The Geothermal Data Repository: Ten
Years of Supporting the Geothermal
Industry with Open Access to
Geothermal Data
Preprint
Jon Weers,1 Arlene Anderson,2 and Nicole Taverna1
1 National Renewable Energy Laboratory
2 U.S. Department of Energy
Presented at the Geothermal Rising Conference (GRC)
Reno, Nevada
August 28-31, 2022

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308

Conference Paper
NREL/CP-6A20-82837
July 2022

The Geothermal Data Repository: Ten
Years of Supporting the Geothermal
Industry with Open Access to
Geothermal Data
Preprint
Jon Weers,1 Arlene Anderson,2 and Nicole Taverna1
1 National Renewable Energy Laboratory
2 U.S. Department of Energy
Suggested Citation

Weers, Jon, Arlene Anderson, and Nicole Taverna. 2022. The Geothermal Data
Repository: Ten Years of Supporting the Geothermal Industry with Open Access to
Geothermal Data: Preprint. Golden, CO: National Renewable Energy Laboratory.
NREL/CP-6A20-82837. https://www.nrel.gov/docs/fy22osti/82837.pdf.

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC

Conference Paper
NREL/CP-6A20-82837
July 2022

This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.

National Renewable Energy Laboratory
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Contract No. DE-AC36-08GO28308

NOTICEThis work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable
Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding
provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy GeothermalTechnologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S.
Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges
that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce
the published form of this work, or allow others to do so, for U.S. Government purposes.
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The Geothermal Data Repository:
Ten Years of Supporting the Geothermal Industry with
Open Access to Geothermal Data
Jon Weers (a), Arlene Anderson (b), and Nicole Taverna (a)(a) National Renewable Energy Laboratory (NREL)

15013 Denver West Parkway
Golden, CO 80401-3305(b) U.S. Department of Energy (DOE)1000 Independence Ave. SW
Washington D.C. 20004, USA
Keywords
Geothermal, data, repository, management, standards, dissemination, open, access, lake, GDR,OpenEI, DOE, OEDI, collaboration, big, storage, transfer, discoverability, usability,
accessibility, pipeline, equality, provenance, innovation, cloud, submission, future, NRELABSTRACTThe Department of Energy’s (DOE) Geothermal Data Repository (GDR) is celebrating its tenthanniversary! Over the last decade it has grown from the simple idea of storing public data in acentralized location to a valuable tool at the center of the US geothermal scientific community andan 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 andstandardized, interoperable data have further increased the value of GDR data, making themavailable across a distributed network of data sharing partners and improving their utility to otherindustries 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 tomeet the evolving needs of the geothermal community. This paper will explore some of manychanges that occurred throughout the GDRs tenure and the lessons learned along the way, as wellas 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.

1This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

1. Introduction
The U.S. Department of Energy (DOE) Geothermal Data Repository (GDR) is the repository andcatalog for data generated by projects funded by the DOE Geothermal Technologies Office (GTO).
The GDR was developed in 2012 by the National Renewable Energy Laboratory (NREL) in
accordance with DOE’s 2011 Strategic Plan, which stated that “DOE’s success should bemeasured not when a project is completed or an experiment concluded, but when scientific andtechnical information is disseminated.” The GDR was designed primarily to disseminate
information and to protect DOE’s investment in analysis, research and development activities bypreserving data from those activities and communicating the resulting information to the largest
audience possible. The metadata describing each dataset within the GDR are federated (i.e.,transmitted) to a network of data sharing partners (Figure 1), greatly increasing the exposure anddiscoverability of GDR data.

Figure 1. Diagram of GDR metadata propagation through a network of data sharing partners.

