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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2017

NASA's Big Earth Data Initiative Accomplishments

The goal of NASA's effort for BEDI is to improve the usability, discoverability, and accessibility of Earth Observation data in support of societal benefit areas. Accomplishments: In support of BEDI goals, datasets have been entered into Common Metadata Repository(CMR), made available via the Open-source Project for a Network Data Access Protocol (OPeNDAP), have a Digital Object Identifier (DOI) registered for the dataset, and to support fast visualization many layers have been added in to the Global Imagery Browse Services (GIBS).

CMR↗
NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗
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 NTRS1994

Three-dimensional magnetic bubble memory system

A compact memory uses magnetic bubble technology for providing data storage. A three-dimensional arrangement, in the form of stacks of magnetic bubble layers, is used to achieve high volumetric storage density. Output tracks are used within each layer to allow data to be accessed uniquely and unambiguously. Storage can be achieved using either current access or field access magnetic bubble technology. Optical sensing via the Faraday effect is used to detect data. Optical sensing facilitates the accessing of data from within the three-dimensional package and lends itself to parallel operation for supporting high data rates and vector and parallel processing.

Stadler, Henry L.↗
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 NTRS2014

Earth Science Informatics Comes of Age

The volume and complexity of Earth science data have steadily increased, placing ever-greater demands on researchers, software developers and data managers tasked with handling such data. Additional demands arise from requirements being levied by funding agencies and governments to better manage, preserve and provide open access to data. Fortunately, over the past 10-15 years significant advances in information technology, such as increased processing power, advanced programming languages, more sophisticated and practical standards, and near-ubiquitous internet access have made the jobs of those acquiring, processing, distributing and archiving data easier. These advances have also led to an increasing number of individuals entering the field of informatics as it applies to Geoscience and Remote Sensing. Informatics is the science and technology of applying computers and computational methods to the systematic analysis, management, interchange, and representation of data, information, and knowledge. Informatics also encompasses the use of computers and computational methods to support decisionmaking and other applications for societal benefits.

Jodha, Siri↗
DOE OSTI2022

AMM: Adaptive Multilinear Meshes

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Additionally, recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

The Role and Evolution of NASA's Earth Science Data Systems

One of the three strategic goals of NASA is to Advance understanding of Earth and develop technologies to improve the quality of life on our home planet (NASA strategic plan 2014). NASA's Earth Science Data System (ESDS) Program directly supports this goal. NASA has been launching satellites for civilian Earth observations for over 40 years, and collecting data from various types of instruments. Especially since 1990, with the start of the Earth Observing System (EOS) Program, which was a part of the Mission to Planet Earth, the observations have been significantly more extensive in their volumes, variety and velocity. Frequent, global observations are made in support of Earth system science. An open data policy has been in effect since 1990, with no period of exclusive access and non-discriminatory access to data, free of charge. NASA currently holds nearly 10 petabytes of Earth science data including satellite, air-borne, and ground-based measurements and derived geophysical parameter products in digital form. Millions of users around the world are using NASA data for Earth science research and applications. In 2014, over a billion data files were downloaded by users from NASAs EOS Data and Information System (EOSDIS), a system with 12 Distributed Active Archive Centers (DAACs) across the U. S. As a core component of the ESDS Program, EOSDIS has been operating since 1994, and has been evolving continuously with advances in information technology. The ESDS Program influences as well as benefits from advances in Earth Science Informatics. The presentation will provide an overview of the role and evolution of NASAs ESDS Program.

Remote Sensing↗
NASA NTRSDate not supplied

North American Land Data Assimilation (NLDAS) Data and Services at NASA GES DISC

The North American Land Data Assimilation System (NLDAS, https://ldas.gsfc.nasa.gov/nldas) integrates a large quantity of observation-based and model reanalysis data to drive offline (not coupled to the atmosphere) land surface models (LSMs) to produce fields of soil moisture, snow, and surface fluxes. The NLDAS-2 data sets currently archived at, and distributed by, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC, https://disc.gsfc.nasa.gov) have been updated to NLDAS Version 2.0 (NLDAS-2.0). This presentation provides an overview of the updated NLDAS-2.0 data at NASA GES DISC and discusses the methods for the public to access the data as well as the data services available.

