Search NASA⌕ Search

SEARCH · Search NASA

Results for “Metadata”

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.

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

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

KBase Credit Metadata Schema

As part of KBase’s commitment to promote open science, we offer users the ability to obtain a DOI (Digital Object Identifier) for their work, which can then be cited in an associated science publication. To further support the community-wide shift towards FAIR (Findable, Accessible, Interoperable, Reusable) data, KBase is expanding our data descriptors so that KBase DOIs have comprehensive citations for datasets, in addition to referencing publications or software used in the workflow. This helps encourage a culture of giving attribution for all research inputs and outputs; standard practice for literature, but still relatively new for software products or datasets. It also promotes open science by building trust that contributors get credit for their work, and accelerates knowledge discovery by supporting and incentivizing the release of data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Using electronic health record metadata to predict housing instability amongst veterans

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Seismic Survey 2016 Metadata at San Emidio, Nevada

1301 Vertical Component seismic instruments were deployed at San Emidio Geothermal field in Nevada in December 2016. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in one-minute increments. Data includes seismic station locations, seismic data logger specifications, instrumentation specifications, descriptions of data, a fracture finding summary and the final report for the 2016 WHOLESCALE seismic survey done in San Emidio, Nevada.

15 GEOTHERMAL ENERGY↗
NASA NTRS2015

Restoration of the Apollo Heat Flow Experiments Metadata

Geothermal heat flow probes were deployed on the Apollo 15 and 17 missions as part of the Apollo Lunar Surface Experiments Package (ALSEP). At each landing site, the astronauts drilled 2 holes, 10-m apart, and installed a probe in each. The holes were 1- and 1.5-m deep at the Apollo 15 site and 2.5-m deep at the Apollo 17 sites. The probes monitored surface temperature and subsurface temperatures at different depths. At the Apollo 15 site, the monitoring continued from July 1971 to January 1977. At the Apollo 17 site, it did from December 1972 to September 1977. Based on the observations made through December 1974, Marcus Langseth, the principal investigator of the heat flow experiments (HFE), determined the thermal conductivity of the lunar regolith by mathematically modeling how the seasonal temperature fluctuation propagated down through the regolith. He also determined the temperature unaffected by diurnal and seasonal thermal waves of the regolith at different depths, which yielded the geothermal gradient. By multiplying the thermal gradient and the thermal conductivity, Langseth obtained the endogenic heat flow of the Moon as 21 mW/m(exp 2) at Site 15 and 16 mW/m(exp 2) at Site 17.

HFE metadata↗
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↗