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All sources NASA NTRS DOE OSTI

NASA NTRS2009

Approach to Managing MeaSURES Data at the GSFC Earth Science Data and Information Services Center (GES DISC)

A major need stated by the NASA Earth science research strategy is to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. (NASA Solicitation for Making Earth System data records for Use in Research Environments (MEaSUREs) 2006-2010) Selected projects create long term records of a given parameter, called Earth Science Data Records (ESDRs), based on mature algorithms that bring together continuous multi-sensor data. ESDRs, associated algorithms, vetted by the appropriate community, are archived at a NASA affiliated data center for archive, stewardship, and distribution. See http://measures-projects.gsfc.nasa.gov/ for more details. This presentation describes the NASA GSFC Earth Science Data and Information Services Center (GES DISC) approach to managing the MEaSUREs ESDR datasets assigned to GES DISC. (Energy/water cycle related and atmospheric composition ESDRs) GES DISC will utilize its experience to integrate existing and proven reusable data management components to accommodate the new ESDRs. Components include a data archive system (S4PA), a data discovery and access system (Mirador), and various web services for data access. In addition, if determined to be useful to the user community, the Giovanni data exploration tool will be made available to ESDRs. The GES DISC data integration methodology to be used for the MEaSUREs datasets is presented. The goals of this presentation are to share an approach to ESDR integration, and initiate discussions amongst the data centers, data managers and data providers for the purpose of gaining efficiencies in data management for MEaSUREs projects.

Vollmer, Bruce↗

NASA NTRS2017

Cyclotron Resonant Scattering Feature Simulations II. Description of the CRSF Simulation Process

Context. Cyclotron resonant scattering features (CRSFs) are formed by scattering of X-ray photons o_ quantized plasma electrons in the strong magnetic field (of the order 1012 G) close to the surface of an accreting X-ray pulsar. Due to the complex scattering cross-sections, the line profiles of CRSFs cannot be described by an analytic expression. Numerical methods, such as Monte Carlo (MC) simulations of the scattering processes, are required in order to predict precise line shapes for a given physical setup, which can be compared to observations to gain information about the underlying physics in these systems.Aims. A versatile simulation code is needed for the generation of synthetic cyclotron lines. Sophisticated geometries should be investigatable by making their simulation possible for the first time.Methods. The simulation utilizes the mean free path tables described in the first paper of this series for the fast interpolation of propagation lengths. The code is parallelized to make the very time-consuming simulations possible on convenient time scales. Furthermore, it can generate responses to monoenergetic photon injections, producing Green's functions, which can be used later to generate spectra for arbitrary continua.Results. We develop a new simulation code to generate synthetic cyclotron lines for complex scenarios, allowing for unprecedented physical interpretation of the observed data. An associated XSPEC model implementation is used to fit synthetic line profiles to NuSTAR data of Cep X-4. The code has been developed with the main goal of overcoming previous geometrical constraints in MC simulations of CRSFs. By applying this code also to more simple, classic geometries used in previous works, we furthermore address issues of code verification and cross-comparison of various models. The XSPEC model and the Green's function tables are available online (see link in footnote, page 1).

Schwarm, F.-W.↗

DOE OSTI2021

Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning

Discovering hidden geothermal resources is a very challenging task. It requires the mining of large datasets, including various diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used Play Fairway Analysis (PFA) typically relies on subject-matter expertise to analyze site or regional data to estimate geothermal conditions and prospectivity. Here, we demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset of Southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. However, most of these systems are poorly characterized because of insufficient existing data and limited past explorative studies. This study aims to discover hidden patterns and relationships in the SWNM geothermal dataset to better understand regional hydrothermal conditions. This is achieved by applying an unsupervised machine learning algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden (latent) signatures characterizing datasets, (2) the optimal number of these signatures, (3) dominant data attributes associated with each signature, and (4) spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, geothermal attributes at 44 locations in SWNM. NMFk successfully finds data patterns and identifies the spatial associations of hydrothermal signatures with the four physiographic provinces in SWNM (Colorado Plateau, Volcanic Field, Basin and Range, and the Rio Grande rift). The algorithm identified up to 5 hydrothermal signatures in the SWNM datasets that differentiate between low- and medium-temperature hydrothermal systems in different provinces. Also, the algorithm identifies two medium-temperature hydrothermal systems in SWNM that require further exploration for geothermal resource development. Based on our analyses, 12 of the attributes are important to identify medium-temperature hydrothermal systems, and the remaining six attributes are critical to characterize low-temperature hydrothermal systems. Based on the obtained results, we identify potential physiographic provinces for further exploration to characterize them as geothermal resources. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored areas.