Ashley Heath↗
DOE OSTI2021

SIRIUS: Science-Driven Data Management for Multi-Tiered Storage

The data sets being generated by large applications on very large-scale systems are increasing in both size and complexity. At the same time, there are new ways available to store and access these data sets. The goal in this project is to develop software that applications can use to make use of new and existing storage technologies in more sophisticated ways. One challenge in scientific data management is handling ‘hot’ vs ‘cold’ data. Data that is hot is data that is needed (or will be needed soon) in order for the program to continue progressing, while cold data is either output (and so will not be need further during the life of the program) or will not be needed until significantly later in the program’s run. Hot data should be stored in a way that allows fast access. On most systems, economic factors lead to an inverse relationship between storage performance and storage capacity and so fast access storage is limited. This makes it important to correctly place hot and cold data and avoid cold data unnecessarily consuming precious resources. In this reporting period, we addressed this challenge in various ways and at various levels. Data management frameworks offer only limited control to applications in how data is stored. We have added software capabilities for seamlessly moving data between layers of the storage technology using promote and demote functions to existing software frameworks. This gives direct control to applications in deciding what priority data receives. Additionally, we integrated different storage layer management frameworks in order to allow data to be exchanged and moved between storage layers in a consistent way across the application. Further, applications are not always able to directly decide what storage level makes sense for a given piece of data without an understanding of the underlying storage technologies. Data storage frameworks are often positioned to make these sorts of decisions in service of the application. We have added machine-learning based capabilities to data staging frameworks in order to make intelligent decisions about where data should be stored given learning about patterns in previous usage of similar data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Small Aerostationary Telecommunications Orbiter Concept for Mars in the 2020s

Current Mars science orbiters carry UHF proximity payloads to provide limited access and data services to landers and rovers on Mars surface. In the era of human spaceflight to Mars, very high rate and reliable relay services will be needed to serve a large number of supporting vehicles, habitats, and orbiters, as well as astronaut EVAs. These will likely be provided by a robust network of orbiting assets in very high orbits, such as areostationary orbits. In the decade leading to that era, telecommunications orbits can be operated at areostationary orbit that can support a significant population of robotic precursor missions and build the network capabilities needed for the human spaceflight era. Telecommunications orbiters of modest size and cost, delivered by Solar Electric Propulsion to areostationary orbit, can provide continuous access at very high data rates to users on the surface and in Mars orbit.In the era of human spaceflight to Mars very high rate andreliable relay services will be needed to serve a largenumber of supporting vehicles, habitats, and orbiters, aswell as astronaut EVAs. These could be provided by arobust network of orbiting assets in very high orbits. In thedecade leading to that era, telecommunications orbiterscould be operated at areostationary orbit that could support asignificant population of robotic precursor missions andbuild the network capabilities needed for the humanspaceflight era. These orbiters could demonstrate thecapabilities and services needed for the future but withoutthe high bandwidth and high reliability requirements neededfor human spaceflight.Telecommunications orbiters of modest size and cost,delivered by Solar Electric Propulsion to areostationaryorbit, could provide continuous access at very high datarates to users on the surface and in Mars orbit. Twoexamples highlighting the wide variety of orbiter deliveryand configuration options were shown that could providehigh-performance service to users.

Lock, Robert E.↗
DOE OSTI2024

Protein data bank: From two epidemics to the global pandemic to mRNA vaccines and Paxlovid

Structural biologists and the open-access Protein Data Bank (PDB) played decisive roles in combating the COVID-19 pandemic. Global biostructure data were turned into global knowledge, allowing scientists and engineers to understand the inner workings of coronaviruses and develop effective countermeasures. Two mRNA vaccines, initially designed with guidance from PDB structures of the SARS-CoV-1 and MERS-CoV spike proteins, prevented infections entirely or reduced the likelihood of morbidity and mortality for more than five billion individual recipients worldwide. Structure-guided drug discovery by Pfizer, Inc (facilitated by PDB structures), initiated in the 2000s in response to SARS-CoV-1 and resumed in 2020, yielded nirmatrelvir (the active ingredient of Paxlovid) -- a potent, orally-bioavailable inhibitor of the SARS-CoV-2 main protease. You've got to love the Protein Data Bank!

