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Computer program is written to display Nimbus high resolution infrared radiometer data in optimum form for experiment usage. Program produces three maps for Nimbus experimental data. Functions of program are to process data and prepare print maps from modified temperature measurements.
This viewgraph presentation gives a detailed description of the avionics associated with the Space Shuttle's data processing system and its usage of z/OS. The contents include: 1) Mission, Products, and Customers; 2) Facility Overview; 3) Shuttle Data Processing System; 4) Languages and Compilers; 5) Application Tools; 6) Shuttle Flight Software Simulator; 7) Software Development and Build Tools; and 8) Fun Facts and Acronyms.
An approximately 2% sampling error was achieved in LACIE by sampling only approximately 2% of the sampling frame. The sample design in the yardstick region for which historical data were available down to a substratum level to support missing data resulting from cloud cover provided the most accurate estimate possible. The implemented strategy provided data of sufficient quality and quantity to support required performance levels and also to satisfy the existing constraints. The allocation scheme appeared to provide the most efficient usage of the available data and gave segment coverage of major producing areas and thus improved the probability of an accurate estimate.
IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830
Determining the appropriate material data is often a bottleneck for performing calculations/simulations of industrial/experimental processes and resulting material structures and properties. Beyond the time it takes to find the appropriate values in the literature, many judgement calls are involved in choosing the values. These judgement calls can lead to inconsistencies between steps in research workflow, where different material parameter values are used. Mist solves this problem by providing a mechanism to store, share, and use material information in convenient human-readable and machine-readable formats. Mist has an extensible ontology for defining a wide variety of material information, currently focused on metal alloy applications. Examples include: alloy composition, density, liquidus temperature, and the coefficient of thermal expansion. Mist converts between standardized machine-readable data formats (e.g. JSON), specialized input format for simulation tools, and human-readable documents (e.g. LaTeX, Markdown). For parameters defined by an equation (e.g. a polynomial function) or a list of tabulated values, Mist can evaluate parameter values at requested conditions. Mist also provides an API for direct usage of the Mist data structures in calculations, if supported.
This paper updates prior research on aircraft leasing and contrasts the findings of current data with prior results. Usage of leases by air carriers is a means to lessen the impact of financial obligations from fleet purchases. The study revisits two previous studies, one in 1969 and one in 1991, which is analyzed the incidence of leases by major air carriers. The current study updates these past studies to consider air carriers current usage of leases. Additionally, since operating leases are not reflected in the balance sheets of airlines, operating lease information was capitalized using a present value of future operating lease payments. Then, financial debt burden ratios were computed to determine the impact from the capitalization of lease information. The usage of operating leases increased, significantly from the first study to the 1991 study, and this trend continues. The incidence of leasing, the classification of leases as operating, and the percentage of operating leases to total fleet have all increased for the majority of the airlines reviewed. When operating lease data were capitalized, debt ratios weakened, providing further evidence of deterioration in the financial health of air carriers.
Scientific datasets are increasingly cited in peer-reviewed journal publications, facilitating easy access to research utilizing those datasets. Datasets undergo a life cycle where older versions of datasets are replaced by newer versions often due to improvements in data resolution, algorithms, and other factors. Unlike peer reviewed documents registered with a single Digital Unique Identifier (DOI), datasets can be updated over time and the newer version of the datasets are registered with a new DOI which is not necessarily linked to the previous version of the dataset. It is challenging when publications citing a dataset need to be traced over the entire life cycle of that dataset. We provide an innovative approach to link the dataset versions and publications using a knowledge graph (KG). KG can help to trace the dataset cited in publications over the entire dataset life cycle and shed light into dataset usage in various applied research areas. We fine-tuned the pretrained NASA IMPACTINDUS Large Language Model (LLM) on a set of labeled publications abstracts. Our results showed that 87% of the publications were classified into one of twenty applied research areas, while the remaining 13% were classified into non-applied research areas. By linking datasets to applied research areas through the KG and employing Global Change Master Directory(GCMD), a well-established controlled vocabulary of scientific keywords describing Earth science datasets, we contribute to a transparent and advanced search and discovery mechanism for datasets across the Earth data ecosystem. The integrated KG and LLM approach is now incorporated and operational in dataset publication management at one of NASA’s Earth science data archival centers.
Giovanni, the NASA Goddard online visualization and analysis tool (http://giovanni.gsfc.nasa.gov) allows users explore various atmospheric phenomena without learning remote sensing data formats and downloading voluminous data. Using NASA MODIS (Terra and Aqua) and ESA MERIS (ENVISAT) aerosol data as an example, we demonstrate Giovanni usage for online multi-sensor remote sensing data comparison and analysis.
The report spans the three year period beginning in June of 2001 and ending June of 2004. Joint Interdisciplinary Earth Science Information Center's (JIESIC) primary purpose has been to carry out research in support of the Global Change Data Center and other Earth science laboratories at Goddard involved in Earth science, remote sensing and applications data and information services. The purpose is to extend the usage of NASA Earth Observing System data, microwave data and other Earth observing data. JIESIC projects fall within the following categories: research and development; STW and WW prototyping; science data, information products and services; and science algorithm support. JIESIC facilitates extending the utility of NASA's Earth System Enterprise (ESE) data, information products and services to better meet the science data and information needs of a number of science and applications user communities, including domain users such as discipline Earth scientists, interdisciplinary Earth scientists, Earth science applications users and educators.
