover and thermal exchanges, but other, more coarse-resolution data sets are more appropriate for large-area thermal studies. Thermal response per land cover is consistent at both levels, which suggests potential for urban climate modeling at multiple scales.

Gluch, Renne↗

NASA NTRS1995

Latitudinal variation of helicity of photospheric magnetic fields

Using a 1988-1994 data set of original photospheric vector magnetograms as well as published data, we have studied the average magnetic helicity of 69 diverse active regions, adopting the linear force-free field parameter alpha as a measure. This average value was determined by minimizing the differences between the computed constant-alpha force-free and observed horizontal magnetic fields. The average magnetic helicity shows a sign difference at the 2 sigma level in opposite hemispheres. In our data set, 76% of the active regions in the northern hemisphere have negative helicity, and 69% in the southern hemisphere, positive. Although the data show considerable variation from one active region to the next, the data set as a whole suggest that the magnitude of the average helicity increases with solar latitude, starting at zero near the equator, reaches a maximum near 15 deg - 25 deg in both hemispheres, and drops back toward smaller values avove 35 deg - 40 deg. Qualitative comparison with published models shows that such latitudinal variation of the average magnetic helicity may result from either turbulent convective motions or differential rotation, although our studies of rotating sunspots lead us to favor the former.

Pevtsov, Alexei A.↗

DOE OSTI2022

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Abstract Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Gan, Yanglan↗

DOE OSTI2011

Smooth full field reconstruction of velocity and its gradients from noisy scattered velocimetry data in a cross-slot flow

We present a method combining generalized Tikhonov regularization with a finite element approximation for reconstructing smooth velocity and velocity gradient fields from spatially scattered and noisy velocity data in a two-dimensional complex flow domain. Synthetic velocity data for a cross-slot geometry are generated using the Oldroyd-B solution, subsequently perturbed by random noise. Performances of diverse finite element continuity-regularization criterion combinations are tested against noise-free data, while the optimum regularization parameter is determined using generalized cross-validation. The best performance is achieved for the velocity field and its gradients simultaneously by C{sup 2} continuous Hermite finite elements and minimization of a norm of the velocity's third derivative. The standard regularization criterion based on the second derivative is shown to lead to systematic distortions in boundary regions, allowing therefore a lower reduction in the statistical error. Furthermore, optical fields are calculated by applying a differential constitutive equation directly to the reconstructed flow kinematics; high quality velocity gradient fields are shown to be an essential prerequisite for their reliable prediction. Overall, the method is expedient to implement and does not require boundary conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1987

High resolution Viking Orbiter images: A useful data source for testing the viability of geomorphic processes attributed to Martian landforms

The diversity of landforms visible in the Viking images of Mars have led to a proliforation of geomorphic agents proposed to be active in shaping the Martian surface. While it is likely that numerous different processes have contributed to the geomorphology of Mars throughout Martian history, it is important that proposed Martian geomorphic agents be subjected to critical scrutiny by the scientific community. High resolution Viking Orbiter images represent a data set for investigating geomorphic processes on Mars. Geologic mapping of Mars can take place at a variety of scales, utilizing a variety of image resolutions, but an interpretation of the history of individual landforms is dependent upon the best available spatial resolution. These high resolution images provide the opportunity to examine proposed Martian geomorphic processes. It is clear that researchers may not interpret features in the same way but it is important that high resolution images of candidate features be made available to the scientific community so that a consensus can be reached. This procedure can provide a way to refine the understanding of geomorphic processes on Mars.