Burley, Stephen K.↗
DOE OSTI2023

The UCLA Cosmochemistry Database

Abstract The UCLA Cosmochemistry Database was initiated as part of a data-rescue and -storage project aimed at archiving a variety of cosmochemical data acquired at University of California, Los Angeles (UCLA). The data collection includes elemental compositions of extraterrestrial materials analyzed by UCLA cosmochemists over the last five decades. The analytical techniques include atomic absorption spectrometry (AAS) and neutron activation analysis (NAA) at UCLA. The data collection is stored on the Astromaterials Data System (Astromat). We provide both interactive tables and downloadable datasheets for users to access all data. The UCLA Cosmochemistry Database archives cosmochemical data that are essential tools for increasing our understanding of the nature and origin of extraterrestrial materials. Future studies can reference the data collection in the examination, analysis, and classification of newly acquired extraterrestrial samples.

Science & Technology - Other Topics↗
NASA NTRS1985

Dynamic-RAM Data Storage Unit

Dynamic random-access-memory (RAM) data delay and storage unit developed to insure data received from satellite is stored and not lost when satellite is not within range of ground station. Stores 256K of serial data, with independent read and write capability.

Sturman, J. C.↗
NASA NTRS1986

The measurement of boundary layers on a compressor blade in cascade at high positive incidence angle. 2: Data report

Boundary layer and near-wake velocity measurements have been made in the well documented flow field about a double circular arc compressor blade in cascade, at an incidence angle of 5 deg. and a chord Reynolds number of 500,000. In Part 2 of this report these measurements were analyzed and presented in standard graphical format. The flow geometry, measurement techniques, and physics of the flow field were also discussed. In this, part 2 of the report, raw and analyzed data are presented in tabulated form in an attempt to make this data more accessible to computational comparison. Also included in part 2 is a description of the data analysis employed. A computer tape containing the data is available.

Deutsch, S.↗
NASA NTRS1992

Collection, analysis, and archival of LDEF activation data

Samples intentionally placed aboard the LDEF and samples obtained from the LDEF structure were studied at NASA Marshall and seven national labs to determine the radioactivity produced in orbit. The gamma ray spectra from these studies have provided information concerning the type and quantity of radioactive nuclei produced by various activating particles. The gamma ray spectra, the resulting activation, and the experimental arrangements are being collected at Marshall and Eastern Kentucky Univ. for review, further analysis, and future archival. An overview of this process and the type of information that will be available for future reference is given. This information includes the samples studied, the location of the samples on LDEF, the amount and type of covering material, the types of detector systems, the format of the gamma ray spectra, and the corrections for geometry, self-absorption, detector efficiency, and background needed to obtain accurate specific activations (activation per kilogram) of material. Plans are given as to the archival of the data for such future reference and how other scientific investigators or spacecraft designers can access the data.

Laird, C. E.↗
NASA NTRS1991

The interconnected data directory system and Catalog Interoperability

The Catalog Interoperability (CI) project, designed to enable rapid and efficient identification, location, and access to data of interest to the science community, is considered. The CI goal is to create a worldwide data information network composed of interconnected directory, guide (catalog), and inventory systems. Directories were created to aid in finding data. The directories contain brief information about the data sets, sufficient for determining whether further investigation is warranted. They also provide automated links to other information systems which give more detail on data of interest. A common format for describing data sets has been developed, called the Directory Interchange Format (DIF), which is used as the basis of information to be shared among the directories. These DIF files can be passed among the directories to keep their information up to date.

Thieman, James R.↗