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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), v4

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2025) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2025) – Power plants that are listed in the 2025 U.S. Hydropower Development Pipeline Data or were listed in previous versions of the dataset These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) – EPA SuRGE sampling locations Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

Hansen, Carly [ORNL] (ORCID:0000000193280838)↗
NASA NTRS2024

Bridging the Gap: Enhancing Prominence and Provenance of NASA Datasets in Research Publications

Attribution of datasets that were used to generate research results described in peer-reviewed publications to the original source of these datasets (which are often archived at NASA Earth Science data centers) has been very challenging. Even though the data citation standard of citing datasets as research artifacts and citing them with Digital Object Identifiers (DOIs) was introduced over a decade ago, most authors do not properly reference the data used in their studies and merely mention them in the text. The lack of proper citations of datasets makes the peer-reviewed publication less transparent, imperils reproducibility, and impedes open science. We offer an open-source publication management methodology and a tool that can help to enhance usage-based data discovery, prominence, and provenance of the data; reproducibility of the research results; and potentially increase the return on investment on NASA-funded research.

open-source↗
NASA NTRS1990

Data dictionaries in information systems - Standards, usage , and application

An overview of data dictionary systems and the role of standardization in the interchange of data dictionaries is presented. The development of the data dictionary for the Planetary Data System is cited as an example. The data element dictionary (DED), which is the repository of the definitions of the vocabulary utilized in an information system, is an important part of this service. A DED provides the definitions of the fields of the data set as well as the data elements of the catalog system. Finally, international efforts such as the Consultative Committee on Space Data Systems and other committees set up to provide standard recommendations on the usage and structure of data dictionaries in the international space science community are discussed.

Johnson, Margaret↗
DOE OSTI2024

Hydropower Infrastructure – LAkes, Reservoirs, and RIvers (HILARRI)

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2024) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2024) These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

13 HYDRO ENERGY↗
NASA NTRS2023

Spatially-Coordinated Airborne Data and Complementary Products for Aerosol, Gas, Cloud, and Meteorological Studies: the Nasa Activate Dataset

The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions, with applications extending from process-based studies to multi-scale model intercomparison and improvement as well as to remote-sensing algorithm assessments and advancements. ACTIVATE used two NASA Langley Research Center aircraft, a HU-25 Falcon and King Air, to conduct systematic and spatially coordinated flights over the northwest Atlantic Ocean, resulting in 162 joint flights and 17 other single-aircraft flights between 2020 and 2022 across all seasons. Data cover 574 and 592 cumulative flights hours for the HU-25 Falcon and King Air, respectively. The HU-25 Falcon conducted profiling at different level legs below, in, and just above boundary layer clouds (< 3 km) and obtained in situ measurements of trace gases, aerosol particles, clouds, and atmospheric state parameters. Under cloud-free conditions, the HU-25 Falcon similarly conducted profiling at different level legs within and immediately above the boundary layer. The King Air (the high-flying aircraft) flew at approximately ∼ 9 km and conducted remote sensing with a lidar and polarimeter while also launching dropsondes (785 in total). Collectively, simultaneous data from both aircraft help to characterize the same vertical column of the atmosphere. In addition to individual instrument files, data from the HU-25 Falcon aircraft are combined into “merge files” on the publicly available data archive that are created at different time resolutions of interest (e.g., 1, 5, 10, 15, 30, 60 s, or matching an individual data product's start and stop times). This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes. The data are publicly accessible through https://doi.org/10.5067/SUBORBITAL/ACTIVATE/DATA001 (ACTIVATE Science Team, 2020).

Aerosol Cloud meTeorology Interactions oVer the we↗
NASA NTRS2005

Atmospheric Composition Data and Information Services Center (ACDISC)

NASA's GSFC Earth Sciences (GES) Data and Information and Data Services Center (DISC) manages the archive, distribution and data access for atmospheric composition data from AURA'S OMI, MLS, and hopefully one day, HIRDLS instruments, as well as heritage datasets from TOMS, UARS, MODIS, and AIRS. This data is currently archived in the GES Distributed Active Archive Center (DAAC). The GES DISC has begun the development of a community driven data management system that's sole purpose is to manage and provide value added services to NASA's Atmospheric Composition (AC) Data. This system, called the Atmospheric Composition Data and Information Services Center (ACDISC) will provide access all AC datasets from the above mentioned instruments, as well as AC datasets residing at remote archive sites (e.g, LaRC DAAC) The goals of the ACDISC are to: 1) Provide a data center for Atmospheric Scientists, guided by Atmospheric Scientists; 2) Be absolutely responsive to the data and data service needs of the Atmospheric Composition (AC) community; 3) Provide services (i.e., expertise) that will facilitate the effortless access to and usage of AC data; 4) Collaborate with AC scientists to facilitate the use of data from multiple sensors for long term atmospheric research. The ACDISC is an AC specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques that facilitate science data usage. The purpose of this presentation is to provide the evolution path that the GES DISC in order to better serve AC data, and also to receive continued community feedback and further foster collaboration with AC data users and providers.

