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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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DOE OSTI2025

EV-ELM (Electric Vehicle Policies with the Energy Language Model) [SWR-25-156]

Electric Vehicle Policies with the Energy Language Model (EV-ELM) leverages previous work using Large Language Models (LLMs) to find, download, and parse policy information related to energy infrastructure. In this application, we use LLMs to find policy documents related to the permitting and installation of electric vehicle charging infrastructure. This software contains the code to find, download, and parse these documents, while a related data record in the Open Energy Data Initiative (OEDI) will include the resulting output dataset that can be used for downstream analysis. The EV-ELM repository contains code for the EV-ELM project, which focuses on retrieving and processing EV permitting processes using large language models. The project is composed of two pipelines: (1) a web scraping pipeline for discovering and downloading EV permitting documents, and (2) a document parsing and extraction pipeline that processes the downloaded files to produce structured data. The web scraping pipeline is designed to extract relevant information from various websites, while the document parsing pipeline processes and analyzes the extracted documents to derive meaningful insights. Both pipelines depend on the NLR elm repository, which provides essential tools and functionalities for handling and processing the data. The web scraping pipeline is a modified version of the ordinance_gpt example within the elm repository. It has been adapted to fit the specific requirements of the EV-ELM project, ensuring that it effectively captures and processes the necessary information related to EV permitting.

Olson, Reid [National Laboratory of the Rockies (N↗

NASA NTRS2010

Phase I for the Use of TOPEX-Poseidon and Jason-1 Radar Altimetry to Monitor Coastal Wetland Inundation and Sea Level Rise in Coastal Louisiana

The objective of the first phase of this project was to determine the feasibility of applying satellite altimetry data to monitor sea level rise and inundation within coastal Louisiana. Global sea level is rising, and coastal Louisiana is subsiding. Therefore, there is a need to monitor these trends over time for coastal restoration and hazard mitigation efforts. TOPEX/POSEIDON and Jason-data are used for global sea level estimates and have also been demonstrated successfully in water level studies of lakes, river basins, and floodplains throughout the world. To employ TOPEX/POSEIDON and Jason-1 data in coastal regions, the numerous steps involved in processing the data over non-open ocean areas must be assessed. This project outlined the appropriate methodology for processing non-open ocean data, including retracking and atmospheric corrections. It also inventoried the many factors in coastal land loss including subsidence, sea level rise, coastal geomorphology, and salinity levels, among others, through a review of remote sensing and field methods. In addition, the project analyzed the socioeconomic factors within the Coastal Zone as compared to the rest of Louisiana. While sensor data uncertainty must be addressed, it was determined that it is feasible to apply radar altimetry data from TOPEX/POSEIDON and Jason 1 to see trends in change within Coastal Louisiana since

Brozen, Madeline↗

NASA NTRS2012

Demystifying Kepler Data: A Primer for Systematic Artifact Mitigation

The Kepler spacecraft has collected data of high photometric precision and cadence almost continuously since operations began on 2009 May 2. Primarily designed to detect planetary transits and asteroseismological signals from solar-like stars, Kepler has provided high quality data for many areas of investigation. Unconditioned simple aperture time-series photometry are however affected by systematic structure. Examples of these systematics are differential velocity aberration, thermal gradients across the spacecraft, and pointing variations. While exhibiting some impact on Kepler's primary science, these systematics can critically handicap potentially ground-breaking scientific gains in other astrophysical areas, especially over long timescales greater than 10 days. As the data archive grows to provide light curves for 10(exp 5) stars of many years in length, Kepler will only fulfill its broad potential for stellar astrophysics if these systematics are understood and mitigated. Post-launch developments in the Kepler archive, data reduction pipeline and open source data analysis software have occurred to remove or reduce systematic artifacts. This paper provides a conceptual primer for users of the Kepler data archive to understand and recognize systematic artifacts within light curves and some methods for their removal. Specific examples of artifact mitigation are provided using data available within the archive. Through the methods defined here, the Kepler community will find a road map to maximizing the quality and employment of the Kepler legacy archive.

