Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Science 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.

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Interoperable Map Services with Performance Tuning for Earth Science Data through API-Tiles and Dynamic API-Styles

NASA’s Goddard Earth Sciences Data and Information Services Center (GES DISC) provides access to a wide range of global climate data from various satellite missions and models. However, the visualization and analysis of these data can be challenging due to their large volume, complex structure, and diverse formats. This study presents the implementation of interoperable map services (API-Maps) with performance tuning using API-Tiles and dynamic API-Styles. API-Maps is a standard for defining and exposing map services through RESTful (representational state transfer) APIs (application programming interfaces). API-Tiles is a technique for generating and delivering map tiles on demand from any data source. API-Styles is a method for dynamically applying styles to map tiles based on user preferences or data attributes. The use of API-Tiles and dynamic API-Styles enhances the performance and scalability of the map services, allowing for smooth and interactive visualization of large datasets. Two types of Earth Science data sources from the NASA GES DISC are used in the experiment: regularly gridded data, such as Global Precipitation Measurement (GPM) precipitation data, and low processing level data, such as low-level data of atmospheric composite measurements from the TROPOspheric Monitoring Instrument (TROPOMI) mission. Re-gridding of swath data (low level data - e.g. Level 2) of atmospheric composites (e.g. TROPOMI products, such as nitrogen dioxide, ozone and aerosol optical depth) is applied to enable the Web-based, interoperable, tiled, and styled mapping (rendering) services of such data. The results demonstrate the effectiveness of the proposed approach in providing fast and efficient access to Earth science data through interoperable map services.

Geographic Information System↗
NASA NTRS1993

The Space and Earth Science Data Compression Workshop

This document is the proceedings from a Space and Earth Science Data Compression Workshop, which was held on March 27, 1992, at the Snowbird Conference Center in Snowbird, Utah. This workshop was held in conjunction with the 1992 Data Compression Conference (DCC '92), which was held at the same location, March 24-26, 1992. The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The workshop consisted of eleven papers presented in four sessions. These papers describe research that is integrated into, or has the potential of being integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientists's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system.

Tilton, James C.↗
NASA NTRS2007

Online Analysis Enhances Use of NASA Earth Science Data

Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.

Acker, James G.↗
NASA NTRSDate not supplied

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗
NASA NTRS2020

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗
NASA NTRS1994

The 1994 Space and Earth Science Data Compression Workshop

This document is the proceedings from the fourth annual 'Space and Earth Science Data Compression Workshop,' which was held on April 2, 1994, at the University of Utah in Salt Lake City, Utah. This workshop was held in cooperation with the 1994 Data Compression Conference, which was held at Snowbird, Utah, March 29-31 1994. The Workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. It consisted of 13 papers presented in 4 sessions. The papers focus on data compression research that is integrated into, or has the potential to be integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientist's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system.

Tilton, James C.↗
NASA NTRSDate not supplied

Earth Science Data Processing With Nextflow

Earth science data processing tasks present many challenges. These tasks often process large input datasets and require scores of CPU-hours to generate results. All but the simplest tasks will be decomposed into a series of computational or data manipulation steps, also known as a scientific workflow. In order to reduce the burden of orchestrating and running the dependent processing steps, a workflow execution engine is required. This poster describes the lessons learned by the CLARREO Pathfinder (CPF) team while developing multiple scientific workflows and utilizing the open-source Nextflow engine to execute them in a cloud computing environment. The Nextflow engine is designed with the following stated goals: first, the engine does not dictate how individual steps in the task are implemented (i.e. it is language and interface agnostic); second, the engine supports easy configuration and modularity at the workflow level so that others can easily execute our workflows to reproduce results; lastly, the engine eases development by transparently scaling execution from local to remote environments. Nextflow was developed for the bioinformatics domain but is a good fit for other scientific workflows where the overall task is well-described by a dataflow diagram. The CPF team has developed Nextflow pipelines (i.e. scientific workflows) to simulate CLARREO radiance, generate large look-up tables for inter-calibration algorithms, and generate L4 intercalibration data products. These pipelines consume from single-digits to hundreds of thousands of CPU-hours. In the development and evolution of these pipelines we have discovered many design patterns, pitfalls, and solutions to common problems. Our goal is to demonstrate important aspects of how to design, implement, run, and ultimately share Nextflow pipelines in the domain of Earth science.

