SEARCH · Search NASA

Results for “linked data”

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 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2018

The Second Data Release of the Beijing–Arizona Sky Survey

This paper describes the second data release (DR2) of the Beijing-Arizona Sky Survey (BASS). BASS is an imaging survey covering a 5400 deg 2 footprint in the g and r bands using the 2.3 m Bok telescope. DR2 includes the observations through 2017 July obtained by BASS and by the Mayall z-band Legacy Survey (MzLS), which used the 4 m Mayall telescope to observe the same footprint. BASS and MzLS have completed 72% and 76% of their observations. The two surveys will be served for the spectroscopic targeting of the upcoming Dark Energy Spectroscopic Instrument. Both BASS and MzLS data are reduced by the same pipeline. We have updated the basic data reduction and photometric calibrations in DR2. In particular, source detections are performed on stacked images, and photometric measurements are co-added from single-epoch images based on these sources. The median 5σ point-source depths after Galactic extinction corrections are 24.05, 23.61, and 23.10 mag for the g, r, and z bands, respectively. The DR2 data products include stacked images, co-added catalogs, and single-epoch images and catalogs. The BASS website (http://batc.bao.ac.cn/BASS/) provides detailed information and links to download the data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2.

More information: http://daymet.ornl.gov Presenter: Ranjeet Devarakonda Environmental Sciences Division Oak Ridge National Laboratory (ORNL) Daymet: Daily Surface Weather Data and Climatological Summaries provides gridded estimates of daily weather parameters for North America, including daily continuous surfaces of minimum and maximum temperature, precipitation occurrence and amount, humidity, shortwave radiation, snow water equivalent, and day length. The current data product (Version 2) covers the period January 1, 1980 to December 31, 2013 [1]. The prior product (Version 1) only covered from 1980-2008. Data are available on a daily time step at a 1-km x 1-km spatial resolution in Lambert Conformal Conic projection with a spatial extent that covers the conterminous United States, Mexico, and Southern Canada as meteorological station density allows. Daymet data can be downloaded from 1) the ORNL Distributed Active Archive Center (DAAC) search and order tools (http://daac.ornl.gov/cgi-bin/cart/add2cart.pl?add=1219) or directly from the DAAC FTP site (http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1219) and 2) the Single Pixel Tool [2] and THREDDS (Thematic Real-time Environmental Data Services) Data Server [3]. The Single Pixel Data Extraction Tool allows users to enter a single geographic point by latitude and longitude in decimal degrees. A routine is executed that translates the (lon, lat) coordinates into projected Daymet (x,y) coordinates. These coordinates are used to access the Daymet database of daily-interpolated surface weather variables. Daily data from the nearest 1 km x 1 km Daymet grid cell are extracted from the database and formatted as a table with one column for each Daymet variable and one row for each day. All daily data for selected years are returned as a single (long) table, formatted for display in the browser window. At the top of this table is a link to the same data in a simple comma-separated text format, suitable for import into a spreadsheet or other data analysis software. The Single Pixel Data Extraction Tool also provides the option to download multiple coordinates programmatically. A multiple extractor script is freely available to download at http://daymet.ornl.gov/files/daymet.zip. The ORNL DAAC s THREDDS data server (TDS) provides customized visualization and access to Daymet time series of North American mosaics. Users can subset and download Daymet data via a variety of community standards, including OPeNDAP, NetCDF Subset service, and Open Geospatial Consortium (OGC) Web Map/Coverage Service. The ORNL DAAC TDS also exposes Daymet metadata through its ncISO service to facilitate harvesting Daymet metadata records into 3rd party catalogs. References: [1] Thornton, P.E., M.M. Thornton, B.W. Mayer, N. Wilhelmi, Y. Wei, R. Devarakonda, and R.B. Cook. 2014. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. [2] Devarakonda R., et al. 2012. Daymet: Single Pixel Data Extraction Tool. Available on-line [http://daymet.ornl.go/singlepixel.html]. [3] Wei Y., et al. 2014. Daymet: Thematic Real-time Environmental Data Services. Available on-line [http://daymet.ornl.gov/thredds_tiles.html].