Kinemuchi, K.↗

NASA NTRS2018

Developing a Standard for Earth Observation Data Preservation Content - A Path to Future Usability

For datasets to be usable, many pieces of information in addition to the data themselves are essential. During the active parts of the lifecycle of dataset generating projects, the needed information is usually accessible through individuals familiar with the various aspects of the projects. However, the utility of datasets tends to outlive the lives of projects, by several decades in many cases. Thus it is essential to capture all the relevant information about the datasets, data, metadata and associate knowledge that is sufficient to read, understand, interpret and reuse the datasets, while the projects are still active. The capture and preservation should be such that the data are usable when no consultation is available from the original project participants. Identification of specific categories of content through an international standard is beneficial to the user communities of the future, so that projects involving Earth observations and generating data products can consistently plan for preservation and future usability of the project outcomes. While there are existing standards that address archival and preservation in general, there are no existing international standards or specifications today to address what content should be preserved. The standard, ISO 19165-1, titled "Geographic Information - Preservation of digital data and metadata Part 1: Fundamentals" considers geographic information preservation in general. It acknowledges that "specific content items needed to preserve the full provenance and context of the data and associated metadata depend on the needs of the designated community and types of datasets (e.g., maps, remotely sensed data from satellites and airborne instruments, physical samples). Follow-up parts to this standard may be developed detailing content items appropriate to individual disciplines." NASA proposed an extension to this standard, titled "Geographic information -- Preservation of digital data and metadata -- Part 2: Content specifications for Earth observation data and derived digital products." The development of this extension is in progress with participation by an international team representing nine countries. The purpose of this paper is to introduce this standard and report on its status.

Remote Sensing; Data Systems; Open Data;↗

NASA NTRSDate not supplied

NASA’s Earth Observation System Data and Information System (EOSDIS) and FAIR – A Self-Assessment

Explore the source record for details and available documents.

Remote Sensing↗

NASA NTRSDate not supplied

FAIR-ness Assessment of NASA’s Earth Observation System Data and Information System (EOSDIS)

This presentation addresses the challenge of evaluating a multi-disciplinary institutional network of data repositories in operation since 1994 against the relatively recent criteria that constitute FAIR (Findable, Accessible, Interoperable, Reusable) data. NASA’s Earth Observation System Data and Information System (EOSDIS), with its 12 discipline-based Distributed Active Archive Centers (DAACs), preceded the definition and popularization of FAIR by over two decades. An assessment is very useful to describe how well the FAIR principles are met and to identify any improvements needed. In 2020, A “self-assessment” of EOSDIS and DAACs was performed by the ESDIS Project staff and the DAACs from the points of view of human actionability and machine actionability. More recently, a draft of a Science Mission Directorate (SMP) Program Directive (SPD-41a) has been released by NASA Headquarters for comment, where it is recommended that all SMD-funded data should follow the FAIR principles. This presentation is timely to initiate community discussion within the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) and help strategize and develop implementation guidelines for EOSDIS and DAACs to conform to FAIR principles.

Remote sensing↗

NASA NTRS2003

Opening Loads Analyses for Various Disk-Gap-Band Parachutes

Detailed opening loads data is presented for 18 tests of Disk-Gap-Band (DGB) parachutes of varying geometry with nominal diameters ranging from 43.2 to 50.1 ft. All of the test parachutes were deployed from a mortar. Six of these tests were conducted via drop testing with drop test vehicles weighing approximately 3,000 or 8,000 lb. Twelve tests were conducted in the National Full-Scale Aerodynamics Complex 80- by 120-foot wind tunnel at the NASA Ames Research Center. The purpose of these tests was to structurally qualify the parachute for the Mars Exploration Rover mission. A key requirement of all tests was that peak parachute load had to be reached at full inflation to more closely simulate the load profile encountered during operation at Mars. Peak loads measured during the tests were in the range from 12,889 to 30,027 lb. Of the two test methods, the wind tunnel tests yielded more accurate and repeatable data. Application of an apparent mass model to the opening loads data yielded insights into the nature of these loads. Although the apparent mass model could reconstruct specific tests with reasonable accuracy, the use of this model for predictive analyses was not accurate enough to set test conditions for either the drop or wind tunnel tests. A simpler empirical model was found to be suitable for predicting opening loads for the wind tunnel tests to a satisfactory level of accuracy. However, this simple empirical model is not applicable to the drop tests.

