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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Federated Learning and Differential Privacy: What might AI-Enhanced co-design of microelectronics learn?

Data is a valuable commodity, and it is often dispersed over multiple entities. Sharing data or models created from the data is not simple due to concerns regarding security, privacy, ownership, and model inversion. This limitation in sharing can hinder model training and development. Federated learning can enable data or model sharing across multiple entities that control local data without having to share or exchange the data themselves. Differential privacy is a conceptual framework that brings strong mathematical guarantee for privacy protection and helps provide a quantifiable privacy guarantee to any data or models shared. The concepts of federated learning and differential privacy are introduced along with possible connections. Lastly, some open discussion topics on how federated learning and differential privacy can tied to AI-Enhanced co-design of microelectronics are highlighted.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

HydroGEN and H2NEW Data Hub

HydroGEN is a multi-lab consortium focused on early-stage R&D in H2 production, supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Hydrogen and Fuel Cell Technologies Office (HFTO). The consortium advances research and development (R&D) of innovative materials for advanced water splitting (AWS) technologies to enable clean, sustainable and low-cost ($1/kg H2) hydrogen production and fosters cross-cutting innovation using theory-guided applied materials R&D to advance all emerging water-splitting pathways for hydrogen production. Hydrogen from Next-generation Electrolyzers of Water (H2NEW) is a consortium of nine U.S. Department of Energy (DOE) national laboratories focused on making large-scale electrolyzers, which produce hydrogen from electricity and water, more durable, efficient, and affordable. The presentation introduces the HydroGEN data hub to the H2NEW consortium and encourages H2NEW members to use it to store and share data with team members and eventually the public. The two consortia share this data hub.

advanced water splitting technologies↗
NASA NTRS1985

Priority Queuing On A Parallel Data Bus

Queuing strategy for communications along shared data bus minimizes number of data lines while always assuring user of highest priority given access to bus. New system handles up to 32 user demands on 17 data lines that previously serviced only 17 demands.

Wallis, D. E.↗
NASA NTRS2020

CubeSat Array for Detection of RF Emissions from Exoplanets using Inter-Satellite Optical Communicators

We are presenting a CubeSat mission concept wherein a large number of small spacecraft furnished with Ultra-Long Wavelength (ULW) observation antennas is to be implemented to synthesize a large aperture for detecting ULW emissions from selected exoplanets. The CubeSat Array for the Detection of RF Emissions from Exoplanets (CADRE) concept involves the placement of a swarm arrangement of CubeSats into small amplitude Lissajous orbits around the Earth-Moon L2 point. In addition to the ULW receivers, each CADRE CubeSat will be furnished with a novel omnidirectional optical communicator which should allow data sharing at gigabit per second data rates. In this paper we discuss the science goals, potential architecture and key technical challenges of an eventual CADRE implementation. We present the estimated interferometer specifications needed to detect Exoplanet RF emissions, CubeSat orbits and formations as well as CubeSat design considerations. Special focus is placed on the design of the mission-enabling inter-CubeSat optical cross link which allows for fast sharing of the detected RF signals for subsequent correlation.

Bentum, Mark↗
DOE OSTI2017

PV Systems Reliability and Outreach

The project purpose was to collaborate with the Electric Power Research Institute (EPRI) to reach out to the photovoltaic (PV) industry to cooperate, collaborate and facilitate establishment and access to PV plant data for use in the Photovoltaic Reliability Operations and Maintenance (PVROM) database. Sandia National Laboratories (SNL) and EPRI conducted this outreach to expand and accelerate the implementation of PVROM by gathering more data for analyzing trends, with a goal of accelerating the adoption of PV systems as a primary power generation source in the United States. SNL and EPRI analyzed the data from the PVROM database to assess industry trends in operations and maintenance (O&M) and develop guidelines for the industry. More specifically, this project was intended to leverage SNL’s expertise in PV plant reliability data collection and analysis, with data sharing partners that own or operate PV systems who wanted to learn about rigorous reliabilitybased data collection to track issues over time and improve O&M activities. The partnership also worked to host multiple workshops as part of the SNL/EPRI PV Systems Symposium to gather industry feedback on O&M process improvement and data standards, and share insights learned from the collaboration between SNL and EPRI in the area of PV system reliability. Additional outreach included multiple ‘expert elicitation’ interviews led by EPRI to gather best practices and paths forward for improving/standardizing PV plant O&M and asset management practices. Technical accomplishments included further development of the PVROM database process, sharing best practices for reliability data collection and improving industry understanding of existing O&M practices, important knowledge, and new and novel techniques for benefitting EPRI utility partners and the broader PV industry. Improved reliability at a PV power plant leads to a lower levelized cost of energy (LCOE), which is reflected most directly in its operating expenses. Electric utilities that utilize PV as a larger share of plants they own and operate are interested in having a high degree of availability or ‘uptime’, similar to other power generation sources in their service area. Having low cost, reliable PV generation can lead to fewer costs that need to be recovered by the utility over the lifetime of the power plant, and less regulatory uncertainty when considering bulk transmission and distribution system reliability. The public and taxpayers benefit by the electric utility passing through lower electricity costs.

