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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 NTRS2017

Harvesting NASA's Common Metadata Repository (CMR)

As part of NASA's Earth Observing System Data and Information System (EOSDIS), the Common Metadata Repository (CMR) stores metadata for over 30,000 datasets from both NASA and international providers along with over 300M granules. This metadata enables sub-second discovery and facilitates data access. While the CMR offers a robust temporal, spatial and keyword search functionality to the general public and international community, it is sometimes more desirable for international partners to harvest the CMR metadata and merge the CMR metadata into a partner's existing metadata repository. This poster will focus on best practices to follow when harvesting CMR metadata to ensure that any changes made to the CMR can also be updated in a partner's own repository. Additionally, since each partner has distinct metadata formats they are able to consume, the best practices will also include guidance on retrieving the metadata in the desired metadata format using CMR's Unified Metadata Model translation software.

Earth Resources↗
NASA NTRS1990

Saving all the bits

The scientific tradition of saving all the data from experiments for independent validation and for further investigation is under profound challenge by modern satellite data collectors and by supercomputers. The volume of data is beyond the capacity to store, transmit, and comprehend the data. A promising line of study is discovery machines that study the data at the collection site and transmit statistical summaries of patterns observed. Examples of discovery machines are the Autoclass system and the genetic memory system of NASA-Ames, and the proposal for knowbots by Kahn and Cerf.

Denning, Peter J.↗
DOE OSTI2024

Data-Driven Supervised Dimension Reduction for Scientific Discovery (LDRD QTI Report)

This report summarizes the findings of a four months FY24 Advanced Science & Technology (AS&T) LDRD Quick Targeted Investigation (QTI) project focused on the exploration of supervised dimension reduction approaches based on autoencoders. Autoencoders have been extensively employed in literature for unsupervised learning tasks, however, their use for supervised regression tasks, which are common within scientific applications, has been limited. Motivated by linear dimension reduction strategies like Active Subspaces and Adaptive Basis, we explored the possibility of employing autoencoders to discover a non-linear manifold able to represent the original function in fewer dimensions. In this report, we discuss a neural network architecture and we perform a numerical campaign on several problems ranging from simple two-dimensional functions to a model problem for magnetohydrodynamics in five dimensions. In our preliminary results, we show that the proposed approach is found to be superior to linear dimension reduction strategies in representing the target function even with a single latent variable.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Genesis Solar Wind Interstream, Coronal Hole and Coronal Mass Ejection Samples: Update on Availability and Condition

Recent refinement of analysis of ACE/SWICS data (Advanced Composition Explorer/Solar Wind Ion Composition Spectrometer) and of onboard data for Genesis Discovery Mission of 3 regimes of solar wind at Earth-Sun L1 make it an appropriate time to update the availability and condition of Genesis samples specifically collected in these three regimes and currently curated at Johnson Space Center. ACE/SWICS spacecraft data indicate that solar wind flow types emanating from the interstream regions, from coronal holes and from coronal mass ejections are elementally and isotopically fractionated in different ways from the solar photosphere, and that correction of solar wind values to photosphere values is non-trivial. Returned Genesis solar wind samples captured very different kinds of information about these three regimes than spacecraft data. Samples were collected from 11/30/2001 to 4/1/2004 on the declining phase of solar cycle 23. Meshik, et al is an example of precision attainable. Earlier high precision laboratory analyses of noble gases collected in the interstream, coronal hole and coronal mass ejection regimes speak to degree of fractionation in solar wind formation and models that laboratory data support. The current availability and condition of samples captured on collector plates during interstream slow solar wind, coronal hole high speed solar wind and coronal mass ejections are de-scribed here for potential users of these samples.

Allton, J. H.↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, and fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs welcome any ASPB members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, altered photoperiods and many other abiotic stressors. Open Science is the practice of making research available to all, while respecting diverse cultures, to foster collaborations with equity. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) within the Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. GeneLab started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository, providing detailed metadata on investigation, sample, and assay levels. The addition of ALSDA to OSDR expands plant data analysis capabilities across both phenotypic and ‘omics data. Today, OSDR hosts 62+ plant datasets and has enabled 58 peer-reviewed publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate with community members and set new standards for space-relevant data and metadata. The AWGs welcome any ASGSR members interested in contributing plant expertise for space biology, and to serve as subject matter experts as we establish the framework for modern plant data archiving. Investigators are encouraged to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRS2007