GDR data is discoverable and downloadable from each of the sites depicted above, without any
duplication of effort or redundant storage. Requests for downloads from the data sharing network
are routed behind the scenes to the GDR, enabling users to download GDR data without leaving
the network partner’s platform. By making data available throughout this network, the GDR has
been able to increase the discoverability of DOE data by a factor of 12, when comparing direct
downloads from the GDR itself to those originating from network partners (Weers et. al, 2021).
To date, the GDR has received 1,376 submissions and is now home to 5,084 resources and morethan 135 TB of data from 74 different organizations (GDR 2022). Over the last 10 years, GDR
data have been downloaded more than 3 million times by universities, national laboratories, privateorganizations, industry professionals, and government agencies.
The primary objectives of the GDR are to 1), protect DOE’s investment in research anddevelopment by ensuring persistent, universal access to the results of GTO-funded activities; 2)
overcome obstacles to collaborative data sharing; 3) collect and preserve as much data as possible

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by reducing the data management and submission burden for recipients of DOE funding, and 4)
support innovation through the timely dissemination of DOE data.
2. Supporting Innovation through Agility and Adaptability
The GDR was specifically designed from day one to support innovation through the collection,
storage, and dissemination of data resulting from research and development activities. This
regularly required storing new forms of data, organizing data in new ways, and being able toaccommodate new data formats and structures, and data representing entirely new technologies.
The federation of metadata to the many sites in the network of data sharing partners (Figure 1)
required the GDR to adopt numerous metadata standards. NREL developed a series of metadatatranslators that encompass the GDR at strategic endpoints to mimic the behavior of our target data
sharing partners, allowing the GDR to transmit metadata in multiple standards simultaneously. To
each of the sites in the data sharing network, the GDR appears to speak the required metadata
language. The GDR is effectively using all the most popular metadata schemas simultaneously toensure the broadest possible compatibility and to future proof data dissemination against metadata
trends.
Additionally, the very framework of the GDR itself has been designed to be modular andadaptable. NREL utilized agile development methodologies and user-centric design to build a datarepository capable of evolving to meet the ever-changing needs of the geothermal industry. The
nature of supporting innovation requires the ability to quickly and easily expand to include or
reorganize around new concepts and technologies.
2.1 Creating an Adaptable Framework
Support for research and development, especially in emerging fields, requires adaptability. An
effective data management system must be able to receive and classify any type of data in its
purview. Rather than require submitted data to adhere to a previously defined structure or metadatataxonomy, the GDR has employed a folksonomy (i.e. a user-generated system of classifying andorganizing content). This allows the creators of data to introduce the classifications they deem
most important, supporting the submission of raw data, new data formats, and the categorizationof new technologies and concepts in real time.
Since the beginning, the GDR has been capable of accepting any type of data. There are no
restrictions on the types of files that can be uploaded. Over time, the GDR’s secure, cloud-basedarchitecture was expanded to leverage advantageous native cloud technologies, such as infinitely
scalable drives and data lakes, to remove limits on size and number of uploaded files. Today, thestorage capacity of the GDR automatically scales with each submitted file, ever expanding toperfectly meet the needs of the geothermal community.
2.2 Enforcing standards through curation
Data standards and metadata consistency are enforced through the curation process, in which themetadata for each data submission are reviewed by GDR curators for relevance, to ensure themetadata is descriptive of the data submitted; appropriateness, to make sure they are suitable for
eventual public release; and completeness, to ensure no components are missing and sufficient
metadata to promote discovery and proper use of the data. The original submitter’s classifications
are almost always preserved and are augmented by the addition of supplemental keywords and3This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

more verbose descriptions by the GDR curators. This approach has the added advantage ofproviding consistency throughout the data catalog while also accommodating innovative data
structures, topics, and formats.
2.2 Responsive to Users’ Needs
For the entirety of the GDR project, NREL has utilized user-centric design and development
methodologies to be responsive to industry needs in a timely manner. Regular attendance atconferences, quarterly trainings, help desks at major industry events, and other user outreachprograms have allowed the GDR team to collect valuable feedback from users including suggested
improvements, changes in data submission and publication workflows, and strategic additions to
the GDR’s network of data sharing partners. These improvements are prioritized and included inregular, iterative development cycles and are often released as new features or improvements to
the GDR.
Additionally the GDR teams provides technical assistance to data submitters, assisting them witheverything from data organization and data management planning at the beginning of a project todata standardization and large file transfers at the time of submission.
2.3 Encouraging Data Citation and Provenance
For many years now, the GDR has been at the forefront of the open data movement for DOE. In
addition to preserving and disseminating DOE data, the GDR team actively encourages the proper
use and citation of datasets to support scientific posterity and provenance. License to use
information is prominently displayed on every GDR dataset. All eligible datasets receive a DigitalObject Identifier (DOI) from DOE’s Office of Scientific and Technical Information (OSTI), andthe metadata for each dataset is pre-formatted into a popular citation format on every dataset
landing page for easy inclusion in publications. The proper citation of data will enable provenancefrom source to finished product, especially in the case of derivative data products such as modelingor analysis efforts, or data amalgamations. Recently, the GDR citation generator was expanded toinclude all popular citation formats (Figure 2), including MLA, APA, Chicago, and RIS; allowing
for each to be copied to the user’s clipboard with a single click.