Cruz, J. R.↗

NASA NTRSDate not supplied

Transforming NASA Earth Science Data Systems: A Journey from Big Earth Data Initiative (BEDI) to Open-Source Science Initiative (OSSI)

NASA's Earth Science Data and Information Systems (ESDIS) have undergone a significant evolution, particularly with the introduction of the Big Earth Data Initiative (BEDI) and the Open-Source Science Initiative (OSSI). In this talk, I will provide an overview of NASA's Earth Science Data and Information systems, highlighting key components such as EOSDIS, ESDIS, and ESDS. Moving forward, I will delve into the BEDI initiative, discussing its objectives, key players, and lessons learned. The second part of the talk will cover the OSSI initiative, exploring its objectives, strategy, and innovative solutions. Throughout the presentation, I will provide insights into the requests, strategies, and solutions behind both BEDI and OSSI. By the end, you will gain a comprehensive understanding of how NASA's Earth Science Data Systems have evolved over the years and witness the organization's commitment to advancing an open-source and collaborative approach to data science. Join me for an enlightening exploration into the future of Earth science data and the pivotal role played by NASA in shaping this transformative landscape.

Jennifer Wei↗

DOE OSTI2026

Data for A Fluorescence-Based Transient Expression Assay for the Analysis of Upstream Open Reading Frames in Plants

Scripts for the manuscript "A fluorescence-based transient expression assay for the analysis of upstream open reading frames in plant" by Haas et al. Upstream open reading frames (uORFs) are regulatory elements present in the 5′ leaders of mRNA that can significantly impact downstream gene expression in eukaryotes. In crop engineering, editing of uORFs can provide an avenue to upregulate expression of native genes without the need to add persistent transgenic copies. Even with genome- wide methods to identify translated uORFs such as ribosome profiling, their functional characterization depends on validation through reporter gene assays and mutagenesis studies. Current screening methods for plants use luciferases or protoplasts to measure differential gene expression between wild- type and mutated transcript leaders, which requires tissue processing and/or substrate addition. Here, we present a time- and cost- efficient alternative to investigate transcript leaders by co- expression of two fluorescent proteins in Nicotiana benthamiana leaf tissue and test our assay on genes involved in photoprotection, editing of which could provide a pathway to increase CO2 assimilation during sun–shade transitions.

Gene Editing↗

NASA NTRS2009

The Open Source DataTurbine Initiative: Streaming Data Middleware for Environmental Observing Systems

The Open Source DataTurbine Initiative is an international community of scientists and engineers sharing a common interest in real-time streaming data middleware and applications. The technology base of the OSDT Initiative is the DataTurbine open source middleware. Key applications of DataTurbine include coral reef monitoring, lake monitoring and limnology, biodiversity and animal tracking, structural health monitoring and earthquake engineering, airborne environmental monitoring, and environmental sustainability. DataTurbine software emerged as a commercial product in the 1990 s from collaborations between NASA and private industry. In October 2007, a grant from the USA National Science Foundation (NSF) Office of Cyberinfrastructure allowed us to transition DataTurbine from a proprietary software product into an open source software initiative. This paper describes the DataTurbine software and highlights key applications in environmental monitoring.

Fountain T.↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗

NASA NTRSDate not supplied

Fostering Open Science in Earth Data Science Research: Insights From Earthdata Forum By ASDC

In the dynamic landscape of Earth Science research, the promotion of open science principles is paramount for advancing knowledge and collaboration. The Earthdata Forum is an actively maintained and operational user forum for all participating National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), and the Global Change Master Directory (GCMD). The Forum serves as a cross-DAAC platform from which user communities can obtain authoritative information relating to NASA Earth Science. This abstract explores the role of the Earthdata Forum forum.earthdata.nasa.gov as a pivotal platform in fostering open science within the Earth Science community. The platform serves as a hub for researchers to actively engage in discussions, share datasets, and collaboratively tackle challenges in the field. Key aspects discussed include the platform's contribution to data accessibility, collaboration, and knowledge sharing. Forum.earthdata.nasa.gov provides a space where researchers transparently ask questions, discuss methodologies, share insights, and seek advice from a vibrant community. The resulting collaborative environment not only facilitates the exchange of ideas but also bolsters the collective knowledge base.

