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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
DOE OSTI2016

The DOE Geothermal Data Repository and the Future of Geothermal Data

We are, right now, in the midst of the Data Revolution. Just as the Industrial Revolution of the early 1800's marked a major turning point in history with changes to manufacturing that influenced almost every aspect of daily life, the proliferation of big data and the evolution of our collective ability to put it to work for us is having a profound impact on our lives today. The U.S. Department of Energy's (DOE) Geothermal Data Repository (GDR) is leveraging new resources, such as the National Renewable Energy Laboratory's (NREL) new secure cloud environment, and cutting edge approaches to data analysis, to prepare for the challenges that data from energy and geosciences research presents. This paper will explore some of those challenges, highlight some of the benefits of data analysis, and explain the steps DOE and NREL are taking to ensure the GDR is ready for the future of geothermal data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Autoload: a pipeline for expanding the holdings of an Institutional Repository enabled by ResourceSync

Providing local access to locally produced content is a primary goal of the Institutional Repository (IR). Guidelines, requirements, and workflows are among the ways in which institutions attempt to ensure this content is deposited and preserved, but some content is always missed. At Los Alamos National Laboratory, the library implemented a service called LANL Research Online (LARO), to provide public access to a collection of publicly shareable LANL researcher publications authored between 2006 and 2016. LARO exposed the fact that we have full text for only about 10% of eligible publications for this time period, despite a review and release requirement that ought to have resulted in a much higher deposition rate. This discovery motivated a new effort to discover and add more full text content to LARO. Autoload attempts to locate and harvest items that were not deposited locally, but for which archivable copies exist. Here we describe the Autoload pipeline prototype and how it aggregates and utilizes Web services including Crossref, SHERPA/RoMEO, and oaDOI as it attempts to retrieve archivable copies of resources. Autoload employs a bootstrapping mechanism based on the ResourceSync standard, a NISO standard for resource replication and synchronization. We implemented support for ResourceSync atop the LARO Solr index, which exposes metadata contained in the local IR. This allowed us to utilize ResourceSync without modifying our IR. Here, we close with a brief discussion of other uses we envision for our ResourceSync-Solr implementation, and describe how a new effort called Signposting can replace cumbersome screen scraping with a robust autodiscovery path to content which leverages Web protocols.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

The Geothermal Data Repository: Five Years of Open Geothermal Data, Benefits to the Community: Preprint

In the five years since its inception, the Department of Energy's (DOE) Geothermal Data Repository (GDR) has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the DOE open data movement where it is providing a tangible benefit to the geothermal scientific community. Throughout this time, the GDR project team has been working closely with the community to refine the data submission process, improve the quality of submitted data, and embrace modern proper data management strategies to maximize the value and utility of submitted data. This paper explores some of the motivations behind various improvements to the GDR over the last 5 years, changes in data submission trends, and the ways in which these improvements have helped to drive research, fuel innovation, and accelerate the adoption of geothermal technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

The Geothermal Data Repository: Five Years of Open Geothermal Data, Benefits to the Community

In the five years since its inception, the Department of Energy's (DOE) Geothermal Data Repository (GDR) has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the DOE open data movement where it is providing a tangible benefit to the geothermal scientific community. Throughout this time, the GDR project team has been working closely with the community to refine the data submission process, improve the quality of submitted data, and embrace modern proper data management strategies to maximize the value and utility of submitted data. This paper explores some of the motivations behind various improvements to the GDR over the last 5 years, changes in data submission trends, and the ways in which these improvements have helped to drive research, fuel innovation, and accelerate the adoption of geothermal technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Improving the Accessibility and Usability of Geothermal Information with Data Lakes and Data Pipelines on the Geothermal Data Repository: Preprint

The Geothermal Data Repository (GDR) provides universal access to data and information resulting from research and development activities funded by the Department of Energy (DOE). The GDR has extended this universal access to big data through integration with data lakes developed by the Open Energy Data Initiative (OEDI). Previously, large datasets such as seismic waveform or distributed acoustic sensing (DAS) data could only be accessed by institutions with high performance data storage and compute capabilities, effectively limiting the accessibility of big data to national labs, larger universities, and major corporations. Moreover, the time and resources needed to transport big data and configure them can produce additional barriers to use. Many of the standard formats used for structured data models (also known as content models) are incapable of handling big data and can introduce additional usability problems, often requiring data to be reformatted prior to use. This paper will explore how recent integrations between the GDR and the OEDI data lake have improved the accessibility and usability of geothermal data in a big way, making the data available to a broader audience, and enabling collaborative analysis and innovation across the greater geothermal industry.