Figure 2 The citation selector from a GDR dataset showing the MLA formatted citation.

3 Lessons learned
Not everything that exists in the GDR today was conceived of ten years ago. Some of the more
impressive features came about through years of iteration, refactoring in response to user feedback,or incorporating lessons learned and data management success stories from other industries.
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Several valuable lessons learned have paved the way for automation, smarter submission forms,
improved training, and a new paradigm for data standardization.
3.1 Automation and Simplification
One of the more significant improvements of the last decade has been the transition toward anautomated, simplified data submission process. To meet the needs of users at the time it was
created, the GDR originally supported 3 distinct and separate data submission pathways, including
a web form and the ability to upload an Excel metadata template (Weers and Anderson, 2013).
This proved to be confusing to users and costly to maintain as improvements had to be made to all
three data ingress methods for consistency. The NREL team transitioned the GDR to a singularsubmission pathway and allocated resources saved on maintenance towards the automation andstreamlining of the submission process. The development of a smart form made the process eveneasier for data submitters by automatically populating key metadata fields based on intelligent
assumptions, prior submissions, detected file types, and other input factors. For example, users
submitting open-source code as part of a data submission need only link to the GitHub repo
address. The GDR will automatically reach out to GitHub and import any associated metadata,
populating the required metadata for the code submission automatically and in most cases, without
any further interaction from the submitter. The development and refinement of the smart form
makes submitting data easier, results in better quality metadata, and reduces the burden placed ondata submitters, allow more DOE resources to be spent on research and development activities.
3.2 Improved Training
NREL has always strived to create an intuitive interface for the GDR and to minimize the amountof training necessary. However, the GDR team quickly realized that proper data management
extends beyond the GDR application and that quality data begins at project inception. Early
training sessions were modified to include guidance on the organization and management of data
throughout the project lifecycle to ensure that sufficient metadata were collected along the way tosupport the eventual submission of project data to the GDR. These long, comprehensive training
sessions were often done as a quarterly webinar or conducted on-demand for new DOE funds
awardees. A recording of the training session was made available on the OpenEI YouTube channel
and, in response to user feedback, a written version was created for those that prefer to read bestpractices documents. Eventually, a series of short, targeted micro-video tutorials (no more than 12 minutes) were created, allowing the GDR team to go into more depth on each individual datasubmission field without overwhelming general audiences. These videos have been organized intoa GDR tutorials playlist on YouTube.
Leading by example, the GDR team recently created GDR submission #1 to house and showcase
data management best practices for GDR submitters and curators (Weers et al, 2021). The
submission contains all the resources listed above and serves and an example submission to others.
3.3 Evolution of Data Standards
One of the greatest lessons learned in the last ten years has led the GDR team to revisit the practices
around data standardization. Originally, data submitters were encouraged to submit standardized
structure data, in the form of content models created by topic for use across the National
Geothermal Data System (NGDS), of which the GDR is a node. While these standards are still
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supported by the GDR, their adoption by data submitters has been sparse and users havecomplained that the required formats (XML or Excel spreadsheet) are limited and don’t allow for
the standardization of non-tabular data formats such a time-series data, geospatial data, or multimedia formats such as video files. Furthermore, the requirement for data submitters to translate
vast amounts of data from industry standard formats such as SEG-Y into NGDS data standards
proved to be costly and time consuming.
The GDR team’s new approach to data standardization takes advantage of modern data formats
such as JSON-LD and builds upon the successful standardization efforts and data management
best practices used by the banking and medical industries. Rather than require awardees totranslate data into standard formats, the GDR will now utilize data pipelines to detect select data
in its original form and automatically standardize it. For example, drilling data submitted to the
GDR are automatically detected as drilling data and converted into a standard format (Figure 3).

Figure 3. Data flow through the GDR data pipeline for the automated standardization of drilling data.