Earthdata FORUM↗

NASA NTRSDate not supplied

NASA EOSDIS 20 Years of Data Usage and User Assessment in Support of Open Science Initiative

NASA EOS Data and Information System (EOSDIS) has been distributing data to world-wide users free with open access. Since the launch of NASA’s Terra satellite in 1999, more than 10,000 distinct EOS data products have been archived and distributed by NASA-funded Earth Science data centers encompassed by the EOSDIS. As of September 30, 2022, more than 90 PB of data archived by EOSDIS have been made available to public users and during FY 2023 over 60 PB have been distributed to public users worldwide. Over these twenty and more years, it has shown significant increase in the distribution of various data products. This has been possible due to free and open access of the data thereby a step towards open science initiative. The purposes of this study are 1) to perform a comprehensive investigation of the archive and distribution patterns of EOSDIS data products for last 20 years, 2) to identify and characterize the global user community for those data, 3) analyze the increased demand for data products, 4) evaluate distribution of higher level products because those are the ones most frequently used in the studies of natural disasters by public users (those data requestors not involved directly in the production or validation of the data products.) and contribute globally to the advance scientific understanding of the Earth-Atmosphere Systems. Funded by the Earth Science Data and Information System (ESDIS) Project, the ESDIS Metrics System (EMS) collects archive, distribution, and user information from EOSDIS data centers. The information (comprising all data products including heritage datasets going back to the 1990s) is stored in a relational database from which it can be analyzed in many ways. We present several metrics analyses that include data distribution patterns for all, as well as the most frequently requested data products; and user characterizations by country, domain, and Earth Science discipline (e.g., Land, Ocean, Cryosphere) of the requested products. Due to the enormous quantity of data handled by EOSDIS data centers and requirements of future data systems to archive increasing amounts of Earth Science data from future and current Earth Science missions effectively, the results of this study can provide insight on how the user communities have accessed the data and provide guidance for open science initiative.

Lalit Wanchoo↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

RadLab, a component of the NASA Open Science Data Repository (OSDR), is a database of radiation measurements from multiple instruments and spacecraft that provides visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. ISS modules), associated celestial bodies, trajectories, and spacecraft coordinates; the primary type of data is the absorbed dose rate, as well as flux and dose equivalent rate where available. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations (time series plots, comparison plots, geospatial visualizations) which provide easy means to assess data availability, iteratively refine search parameters, interactively inspect the data, and export target data subsets. Datasets are continuously being added to the RadLab database as part of the rolling release process. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit. The current release contains datasets provided by US and international collaborators and includes readings from multiple modules of the ISS, the BioSentinel CubeSat, Chang’e 4, the Lunar Reconnaissance Orbiter, the ExoMars Orbiter, and the Curiosity rover. Datasets are associated with respective RadLab knowledgebase articles which include instrument descriptions and provide bibliographical references. RadLab aims to provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments. Some of its applications include inference of absorbed radiation dose for NASA GeneLab payloads, and training predictive models as part of the 2024 FDL-X challenge. The platform is actively expanding and seeking additional data, with plans to also cover past (e.g. Shuttle, Mir) and future (e.g. Artemis) missions. The RadLab Working Group has been created to aid in this process as well as to foster collaborations among data contributors and users, to develop standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the radiation environment in outer space.

Kirill Grigorev↗

NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Space Radiation Data

Kirill Grigorev↗

NASA NTRS2017

Inverse Method for Estimation of Composite Kink-Band Toughness from Open-Hole Compression Strength Data

Fiber-reinforced polymer matrix composite materials can fail by kink-band propagation mechanism when subjected to in-plane compressive loading. This mode of failure is especially prevalent in compressive loading of laminates with holes, cut-outs, or impact damage. Most of the successful models for predicting compressive strength of such laminates require “fracture” toughness associated with kink-band propagation under in-plane compression. However, this property is difficult to measure experimentally, limiting the use of such models in design practice. In this paper an inverse method is proposed to estimate the kink-band toughness of the laminate from its open-hole compression strength data, which is an easier property to measure experimentally. Furthermore, a scaling relationship is proposed to estimate kink-band toughness for other laminate configurations of the same material.

Luke Borkowski↗