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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

GeneLab: Multi-Omics Investigation of Rodent Research-1 Bio-Banked Tissues

NASAs Rodent Research (RR) project is playing a critical role in advancing biomedical research on the physiological effects of space environments. Due to the limited resources for conducting biological experiments aboard the International Space Station (ISS), it is imperative to use crew time efficiently while maximizing high-quality science return. NASAs GeneLab project has as its primary objectives to 1) further increase the value of these experiments using a multi-omics, systems biology-based approach, and 2) disseminate these data without restrictions to the scientific community. The current investigation assessed viability of RNA, DNA, and protein extracted from archived RR-1 tissue samples for epigenomic, transcriptomic, and proteomic assays. During the first RR spaceflight experiment, a variety of tissue types were harvested from subjects, snap-frozen or RNAlater-preserved, and then stored at least a year at -80OC after return to Earth. They were then prioritized for this investigation based on likelihood of significant scientific value for spaceflight research. All tissues were made available to GeneLab through the bio-specimen sharing program managed by the Ames Life Science Data Archive and included mouse adrenal glands, quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, soleus, eye, and kidney. We report here protocols for and results of these tissue extractions, and thus, the feasibility and value of these kinds of omics analyses. In addition to providing additional opportunities for investigation of spaceflight effects on the mouse transcriptome and proteome in new kinds of tissues, our results may also be of value to program managers for the prioritization of ISS crew time for rodent research activities. Support from the NASA Space Life and Physical Sciences Division and the International Space Station Program is gratefully acknowledged.

GeneLab↗
NASA NTRS1990

Use of antarctic analogs to support the space exploration initiative

This report has discussed the Space Exploration Initiative (SEI) and the U.S. Antarctic Program (USAP) in the context of assessing the potential rationale and strategy for conducting a cooperative NASA/NSF (National Science Foundation) effort. Specifically, such an effort would address shared research and data on living and conducting scientific research in isolated, confined, hostile, and remote environments. A review of the respective goals and requirements of NASA and the NSF indicates that numerous opportunities exist to mutually benefit from sharing relevant technologies, data, and systems. Two major conclusions can be drawn: (1) The technologies, experience, and capabilities existing and developing in the aerospace community would enhance scientific research capabilities and the efficiency and effectiveness of operations in Antarctica. The transfer and application of critical technologies (e.g., power, waste management, life support) and collaboration on crew research needs (e.g., human behavior and medical support needs) would streamline the USAP operations and provide the scientific community with advancements in facilities and tools for Antarctic research. (2) Antarctica is the most appropriate earth analog for the environments of the the Moon and Mars. Using Antarctica in this way would contribute substantially to near- and long-term needs and plans for the SEI. Antarctica is one of the few ground-based analogs that would permit comprehensive and integrated studies of three areas deemed critical to productive and safe operations on the Moon and Mars: human health and productivity; innovative scientific research techniques; and reliable, efficient technologies and facilities.

Wharton, Robert↗
DOE OSTI2023

Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data as a Key Resource in Catalysis: A Community Account

The deployment of artificial intelligence (AI) is transforming the scientific fields central to interdisciplinary catalysis research. By enabling more effective use of data, AI (including simpler machine learning and data science tools) holds great promise for accelerating discoveries. However, progress has so far been modest, largely due to the lack of standardized, machine-readable, and openly shared catalysis data. This perspective, accounting for community insights emerging at conferences, analyses the underlying reasons for these challenges and proposes solutions to a future whereFAIR data management becomes an integral part of research in catalysis. In the short-term, we deem that mandatory FAIR data depositing prior to scientific publications along with consensualized top-down guidelines on data sharing powered by ease-to-use tools can make the necessary step change happen to catalyse data as key resource in our community.