This approach promotes data sharing by allowing submitters to submit data in whatever formats
they are generated, even if the formats are raw or proprietary, transferring the burden of
standardization from individual project teams to the GDR’s team of data experts, resulting in
reduced duplication of effort and greater efficiencies across the DOE portfolio and allowing moreresources to be spent on critical project objectives.
4 Support for Big Data Through Data Lakes and Integration with the Open Energy Data
Initiative (OEDI)The Open Energy Data Initiative (OEDI) provides universal access to high-value datasets throughcloud-based data lakes to accelerate analysis and advance innovation. NREL has partnered withmajor cloud providers, including Amazon Web Services (AWS), Google Cloud, and Microsoft
Azure to make more than 1 petabyte (1 PB) of high-profile data universally accessible to the public.
The GDR was integrated with OEDI to create a Geothermal Data Lake (Weers et al 2021) toovercome the challenges of working with big data and make big geothermal data available to abroader audience.

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Figure 4 GDR integration with the Open Energy Data Initiative (OEDI) to produce a geothermal data lake

In the past, large datasets had to be downloaded or otherwise transferred to an organization with a
High-Performance Compute (HPC) capability where researchers would analyze their findings andrun analyses to get answers to their research questions. In the data lake model, researchers send
their research questions to the data and only download answers. There are numerous ways toconnect to a data lake, represented by the different arrows in Figure 4, including encapsulation of
the research question in a container, the use of modular code, the development of a cloud based
HPC by spinning up new servers, or clusters of servers, in an adjacent cloud, or through connected
applications and support tools such as Jupyter notebooks. Data in the data lake remain intact,
available to all research partners, enabling real-time collaboration on big data without the need forlarge data transfers between partners. Users of data lakes can accelerate their research timelines
and save money by removing the need for costly data transfers and synchronization among
partners. The use of a centralized data store also ensures consistent access to the data for all
collaborators, reducing the risk of data corruption and the duplication of effort needed to make thedata operable at each institution.
Figure 5 shows a portion of the PoroTomo GDR submission, which includes links to the PoroTomo
distributed acoustic sensing (DAS) dataset hosted by the OEDI data lake. This submission provides
access to the data in a more traditional industry standard format (SEG-Y) along with a cloudoptimized hierarchical data format (HDF5). It also includes a tutorial Jupyter Notebook whichallows the user to walk through an example of accessing the data and applying some basic
processing techniques within a Jupyter Notebook, all without downloading any data. The
PoroTomo DAS data has quickly become one of the GDR’s most popular datasets due to it beingof high value for research in DAS applied to geothermal resource characterization, acting as oneof the first cloud-accessible DAS datasets, and providing an example of how to access data stored
in the cloud.

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Figure 5. Screenshot of the PoroTomo Horizontal and Vertical DAS Data (Feigl 2017) on the GDR showing
access to PoroTomo data in in SEG-Y and HDF5 formats via the “View Data Lake” links (blue arrows).
This GDR submission is available at: http://gdr.openei.org/submissions/980.

Access to data in the GDR data lake are no longer limited to national labs, larger universities, andother organizations that possess their own big data storage and HPC infrastructure, enabling
universal access to data for organizations of all sizes and opening the door to potential
collaboration with smaller universities, high schools, startup companies, underprivileged
communities and other innovators.
5 Overcoming the Challenges of Data Sharing
There are many challenges to creating a culture of proper data management and collaborative data
sharing (Figure 6), including technological (e.g. transfer speeds and storage limits), organization(e.g. proprietary data, institutional policy), financial (e.g. cost of infrastructure needed to support
big data), and cultural (e.g. believe that my data is my asset). The GDR team has experienced
these challenges first-hand, and has employed a several approaches to combat them, including astate-of-the-art cloud architecture, infinitely scalable drives, integration with the Open Energy
Data Initiative (OEDI), the formation of a geothermal data lake, and annual training for awardees
on data management best practices and data sharing success stories.

