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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.
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In this paper we present how research projects at Oak Ridge National Laboratory are using Semantic Search capabilities to help scientists perform their research. We will discuss how the Mercury metadata search system, with the help of the semantic search capability, is being used to find, retrieve, and link climate change data. DOI: 10.1109/CTS.2014.6867639
Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through December 2020.
Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through November 2021.
Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through June 2022.
Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC ABSTRACT = The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Ma-chine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.
This submission provides reports on the results of seismic data analyses, statistical data sets of rock properties under the DVGWF, GIS data for new maps of EGS favorability, and a link to raw seismogram data archived by the IRIS-DMS.
Methodology that interpolates low time-resolution data (e.g., hourly) to high time-resolution (e.g., minutely) with variability patterns extracted from historical records. Magnitude of the variability inserted into the low time resolution data can be adjusted according to the installed capacity represented by the low time-resolution data compared to that by historical records. This approach enables detailed analysis of the impacts from wind and solar on power system intra-hour operations and balancing reserve requirements even with only hourly data. It also allows convenient creation of high resolution wind or solar generation data with various degree of variability to investigate their operational impacts. The methodology comprises of the following steps: 1. Smooth the historical data (set A) with an appropriate window length l to get its trend (set B); l can be a fraction of an hour (e.g., 15 minutes) or longer than an hour, of which the length of the variability patterns will be; 2. Extract the variable component (set C) of historical data by subtracting the smooth trend from it, i.e. set C = set A - set B 3. For each window length l of the variable component data set, find the average value x (will call it base component) of the corresponding window of the historical data set; 4. Define a series of segments (set D) that the values of data will be grouped into, e.g. (0, 0.1), (0.1, 0.2), ..., (0.9, 1.0) after normalization; Link each variability pattern to a data segment based on its corresponding base component x; after this step, each data segment should be linked to multiple variability patterns after this step; 5. Use spline function to interpolate the low time-resolution forecast data (set E) to become a high time-resolution smooth curve (set F); 6. Based on the window length l , calculate the average value y in each window length of set F; find the data segment that y belongs to; then randomly select one of the variability patterns linked to this data segment; 7. Add the selected variability pattern in each window to set F to get the high time-resolution data (set G) with variability observed from historical data set A; a coefficient k can be multiplied to the selected variability pattern to reflect the potential difference in variability between forecast set E and historical data set A caused by installation capacity and other factors. 8. After set G is constructed, variability statistics can be checked for set A and set G for comparison. Adjust the coefficient k if necessary to achieve desired variability in the reconstructed data.
The Data Processing Pipeline ('Pipeline') has been developed for the Gamma-Ray Large Area Space Telescope (GLAST) which launched June 11, 2008. It generically processes graphs of dependent tasks, maintaining a full record of its state, history and data products. The Pipeline is used to automatically process the data down-linked from the satellite and to deliver science products to the GLAST collaboration and the Science Support Center and has been in continuous use since launch with great success. The pipeline handles up to 2000 concurrent jobs and in reconstructing science data produces approximately 750GB of data products using 1/2 CPU-year of processing time per day.
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Confident identification of unknown chemicals in high resolution mass spectrometry (HRMS) screening studies requires cohesive workflows and complementary data, tools, and software. Chemistry databases, screening libraries, and chemical metadata have become fixtures in identification workflows. To increase confidence in compound identifications, the use of structural fragmentation data collected via tandem mass spectrometry (MS/MS or MS2) is vital. However, the availability of empirically collected MS/MS data for identification of unknowns is limited. Researchers have therefore turned to in silico generation of MS/MS data for use in HRMS-based screening studies. This paper describes the generation en masse of predicted MS/MS spectra for the entirety of the US EPA’s DSSTox database using competitive fragmentation modelling and a freely available open source tool, CFM-ID. The generated dataset comprises predicted MS/MS spectra for ~700,000 structures, and mappings between predicted spectra, structures, associated substances, and chemical metadata. Together, these resources facilitate improved compound identifications in HRMS screening studies. These data are accessible via an SQL database, a comma-separated export file (.csv), and EPA’s CompTox Chemicals Dashboard.
The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and welldefined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.