Thornton, Peter E↗

DOE OSTI2018

Downregulation of p- COUMAROYL ESTER 3-HYDROXYLASE in rice leads to altered cell wall structures and improves biomass saccharification

p-Coumaroyl ester 3-hydroxylase (C3'H) is a key enzyme involved in the biosynthesis of lignin, a phenylpropanoid polymer that is the major constituent in vascular plants’ secondary cell walls. Although the crucial role of C3'H in lignification and its manipulation to upgrade lignocellulose have been investigated in eudicots, limited information is available in monocotyledonous grass species despite their potential as biomass feedstocks. Here in this paper we address the pronounced impacts of C3'H-deficiency on the structure and properties of grass cell walls. C3'H-knockdown lines generated via RNAi-mediated gene silencing, with ~0.5% of residual expression levels, reached maturity and set seeds, whereas, in contrast, C3'H-knockout rice mutants generated via CRISPR/Cas9-mediated mutagenesis were severely dwarfed and sterile. Cell wall analysis of the mature C3'H-knockdown RNAi lines revealed that their lignins were largely enriched in p-hydroxyphenyl (H) units while substantially reduced in the normally dominant guaiacyl (G) and syringyl (S) units. Interestingly, however, the enrichment of H units was limited to being within the non-acylated lignin units, with grass-specific γ-p-coumaroylated lignin units remaining apparently unchanged. Suppression of C3'H also resulted in relative augmentation in tricin residues in lignin as well as substantial reduction in wall cross-linking ferulates. Collectively, our data demonstrate that C3'H expression is an important determinant, not only of lignin content and composition, but also of the degree of cell wall cross-linking. We also demonstrated that C3'H-suppressed rice displays enhanced biomass saccharification.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Driver Identification Dataset

The ORNL Driver Identification Dataset was created to collect and analyze driving behavior data from 50 different drivers. Each driver operated a 2014 Kenworth T270 Class 6 truck around Fort Collins, Colorado while various data sources recorded their driving behavior and vehicle performance. The dataset includes CANbus (Controller Area Network) data, GPS data, inertial measurement data, and biometric data from a heart rate monitor. A cyberattack was executed during each drive, which caused multiple dashboard warning lights to illuminate and set the tachometer and speedometer to zero, regardless of actual speed. The attack was stopped either after one minute or if the driver pulled over. By downloading the dataset, you agree to the following: 1) I will not use or disclose the data for any purpose other than Research as that term is defined in 10 CFR 745.102. 2) I will not, under any circumstances, request or accept private or linking identifiers for the data used. 3) I will not attempt to determine the identity of the individuals associated with the data. 4) I will use appropriate safeguards to prevent the use or disclose of the data for any purpose other than Research.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Summary of Methodology for Mitigating Risks Associated with Licensing and Qualifying AM Nuclear Materials

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program focuses on accelerating the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Laser powder bed fusion (LPBF) is one of the most popular additive manufacturing (AM) processes for fabricating components with intrinsically complex geometries. LPBF was extensively explored for nuclear applications under the previous Transformational Challenge Reactor program. Additionally, Oak Ridge National Laboratory developed and licensed the Peregrine software and larger digital platform that couples machine learning and in situ data collection during AM to detect anomalies and any evolved defects. The digital platform will be critical to (1) the qualification of AM components for nuclear applications that link location-specific data to macroscopic properties and (2) predict final component performance. Current in situ process monitoring tools are valuable for observing the formation of stochastic flaws, but additional data are needed to predict the resulting microstructures and associated material performance. Rapid cooling rates and large thermal gradients have caused large heterogeneities in the microstructure, which cause anisotropy in mechanical performance. The AMMT program is evaluating the best approaches for addressing these heterogeneities and their effect on component performance using a combination of multiscale modeling, enhanced in situ process monitoring, and high throughput experimental testing. This report summarizes strategies for mitigating the risks associated with qualifying AM components, including developing new sensing capabilities for in situ process monitoring and characterizing melt pool solidification and residual stresses to inform multiscale modeling efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Using Xrootd to federate regional storage