Zimbelman, James R.↗

DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q2

The vision of the National Microbiome Data Collaborative (NMDC) centers on the concept of connecting data, people, and ideas to advance microbiome innovation and discovery. Building data infrastructure, while key to NMDC’s ability to execute on our vision, can only go so far in creating scientific impact. By fostering strong community partnerships and developing a set of robust community outreach and training programs, we are able to turn our products – the Submission Portal, NMDC EDGE, and the Data Portal – into tools that empower the scientific community. Our multi-pronged community building approach spans individual researchers, research teams, consortia and scientific societies, and institutions and federal agencies. To foster a collaborative and inclusive community-centered environment, we have identified three strategic objectives to promote an inclusive and connected community: (1) recognize and support the diverse research needs and perspectives of the microbiome research community; (2) promote best practices across the microbiome community, from researchers to funders, through community-driven practices (FAIR, CARE, and TRUST); and (3) build a microbiome ecosystem that enables scientific discovery and innovation across stakeholders. These strategic objectives allow our team to focus on impact across a diverse range of activities, from launching the American Society for Microbiology (ASM) Microbiome Data Prize to supporting the Ambassador and Champions programs fostering learning and building a collaborative network. We broadly communicate our work through social media (X/Twitter, LinkedIn, and Instagram), The Microbiome Standard (our quarterly newsletter), and Annual Reports. All our work is underpinned by a strong commitment to diversity, equity, and inclusion as articulated in our Action Plan that tracks progress towards key metrics. A core component of our engagement strategy is user research. User research ensures the Submission Portal, NMDC EDGE, Data Portal, and the new Field Notes mobile app are designed with and for the scientific community. Our user research efforts consist of asking researchers exploratory questions to collect information on researcher priorities, methodologies, and perceptions to ensure that we are aware of the current state of microbiome research. Our usability testing provides researchers with prototypes or test environments of the NMDC products, and we capture valuable information on how users interact with the products to make improvements. Given the diverse nature of microbiome work, we acknowledge that we are not aware of all pressing data challenges and thus rely on the research community to help us identify the most important issues to prioritize. To date, we have conducted 24 interviews and one beta-testing call with 10 participants across all NMDC products, which have generated 321 insights and 120 action items. Herein, we describe the ways we engage with the microbiome research community to advance the NMDC mission.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

A novel procedure on next generation sequencing data analysis using text mining algorithm

Background: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Methods: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. Results: The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. Conclusion: The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

WHONDRS Surface Water and Sediment Non-Purgeable Organic Carbon and FTICR-MS across Glacial Features in Svalbard 2021 (v3)

This dataset supports a broader study examining land-atmosphere gas exchange associated with permafrost and glaciers in Svalbard. The dataset provides geochemistry and organic matter characterization data generated from surface water and sediment. Samples were collected from 8 spatially diverse sites across central Svalbard and also from a more comprehensive temporal and spatial study within one glacial catchment area throughout one summer melt season. Related data were collected and will be published separately in collaboration with Yde and Kleber.This data package was originally published in September 2022. It was updated in March 2023 (v2; new and modified files), and July 2025 (v3; modified files). See the change history section in the readme for details.For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved organic carbon (DOC; measure as non-purgeable organic carbon; NPOC); (5) surface water sampling protocol; (6) sediment extraction protocol; (7) readme; (8) methods codes; (9) international generic sample number (IGSN) mapping file; and (10) folder of high resolution characterization of organic matter via 7 and 12 Tesla (7T and 12T) Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR folder contains two subfolders containing the 7T and 12T .xml data files and another subfolder containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1982

Evaluation of the potential of one to three SEASAT-SMMR channels in retrieving sea surface temperature

The scanning multichannel microwave radiometer (SMMR) aboard the SEASAT satellite measured emitted radiation in both horizontal and vertical polarizations at microwave frequencies of 6.6, 10.69, 18.0, 21.0 and 37.0 GHz. Retrieval algorithms, for sea surface temperature (SST) determination, from subsets of one to three SMMR channels are obtained by a two step statistical technique. The technique first selects the best subsets of a given size defined by an R2 criterion (coefficient of determination), of a given size by the application of an efficient 'leaps and bounds' technique on a statistical data base. It then performs a regression analysis on the selected subsets. The statistical data base employed a large (600) set of seasonally and geographically diverse atmospheric and surface parameters for radiative transfer calculations. The results of the study of one to three channel subset retrieval algorithms indicate the possibility of using 6.6V, 6.6H and 18V channels for SST determination from SEASAT-SMMR data.

Pandey, P. C.↗