An Overview of the Mars Reconnaissance Orbiter (MRO) Science Mission

The Mars Reconnaissance Orbiter (MRO) is the latest addition to the suite of missions on or orbiting Mars as part of the NASA Mars Exploration Program. Launched on 12 August 2005, the orbiter successfully entered Mars orbit on 10 March 2006 and finished aerobraking on 30 August 2006. Now in its near-polar, near-circular, low-altitude (approximately 300 km), 3 p.m. orbit, the spacecraft is operating its payload of six scientific instruments throughout a one-Mars-year Primary Science Phase (PSP) of global mapping, regional survey, and targeted observations. Eight scientific investigations were chosen for MRO, two of which use either the spacecraft accelerometers or tracking of the spacecraft telecom signal to acquire data needed for analysis. Six instruments, including three imaging systems, a visible-near infrared spectrometer, a shallow-probing subsurface radar, and a thermal-infrared profiler, were selected to complement and extend the capabilities of current working spacecraft at Mars. Whether observing the atmosphere, surface, or subsurface, the MRO instruments are designed to achieve significantly higher resolution while maintaining coverage comparable to the current best observations. The requirements to return higher-resolution data, to target routinely from a low-altitude orbit, and to operate a complex suite of instruments were major challenges successfully met in the design and build of the spacecraft, as well as by the mission design. Calibration activities during the seven-month cruise to Mars and limited payload operations during a three-day checkout prior to the start of aerobraking demonstrated, where possible, that the spacecraft and payload still had the functions critical to the science mission. Two critical events, the deployment of the SHARAD radar antenna and the opening of the CRISM telescope cover, were successfully accomplished in September 2006. Normal data collection began 7 November 2006 after solar conjunction. As part of its science mission, MRO will also aid identification and characterization of the most promising sites for future landed missions, both in terms of safety and in terms of the scientific potential for future discovery. Ultimately, MRO data will advance our understanding of how Mars has evolved and by which processes that change occurs, all within a framework of identifying the presence, extent, and role of water in shaping the planet s climate over time.

astronomical instruments↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions rely on plants and crops for crew and ecosystem health. Access to space plant data enables scientists to gain a deeper understanding of biological responses to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, and altered photoperiods. Open Science is the practice of making research available to all, while respecting diverse cultures, fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) mission designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) developed by NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. OSDR started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository (GeneLab), providing detailed metadata on investigation, sample, and assay levels. Today, GeneLab hosts 62 plant datasets which have led to 5 published peer-reviewed meta-analysis publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs are welcoming any ASPB members interested in providing plant expertise for space biology. The addition of ALSDA to OSDR is also expanding analysis capability beyond ‘omics. Now is the time to get involved as a Subject Matter Expert as we establish the framework for modern plant data archiving through the AWGs. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRS1991

A search for periodicity in the x ray spectrum of black hole candidate A0620-00

The archived data from the SAS-3 observations of the X-ray nova A0620-00, the best of the stellar blackhole candidates, were exhaustively examined for evidence of variable phenomena correlated with the orbital motion of the binary system of which it is a member. The original analysis of these data was completed before discovery of the binary companion and determination of the orbital period of the system. New interest was drawn to the task of a reexamination of the archive data by the recent discovery of the massive nature of the X-ray source through analysis of the Doppler variations and ellipsoidal light variations of the faint K-star companion by McClintock and Remillard. The archive research, carried out under the supervision of the principal investigator, was the topic of the thesis submitted to the MIT Department of Physics by Kenneth Plaks in partial fulfillment of the requirements for the degree of Master of Science. Plaks' effort was focused on the elimination of fluctuations in the data due to errors in attitude solutions and other extraneous causes. The first products of his work were long-term light curves of the X-ray intensities in the various energy channels as functions of time during the time from outbursts in August 1975 to quiescence approximately 6 months later. These curves, are refined versions of the preliminary results published in 1976 (Matilsky et al. 1976). Smooth exponentials were fitted to these long term light curves to provide the basis for detrending the data, thereby permitting a calculation of residuals derived by subtracting the fitted curve from the data. The residuals were then analyzed by Fourier analysis to search for variations with the period of the binary orbit, namely 7.75 hours. No evidence of an orbital periodicity was found. However, the refined light curve provides a much clearer picture of the outburst and subsequent decay of the X-ray luminosity. In fact, there were two outbursts, each followed by an exponential decay with similar time constants of about 25 days. Previous evidence of a three-oscillation variation with a 7.8 day period were confirmed. Substantial theoretical effort has been devoted to attempts to account for the decay characteristics as the result of the gradual eating up of an accretion disk by a stellar-mass blackhole (e.g., Huang and Wheeler 1989). The improved decay curves will provide significant new constraints on the theoretical analyses.