58 GEOSCIENCES↗

DOE OSTI2026

Data for Sugar Accumulation Enhancement in Sorghum Stem is Associated with Reduced Reproductive Sink Strength and Increased Phloem Unloading Activity

Sweet sorghum has emerged as a promising source of bioenergy mainly due to its high biomass and high soluble sugar yield in stems. Studies have shown that loss-of-function Dry locus alleles have been selected during sweet sorghum domestication, and decapitation can further boost sugar accumulation in sweet sorghum, indicating that the potential for improving sugar yields is yet to be fully realized. To maximize sugar accumulation, it is essential to gain a better understanding of the mechanism underlying the massive accumulation of soluble sugars in sweet sorghum stems in addition to the Dry locus. We performed a transcriptomic analysis upon decapitation of near-isogenic lines for mutant (d, juicy stems, and green leaf midrib) and functional (D, dry stems and white leaf midrib) alleles at the Dry locus. Our analysis revealed that decapitation suppressed photosynthesis in leaves, but accelerated starch metabolic processes in stems. SbbHLH093 negatively correlates with sugar levels supported by genotypes (DD vs. dd), treatments (control vs. decapitation), and developmental stages post anthesis (3d vs.10d). D locus gene SbNAC074A and other programmed cell death-related genes were down regulated by decapitation, while sugar transporter-encoding gene SbSWEET1A was induced. Both SbSWEET1A and Invertase 5 were detected in phloem companion cells by RNA in situ assay. Loss of the SbbHLH093 homolog, AtbHLH093, in Arabidopsis led to a sugar accumulation increase. This study provides new insights into sugar accumulation enhancement in bioenergy crops, which can be potentially achieved by reducing reproductive sink strength and enhancing phloem unloading.

Transcriptomics↗

DOE OSTI2020

Generation and representation of synthetic smart meter data

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This paper describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Electrolyte Solvation and Ionic Association. VII. Correlating Raman Spectroscopic Data with Solvate Species

Raman spectroscopy is a widely utilized method for determining information about the solvate species present in electrolyte solutions. Difficulties arise, however, when interpreting the Raman data in terms of correlating the spectroscopic vibrational bands with specific solvate species. Examples are provided to demonstrate how erroneous conclusions may be drawn with regard to the identity and population (distribution) of the solvent-separated ion pair (SSIP), contact ion pair (CIP) and aggregate (AGG) solvate species frequently mentioned in the scientific literature.

contact ion pairs↗

DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

TCR Data Management Plan

The Oak Ridge National Laboratory (ORNL) Transformation Challenge Reactor (TCR) program is developing additive manufacturing and artificial intelligence (AI) to deliver enabling technologies for advanced reactors. Through the application of these advanced technologies, the program targets delivering solutions to the high costs and lengthy deployment timelines that threaten the future of nuclear energy—the country’s largest source of carbon-free energy. This document describes the plan for integrating and managing TCR data from multiple sources throughout ORNL and external sources, including novel data sources associated with advanced manufacturing, characterization systems, and the TCR Digital Platform that incorporates various levels of AI and data analytics.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1985

Generative engineering databases - Toward expert systems

Engineering data management, incorporating concepts of optimization with data representation, is receiving increasing attention as the amount and complexity of information necessary for performing engineering operations increases and the need to coordinate its representation and use increases. Research in this area promises advantages for a wide variety of engineering applications, particularly those which seek to use data in innovative ways in the engineering process. This paper presents a framework for a comprehensive, relational database management system that combines a knowledge base of design constraints with a database of engineering data items in order to achieve a 'generative database' - one which automatically generates new engineering design data according to the design constraints stored in the knowledge base. The representation requires a database that is able to store all of the data normally associated with engineering design and to accurately represent the interactions between constraints and the stored data while guaranteeing its integrity. The representation also requires a knowledge base that is able to store all the constraints imposed upon the engineering design process.