Development of a hierarchical Bayesian model to estimate the spatiotemporal distribution of aqueous geochemical parameters associated with in-situ bioremediation using surface spectral induced polarization (SIP) data and borehole geochemical measurements collected during a bioremediation experiment at a uranium-contaminated site near Rifle, Colorado. The SIP data are first inverted for Cole-Cole parameters including chargeability, time constant, resistivity at the DC frequency and dependence factor, at each pixel of two-dimensional grids using a previously developed stochastic method. Correlations between the inverted Cole-Cole parameters and the wellbore-based groundwater chemistry measurements indicative of key metabolic processes within the aquifer (e.g. ferrous iron, sulfate, uranium) were established and used as a basis for petrophysical model development. The developed Bayesian model consists of three levels of statistical sub-models: 1) data model, providing links between geochemical and geophysical attributes, 2) process model, describing the spatial and temporal variability of geochemical properties in the subsurface system, and 3) parameter model, describing prior distributions of various parameters and initial conditions. The unknown parameters are estimated using Markov chain Monte Carlo methods. By combining the temporally distributed geochemical data with the spatially distributed geophysical data, we obtain the spatio-temporal distribution of ferrous iron, sulfate and sulfide, and their associated uncertainity information. The obtained results can be used to assess the efficacy of the bioremediation treatment over space and time and to constrain reactive transport models.
Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. Here, we describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.
We report the first O-band link with electrically reconfigurable intensity-modulation direct-detection (IMDD) and coherent operation using custom silicon photonic chips packaged with commercial electronic chips. Transmission below the KP4-FEC threshold is shown using commercial 53 Gbaud PAM4 digital signal processing (DSP) for 16QAM (200 Gbps/ λ ) and PAM4 (100 Gbps/ λ ). Efficient operation of the packaged full link at 12 and 11.2 pJ/bit is achieved for the PAM4 and 16QAM modes, respectively.
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.
The International Arctic Systems for Observing the Atmosphere (IASOA) was initiated as an International Polar Year (IPY) project (Uttal et al. 2015, on-line release) to address key atmospheric science questions through coordinating the considerable atmospheric observing assets at 10 pan-arctic observatories (http://iasoa.org). The mission of IASOA is to advance cross-site research objectives from independent arctic atmospheric observatories through: (1) strategically developing comprehensive observational capacity; (2) facilitating data access and usability through a single gateway; and (3) mobilizing contributions to synergistic science and socially relevant services derived from IASOA assets and expertise. In April 2015 the IASOA science steering committee invited the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s Oliktok Point observatory to contribute to consortium activities. The IASOA steering committee identified enhanced, cross-site data sharing through an IASOA-specific data access portal as a focus for ongoing consortium activities. The design intent of the portal has been to display the data inventories of all IASOA observatories in a succinct summary (Data-at-a-Glance) and provide the most proximal access links available to those data sets. The IASOA data access portal is not intended to replace the functions of existing archives. This succinct summary of data availability supports cross-site and interdisciplinary synthesis science activities. Efforts to enhance the legacy IASOA data access portal functionality have focused on two areas: developing a comprehensive and coherent data vocabulary for IASOA data and implementing an interoperable metadata standard for all IASOA data. The technical goal of this proposal is to work with ARM data managers to operationalize the export of metadata for observational data sets for Oliktok Point from the ARM metadata database. IASOA harvests data in the commonly used International Organization for Standardization (ISO) format. ARM has successfully generated metatdata matching this format in the past to provide metadata from its North Slope of Alaska (NSA) Barrow observatory.
This data set provides links to the results of metagenomic analyses of 44 peat samples collected on 13 June 2016 from SPRUCE experiment treatment and ambient plots. Experimental plots had received approximately 24 months of belowground warming (deep peat heating (DPH), Hanson et al. 2015) with the last 9 of those months including air warming for implementation of whole ecosystems warming (WEW – Hanson et al. 2016). The SPRUCE experiment site is located in an ombrotrophic bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). WEW Metagenomes: Data from these metagenomes are archived in the U.S. Department of Energy Joint Genome Institute (DOE JGI) Integrated Microbial Genomes (IMG) system and are available at the accession numbers provided below (Table 2) and in the accompanying inventory file. The easiest way to find results on IMG is at this link, https://img.jgi.doe.gov/cgi-bin/m/main.cgi, and then enter “June2016WEW” as a search term in the “Quick Genome Search:” box at the top of the page. This work was done in collaboration between ORNL, USFS and the DOE Joint Genome Institute (JGI) study that is targeting understanding the microbial metagenomes of “fungal, bacterial, and archaeal communities mediating C cycling and trace gas flux in peatland ecosystems subject to climate change” (Proposal ID: 1445).
This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2020. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top. The revision dataset included projects with a total installed capacity in 2020 under the state-level program but missing the project level details.