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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 NTRS1991

Magellan - Radar performance and data products

The Magellan Venus orbiter carries only one scientific instrument: a 12.6-centimeter-wavelength radar system shared among three data-taking modes. The synthetic-aperture mode images radar echoes from the Venus surface at a resolution of between 120 and 300 meters, depending on spacecraft altitude. In the altimetric mode, relative height measurement accuracies may approach 5 meters, depending on the terrain's roughness, although orbital uncertainties place a floor of about 50 meters on the absolute uncertainty. In areas of extremely rough topography, accuracy is limited by the inherent line-of-sight radar resolution of about 88 meters. The maximum elevation observed to date, corresponding to a planetary radius of 6062 kilometers, lies within Maxwell Mons. When used as a thermal emission radiometer, the system can determine surface emissivities to an absolute accuracy of about 0.02. Mosaicked and archival digital data products will be released in compact disk (CDROM) format.

Pettengill, Gordon H.↗
NASA NTRS2004

Harnessing: Technologies for Sustainable Reindeer Husbandry in the Arctic

To accelerate the development of sustainable reindeer husbandry under the lead of indigenous reindeer herders, it is critical to empower reindeer herders with the best available technologies and to promote a new kind of science where traditional knowledge is fully integrated into the scientific management of the natural environment in the Arctic. This is particularly true given the dramatic environmental, climatic, economic, social and industrial changes, which have taken place across the Arctic in recent years, all of which have had serious impacts on the reindeer herding communities of the North. The Anar Declaration, adopted by the 2d World Reindeer Herders Congress (WRHC), in Inari, Finland, June 2001drew guidelines for the development of a sustainable reindeer husbandry based on reindeer peoples values and goals. The declaration calls for the reindeer herding peoples to be given the possibilities to develop and influence the management of the reindeer industry and its natural environment because of their knowledge and traditional practices. At the same time, Arctic scientists from many institutions and governments are carrying out increasingly highly technical reindeer related research activities. It is important that the technologies and results of these activities be more commonly co-produced with the reindeer herder community and/or made more readily available to the reindeer peoples for comparison with traditional knowledge for improved herd management. This paper describes a project in which reindeer herders and scientists are utilizing technologies to create a system for collecting and sharing knowledge. The project, Reindeer Mapper, is creating an information management and knowledge sharing system, which will help make technologies more readily available to the herder community for observing, data collection and analysis, monitoring, sharing, communications, and dissemination of information - to be integrated with traditional, local knowledge. The paper describes some of the technologies which comprise the system including an intranet system to enable the team members to work together and share information electronically, remote sensing data for monitoring environmental parameters important to reindeer husbandry (e.g. SAR, Landsat), acquisition of ground-based measurements, and the GIS-based information management and knowledge sharing system.

Maynard, Nancy G.↗
NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗
NASA NTRS1967

A user-oriented time-shared online system.

Computerized method for on-line data analysis, elucidating SLIP language, request, processing, interpretation, time sharing, background activity, etc

LANGUAGE PROGRAMMING↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2019

Cumulus Lessons Learned: Building, Testing, and Sharing a Cloud Archive

Cumulus is a scalable, extensible cloud-based archive system which is capable of ingesting, archiving, and distributing data from both existing on-prem sources and new cloud-native missions. As we have built and evolved the system with contributions from seven NASA EOSDIS organizations, we have learned several lessons about how to build a robust, broadly-applicable, microservices-based cloud system for geospatial data which we will share in this talk.

Quinn, Patrick↗
NASA NTRS1989

Thin film memory matrix using amorphous and high resistive layers

Memory cells in a matrix are provided by a thin film of amorphous semiconductor material overlayed by a thin film of resistive material. An array of parallel conductors on one side perpendicular to an array of parallel conductors on the other side enable the amorphous semiconductor material to be switched in addressed areas to be switched from a high resistance state to a low resistance state with a predetermined level of electrical energy applied through selected conductors, and thereafter to be read out with a lower level of electrical energy. Each cell may be fabricated in the channel of an MIS field-effect transistor with a separate common gate over each section to enable the memory matrix to be selectively blanked in sections during storing or reading out of data. This allows for time sharing of addressing circuitry for storing and reading out data in a synaptic network, which may be under control of a microprocessor.