Rasdorf, W. J.↗

NASA NTRS2017

Biosensor Integration Development ExMC/Canadian Space Agency Collaboration

In support of the NASA Human Research Program Exploration Medical Capability (ExMC) Element, NASA Ames Research Center (ARC) established a collaborative effort with the Canadian Space Agency (CSA). The collaboration focuses on leveraging CSA capability in the areas of biosensors and decision support that will augment future development of such components for Exploration Missions. The CSA advancement of biosensors enables NASA to focus on the integration and data management associated with these types of components through the system currently under development by the Medical Data Architecture (MDA) project. This approach has enabled the establishment of a successful collaborative working relationship between ExMC and CSA.Applying lessons learned from the fiscal year 2016 (FY16) Human Exploration Research Analog (HERA) campaign, CSA and NASA ARC developed a solution to provide real-time feedback to researchers who monitor the collection of vital signs data from a wearable Astroskin garment. The advances in the interfaces included the development of an iPad application (by CSA) to wirelessly forward the vital signs data to the MDA system, which collected the vital signs data through a receiver developed by NASA ARC. The development of these interfaces aims to provide communications between the Astroskin and the MDA system such that data may be seamlessly collected, stored and retrieved by the MDA. The first steps towards this goal were demonstrated in FY16. In FY17, ExMC will complete the first in a series of test beds that establishes a system to automate collection and management of vital sign data from the Astroskin, and other sources of data, to provide information for a crewmember to make medical decisions. In addition, the MDA Test Bed 1 will enable CSA to evaluate and optimize biosensor advancement and facilitate decision support algorithm development.

Vital signs↗

NASA NTRS2017

Biosensor Integration Development ExMC/Canadian Space Agency Collaboration

Astroskin↗

DOE OSTI2020

CasCollect: targeted assembly of CRISPR-associated operons from high-throughput sequencing data

Abstract CRISPR arrays and CRISPR-associated (Cas) proteins comprise a widespread adaptive immune system in bacteria and archaea. These systems function as a defense against exogenous parasitic mobile genetic elements that include bacteriophages, plasmids and foreign nucleic acids. With the continuous spread of antibiotic resistance, knowledge of pathogen susceptibility to bacteriophage therapy is becoming more critical. Additionally, gene-editing applications would benefit from the discovery of new cas genes with favorable properties. While next-generation sequencing has produced staggering quantities of data, transitioning from raw sequencing reads to the identification of CRISPR/Cas systems has remained challenging. This is especially true for metagenomic data, which has the highest potential for identifying novel cas genes. We report a comprehensive computational pipeline, CasCollect, for the targeted assembly and annotation of cas genes and CRISPR arrays—even isolated arrays—from raw sequencing reads. Benchmarking our targeted assembly pipeline demonstrates significantly improved timing by almost two orders of magnitude compared with conventional assembly and annotation, while retaining the ability to detect CRISPR arrays and cas genes. CasCollect is a highly versatile pipeline and can be used for targeted assembly of any specialty gene set, reconfigurable for user provided Hidden Markov Models and/or reference nucleotide sequences.

Podlevsky, Joshua D.↗

DOE OSTI2020

Aligned FTICR-MS, Respiration, and Biochemical Transformation Data from Columbia River Sediment Incubations Associated with: “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism”

Columbia River hyporheic zone sediments from the Hanford Site 300 Area in eastern Washington were incubated in a full factorial design with different organic matter (OM) amendments. The aim of the experiment was to test the influence of OM thermodynamics on aerobic respiration across different organic carbon pools and concentrations. This dataset is comprised of (1) a csv with high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory), (2) a csv with sample IDs, replicates, amendments, dissolved oxygen (DO) measurements, and sampling time points, (3) a csv with sample IDs, replicates, respiration rates, FTICR summary metrics, and relative abundance of chemical classes; and (4) a csv with the number of chemical transformations in each sample replicate (microcosm). The data are associated with the publication “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism” published in ES&T Letters (Garayburu-Caruso et al., 2020). Please use the data package’s DOI to cite the data package. We ask that you acknowledge the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Subsurface Biogeochemical Research (SBR) program when using the data. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include data package authors as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Carbon Ore Resources Database (CORD)

The Carbon Ore Resources Database (CORD) is a working collection of 399 data files associated with carbon ore resources in the United States. The collection includes spatial/non-spatial, filtered, processed, and secondary data files with original data acquisition efforts focused on domestic coal resources. The current version 1.0 is on NETL ArcGIS Enterprise.