36 - MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗
NASA NTRS1987

The development of a prototype intelligent user interface subsystem for NASA's scientific database systems

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components the development of an Intelligent User Interface (IUI).The intent of the latter is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. The purpose is to support the large number of potential scientific and engineering users presently having need of space and land related research and technical data but who have little or no experience in query languages or understanding of the information content or architecture of the databases involved. This technical memorandum presents prototype Intelligent User Interface Subsystem (IUIS) using the Crustal Dynamics Project Database as a test bed for the implementation of the CRUDDES (Crustal Dynamics Expert System). The knowledge base has more than 200 rules and represents a single application view and the architectural view. Operational performance using CRUDDES has allowed nondatabase users to obtain useful information from the database previously accessible only to an expert database user or the database designer.

Campbell, William J.↗
NASA NTRS1990

Analysis of AVHRR, CZCS and historical in situ data off the Oregon Coast

The original scientific objectives of this grant were to: (1) characterize the seasonal cycles and interannual variability for phytoplankton concentrations and sea surface temperature (SST) in the California Current using satellite data; and (2) to explore the spatial and temporal relationship between these variables and surface wind forcing. An additional methodological objective was to develop statistical methods for forming mean fields, which minimize the effects of random data gaps and errors in the irregularly sampled CZCS (Coastal Zone Color Scanner) and AVHRR (Advanced Very High Resolution Radiometer) satellite data. A final task was to evaluate the level of uncertainty in the wind fields used for the statistical analysis. Funding in the first year included part of the cost of an image processing system to enable this and other projects to process and analyze satellite data. This report consists of summaries of the major projects carried out with all or partial support from this grant. The appendices include a list of papers and professional presentations supported by the grant, as well as reprints of the major papers and reports.

Strub, P. Ted↗
NASA NTRS1991

SEAPAK user's guide, version 2.0. Volume 1: System description

The SEAPAK is a user interactive satellite data analysis package that was developed for the processing and interpretation of Nimbus-7/Coastal Zone Color Scanner (CZCS) and the NOAA Advanced Very High Resolution Radiometer (AVHRR) data. Significant revisions were made to version 1.0 of the guide, and the ancillary environmental data analysis module was expanded. The package continues to emphasize user friendliness and user interactive data analyses. Additionally, because the scientific goals of the ocean color research being conducted have shifted to large space and time scales, batch processing capabilities for both satellite and ancillary environmental data analyses were enhanced, thus allowing large quantities of data to be ingested and analyzed in background.

Mcclain, Charles R.↗
NASA NTRS1992

PC-SEAPAK user's guide, version 4.0

PC-SEAPAK is designed to provide a complete and affordable capability for processing and analysis of NOAA Advanced Very High Resolution Radiometer (AVHRR) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. Since the release of version 3.0 over a year ago, significant revisions were made to the AVHRR and CZCS programs and to the statistical data analysis module, and a number of new programs were added. This new version has 114 procedures listed in its menus. The package continues to emphasize user-friendliness and interactive data analysis. Additionally, because the scientific goals of the ocean color research being conducted have shifted to larger space and time scales, batch processing capabilities were enhanced, allowing large quantities of data to be easily ingested and analyzed. The development of PC-SEAPAK was paralled by two other activities that were influential and assistive: the global CZCS processing effort at GSFC and the continued development of VAX-SEAPAK. SEAPAK incorporates the instrument calibration and support all levels of data available from the CZCS archive.

Mcclain, Charles R.↗
NASA NTRS1996

Validation of UARS Microwave Limb Sounder ClO Measurements

Validation of stratospheric ClO measurements by the Microwave Limb Sounder (MLS) on the Upper Atmosphere Research Satellite (UARS) is described. Credibility of the measurements is established by (1) the consistency of the measured ClO spectral emission line with the retrieved ClO profiles and (2) comparisons of ClO from MLS with that from correlative measurements by balloon-based, ground-based, and aircraft-based instruments. Values of "noise" (random), "scaling" (multiplicative), and "bias" (additive) uncertainties are determined for the Version 3 data, in the first version public release of the known artifacts in these data are identified. Comparisons with correlative measurements indicate agreement to within the combined uncertainties expected for MLS and the other measurements being compared. It is concluded that MLS Version 3 ClO data, with proper consideration of the uncertainties and "quality" parameters produced with these data, can be used for scientific analyses at retrieval surfaces between 46 and 1 hPa (approximately 20-50 km in height). Future work is planned to correct known problems in the data and improve their quality.

