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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling

Residential and commercial buildings in the United States accounted for 40% of total energy in 2020. Building energy modeling (BEM) is a useful tool that allows individuals, researchers, companies, or utilities to save energy by optimizing buildings through estimation of building technology savings and performance projection of building energy under various environmental conditions. Urban building energy modeling (UBEM) expands the scope beyond individual buildings to the buildings in a neighborhood, city, utility and more. Yet there is a knowledge gap in the literature as to how these models compare to measured data on an individual and aggregated basis. As UBEM data and methods continue to develop, it is important to consider the accuracy, bias, and limitations of the models. Here, nation-scale data and UBEM software suite named Automatic Building Energy Modeling (AutoBEM) was used to model 50,843 buildings in Chattanooga, Tennessee. The uncalibrated simulation results were compared to aggregated 15-minute electricity data for the year 2019 with visualizations highlighting sources of bias in building data and the AutoBEM framework while considering how they relate to other UBEM methods. Estimation of building type and year of constructions are found to be the major sources of bias. Accounting for the amount of conditioned area per building significantly improves the overall fit of the simulated energy use intensity. it was found that inherent variation in building energy use contributes to R 2 values between 0.008 and 0.095 across building types but slope values near 1 for the total number of buildings. This indicates the need for building aggregation for representative building energy modeling with data sources available at an urban scale while illustrating the need for additional individual building data and model improvement beyond the originally produced UBEM models for individual building analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

COVID-19 pandemic reveals the peril of ignoring metadata standards

Efficient response to the pandemic through the mobilization of the larger scientific community is challenged by the limited reusability of the available primary genomic data. Here, the Genomic Standards Consortium board highlights the essential need for contextual genomic data FAIRness, for empowering key data-driven biological questions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Automated Indexing of Structured Scientific Metadata Using Apache Solr

Scientific datasets are continuously growing with the amount of raw data being collected worldwide. This amount of data poses the biggest challenge to web search engines on how to retrieve them efficiently. This paper discusses how major scientific data centers are using popular open-source search platforms such as Solr [1] to retrieve structured data stored in data sources such as relational database management systems using its import handler mechanisms [2]. Additionally, we will also focus on how we can configure Solr to serve advanced full-text, faceted search capabilities, along with its key features, which simplify representing and delivering better performance to the scientific search interfaces.

Guntupally, Kavya↗
DOE OSTI2016

Brady's Geothermal Field - Metadata for InSAR Holdings

List of synthetic aperture radar (SAR) images acquired by TerraSAR-X and TanDEM-X satellite missions and archived at UNAVCO's WINSAR facility. See file "Bradys TSX Holdings.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 OSTI2017

Marine Corps Base Camp Lejeune AEM-1 Hydrosphere Deployment Metadata

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.Data were collected to characterize the travel time of water and solutes through the tidal freshwater reach of the New River. Travel time of particles and solutes is fundamental for estimating their exposure to reaction sites where biogeochemical processes occur. Travel time provides the time scale for all subsequent calculations of biogeochemical process rates that alter constituent loads through this reach. This metric provides a convenient control volume across which to extrapolate process rates of denitrification and photomineralization of DOC. Analysis of this dataset is provided in Appendix D of Chapter 4 (AEM-4) in the DCERP2 Final Monitoring Report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Raw/Translated data and metadata from sensor and leaf sample measurements at Manaus, Brazil, 24 May 2016 -16 March 2017

Raw and processed data from K34 tower site on a plateau near Manaus, Brazil including automated and manually collected time series data on leaf surface temperature (thermocouples, IR radiometer, thermocamera), leaf gas exchange fluxes of CO2, isoprene emissions, and H2O, sap flow, leaf water potential, soil moisture, and ambient concentrations of volatile organic compounds. IR radiometer data from the B34 tower in the nearby valley is also included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Historic Source Inspection Metadata

Although this data dictionary describes the raw data format, it is relevant only for data entered in 2017 and later. Entries in this date range represent formatted, standardized data that have been verified against original, raw data files by the database manager. She took ownership of the database in September 2022 and was able to standardize entries by comparing against raw data files back to 2017. Furthermore, for comparison of historic pre-source inspection data to new pre-source data supplied by the vendor and intended for action limit analysis purposes, this time period is of sufficient length.

97 MATHEMATICS AND COMPUTING↗