Carbon Ore↗

NASA NTRS1979

On the multiplicity of solutions of the inverse problem for a vibrating beam

The paper considers a specific inverse problem for a vibrating beam with three nonsympathetic spectra. More specifically, the spectral data consist of the natural frequencies of vibration of the beam in the following three configurations: (1) clamped-clamped, (2) clamped-supported, and (3) clamped-free. The basic equations for an inhomogeneous discrete beam are derived, where this simple mechanical system provides valuable insight into the inverse problem. All the necessary ingredients for a consideration of the inverse problem are given. Attention is given to an analysis of the particular inverse problem for which the spectral data are associated with the natural frequencies of vibrations in the above configurations. It is shown that with such data, the inverse problem has a 2exp(N-1)-fold multiplicity of solutions.

Barcilon, V.↗

NASA NTRS1987

Acquisition-Management Program

NASA Acquisition Management Subsystem (AMS) program integrated NASA-wide standard automated-procurement-system program developed in 1985. Designed to provide each NASA installation with procurement data-base concept with on-line terminals for managing, tracking, reporting, and controlling contractual actions and associated procurement data. Subsystem provides control, status, and reporting for various procurement areas. Purpose of standardization is to decrease costs of procurement and operation of automatic data processing; increases procurement productivity; furnishes accurate, on-line management information and improves customer support. Written in the ADABAS NATURAL.

Avery, Don E.↗

NASA NTRS1995

Visualization of International Solar-Terrestrial Physics Program (ISTP) data

The International Solar-Terrestrial Physics Program (ISTP) is a multispacecraft, multinational program whose objective is to promote further understanding of the Earth's complex plasma environment. Extensive data sharing and data analysis will be needed to ensure the success of the overall ISTP program. For this reason, there has been a special emphasis on data standards throughout ISTP. One of the key tools will be the common data format (CDF), developed, maintained, and evolved at the National Space Science Data Center (NSSDC), with the set of ISTP implementation guidelines specially designed for space physics data sets by the Space Physics Data Facility (associated with the NSSDC). The ISTP guidelines were developed to facilitate searching, plotting, merging, and subsetting of data sets. We focus here on the plotting application. A prototype software package was developed to plot key parameter (KP) data from the ISTP program at the Science Planning and Operations Facility (SPOF). The ISTP Key Parameter Visualization Tool is based on the Interactive Data Language (IDL) and is keyed to the ISTP guidelines, reading data stored in CDF. With the combination of CDF, the ISTP guidelines, and the visualization software, we can look forward to easier and more effective data sharing and use among ISTP scientists.

Kessel, Ramona L.↗

DOE OSTI2021

Quantitative Proteomic Analysis of the Senescence-Associated Secretory Phenotype by Data-Independent Acquisition

Cellular senescence is a complex stress response that induces an essentially permanent cell cycle arrest and a complex secretory phenotype termed the senescence-associated secretory phenotype (SASP), which drives numerous aging pathologies. Characterization of the SASP can provide insights into aging and disease mechanisms, aging biomarker candidates, and targets for counteracting the deleterious effects of senescent cells. Here we describe a mass spectrometry (MS)-compatible protocol to (1) generate senescent cells using different stimuli, (2) collect conditioned medium containing proteins secreted by senescent cells (i.e., SASP), and (3) prepare the SASP for quantitative proteomic analysis using data-independent acquisition (DIA) MS. Basic Protocol 1: Generating ionizing radiation-induced senescent and control cells Alternate Protocol 1: Generating doxorubicin-induced senescent and control cells Alternate Protocol 2: Generating oncogenic RAS-induced senescent and control cells Alternate Protocol 3: Generating mitochondrial dysfunction-induced senescent and control cells Alternate Protocol 4: Generating atazanavir/ritonavir-induced senescent and control cells Support Protocol: A multiple-assay approach to confirm the phenotype of senescent cells Basic Protocol 2: Generating conditioned medium from senescent cells cultured in low serum and quiescent control cells Alternate Protocol 5: Generating conditioned medium from senescent cells cultured in complete medium and quiescent control cells Basic Protocol 3: Quantitative proteomic analysis of the SASP.

60 APPLIED LIFE SCIENCES↗