Aron D Bartle↗
NASA NTRS1985

The AMPTE Charge Composition Explorer science data system

The instrument complement on all three Active Magnetospheric Particle Tracer Explorer (AMPTE) spacecraft is devoted to the conduction of the unified measurements which are required to achieve the scientific aims. A single science data base for each spacecaft has, therefore, been established with the objective to facilitate unified analysis. The data are kept in Science Data Centers in the U.S., West Germany, and the United Kingdom. The Science Data Center dedicated to the Charge Composition Explorer (CCE) is located at the Applied Physics Laboratory of Johns Hopkins University where it was developed. The present paper is concerned with aspects of the computing center's design, development and operations.

Holland, B. B.↗
NASA NTRSDate not supplied

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗
NASA NTRS2020

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗
NASA NTRS1991

Solar-terrestrial models at the National Space Science Data Center

The National Space Science Data Center (NSSDC) and World Data Center A for Rockets and Satellites (WDC-A-R&S) has a long record of participation in the worldwide efforts to establish and improve empirical models for the different regions of the solar-terrestrial environment. The center maintains a unique archive of solar-terrestrial models and related applications software, described in a recently published models catalog. The software packages are distributed on tape, diskette, and on-line on the Space Physics Analysis Network (SPAN). Four of the most frequently requested models (IRI, MSIS/CIRA, IGRF, AE-8/AP-8) can also be accessed and run on the NSSDC Online Documentation and Information Service (NODIS) account, which can be reached from any SPAN node.

Bilitza, D.↗
NASA NTRS2019

Improving Earth Science Data Literacy

The improvement of scientific literacy across the public can take on a myriad of forms and activities. The vast archives of NASA’s Earth science data, as one example, reflect the agency’s ongoing commitment to producing the highest quality data, services and tools intended for enabling the public to understand the complexity of Earth systems. The focus of this presentation is to explore and reveal the specific strategies the NASA Earth Science Data and Information System (ESDIS) Project uses to support our very large and diverse user communities access to and understanding of these science data and services. The diversity of our subject matter, spanning the whole Earth science domain, is mirrored by our user communities which are equally as broad in their needs and abilities. ESDIS and the supporting Communication Team leverages an array of internet-based tools and communication strategies to both understand actual user needs and develop optimum pathways for improving our users’ knowledge and understanding of the data. We will focus our limited time on a few of the high-impact activities including Webinars, or video-based, on-line, interactive discussions and presentations that since their introduction in 2013 have become a staple for user-instructor interaction via the internet. Here we discuss the types of content we produce and many lessons learned on how best to use this medium for improving user literacy on the subject at hand. In addition, we include discussion of our approach to using various social media platforms and tools to bolster the awareness of our users for data and services that are likely of interest. The ESDIS project remains active in various social media campaigns where our particular blend of science, data and tools engages new users curious of how to get their science or application started using NASA-available data. We anticipate that these examples may prove to be novel in their application to improving science literacy and skill development among the public.

Lindsay, Francis↗
NASA NTRS2007

NASA's Earth Science Data Systems Standards Process Experiences

NASA has impaneled several internal working groups to provide recommendations to NASA management on ways to evolve and improve Earth Science Data Systems. One of these working groups is the Standards Process Group (SPC). The SPG is drawn from NASA-funded Earth Science Data Systems stakeholders, and it directs a process of community review and evaluation of proposed NASA standards. The working group's goal is to promote interoperability and interuse of NASA Earth Science data through broader use of standards that have proven implementation and operational benefit to NASA Earth science by facilitating the NASA management endorsement of proposed standards. The SPC now has two years of experience with this approach to identification of standards. We will discuss real examples of the different types of candidate standards that have been proposed to NASA's Standards Process Group such as OPeNDAP's Data Access Protocol, the Hierarchical Data Format, and Open Geospatial Consortium's Web Map Server. Each of the three types of proposals requires a different sort of criteria for understanding the broad concepts of "proven implementation" and "operational benefit" in the context of NASA Earth Science data systems. We will discuss how our Standards Process has evolved with our experiences with the three candidate standards.

Ullman, Richard E.↗
NASA NTRS1990

High Rate Science Data Handling

Viewgraphs on high rate science data handling are presented. Topics covered include: science needs versus technology; technology assessment; conceptual architecture; and direct downlink concept.

Masline, Richard C.↗