14 SOLAR ENERGY↗
DOE OSTI2023

Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Abstract We thank the authors for this interesting paper that highlights important ideas and concepts for the future of climate model ensembles and their storage, as well as future uses of stochastic emulators. Stochastic emulators are particularly relevant because of the statistical nature of climate model ensembles, as discussed in previous work of the authors (Castruccio et al. in J Clim 32:8511–8522, 2019; Hu and Castruccio in J Clim 34:8409–8418, 2021). We thank the authors for sharing of some of their data with us in order to illustrate this discussion. In the following, in Sect. 1 we discuss alternative techniques currently used and studied, namely lossy compression and ideas emerging from the climate modeling community, that could feed the discussion on ensemble and storage. In that section, we also present numerical results of compression performed on the data shared by the authors. In Sect. 2, we discuss the current statistical model proposed by the authors and its context. We discuss other potential uses of stochastic emulators in climate and Earth modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

International Radiation Monitoring Information System (IRMIS) – a versatile EPR tool for all member states [Slides]

IRMIS provides Competent Authorities, IEC, and other relevant International Organizations with a data sharing tool that helps to report and share information, evaluate radiation monitoring data to assess if the public is safe, identify protective actions, keep the public informed by Member State Competent Authority, and maintain transparency of data handling and processing.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1994

An overview of the Opus language and runtime system

We have recently introduced a new language, called Opus, which provides a set of Fortran language extensions that allow for integrated support of task and data parallelism. lt also provides shared data abstractions (SDA's) as a method for communication and synchronization among these tasks. In this paper, we first provide a brief description of the language features and then focus on both the language-dependent and language-independent parts of the runtime system that support the language. The language-independent portion of the runtime system supports lightweight threads across multiple address spaces, and is built upon existing lightweight thread and communication systems. The language-dependent portion of the runtime system supports conditional invocation of SDA methods and distributed SDA argument handling.

Mehrotra, Piyush↗
NASA NTRS1994

Runtime support for data parallel tasks

We have recently introduced a set of Fortran language extensions that allow for integrated support of task and data parallelism, and provide for shared data abstractions (SDA's) as a method for communications and synchronization among these tasks. In this paper we discuss the design and implementation issues of the runtime system necessary to support these extensions, and discuss the underlying requirements for such a system. To test the feasibility of this approach, we implement a prototype of the runtime system and use this to support an abstract multidisciplinary optimization (MDO) problem for aircraft design. We give initial results and discuss future plans.

Haines, Matthew↗
NASA NTRS2003

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

knowledge systems↗
DOE OSTI2014

A Qualitative Readiness-Requirements Assessment Model for Enterprise Big-Data Infrastructure Investment

In the last three decades, there has been an exponential growth in the area of information technology providing the information processing needs of data-driven businesses in government, science, and private industry in the form of capturing, staging, integrating, conveying, analyzing, and transferring data that will help knowledge workers and decision makers make sound business decisions. Data integration across enterprise warehouses is one of the most challenging steps in the big data analytics strategy. Several levels of data integration have been identified across enterprise warehouses: data accessibility, common data platform, and consolidated data model. Each level of integration has its own set of complexities that requires a certain amount of time, budget, and resources to implement. Such levels of integration are designed to address the technical challenges inherent in consolidating the disparate data sources. In this paper, we present a methodology based on industry best practices to measure the readiness of an organization and its data sets against the different levels of data integration. We introduce a new Integration Level Model (ILM) tool, which is used for quantifying an organization and data system s readiness to share data at a certain level of data integration. It is based largely on the established and accepted framework provided in the Data Management Association (DAMA-DMBOK). It comprises several key data management functions and supporting activities, together with several environmental elements that describe and apply to each function. The proposed model scores the maturity of a system s data governance processes and provides a pragmatic methodology for evaluating integration risks. The higher the computed scores, the better managed the source data system and the greater the likelihood that the data system can be brought in at a higher level of integration.

Big data↗
DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗
NASA NTRS2023

We Need A Better Way to Share Earth Observations

A more accessible, open data-sharing infrastructure will engage a broader community of contributors, helping to develop satellite data products that benefit Earth science research and applications.

Apps & Software↗
DOE OSTI2022

Validation of standardized data formats and tools for ground-level particle-based gamma-ray observatories

Context. Ground-based γ-ray astronomy is still a rather young field of research, with strong historical connections to particle physics. This is why most observations are conducted by experiments with proprietary data and analysis software, as is usual in the particle physics field. However, in recent years, this paradigm has been slowly shifting toward the development and use of open-source data formats and tools, driven by upcoming observatories such as the Cherenkov Telescope Array (CTA). In this context, a community-driven, shared data format (the gamma-astro-data-format, or GADF) and analysis tools such as Gammapy and ctools have been developed. So far, these efforts have been led by the Imaging Atmospheric Cherenkov Telescope community, leaving out other types of ground-based γ-ray instruments. Aims. We aim to show that the data from ground particle arrays, such as the High-Altitude Water Cherenkov (HAWC) observatory, are also compatible with the GADF and can thus be fully analyzed using the related tools, in this case, Gammapy. Methods. We reproduced several published HAWC results using Gammapy and data products compliant with GADF standard. We also illustrate the capabilities of the shared format and tools by producing a joint fit of the Crab spectrum including data from six different γ-ray experiments. Results. We find excellent agreement with the reference results, a powerful confirmation of both the published results and the tools involved. Conclusions. The data from particle detector arrays such as the HAWC observatory can be adapted to the GADF and thus analyzed with Gammapy. A common data format and shared analysis tools allow multi-instrument joint analysis and effective data sharing. To emphasize this, a sample of Crab nebula event lists is made public with this paper. Because of the complementary nature of pointing and wide-field instruments, this synergy will be distinctly beneficial for the joint scientific exploitation of future observatories such as the Southern Wide-field Gamma-ray Observatory and CTA.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Livewire User Guide

The Livewire Data Platform houses a catalog of transportation- and mobility-related project data, as well as a publications database, making it easy to search and share data. It allows transportation researchers, industry, and academic partners to increase the visibility of their projects within the research community, securely share and preserve data, and leverage datasets from other projects. Public data on Livewire are open to anyone with a Livewire account. This guide will help Livewire users understand how to store project data as a data steward, as well as access data as a data consumer.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations

In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of a posteriori data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

54 ENVIRONMENTAL SCIENCES↗