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DOE OSTI2021

Improving the Accessibility and Usability of Geothermal Information with Data Lakes and Data Pipelines on the Geothermal Data Repository

The Geothermal Data Repository (GDR) provides universal access to data and information resulting from research and development activities funded by the Department of Energy (DOE). The GDR has extended this universal access to big data through integration with data lakes developed by the Open Energy Data Initiative (OEDI). Previously, large datasets such as seismic waveform or distributed acoustic sensing (DAS) data could only be accessed by institutions with high performance data storage and compute capabilities, effectively limiting the accessibility of big data to national labs, larger universities, and major corporations. Moreover, the time and resources needed to transport big data and configure them can produce additional barriers to use. Many of the standard formats used for structured data models (also known as content models) are incapable of handling big data and can introduce additional usability problems, often requiring data to be reformatted prior to use. This paper will explore how recent integrations between the GDR and the OEDI data lake have improved the accessibility and usability of geothermal data in a big way, making the data available to a broader audience, and enabling collaborative analysis and innovation across the greater geothermal industry.

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DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDRs success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

accessibility↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDR's success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

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DOE OSTI2023

The Construction of Reduced Order Models for the HYBRID Repository

Reduced order models (ROMs) present an opportunity to reduce the computational burden of IES analysis using HYBRID models without significantly sacrificing data accuracy. Other applications of ROMs are digital twins, stochastic optimization studies, model exporting, and protection of proprietary information. In this paper the capability of creating ROMs of the models within the HYBRID repository will be demonstrated. Specifically, a linear regression ROM of a gas turbine and a dynamic mode decomposition with control (DMDc) ROM of a lithium-ion battery are constructed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

DOE↗
DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗
DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗
DOE OSTI2013

Sub-Seabed Repository for Nuclear Waste - a Strategic Alternative - 13102

It was recognized at the outset of nuclear power generation in the 1950's that the waste products would require isolation away from humans for periods in excess of 10,000 years. After years studying alternatives, the DOE recommended pursuing the development of a SNF/HLW disposal facility within Yucca Mountain in the desert of Nevada. That recommendation became law with passage of the NWPAA, effectively stopping development of other approaches to the waste problem. In the face of political resistance from the state of Nevada, the 2010 decision to withdraw the license application for the geologic repository at Yucca Mountain has delayed further the most mature option for safe, long-term disposal of SNF and HLW. It is time to revisit an alternative option, sub-seabed disposal within the US Exclusive Economic Zone (EEZ), which would permanently sequester waste out of the biosphere, and out of the reach of saboteurs or terrorists. A proposal is made for a full scale pilot project to demonstrate burying radioactive waste in stable, deep ocean sediments. While much of the scientific work on pelagic clays has been done to develop a sub-seabed waste sequestration capability, this proposal introduces technology from non-traditional sources such as riser-less ocean drilling and the Navy's Sound Surveillance System. The political decisions affecting the issue will come down to site selection and a thorough understanding of comparative risks. The sub-seabed sequestration of nuclear waste has the potential to provide a robust solution to a critical problem for this clean and reliable energy source. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented or is currently implementing data standards and automated data pipelines for the following geothermal data types: 1) drilling data, 2) geospatial datasets, and 3) Distributed Acoustic Sensing (DAS) data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how the GDR team can improve this process.

cloud-optimized↗
DOE OSTI2023

Connecting People to Data: Enabling Data Connected Communities through Enhancements to the Geothermal Data Repository

The Department of Energy's (DOE) Geothermal Data Repository (GDR) has implemented a series of new features designed to connect people to data. These features, which are based on feedback from the GDR user community and surveys of the greater geothermal research community, are designed to improve data quality and empower members of all communities to better engage with geothermal data resources by providing universal access to data and by improving the connections between data providers, subject matter experts, and the communities of people using GDR data. This paper will explore some of the recent enhancements made to the GDR to improve data discoverability, reduce submission time, and result in better quality data submissions. These improvements include the ability for users to save a list of their favorite datasets, search for insight into geothermal datasets or data availability, or sign up to receive notifications of future updates to specific datasets. These improvements aim to enhance the overall user experience of the GDR while further connecting communities to the data they need to inform decisions, advance geothermal research, and develop innovative solutions to local energy problems.

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DOE OSTI2024

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives.

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DOE OSTI2025

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives. This paper will outline the development, integration, output, and efficacy of the AskGDR LLM, including adherence to scientific rigor through improvements designed to increase the accuracy of generated answers, avoid speculation, and provide proper references for all resources used.

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