Kempler, S.↗
NASA NTRS2024

Analyzing EOSDIS Dataset Research Outputs using Knowledge Graphs and Large Language Models

Datasets, unlike publications, can be updated over time, with each new version receiving a DOI but not always being linked to previous ones. This complicates tracking citations across a dataset’s lifecycle. We address this by integrating dataset versions and citations into a knowledge graph (KG), which helps trace dataset citations and analyze dataset usage in applied research. To categorize publications from various journals, we fine-tuned NASA IMPACT INDUS Large Language Model (LLM) on a labeled publication set, assigning publications to one of twenty applied research areas. By linking datasets to these research areas, we improved dataset searchability and discovery through these domains.

open-source↗
NASA NTRS1984

The Design and Usage of the New Data Management Features in NASTRAN

Two new data management features are installed in the April 1984 release of NASTRAN. These two features are the Rigid Format Data Base and the READFILE capability. The Rigid Format Data Base is stored on external files in card image format and can be easily maintained and expanded by the use of standard text editors. This data base provides the user and the NASTRAN maintenance contractor with an easy means for making changes to a Rigid Format or for generating new Rigid Formats without unnecessary compilations and link editing of NASTRAN. Each Rigid Format entry in the data base contains the Direct Matrix Abstraction Program (DMAP), along with the associated restart, DMAP sequence subset and substructure control flags. The READFILE capability allows an user to reference an external secondary file from the NASTRAN primary input file and to read data from this secondary file. There is no limit to the number of external secondary files that may be referenced and read.

Pamidi, P. R.↗
NASA NTRS1973

Development of guidelines for the definition of the relavant information content in data classes

The problem of experiment design is defined as an information system consisting of information source, measurement unit, environmental disturbances, data handling and storage, and the mathematical analysis and usage of data. Based on today's concept of effective computability, general guidelines for the definition of the relevant information content in data classes are derived. The lack of a universally applicable information theory and corresponding mathematical or system structure is restricting the solvable problem classes to a small set. It is expected that a new relativity theory of information, generally described by a universal algebra of relations will lead to new mathematical models and system structures capable of modeling any well defined practical problem isomorphic to an equivalence relation at any corresponding level of abstractness.

Schmitt, E.↗
NASA NTRS2011

Capturing, Harmonizing and Delivering Data and Quality Provenance

Satellite remote sensing data have proven to be vital for various scientific and applications needs. However, the usability of these data depends not only on the data values but also on the ability of data users to assess and understand the quality of these data for various applications and for comparison or inter-usage of data from different sensors and models. In this paper, we describe some aspects of capturing, harmonizing and delivering this information to users in the framework of distributed web-based data tools.

Leptoukh, Gregory↗
NASA NTRS2019

Smart Handoffs: Preserving User Context Between Tools and Services Related to NASA's EOSDIS Data Archive

NASA's Earth Observing System Data and Information System (EOSDIS) is tasked with archiving and distributing Earth Observation data across a range of disciplines, including atmospheric science, oceanography, land processes, natural hazards, solar radiance and even socioeconomic aspects relating to the environment. Given the breadth of disciplines and depth of data that EOSDIS provides, the efficient and intuitive discovery and usage of data by a scientist is of paramount importance. An effective data gathering workflow may involve switching from general use discovery tools to a more bespoke services designed specifically for the scientist's discipline. Providing concrete interoperability between such tools could vastly improve the efficiency of a scientist's workflow.

Analytics↗
NASA NTRS1991

Space transfer vehicle concepts and requirements. Volume 1: Executive summary

The objectives of the Space Transfer Vehicle (STV) Concepts and Requirements studies were to provide sensitivity data on usage, economics, and technology associated with new space transportation systems. The study was structured to utilize data on the emerging launch vehicles, the latest mission scenarios, and Space Exploration Initiative (SEI) payload manifesting and schedules, to define a flexible, high performance, cost effective, evolutionary space transportation system for NASA. Initial activities were to support the MSFC effort in the preparation of inputs to the 90 Day Report to the National Space Council (NSC). With the results of this study establishing a point-of-departure for continuing the STV studies in 1990, additional options and mission architectures were defined. The continuing studies will update and expand the parametrics, assess new cargo and manned ETO vehicles, determine impacts on the redefined Phase 0 Space Station Freedom, and to develop a design that encompasses adequate configuration flexibility to ensure compliance with on-going NASA study recommendations with major system disconnects. In terms of general requirements, the objectives of the STV system and its mission profiles will address crew safety and mission success through a failure-tolerant and forgiving design approach. These objectives were addressed through the following: engine-out capability for all mission phases; built-in-test for vehicle health monitoring to allow testing of all critical functions such as, verification of lunar landing and ascent engines before initiating the landing sequence; critical subsystems will have multiple strings for redundancy plus adequate supplies of onboard spares for removal and replacement of failed items; crew radiation protection; and trajectories that optimize lunar and Mars performance and flyby abort capabilities.

