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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Using the IRAS data to search in the asteroid belt for any potential evidence of galactic colonization

The end product of the biological evolution seems to be the appearance of technological civilizations, which are characterized by superior technology that supercedes biological capabilities. The Search for Extraterrestrial Intelligence (SETI) has gained scientific recognition in recent years. The concept of galactic colonization is debated extensively, with opinions ranging from the impossible to the inevitable, but without a clear resolution. Answers can be obtained only with experimental tests and not with endless debates. A search for large space colonies in the asteroid belt, an ideal source of raw materials for a spaceborne civilization, is a test of the galactic colonization theory. The catalogue of solar system objects obtained form the Infrared Astronomy Satellite (IRAS) observations at 12, 25, 60, and 100 microns, is an ideal source for such a search. The catalog is expected to be ready at the end of 1985 and will contain more than 10,000 objects.

Papagiannis, M. D.↗
NASA NTRS1994

LMC stellar X-ray sources observed with ROSAT. 1: X-ray data and search for optical counterparts

Observations of Einstein Large Magellanic Cloud (LMC) X-ray point sources have been made with ROSAT's High-Resolution Imager to obtain accurate positions from which to search for optical counterparts. This paper is the first in a series reporting results of the ROSAT observations and subsequent optical observations. It includes the X-ray positions and fluxes, information about variability, optical finding charts for each source, a list of identified counterparts, and information about candidates which have been observed spectroscopically in each of the fields. Sixteen point sources were measured at a greater than 3 sigma level, while 15 other sources were either extended or less significant detections. About 50% of the sources are serendipitous detections (not found in previous surveys). More than half of the X-ray sources are variable. Sixteen of the sources have been optically identified or confirmed: six with foreground cool stars, four with Seyfert galaxies, two with signal-to-noise ratio (SNR) in the LMC, and four with peculiar hot LMC stars. Presumably the latter are all binaries, although only one (CAL 83) has been previously studied in detail.

Schmidtke, P. C.↗
NASA NTRS2002

New Data and Search Features in the NASA ADS Abstract Service

The NASA-ADS Abstract Service provides search capability for literature in Astronomy/Planetary Sciences, Physics/Geophysics, and Instrumentation. Scanned articles are available for many journals and proceedings. The ADS service is free for anybody. Additional information is contained in the original extended abstract.

Eichhorn, G.↗
DOE OSTI2024

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2010

Multi-Instrument Tools and Services to Access NASA Earth Science Data from the GSFC Earth Sciences Data and Information Services Center

The presentation purpose is to describe multi-instrument tools and services that facilitate access and usability of NASA Earth science data at Goddard Space Flight Center (GSFC). NASA's Earth observing system includes 14 satellites. Topics include EOSDIS facilities and system architecture, and overview of GSFC Earth Science Data and Information Services Center (GES DISC) mission, Mirador data search, Giovanni, multi-instrument data exploration, Google Earth[TM], data merging, and applications.

Kempler, Steve↗
DOE OSTI2023

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection

Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match tandem mass spectra to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from cow milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected tandem mass spectra with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from cow’s milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2012

The NASA Tournament Laboratory (NTL): Improving Data Access at PDS while Spreading Joy and Engaging Students through 16 Micro-Contests

NASA PDS hosts terabytes of valuable data from hundreds of data sources and spans decades of research. Data is stored on flat-file systems regulated through careful meta dictionaries. PDS's data is available to the public through its website which supports data searches through drill-down navigation. While the system returns data quickly, result sets in response to identical input differ depending on the drill-down path a user follows. To correct this Issue, to allow custom searching, and to improve general accessibility, PDS sought to create a new data structure and API, and to use them to build applications that are a joy to use and showcase the value of the data to students, teachers and citizens. PDS engaged TopCoder and Harvard Business School through the NTL to pursue these objectives in a pilot effort. Scope was limited to Small Bodies Node data. NTL analyzed data, proposed a solution, and implemented it through a series of micro-contests. Contest focused on different segments of the problem; conceptualization, architectural design, implementation, testing, etc. To demonstrate the utility of the completed solution, NTL developed web-based and mobile applications that can compare targets, regardless of mission. To further explore the potential of the solution NTL hosted "Mash-up" challenges that integrated the API with other publically available assets, to produce consumer and teaching applications, including an Augmented Reality iPad tool. Two contests were also posted to middle and high school students via the NoNameSite.com platform, and as a result of these contests, PDS/SBN has initiated a Facebook program. These contests defined and implemented a data warehouse with the necessary migration tools to transform legacy data, produced a public web interface for the new search, developed a public API, and produced four mobile applications that we expect to appeal to users both within and, without the academic community.