While the LHC data movement systems have demonstrated the ability to move data at the necessary throughput, we have identified two weaknesses: the latency for physicists to access data and the complexity of the tools involved. To address these, both ATLAS and CMS have begun to federate regional storage systems using Xrootd. Xrootd, referring to a protocol and implementation, allows us to provide data access to all disk-resident data from a single virtual endpoint. This redirector discovers the actual location of the data and redirects the client to the appropriate site. The approach is particularly advantageous since typically the redirection requires much less than 500 milliseconds and the Xrootd client is conveniently built into LHC physicists analysis tools. Currently, there are three regional storage federations - a US ATLAS region, a European CMS region, and a US CMS region. The US ATLAS and US CMS regions include their respective Tier 1, Tier 2 and some Tier 3 facilities, a large percentage of experimental data is available via the federation. Additionally, US ATLAS has begun studying low-latency regional federations of close-by sites. From the base idea of federating storage behind an endpoint, the implementations and use cases diverge. The CMS software framework is capable of efficiently processing data over high-latency links, so using the remote site directly is comparable to accessing local data. The ATLAS processing model allows a broad spectrum of user applications with varying degrees of performance with regard to latency, a particular focus has been optimizing n-tuple analysis. Both VOs use GSI security. ATLAS has developed a mapping of VOMS roles to specific file system authorizations, while CMS has developed callouts to the site's mapping service. Each federation presents a global namespace to users. For ATLAS, the global-to-local mapping is based on a heuristic-based lookup from the site's local file catalog, while CMS does the mapping based on translations given in a configuration file. We will also cover the latest usage statistics and interesting use cases that have developed over the previous 18 months.

Bauerdick, L.↗

DOE OSTI2018

Global Oil & Gas Features Database

This submission contains a zip file with the developed Global Oil & Gas Features Database (as an ArcGIS geodatabase). Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase This data can be viewed as a web map application at the following link: https://edx.netl.doe.gov/dataset/global-oil-gas-infrastructure-features-database-edx-spatial-webmap Acknowledgements: This work was performed under a CRADA between NETL and EDF, and was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

Database↗

DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗

DOE OSTI2015

Brady Geothermal Field InSAR Raw Data

List of TerraSAR-X/TanDEM-X images acquired between 2015-01-01 and 2015-03-31, and archived at https://winsar.unavco.org. See file "BHS InSAR Data with URLs.csv" for individual links. NOTE: The user must create an account in order to access the data (See "Instructions for Creating an Account" below).

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

WHOLESCALE: Seismic Survey Data from San Emidio Nevada 2021

This dataset includes raw and processed seismic data from the 2021 seismic survey at the San Emidio geothermal field in Nevada. In April and May 2021, 37 tri-axial short period seismographs were deployed in a 1.8km diameter cluster centered on 40.367278 N, 119.409019 W. The first data record started at 2021-04-06T07:09:10Z UTC and the last record ended at 2021-05-11T02:58:52Z UTC. The 37 stations included 29 SmartSolo IGU-16HR 3C all-in-one 5 Hz seismographs and 8 DataCube seismographs with 4.5 Hz HGS HG-6(B coil) tri-axial geophones. The raw format (level 0) data includes 353 GB of 500 sps SmartSolo data in native DLD format, 113 GB of 400 sps DataCube data in native DataCube format, and 3.4 GB of GPS data collected during the RTK GPS survey. The SAC data (level 1) files include 564 GB of hourly SAC files. The experiment was designed to monitor seismic activity before, during, and after the planned three-day plant maintenance shutdown April 19-21, 2021. The pumping stopped at 2021/04/19 12:51:45 UTC and resumed about 2021/04/21 21:00:00 UTC. The dataset is hosted in an AWS data lake, with an associated GDR data set providing the metadata. Links to both of these resources are included below. Additionally, this collection features data and metadata from a 2016 seismic survey at the same site for comparative analysis.