Waters, J. W.↗
DOE OSTI2021

Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. Herein we present a state–of–the–art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time–varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Uncertainty Quantification in Scientific ML

The intricate interactions between data sampling, model selection and the inherent randomness in complex systems strongly emphasize the need for a rigorous characterization of ML algorithms. In conventional statistics, uncertainty quantification (UQ) provides this characterization by measuring how accurately a model reflects the physical reality and by studying the impact of different error sources on the prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Downwelling Shortwave and Longwave Irradiance from CC-RIDER on Mount Soledad

The Clouds and Climate - Remote Integrated Deployment of Radiometers (CC-RIDER) is a suite of five Eppley Laboratory (Inc.) instruments that was deployed during EPCAPE at the secondary Mount Soledad site in La Jolla, CA. A primary and backup Precision Spectral Pyranometer (PSP Primary, PSP Backup) measured broadband downwelling shortwave irradiance in the spectral interval 280-2800 nm. A third Precision Spectral Pyranometer (PSP NIR) was fitted with a near-infrared long pass filter and measured downwelling shortwave irradiance in the spectral interval 780-2800 nm. A Total Ultraviolet Radiometer (TUVR) measured broadband downwelling broadband ultraviolet irradiance in the spectral interval 295-385 nm. A Precision Infrared Radiometer (PIR) Pyrgeometer measured downwelling broadband longwave irradiance in the spectral interval 3.5 - 50 microns. Data collection began on 18 April 2023 at 21:31 UTC and ended on 20 February 2024 at 22:34 UTC. Data were recorded by a Campbell Scientific (Inc.) CR1000X datalogger in one-minute intervals, for a total of 443584 data records. The datalogger was solar powered enabling data collection to proceed without interruption from start to finish.

Clouds and Climate – Remote Integrated DEployement↗
NASA NTRS1986

The development of an intelligent user interface for NASA's scientific databases

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI effort is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. This paper presents the design concepts, development approach and evaluation of performance of a prototype Intelligent User Interface Subsystem (IUIS) supporting an operational database.

Campbell, William J.↗
NASA NTRS1989

Block adaptive quantization of Magellan SAR data

A report is presented on a data compression scheme that will be used to reduce the SAR data rate on the NASA Magellan mission to Venus. The spacecraft has only one scientific instrument, a radar system for imaging the surface, for altimetric profiling of the planet topography, and for measuring radiation from the planet surface. A straightforward implementation of the scientific requirements of the mission results in a data rate higher than can be accommodated by the available system bandwidth. A data-rate-reduction scheme which includes operation of the radar in burst mode and block-adaptive quantization of the SAR data is selected to satisfy the scientific requirements. Descriptions of the quantization scheme and its hardware implementation are given. Burst-mode SAR operation is also briefly discussed.

Kwok, Ronald↗
NASA NTRS1986

The development of an intelligent user interface for NASA's scientific databases

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI effort is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. This paper presents the design concepts, development approach and evaluation of performance of a prototype Intelligent User Interface Subsystem (IUIS) supporting an operational database.

Campbell, William J.↗
NASA NTRS1991

EMASS (trademark): An expandable solution for NASA space data storage needs

The data acquisition, distribution, processing, and archiving requirements of NASA and other U.S. Government data centers present significant data management challenges that must be met in the 1990's. The Earth Observing System (EOS) project alone is expected to generate daily data volumes greater than 2 Terabytes (2 x 10(exp 12) Bytes). As the scientific community makes use of this data, their work will result in larger, increasingly complex data sets to be further exploited and managed. The challenge for data storage systems is to satisfy the initial data management requirements with cost effective solutions that provide for planned growth. The expendable architecture of the E-Systems Modular Automated Storage System (EMASS(TM)), a mass storage system which is designed to support NASA's data capture, storage, distribution, and management requirements into the 21st century is described.

Peterson, Anthony L.↗