Source record↗
DOE OSTI2024

Homomorphic Encryption for Electrical Metering Aggregation: Protecting the Privacy of Building Tenants

Electrical meters are devices that measure consumer electricity usage. The data collected by these meters is necessary for utility billing and electrical grid management but can also be used to assess the environmental impact of buildings. Prior research has found that unprotected metering data could potentially be used to infer some information about the behaviors of building tenants by detecting changes in electricity usage. For example, a period of low electricity usage could suggest that the tenants are not in the building. As smart metering becomes more common, there is a growing need for data privacy protections for metering data that do not negatively impact the quality and availability of data used for energy management and billing applications. To identify potential solutions, we developed a Python-based data aggregation platform to analyze the potential efficacy of privacy-enhancing technologies for energy metering applications. This platform aggregates groups of metering sites into virtual buildings, which could potentially detach changes in electrical activity from individual tenants, making it more difficult to track the activity of a specific tenant. To further protect data during analysis, this project utilizes homomorphic encryption as part of its initial approach. Homomorphic encryption offers a means of protecting energy consumption data while permitting mathematical operations to be performed without the need to know the data contents. This allows for data to be processed into usable statistics without revealing energy consumption information. A series of homomorphic encryption libraries were evaluated to determine their applicability and limitations in the context of metering data. The use of these techniques may help to reassure consumers and encourage further adoption of smart grid infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

An Introduction to Data Visualization

Data visualization can be used to present findings, explore data, and use the human eye to find patterns that a computer would struggle to locate. Borrowing tools from art, storytelling, data analytics and software development, data visualization is an indispensable part of the analysis process. While data visualization usage spans across multiple disciplines and sectors, most never receive formal training in the subject. As such, this tutorial will introduce key data visualization building blocks and how to best use those building blocks for different scenarios and audiences. We will also go over tips on accessibility, design and interactive elements. While this will by no means be a complete overview of the data visualization field, by building a foundation and introducing some rules of thumb, attendees will be better equipped for communicating their findings to their audience.

data visualization↗
NASA NTRSDate not supplied

Revolutionizing Earth Science with Generalized AI Models

Foundation Models (FM) are generalized Artificial Intelligence (AI) models that are designed to replace a task or an application-specific model and can be used for many downstream applications. These FM can be built on any sequence data and are trained utilizing self-supervised approaches. The obstacle of creating a sizable labeled dataset for training is removed by using self-supervised learning. Most FM employ transformer design that takes advantage of the idea of self-attention, allowing the network to represent the impact of distant data points on one another in space and time. The FM models show emergent qualities that are induced from the data. FM can become a valuable tool for Earth science researchers. Due to the size of these models, downstream applications built fine-tuning these FM perform better and exhibit greater accuracy than models created from scratch. FM significantly lowers the entry barrier in terms of both the time and effort required to develop various downstream applications. For some scientific datasets, such as optical remote sensing data, FM can speed up processes like classification, object detection and prediction. By eliminating the training data bottleneck and maximizing the usage of science data, FM can make it simpler to integrate AI into scientific research. Initial results for three different FMs will be presented.

Rahul Ramachandran↗
NASA NTRS2019

NASA GES DISC's Customized Services for Climatology and Meteorology

At the NASA Goddard Earth Sciences (GES) Data and Information Service Center (DISC), we have archived and distributed more than 2,400 Earth science data products, from different missions or projects containing more than 100 M data files/granules with a total volume size nearly 2 PB that broadly serve user needs in science areas such as Atmospheric Composition, Water & Energy Cycles and Climate Variability. To date, GES DISC has developed many pertinent services to facilitate the usage of data products by our research communities, represented by approximately 24,000 registered users. We are facing the big data with increasingly archival volume and data types, moreover, we also encounter increasing users' demands and the demands are more diversified. It is still a challenge for us to better understand exactly what our users' needs are, even after developing more than 70 services, including well-known online tools such as Giovanni and MERRA subsetter. In this presentation, we will try to address how we can accommodate the users' needs from two applicational user communities, Air Quality and Wind Energy, from data or service discovery to guide them properly utilize the data and services to fit their needs.

customizable services for climate and meteorology↗