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Figure 6 Challenges of data sharing and the approaches taken by the GDR team to overcome them

The positive impact of the GDR team’s efforts to overcome the challenges of sharing data can bequantified through the number of downloads (over 3 million), datasets (1,019) , and volume ofsubmitted data (135.28 TB), but just as notable are some the qualitative metrics and success stories
captured over the years.
5.1 Success Stories
The evolution of the questions emailed to the GDR team are one such metric, and over the years
paint a positive trend and a notable impact in the effort to overcome organizational and cultural
challenges to data sharing and management. Many early questions received around the time the
GDR was created, in 2012 and 2013 were focused on trying to understand data submission
requirements and addressing perceived stigmas with sharing data. Questions such as, “Why do I
have to submit my data?” and “What data does DOE want?” were prevalent. The concerns wereaddressed through hosting workshops and training sessions, by placing help desks at majorindustry functions, by handing out awards at DOE events for best data submission and mostdownloads, through technical assistance and one-on-one consultations, and through thecommunication of data management best practices and data sharing success stories. Over time, as
the value of sharing data was demonstrated more successfully, the questions received by the GDRteam switched from inquiries of obligation to inquiries of potential. It became commonplace for
the GDR team to receive questions like, “Can you handle 10 TB of data?” and “What is the bestformat for time-series data?”. These questions not only demonstrated an enthusiasm for the GDR
platform, but they also helped shape the future of GDR development and the allocation of resources
towards support for big data and new data standards. In recent years, support questions havebecome even more flattering, with users asking if they can amend previous submissions with
updated information, asking us to help coordinate the assignment of DOIs with the release of anew publication, or even asking if their personal, non-DOE-funded research projects can be added
to the GDR so that they might benefit from the GDR’s powerful dissemination engine.

9This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

5.1.1 Demonstrating the Value of Raw Data
One of the most significant success stories of the last ten years is the quantifiable impact of thepotential for raw data to be reused in new and exciting ways. Throughout the last decade, GDR
training materials have touted the intrinsic value of raw data as a fuel for innovation. Summarizing
project data is, by its very nature, biased towards the scope and objectives of the project. Whileraw data remains free to be used and reused in any number of different ways. The best exampleof this from the last 10 year shows up as the GDR’s second most downloaded dataset of all time:the Evaluation Data of a High Temperature COTS (Commercial Off-the-Shelf) Flash Memory
Module (TI SM28VLT32) for Use in Geothermal Electronics Packages (Cashion 2014).
Downloaded over 95,000 times in total, the dataset frequently takes the #1 or #2 spot in the top 10
downloads per month (Figure 7), prompting the GDR team to dig a little deeper into the demand
for this data.

Figure 7 Table showing top 3 most downloaded GDR datasets for March 2022.

Looking for a trend to explain the consistently high volume of downloads, the GDR analysts were
expecting to see evidence of a university using the dataset for a class project, a poorly written
script automating download inside a loop, or some other explanation for the astronomical accessnumbers. However, what they found was a long list of legitimate downloads from tens of
thousands of separate individuals from as many different organizations, ranging from universities
to private corporate research firms, many of them operating in industries unrelated to geothermal,
including researchers from aerospace, the automotive industry, and cell phone manufacturers. The
conclusion was that the raw data from of the evaluation of memory modules at high temperatures
was valuable to anyone working with electronics in potentially hostile environments.
6 Conclusion
Over the last 10 years, the DOE and NREL have worked together to ensure that the GDR remains
at the forefront of the DOE open data movement and that it continues to meet the data management
and information dissemination needs of the geothermal community.

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Looking forward, the GDR team aims to expand data pipeline efforts to automate thestandardization of additional data types, develop value-add data products and aggregations,
support innovation, reduce duplication of effort, and advance the geothermal industry.
The GDR protects DOE’s investment in research and development by employing an adaptableapproach to data management that enables the GDR to provide open, transparent access to public
data; security for restricted access data; data provenance and preservation for the geothermalscientific community; and to be ready to support whatever advancements and innovation the futureof geothermal may hold.
Acknowledgement
This work was authored by the National Renewable Energy Laboratory, operated by Alliance for
Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DEAC36-08GO28308 with funding provided by U.S. Department of Energy Office of Energy
Efficiency and Renewable Energy (EERE) Geothermal Technologies Office (GTO). The views
expressed in the article do not necessarily represent the views of the DOE or the United States
Government. The United States Government retains and the publisher, by accepting the article forpublication, acknowledges that the United States Government retains a nonexclusive, paid-up,
irrevocable, worldwide license to publish or reproduce the published form of this work, or allow
others to do so, for United States Government purposes.
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12This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.