Anderson, Arlene | Search NASA

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.

access↗

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.

access↗

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 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

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

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.

access↗

DOE OSTI2023

Improving the Quality of Geothermal Data Through Data Standards and Pipelines Within the Geothermal Data Repository: Preprint

For machine learning outputs to be applicable to real world problems, high quality data are needed to ensure high quality results. With the more recent emphasis on machine learning in geothermal, there is an increasing need for greater focus on the quality of the data available for use in these projects. For example, Geothermal Operational Optimization Using Machine Learning (GOOML) utilized large quantities of geothermal power plant operational data to inform power plant operational configurations to maximize power generation. High quality datasets result from dependable sensors or devices collecting data, high frequency of measurements, sufficient data points, adequate metadata, reliable storage of data, and sufficient data curation. Another component that contributes to high quality data is reusability, which can be enhanced through data standardization. Data Standardization creates consistency in formatting and contents of like datasets, lessening preprocessing requirements and ensuring adequate information provided by a given dataset. The Geothermal Data Repository (GDR) aims to help improve data quality through automated data standardization for high-value datasets through the implementation of data pipelines alongside reliable and accessible long-term storage for datasets. As such, the GDR has decided to shift away from recommending the use of Excel-based content models and towards the implementation of automated data pipelines. This takes the burden of data standardization off the user and project team and will increase the availability of standardized geothermal data available through the GDR. A set of recommendations, or a data standard for each data type will exist with each data pipeline in order to advise data collection for maximum usability for future research. This paper serves to describe the GDR's proposed transition towards data standardization through automated data pipelines, to discuss the need for and value of such a shift, and to call for suggestions from the community regarding the most useful data standards and pipelines.

data↗

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.

access↗

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 OSTI2021

Geothermal Energy R&D: An Overview of the U.S. Department of Energy’s Geothermal Technologies Office

Geothermal energy can provide answers to many of America’s essential energy questions. The United States has tremendous geothermal resources, as illustrated by the results of the DOE GeoVision analysis, but technical and non-technical barriers have historically stood in the way of widespread deployment of geothermal energy. The U.S. Department of Energy’s Geothermal Technologies Office within the Office of Energy Efficiency and Renewable Energy has invested more than $470 million in research and development (R&D) since 2015 to meet its three strategic goals: (1) unlock the potential of enhanced geothermal systems, (2) advance technologies to increase geothermal energy on the U.S. electricity grid, and (3) support R&D to expand geothermal energy opportunities throughout the United States. Here, we describe many of those R&D initiatives and outlines future directions in geothermal research.

energy storage systems↗

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 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 OSTI2015

DOE Geothermal Data Repository: Getting More Mileage Out of Your Data: Preprint

All data submitted to the U.S. Department of Energy's Geothermal Data Repository (GDR) is eventually made public. The metadata for these data submissions is searchable in multiple data catalogs, including the GDR catalog and the data catalog on OpenEI.org. Because it is a node on the National Geothermal Data System (NGDS), all data on the GDR are also discoverable through both the regular Identifier (DOI), and as a byproduct of this assignment, these submissions are automatically registered in the Office of Science and Technical Information (OSTI) DataCite catalog. From there, these data are federated to additional sites both domestic and international, including Science.gov and WorldWideScience.org. This paper will explore in detail the wide reach of data submitted to the GDR from and how this exposure can dramatically increase the utility of submitted data.

15 GEOTHERMAL ENERGY↗

DOE OSTI2015

Geothermal Prospector: Supporting Geothermal Analysis Through Spatial Data Visualization and Querying Tools

Determining opportunities for geothermal energy can involve a significant investment in data collection and analysis. Analysts within a variety of industry and research domains collect and use these data; however, determining the existence and availability of data needed for a specific analysis activity can be challenging and represents one of the initial barriers to geothermal development [2]. This paper describes the motivating factors involved in designing and building the Geothermal Prospector application, how it can be used to reduce risks and costs related to geothermal exploration, and where it fits within the larger collection of tools that is the National Geothermal Data System (NGDS) [5].

15 GEOTHERMAL ENERGY↗

DOE OSTI2014

National Geothermal Data System: Case Studies on Exploration and Development of Potential Geothermal Sites Through Distributed Data Sharing

The NGDS released version 1 of the system on April 30, 2014 using the US Geoscience Information Network (USGIN) as its data integration platform. NGDS supports the 2013 Open Data Policy, and as such, the launch was featured at the 2014 Energy Datapalooza. Currently, the NGDS features a comprehensive user interface for searching and accessing nearly 41,000 documents and more than 9 million data points shared by scores of data providers across the U.S. The NGDS supports distributed data sharing, permitting the data owners to maintain the raw data that is made available to the consumer. Researchers and industry have been utilizing the NGDS as a mechanism for promoting geothermal development across the country, from hydrothermal to ground source heat pump applications. Case studies in geothermal research and exploration from across the country are highlighted.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

National Geothermal Data System (NGDS) Geothermal Data: Community Requirements and Information Engineering

To satisfy the critical need for geothermal data to advance geothermal energy as a viable renewable energy contender, the U.S. Department of Energy is investing in the development of the National Geothermal Data System (NGDS). This paper outlines efforts among geothermal data providers nationwide to supply cutting edge geo-informatics. NGDS geothermal data acquisition, delivery, and methodology are discussed. In particular, this paper addresses the various types of data required to effectively assess geothermal energy potential and why simple links to existing data are insufficient. To create a platform for ready access by all geothermal stakeholders, the NGDS includes a work plan that addresses data assets and resources of interest to users, a survey of data providers, data content models, and how data will be exchanged and promoted, as well as lessons learned within the geothermal community.

15 GEOTHERMAL ENERGY↗