LaMora, Andy↗
DOE OSTI2020

ESS-DIVE reporting format for leaf-level gas exchange data and metadata

Here we present documentation of the ESS-DIVE reporting format for leaf-level gas exchange data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-leaf-gas-exchange v1.0. The zip contains the reporting format description, guidelines, variable tables and instructions in GitHub markdown language (*.md) and 2 metadata templates as spreadsheets with drop down options (*.xlsx files, also function in GoogleSheets).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ESS-DIVE Unoccupied Aerial Systems (UAS) Reporting Format v1

Here we present documentation of the ESS-DIVE reporting format for Unoccupied Aerial System (UAS) data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format provides templates and guidance for the reporting of metadata for UAS experimental campaigns, individual flights, platform and sensor description. To improve data access and discoverability, the reporting format proposes a data description scheme of Levels based on the degree of processing, where Level 0 includes raw data, through to Level 3 being derived data end products. A range of examples of data types for each Level are given, with suggested file naming schemes. The reporting format presented here is intended to form a foundation for future development that will accommodate new UAS technologies and approaches to data access and use in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub at https://github.com/ess-dive-community/essdive-uas. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-uas v1.0. The zip contains the reporting format description, instructions and variable definitions in GitHub markdown language (*.md) and metadata templates in csv format. The reporting format is designed to be compatible with other ESS-DIVE formats, and it is specifically recommended that this reporting format be used in conjunction with the File-level metadata (FLMD) and comma separated values (csv) reporting formats for submission to the ESS-DIVE repository.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Satellite image analysis using neural networks

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Sheldon, Roger A.↗
NASA NTRS2021

GstLAL: A software framework for gravitational wave discovery

The GstLAL library, derived from Gstreamer and the LIGO Algorithm Library, supports a stream-based approach to gravitational-wave data processing. Although GstLAL was primarily designed to search for gravitational-wave signatures of merging black holes and neutron stars, it has also contributed to other gravitational-wave searches, data calibration, and detector-characterization efforts. GstLAL has played an integral role in all of the LIGO-Virgo collaboration detections, and its low-latency configuration has enabled rapid electromagnetic follow-up for dozens of compact binary candidates.

Gravitational waves↗
NASA NTRSDate not supplied

pyQuARC: Preparing for Full Release

Metadata holds the contextual information about data and is the underlying structure for many data search portals. High quality metadata optimizes search results, allowing users to quickly retrieve the data they need. With the abundant volume and diversity of Earth observation datasets, data discovery and metadata quality are critical for end users. The Common Metadata Repository (CMR), for example, currently hosts metadata for over 9,000 Earth observation data products archived across 12 NASA Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, assesses the completeness, correctness, and consistency of these metadata records to ensure they are accessible, usable, and discoverable. In 2021, ARC began developing pyQuARC, an open source library for Earth Observation Metadata Quality Assessment to automate this effort. The tool uses ARC’s existing metadata quality framework to provide prioritized recommendations for metadata improvement. During initial testing, pyQuARC automatically identified 58% of metadata findings when compared with a sample of manually reviewed records. Using the results from initial testing, this presentation will focus on recent advancements and improvements of the tool as the ARC team prepares for pyQuARC’s full release. It will also demonstrate pyQuARC's enrichment value, not only for the ARC team, but the broader EOSDIS metadata community as well.

Essence Raphael↗
NASA NTRS2008

The NASA NEESPI Data Portal: Products, Information, and Services

Studies have indicated that land cover and use changes in Northern Eurasia influence global climate system. However, the procedures are not fully understood and it is challenging to understand the interactions between the land changes in this region and the global climate. Having integrated data collections form multiple disciplines are important for studies of climate and environmental changes. Remote sensed and model data are particularly important die to sparse in situ measurements in many Eurasia regions especially in Siberia. The NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) NEESPI data portal has generated infrastructure to provide satellite remote sensing and numerical model data for atmospheric, land surface, and cryosphere. Data searching, subsetting, and downloading functions are available. ONe useful tool is the Web-based online data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure), which allows scientists to assess easily the state and dynamics of terrestrial ecosystems in Northern Eurasia and their interactions with global climate system. Recently, we have created a metadata database prototype to expand the NASA NEESPI data portal for providing a venue for NEESPI scientists fo find the desired data easily and leveraging data sharing within NEESPI projects. The database provides product level information. The desired data can be found through navigation and free text search and narrowed down by filtering with a number of constraints. In addition, we have developed a Web Map Service (WMS) prototype to allow access data and images from difference data resources.