15 GEOTHERMAL ENERGY↗

DOE OSTI2011

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

Background: Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results: This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multirelational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions: We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

National Cancer Institute (NCI) Exposomic Linkage Protocol

The purpose of this work is to create point-level linkages of residential history data, which is provided by the Surveillance, Epidemiology, and End Results (SEER) program, to air pollution exposure data so that we can develop longitudinal measures of exposure and investigate their effects on cancer incidence, treatment response, and survival. The Louisiana, New Jersey, Kentucky, and Iowa registries were previously linked to LexisNexis residential history data through the National Cancer Institute (NCI). We will enhance the utility of the existing residential location data by geocoding addresses based on data from between 1995 and 2024 and spatially linking the locations to air pollution, indoor radon, and the US Environmental Protection Agency’s (EPA) Risk-Screening Environmental Indicators (RSEI) exposure data (Figure 1).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2017

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system that allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

CMS ECAL Upgrade Front End card: design and prototype test results

The CMS ECAL Phase 2 Front-End (FE) card is designed to provide streaming of digitized datagenerated on the Very-Frond-End (VFE) cards to the CMS ECAL back-end electronics system based onthe Barrel Calorimeter Processor (BCP) ATCA modules. The FE card is intended to utilize data linksdeveloped within the Versatile Link common project. The card will contain four lpGBT ASICS withcorresponding versatile link plus optical link modules.The first prototype FE card was developed to validate the clock distribution, the high speed serial data linksas well as other specific technical features of the future design using the currently available GBT chipsetand versatile link components

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Gaining Insights in Loading Events for Wind Turbine Drivetrain Prognostics

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (WEC). This can cause the bearing lifetime to be reduced to 5–10% of its design value. Multiple hypotheses are available in literature concerning its cause. To be able to validate or disprove these hypotheses, it is essential to have historic high-frequency measurement data (e.g., load and vibration levels) available. In time, this will allow linking to the history of the turbine operating data with failure data. This paper discusses the dynamic loading on the turbine during certain events (e.g., emergency stops, run-ups, and during normal operating conditions). By combining the number of specific events that each turbine has seen with the severity of each event, it becomes possible to assess which turbines are most likely to show signs of damage.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2019

A data acquisition system for HV-CMOS sensor research and development in the upgrade of ATLAS experiment

In the High Luminosity LHC (HL-LHC) upgrade of ATLAS experiment in 2026, the new inner tracker detector will be installed. As a promising candidate for the outer layer of the inner tracker detector, the High Voltage CMOS (HV-CMOS) pixel sensors are being investigated by the ATLAS collaboration. A Front-End LInk eXchange (FELIX) based Data Acquisition (DAQ) system is developed for the R&D of the HV-CMOS sensors. A FELIX card with PCIe interface in the back-end is used to control, monitor, calibrate and read out the front-end electronics. In the front-end, a Xilinx evaluation board ZC706 works as the interface between the back- end and custom boards with HV-CMOS sensors and the readout ASIC. In conclusion, the DAQ system was successfully deployed for the testbeam experiment at CERN. Design of the DAQ system, the tuning of sensors and testbeam results will be described in details.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2011

ULTRA SECURE HIGH RELIABILITY WIRELESS RADIATION MONITOR

Radiation monitoring in nuclear facilities is essential to safe operation of the equipment as well as protecting personnel. In specific, typical air monitoring of radioactive gases or particulate involves complex systems of valves, pumps, piping and electronics. The challenge is to measure a representative sample in areas that are radioactively contaminated. Running cables and piping to these locations is very expensive due to the containment requirements. Penetration into and out of an airborne or containment area is complex and costly. The process rooms are built with thick rebar-enforced concrete walls with glove box containment chambers inside. Figure 1 shows high temperature radiation resistance cabling entering the top of a typical glove box. In some case, the entire processing area must be contained in a 'hot cell' where the only access into the chamber is via manipulators. An example is shown in Figure 2. A short range wireless network provides an ideal communication link for transmitting the data from the radiation sensor to a 'clean area', or area absent of any radiation fields or radioactive contamination. Radiation monitoring systems that protect personnel and equipment must meet stringent codes and standards due to the consequences of failure. At first glance a wired system would seem more desirable. Concerns with wireless communication include latency, jamming, spoofing, man in the middle attacks, and hacking. The Department of Energy's Savannah River National Laboratory (SRNL) has developed a prototype wireless radiation air monitoring system that address many of the concerns with wireless and allows quick deployment in radiation and contamination areas. It is stand alone and only requires a standard 120 VAC, 60 Hz power source. It is designed to be mounted or portable. The wireless link uses a National Security Agency (NSA) Suite B compliant wireless network from Fortress Technologies that is considered robust enough to be used for classified data transmission in place of NSA Type 1 devices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