Thakoor, Anilkumar P.↗
DOE OSTI2024

BigPanDA monitoring system evolution in the ATLAS Experiment

Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Investigating the Future of Scientific Data Search [Slides]

Searching for usable, actionable, data in a trustworthy manner is a challenge across scientific communities. Artificial Intelligence (AI) and Machine Learning (ML) techniques may be leveraged to increase the utility of scientific data by: Demystify unstructured data to aid curation & sharing Surfacing hard to find datasets. User Experience (UX) Research can help uncover scientists needs & challenges finding data and using AI/ML enabled tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Geocuration Lessons Learned from the Climate Data Initiative Project

Curation is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest and typically occurs in museums, art galleries, and libraries. The task of organizing data around specific topics or themes is a vibrant and growing effort in the biological sciences but to date this effort has not been actively pursued in the Earth sciences. This presentation will introduce the concept of geocuration, which we define it as the act of searching, selecting, and synthesizing Earth science data/metadata and information from across disciplines and repositories into a single, cohesive, and useful compendium. We also present the Climate Data Initiative (CDI) project as an prototypical example. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. CDI is a broad multi-agency effort of the U.S. government and seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate change preparedness. The geocuration process used in the CDI project, key lessons learned, and suggestions to improve similar geocuration efforts in the future will be part of this presentation.

climate↗
NASA NTRS2015

Climate Data Initiative: A Geocuration Effort to Support Climate Resilience

Curation is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest and typically occurs in museums, art galleries, and libraries. The task of organizing data around specific topics or themes is a vibrant and growing effort in the biological sciences but to date this effort has not been actively pursued in the Earth sciences. In this paper, we introduce the concept of geocuration and define it as the act of searching, selecting, and synthesizing Earth science data/metadata and information from across disciplines and repositories into a single, cohesive, and useful collection. We present the Climate Data Initiative (CDI) project as a prototypical example. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. CDI is a broad multi-agency effort of the U.S. government and seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate-change preparedness. We describe the geocuration process used in the CDI project, lessons learned, and suggestions to improve similar geocuration efforts in the future.

Metada↗
NASA NTRS2015

Climate Data Initiative: A Geocuration Effort to Support Climate Resilience

Curation is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest and typically occurs in museums, art galleries, and libraries. The task of organizing data around specific topics or themes is a vibrant and growing effort in the biological sciences but to date this effort has not been actively pursued in the Earth sciences. In this paper, we introduce the concept of geocuration and define it as the act of searching, selecting, and synthesizing Earth science data/metadata and information from across disciplines and repositories into a single, cohesive, and useful compendium We present the Climate Data Initiative (CDI) project as an exemplar example. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. CDI is a broad multi-agency effort of the U.S. government and seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate-change preparedness. We describe the geocuration process used in CDI project, lessons learned, and suggestions to improve similar geocuration efforts in the future.

virtual collections↗
DOE OSTI2025

Recommendations for Minimum Required Diagnostics Information

The rapid growth of electrified transportation, including light- and medium-duty electric vehicles (EVs) as a mobility solution requires a reliable EV-charging infrastructure. To advance charging reliability, the ChargeX Consortium reports “Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure” and “Implementation Guide for Minimum Required Error Codes in Electric Vehicle Charging Infrastructure” provided recommendations for a set of minimum required error codes (MRECs), their functional and responsibility classifications, and a guide for their implementation, using Open Charge Point Protocol (OCPP) versions 1.6J4 and 2.0.1.5 These reports outline a recommended practice for consistent error reporting and interpretation, which is essential for communicating issues uniformly across the complex and diverse EV-charging ecosystem. However, MRECs are just one part of diagnosing issues; another critical part is obtaining enough information about the current state and performance of the various charging components to identify root causes for each of the error codes. This additional diagnostics data can be used by technicians or automated systems to understand the context around an issue, allowing for timely resolution, decreased maintenance costs, and increased charging reliability. During everyday operations, data are regularly collected and analyzed across the ecosystem. Although sharing of all that available data would be great for diagnostics, concerns on data ownership, privacy, and original equipment manufacturer (OEM) intellectual property pose a challenge. To overcome this obstacle, this report proposes a set of minimum required diagnostic information (MRDI) and recommends that the industry implement these uniformly across the North American EV charging ecosystem. MRDI provides a means to exchange only data deemed necessary for root cause determination.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Recommendations for Minimum Required Diagnostics Information for Electric Vehicle Charging Infrastructure