Data and analyses are presented on the utilization of aircraft fuel in the U.S. and the resulting greenhouse gas emissions. Commercial passenger and freight flight data and airport fuel consumption usage from 2016-2021 are captured from the Bureau of Transportation Statistics website and the Sherlock Data Warehouse managed at NASA Ames Research Center. The resulting dataset is used to determine the miles flown by major aircraft. The corresponding aircraft fuel burn is estimated based on the International Civilian Aviation Organization fuel burn tables, and carbon dioxide emissions are calculated using a fuel-burn multiplicative factor. One conclusion of this analysis is that long-haul flights (flight distances > 2485 statute miles) create a disproportionately large amount of carbon dioxide emissions in the U.S, while short flights (< 311 statute miles) contribute less than five percent of the U.S. aviation-related carbon dioxide emissions. Although these short-haul flights may not have a large impact on overall carbon dioxide emissions, they will be valuable as demonstration missions for the next generation of electric, hybrid, and hydrogen-powered vehicles and their supporting energy infrastructures. This paper discusses recent trends in short-haul missions, their associated aircraft and airport types, and extracts several key requirements for future short-haul vehicles.
Data and analyses are presented on the utilization of aircraft fuel in the U.S. and the resulting greenhouse gas emissions. Commercial passenger and freight flight data and airport fuel consumption usage from 2016-2021 are captured from the Bureau of Transportation Statistics website and the Sherlock Data Warehouse managed at NASA Ames Research Center. The resulting dataset is used to determine the miles flown by major aircraft. The corresponding aircraft fuel burn is estimated based on the International Civilian Aviation Organization fuel burn tables, and carbon dioxide emissions are calculated using a fuel-burn multiplicative factor. One conclusion of this analysis is that long-haul flights (flight distances > 2485 statute miles) create a disproportionately large amount of carbon dioxide emissions in the U.S, while short flights (< 311 statute miles) contribute less than five percent of the U.S. aviation-related carbon dioxide emissions. Although these short-haul flights may not have a large impact on overall carbon dioxide emissions, they will be valuable as demonstration missions for the next generation of electric, hybrid, and hydrogen-powered vehicles and their supporting energy infrastructures. This paper discusses recent trends in short-haul missions, their associated aircraft and airport types, and extracts several key requirements for future short-haul vehicles.
The programmatic, technical, user application, and cost factors associated with the development of an operational, statewide land use inventory from LANDSAT data are described. The LANDSAT multispectral data are subjected to geometrical and categorical processing to produce map files for each of the 200 fifteen (15) minute quads covering Ohio. Computer compatible tapes are rescanned to produce inventory tapes which identify eight (8) Level I land use categories and a variety of Level II categories. The inventory tapes are processed through a series of ten (10) software programs developed by the State of Ohio. The net result is a computerized inventory which can be displayed in map or tabular form for various geographic units, at a variety of scales and for selected categories of usage. The computerized inventory data files are applied to technical programs developed by the various state agencies to be used in state, regional, and local planning programs.
Devices, systems, and techniques for data collecting network such as wireless sensors are disclosed. A described technique includes detecting one or more remote nodes included in the wireless sensor network using a local power level that controls a radio range of the local node. The technique includes transmitting a local outdegree. The local outdegree can be based on a quantity of the one or more remote nodes. The technique includes receiving one or more remote outdegrees from the one or more remote nodes. The technique includes determining a local node type of the local node based on detecting a node type of the one or more remote nodes, using the one or more remote outdegrees, and using the local outdegree. The technique includes adjusting characteristics, including an energy usage characteristic and a data compression characteristic, of the wireless sensor network by selectively modifying the local power level and selectively changing the local node type.
About 2,200 samples were collected from the Moon during the Apollo missions, forming a unique and irreplaceable legacy of the Apollo program. These samples, obtained at tremendous cost and great risk, are the only samples that have ever been returned by astronauts from the surface of another planetary body. These lunar samples have been curated at NASA Johnson Space Center and made available to the global research community. Over more than 45 years, a vast body of petrological, geochemical, and geochronological studies of these samples have been amassed, which helped to expand our understanding of the history and evolution of the Moon, the Earth itself, and the history of our entire solar system. Unfortunately, data from these studies are dispersed in the literature, often only available in analog format in older publications, and/or lacking sample metadata and analytical metadata (e.g., information about analytical procedure and data quality), which greatly limits their usage for new scientific endeavors. Even worse is that much lunar data have never been published, simply because no forum existed at the time (e.g., electronic supplements). Thousands of valuable analyses remain inaccessible, often preserved only in personal records, and are in danger of being lost forever, when investigators retire or pass away. Making these data and metadata publicly accessible in a digital format would dramatically help guide current and future research and eliminate duplicated analyses of precious lunar samples.
Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era.
Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.
While load balancing in distributed-memory computing has been well-studied, we present an innovative approach to this problem: a unified, reduced-order model that combines three key components to describe “work” in a distributed system: computation, communication, and memory. Our model enables an optimizer to explore complex tradeoffs in task placement, such as augmented parallelism, at the expense of data replication increasing memory usage. We propose a fully distributed, heuristic-based load balancing optimization algorithm, and demonstrate that it quickly finds close-to-optimal solutions. We formalize the complex optimization problem as a mixed-integer linear program, and compare it to our strategy. Finally, we show that when applied to an electromagnetics code, our approach obtains up to 2.3x speedups for the imbalanced execution.