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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

The 1970/71 spectral data management programs

The data management programs used by the Stanford Remote Sensing Laboratory to access, modify, and reduce the data obtained from both the NASA IR airborne spectrometer, and Stanford's SG-4 field spectrometer are reported. Many details covered in previous reports are not repeated. References are provided. These programs are written in FORTRAN 4 and S/360 Assembler Language, and are currently running on a S/360 model 67 (operating under OS/MFT) at the Stanford Computation Center Campus Facility.

Marshall, A. A.↗
NASA NTRS1971

A guide to onboard checkout. Volume 5: Data management

The baseline data management subsystem for a space station is discussed. The subsystem consists of equipment necessary to transfer, store, and process data to and from users and subsystems. It acquires and conditions a wide variety of input data from experiments, vehicle subsystems sensors, uplinked ground communications, and astronaut-activated controls. Computer techniques for failure analysis, reliability, and maintenance checkout onboard the space station are considered.

Source record↗
NASA NTRS1975

Langley applications experiments data management system study

A data management system study is presented that defines, in functional terms, the most cost effective ground data management system to support Advanced Technology Laboratory (ATL) flights of the space shuttle. Results from each subtask performed and the recommended system configuration for reformatting the experiment instrumentation tapes to computer compatible tape are examined. Included are cost factors for development of a mini control center for real-time support of the ATL flights.

Lanham, C. C., Jr.↗
NASA NTRS1972

Redundant data management system

Redundant data management system solves problem of operating redundant equipment in real time environment where failures are detected, isolated, and switched in simple manner. System consists of quadruply-redundant computer, input/output control units, and data buses. System inherently contains failure detection, isolation, and switching function.

Hall, J. R.↗
NASA NTRS1990

Modeling of the Space Station Freedom data management system

The data management system (DMS) is the information and communications system onboard Space Station Freedom. Extensive modeling of the DMS is being conducted throughout NASA to aid in the design and development of this vital system. Activities to model the DMS network infrastructure are described, focusing on modeling of the fiber distributed data interface (FDDI) token-ring protocol and experimental testbedding of networking aspects of the DMS.

Johnson, Marjory J.↗
NASA NTRS1990

Modeling of the Space Station Freedom data management system

The Data Management System (DMS) is the information and communications system onboard Space Station Freedom (SSF). Extensive modeling of the DMS is being conducted throughout NASA to aid in the design and development of this vital system. Activities discussed at NASA Ames Research Center to model the DMS network infrastructure are discussed with focus on the modeling of the Fiber Distributed Data Interface (FDDI) token-ring protocol and experimental testbedding of networking aspects of the DMS.

Johnson, Marjory J.↗
NASA NTRS1983

Data management support for selected climate data sets using the climate data access system

The functional capabilities of the Goddard Space Flight Center (GSFC) Climate Data Access System (CDAS), an interactive data storage and retrieval system, and the archival data sets which this system manages are discussed. The CDAS manages several climate-related data sets, such as the First Global Atmospheric Research Program (GARP) Global Experiment (FGGE) Level 2-b and Level 3-a data tapes. CDAS data management support consists of three basic functions: (1) an inventory capability which allows users to search or update a disk-resident inventory describing the contents of each tape in a data set, (2) a capability to depict graphically the spatial coverage of a tape in a data set, and (3) a data set selection capability which allows users to extract portions of a data set using criteria such as time, location, and data source/parameter and output the data to tape, user terminal, or system printer. This report includes figures that illustrate menu displays and output listings for each CDAS function.

Reph, M. G.↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Centralized Data Management Platform

The technology is an adaptive data management and integration platform designed for disparate data sources. It is built to support multitenancy, manage data governance, handle heterogeneous data formats and advance data democratization using a suite of connected, independent microservices. Each service can be used within an integrated environment, or as a standalone product, with a dedicated set of functionalities, such as metadata management, data versioning, access control, data tagging, link management, and analytics, among others.

Technology Transfer↗
NASA NTRS1971

Analysis of the Apollo spacecraft operational data management system. Executive summary

A study was made of Apollo, Skylab, and several other data management systems to determine those techniques which could be applied to the management of operational data for future manned spacecraft programs. The results of the study are presented and include: (1) an analysis of present data management systems, (2) a list of requirements for future operational data management systems, (3) an evaluation of automated data management techniques, and (4) a plan for data management applicable to future space programs.

Source record↗
NASA NTRSDate not supplied

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRS1995

Data management, archiving, visualization and analysis of space physics data

A series of programs for the visualization and analysis of space physics data has been developed at UCLA. In the course of those developments, a number of lessons have been learned regarding data management and data archiving, as well as data analysis. The issues now facing those wishing to develop such software, as well as the lessons learned, are reviewed. Modern media have eased many of the earlier problems of the physical volume required to store data, the speed of access, and the permanence of the records. However, the ultimate longevity of these media is still a question of debate. Finally, while software development has become easier, cost is still a limiting factor in developing visualization and analysis software.

Russell, C. T.↗
NASA NTRS2016

Ontologies for Aviation Data Management

Managing complex aviation data can be a significant challenge for any enterprise – whether a government agency, airline, airframe manufacturer, or aviation service provider. To handle this challenge, data models are typically developed to characterize and manage the data generated, used, and stored by a given enterprise. Unfortunately, different data providers employ qualitatively different data models, and this gives rise to problems exchanging data across organizational boundaries. Over the past decade, these problems have motivated data producers and consumers to look toward standardized data exchange models to address data interoperability. In this paper we examine some of these standardized data exchange models and compare them with a new type of data model based on ontologies. Ontology models have emerged in recent years from a confluence of research in the artificial intelligence, semantic web, and information science communities. This paper introduces ontology models, provides several use cases for ontologies relevant to aviation data management, and summarizes state of the art aviation prototype applications that utilize ontologies.

artificial intelligence↗
NASA NTRS1984

CAD/CAM data management

The role of data base management in CAD/CAM, particularly for geometric data is described. First, long term and short term objectives for CAD/CAM data management are identified. Second, the benefits of the data base management approach are explained. Third, some of the additional work needed in the data base area is discussed.

Bray, O. H.↗