The rapid growth of electrified transportation, including light- and medium-duty electric vehicles (EVs) as a mobility solution requires a reliable EV charging infrastructure. To advance charging reliability, the ChargeX Consortium reports “Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure” and “Implementation Guide for Minimum Required Error Codes in Electric Vehicle Charging Infrastructure” have provided recommendations for a set of minimum required error codes (MRECs), their functional and responsibility classifications, and a guide for their implementation using OCPP versions 1.6J and 2.0.1. These reports outline a recommended practice for consistent error reporting and interpretation, which is essential for communicating issues uniformly across the complex and diverse EV charging ecosystem. However, MRECs are just one part of diagnosing issues, another critical part is obtaining enough information about the current state and performance of the various charging components to identify root causes for each of the error codes. This additional diagnostics data can be used by technicians or automated systems to understand the context around an issue, allowing for timely resolution, decreased maintenance costs, and increased charging reliability. During everyday operations, data is regularly collected and analyzed across the ecosystem. Although sharing of all that available data would be great for diagnostics, concerns on data ownership, privacy, and OEM intellectual property pose a challenge. To overcome this obstacle, this report proposes a set of Minimum Required Diagnostic Information (MRDI) and recommends that the industry implement these uniformly across the North American EV charging ecosystem. MRDI provides a means to exchange only data deemed necessary for root cause determination.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Use NASA Earthdata in the Cloud

With the impending arrival of new, high-data-volume Earth observation missions, NASA’s ability to effectively ingest, process, and archive large amounts of data requires the most cost-effective, flexible, and scalable data-management architectures and technologies. To meet these demands, NASA's Earth Science Data Systems (ESDS) Program is implementing a strategic vision to develop and operate multiple components of NASA's Earth Observing System Data and Information System (EOSDIS) in a commercial cloud environment. As more earth observing data is moved into the cloud, NASA EOSDIS wants to share our reasoning for moving data into the commercial cloud, as well as demonstrate how end-users can take advantage of both data in the cloud and cloud-deployed services. This presentation explains the enterprise reasons for moving data into the cloud, specifically the scalability of cloud systems, the flexibility to pivot to the most optimal language/system/architecture, and the reduced redundancy of a common architecture. Then we primarily focus on the user-driven reasons for moving to the cloud. Equal access to high performance computing helps all parties, especially users that don't have access to their own on-premise computing infrastructure. Rapid deployment allows users to spin up their own systems without needing the extensive platform previously required for large scale development. Cost effectiveness allows users a wide breadth of computing options, from super computer ability to small scale development, which can help underserved communities. Finally we link to internal resources that users can utilize to begin their cloud based development journey without having to be an expert in the field.

Nicholas Doty↗
NASA NTRS2017

Growing Beyond Earth; Students Exploring Plant Varieties for Future Space Exploration

Future space exploration and long duration space flight will pose an array of challenges to the health and wellbeing of astronauts. Since 2015, Fairchild Tropical Botanic Garden (FTBG), in partnership with NASA's Veggie team, has been testing edible crops for space flight potential through a series of citizen science experiments. FTBG's interest in classroom-based science projects, along with NASA's successful operation of the Veggie system aboard the International Space Station (ISS), led to a NASA-FTBG partnership that gave rise to the Growing Beyond Earth STEM Initiative (GBE). Established in 2015, GBE now involves 131 middle and high school classrooms in South Florida, all conducting simultaneous plant science experiments. The results of those experiments (both numeric and visual) are directly shared with the space food production researchers at KSC. Through this session, we will explore the successful classroom implementation and integration into the curriculum, how the data is being used and the impact of the project on participating researchers, teachers, and students. Participating schools were supplied with specialized LED-lit growth chambers, mimicking the Veggie system on ISS, for growing edible plants under similar physical and environmental constraints. Research protocols were provided by KSC scientists, while edible plant varieties were selected mainly by the botanists at FTBG. In a jointly-led professional development workshop, participating teachers were trained to conduct GBE experiments in their classrooms. Teachers were instructed to not only teach basic botany concepts, but to also demonstrate practical applications of math, physics and chemistry. As experiments were underway, students shared data on plant germination, growth, and health in an online spreadsheet. Results from the students research show a promising selection of new plant candidates for possible further testing. Over a two year period, more than 5000 South Florida students, ages 11 to 18, participated in GBE. Evaluation of the program shows an increased knowledge of and interest in science and science careers among students. The program has also boosted the demand for summer high school internships at FTBG, further developing expertise in plant research and science related to space exploration. Supported by a grant from NASA (NNX16AM32G) to Fairchild Tropical Botanic Garden.

Veggie↗
NASA NTRS2017

Building A Cloud Based Distributed Active Data Archive Center

NASA's Earth Science Data System (ESDS) Program facilitates the implementation of NASA's Earth Science strategic plan, which is committed to the full and open sharing of Earth science data obtained from NASA instruments to all users. The Earth Science Data information System (ESDIS) project manages the Earth Observing System Data and Information System (EOSDIS). Data within EOSDIS are held at Distributed Active Archive Centers (DAACs). One of the key responsibilities of the ESDS Program is to continuously evolve the entire data and information system to maximize returns on the collected NASA data.

Earth Science Informatics↗