Shen, Suhung↗
DOE OSTI2021

Integrating Applied Energy and BER Smart Data Capabilities to Develop a DOE Data Fabric for Energy-Water R&D

Focal Area(s): 1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: DOE R&D, including DOE’s Basic Energy Research (BER)’s Environmental Systems Science Division (EESSD) program and DOE’s applied energy research (AER) programs (EERE, FE, and NE) are producers and consumers of Earth systems datasets. This white paper focuses on the first topic area from the call in relation to how crosscutting resources and innovations from DOE’s EESSD and AER can be brought to bear to mutual benefit and more efficient energy-water, Earth system data resources through improved. The overarching challenge posed by this call focuses on how DOE can directly leverage artificial intelligence (AI) to engineer a substantial (paradigm-changing) improvement in Earth System Predictability? While stemming from DOE BER’s EESSD program, this is a challenge that is faced and also being addressed by DOE’s AER programs. Over the past decade plus, FE, EERE, and NE programs have made important strides towards addressing this need. These strides are in many ways highly complementary to EESSD’s MODEX efforts. Energy water systems spanning metocean to groundwater to surface water systems all are data driven whether for basic energy or applied energy. These are remote, multi-variate, complex natural, and in many cases engineered, systems. Key needs and challenges of both EESSD and AER include developing data-focused tools to enhance data search and discovery to fill in knowledge gaps (address sparse data challenge), and rapidly transform datasets, including disparate and multi-source data. Leveraging DOE on-premise computing (HPC, exascale) infrastructure supports the computing-intensive algorithms required to execute these data acquisition and transformation processes to derive enriched knowledge and data, driving AI/ML and big data analytics for these systems. The opportunity lies in combining BER and AER efforts to provide a more robust, advanced, efficient and complete computing data fabric to address energy-water data acquisition and assimilation needs which currently pose significant impediments to AI/ML predictions and research.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

The NASA NEESPI Data Portal to Support Studies of Climate and Environmental Changes in Non-Boreal Europe

NASA NEESPI (Northern Eurasia Earth Science Partnership Initiative) data portal is a NASA funded project that focuses on collecting satellite remote sensing data, providing tools, information, and services in support of NEESPI scientific objectives (Leptoukh, et al., 2007). The data can be accessed online through anonymous ftp, through an advanced data searching and ordering system Mirador that uses keywords to find data quickly in a Google-like interface, and through the Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni). The portal provides preprocessed data from different satellite sensors and numerical models to the same spatial and temporal resolution and the same projection so that the data can be used easily to perform inter-comparison or relationship studies. In addition, it provides parameter and spatially subsetted data for regional studies. Studies of regional carbon, hydrology, aerosols in non-boreal Europe and their interactions with global climate are very challenging research topics. The NASA NEESPI data portal makes many satellite data available for such studies, including information on land cover types, fire, vegetation index, aerosols, land surface temperature, soil moisture, precipitation, snow/ice, and other parameters. This paper will introduce the features and products available in the system, focusing on the online data 1 tool, Giovanni NEESPI. An example that explores different data through Giovanni NEESPI in temperate region of non-boreal Europe will be presented.

Shen, Suhung↗
NASA NTRS2006

The Virtual Space Physics Observatory: Quick Access to Data and Tools

The Virtual Space Physics Observatory (VSPO; see http://vspo.gsfc.nasa.gov) has grown to provide a way to find and access about 375 data products and services from over 100 spacecraft/observatories in space and solar physics. The datasets are mainly chosen to be the most requested, and include most of the publicly available data products from operating NASA Heliophysics spacecraft as well as from solar observatories measuring across the frequency spectrum. Service links include a "quick orbits" page that uses SSCWeb Web Services to provide a rapid answer to questions such as "What spacecraft were in orbit in July 1992?" and "Where were Geotail, Cluster, and Polar on 2 June 2001?" These queries are linked back to the data search page. The VSPO interface provides many ways of looking for data based on terms used in a registry of resources using the SPASE Data Model that will be the standard for Heliophysics Virtual Observatories. VSPO itself is accessible via an API that allows other applications to use it as a Web Service; this has been implemented in one instance using the ViSBARD visualization program. The VSPO will become part of the Space Physics Data Facility, and will continue to expand its access to data. A challenge for all VOs will be to provide uniform access to data at the variable level, and we will be addressing this question in a number of ways.

Cornwell, Carl↗
NASA NTRS1983

Documentation for the machine-readable version of the Survey of the Astrographic Catalogue From 1 to 31 Degrees of Northern Declination (Fresneau 1983)

A description of the machine readable catalog, including detailed format and tape file characteristics, is given. The machine file is a computation of mean values for position and magnitude at a mean epoch of observation for each unique star in the Oxford, Paris, Bordeaux, Toulouse and Northern Hemisphere Algiers zone. The format was changed to effect more efficient data searching by position and additional duplicate entries were removed. The final catalog contains data for 997311 stars.

Warren, W. H., Jr.↗