Clark, George W.↗
DOE OSTI2025

Enabling pan-repository reanalysis for big data science of public metabolomics data

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

El Abiead, Yasin↗
NASA NTRS1983

Discovery of s-process Nd in Allende residue

New interpretation is given to the isotopic anomalies detected by Lugmair et al. (1983) in an acid-resistant residue of the Allende meteorite. If the Nd-142 excess is due to Sm-146 decay, as the discoverers proposed, it is argued that the decay has occurred in interstellar grains, so that the conclusion that Sm-146 (1.03 x 10 to the 8th yr) was alive in the solar system is premature. It is shown by renormalizing their data that the discovery is likely to be s-process Nd, confirming the survival of red-giant stardust in carbonaceous interstellar dust.

Clayton, D. D.↗
DOE OSTI2026

Building the Eyes of Discovery

My presentation focuses on the development and enhancement of sensor modules that help particle detectors “see” particles invisible to the human eye, specifically the Compact Muon Shield detector, which is part of the High-Luminosity Large Hadron Collider. As a CCI intern at Fermilab’s Silicon Detector Facility, I worked on testing and visual inspection of different module parts before assembly and on testing the modules after assembly. My talk highlights how small sensors carry the capability to collect tiny particle signals within the detector and the reliability of control checks pre-assembly for scientists to produce reliable data for future discoveries.

Siddiqui, Hooriya [Fermilab] (ORCID:00090001510973↗
DOE OSTI2025

SEAFORML (Smart Exploration and Analysis For Optimal and Robust Machine Learning)

The poster discusses data analysis of the WAVgraph database and applied machine learning methods for it. The database is a long-term project that seeks to be a comprehensive repository of information on cyber threats and is updated regularly. It was previously unanalyzed and unexplored. The goal was to learn more about it and its contents in order to have a better understanding and enable better use. The data analysis and discovery enabled further exploration through natural language processing, similarity, and clustering methods. The poster shows some of the insights from the analysis and explains the methods used for the machine learning applications.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2021

TIC 172900988: A Transiting Circumbinary Planet Detected in One Sector of TESS Data

We report the first discovery of a transiting circumbinary planet detected from a single sector of Transiting Exoplanet Survey Satellite (TESS) data. During Sector 21, the planet TIC 172900988b transited the primary star and then five days later it transited the secondary star. The binary is itself eclipsing, with a period P ≈ 19.7 days and an eccentricity e ≈ 0.45. Archival data from ASAS-SN, Evryscope, KELT, and SuperWASP reveal a prominent apsidal motion of the binary orbit, caused by the dynamical interactions between the binary and the planet. A comprehensive photodynamical analysis of the TESS, archival and follow-up data yields stellar masses and radii of M(1) = 1.2384 ± 0.0007 Mꙩ and R(1) = 1.3827 ± 0.0016 Rꙩ for the primary and M(2) = 1.2019 ± 0.0007 Mꙩ and R(2) = 1.3124 ± 0.0012 Rꙩ for the secondary. The radius of the planet is R(3) = 11.25 ± 0.44 Rꚛ (1.004 ± 0.039R(Jup)). The planet’s mass and orbital properties are not uniquely determined—there are six solutions with nearly equal likelihood. Specifically, we find that the planet’s mass is in the range of 824 ≲ M3 ≲ 981 Mꚛ (2.65 ≲ M3 ≲ 3.09M(Jup)), its orbital period could be 188.8, 190.4, 194.0, 199.0, 200.4, or 204.1 days, and the eccentricity is between 0.02 and 0.09. At V = 10.141 mag, the system is accessible for high-resolution spectroscopic observations, e.g., the Rossiter–McLaughlin effect and transit spectroscopy.

Veselin B. Kostov↗
NASA NTRS1996

A Note on Interfacing Object Warehouses and Mass Storage Systems for Data Mining Applications

Data mining is the automatic discovery of patterns, associations, and anomalies in data sets. Data mining requires numerically and statistically intensive queries. Our assumption is that data mining requires a specialized data management infrastructure to support the aforementioned intensive queries, but because of the sizes of data involved, this infrastructure is layered over a hierarchical storage system. In this paper, we discuss the architecture of a system which is layered for modularity, but exploits specialized lightweight services to maintain efficiency. Rather than use a full functioned database for example, we use light weight object services specialized for data mining. We propose using information repositories between layers so that components on either side of the layer can access information in the repositories to assist in making decisions about data layout, the caching and migration of data, the scheduling of queries, and related matters.

Grossman, Robert L.↗
NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