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Contract No. DE-AC36-08GO28308

DOE Geothermal Data 

Repository – Tethering Data to 

Information

Preprint

Jon Weers

National Renewable Energy Laboratory

Arlene Anderson

U.S. Department of Energy

To be presented at the Thirty-Ninth Workshop of Geothermal 

Reservoir Engineering

Stanford, California

February 24-26, 2014

Conference Paper

NREL/CP-6A20-61403

February 2014
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DOE Geothermal Data Repository – Tethering Data to Information

Jon Weers (a), Arlene Anderson (b)

(a) National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401-3305

(b) U.S. Department of Energy, 1000 Independence Ave. SW, Washington D.C. 20004, USA

jon.weers@nrel.gov (a), Arlene.anderson@ee.doe.gov (b)

Keywords: GDR, NGDS, Geothermal, Data, Repository, Information, Node, DOE

ABSTRACT

Data are not inherently information. Without context, data are just numbers, figures, names, or points on a line. By assigning context to 

data, we can validate ideas, form opinions, and generate knowledge. This is an important distinction to information scientists, as we 

recognize that the context in which we keep our data plays a big part in generating its value. The mechanisms used to assign this 

context often include their own data, supplemental to the data being described and defining semantic relationships, commonly referred 

to as metadata.

This paper provides the status of the DOE Geothermal Data Repository (DOE GDR), including recent efforts to tether data submissions 

to information, discusses the important distinction between data and information, outlines a path to generate useful knowledge from raw 

data, and details the steps taken in order to become a node on the National Geothermal Data System (NGDS).

1. THE DIFFERENCE BETWEEN DATA AND INFORMATION

To fully understand the difference between data and information, we must explore the process of deriving knowledge from data. In the 

pursuit of knowledge, we often start with raw data and work our way up. The relationship between data, information, knowledge, and 

wisdom can be described in a hierarchical arrangement (Ackoff 1989):

Figure 1: The hierarchical relationship between data, information, knowledge and wisdom.

1.1 What are data?

Data start as an observation. In their most basic form, data can be numbers, figures, names, or single bits, which are observed, collected 

or otherwise assembled in a (hopefully) reusable format. Without context, data are not informative. This is an important distinction to 

information scientists, as we recognize that the context in which we keep our data plays a big part in generating value. A string of 

numbers, for example, is difficult to appreciate without context. It is the context associated with data that elevates it to the status of 

information.

1.2 What is information?

Information is data with context. It is data about something. This contextual association is what gives data its value. The more 

carefully crafted this association is, the more useful the information becomes. One of the most obvious contexts is the subject of the 

data. Associating a subject with the data allows us to answer the question, “What is this data about?” The process of creating this 

association is surprisingly easy to overlook, being so obvious at the time that data collectors often forget to document it, rendering the 

data unusable by anyone not privy to the conditions of collection.
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Another common context is location. By associating data with a given area or locale, we can begin to associate the information 

generated from the data with other information related to the area. This helps to build context and greatly increases the value of the 

information. To better enable these associations, the DOE GDR has recently expanded its location metadata to associate individual files 

within a submission with either an area or a point.

Figure 2: Specifying location metadata in the DOE GDR submission form (DOE GDR 2014, Map from Google Imagery 2014).

Today, attribution of location information is more important than ever. Users of data are searching for information by location in 

greater frequency than in years past (Google Analytics 2014), and location-aware devices, including modern laptops and mobile phones,

are programmatically limiting non-geographic searches to local results using complex algorithms (Agrawal & Shanahan 2010). As a 

result, data associated with a location become easier to discover and are consequently more useful.

The National Geothermal Data System (NGDS) is a system of interconnected data nodes, of which the GDR is one. The NGDS features 

a geospatially driven search interface. To empower it, all data federated to the NGDS should include a geographic extent. For these 

reasons, locations are now mandatory metadata in the GDR. Submissions that apply to entire countries or continents, such as location 

agnostic technological improvements, should specify those continents or countries as an area in the metadata. Figure 2 demonstrates the 

area definition during the GDR submission process and is just one of many improvements made to the GDR recently to improve data 

discovery and interoperability with the NGDS.

Another important context is time. Temporal context can be easy to overlook because present relevance can appear obvious to data 

collectors. Temporal context is especially relevant in original data submissions. Far too often, original data are submitted without 

consideration of future iterations on the data. In order to ensure the information is usable for the foreseeable future, the temporal 

context around its collection must be clarified. For example, the potential generation capacity of a geothermal reservoir might be 50 

megawatts. The reservoir may even be commonly referred to as a 50-megawatt reservoir. However, as our knowledge of the reservoir 

improves, and the efficiencies of our thermal conversion technologies improve, the megawatt rating of the reservoir could change. A 

more accurate moniker might be “50 megawatts in 2014 using 2012 technology” reservoir. Associating the measurement with the date 

it was taken and the technology used to collect it can help clarify these types of issues. For this reason, the GDR collects the “sample 

date” for each resource in a submission and an overall submission “origination date.”

There are a number of other ways to assign context to data. Many of them are, in a sense, pieces of data themselves, which are 

collectively referred to as “metadata”.

Metadata are commonly defined as “data about data” (Wikimedia Foundation 2013). When properly attributed, metadata serve to 

define the context of the data they describe, generating information. “By describing the contents and context of data files, the quality of 

the original data/files is greatly increased” (Wikimedia Foundation 2013).

1.3 Completing the pyramid

Knowledge is understanding. We gather information and apply reason to determine the “why” and “how” of things. The quality of our 

information is key to our understanding. When little context is available, assumptions must be made to fill the void. Numerous or false 

assumptions can lead to false knowledge or even unintentional deception. The more context tying our data to information, the more 

reliable our understanding of that information is.
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Our history of knowledge builds wisdom. To use a colloquialism, wisdom comes from experience. From this we can analyze patterns, 

build predictive models, and anticipate outcomes, none of which would be possible without the proper contextualization of our original 

data. As the summit of Ackoff’s hierarchical pyramid, wisdom is heavily dependent upon the quality of the supporting information, or 

in our case, the combination of reusable data and descriptive metadata.

2. TETHERING DATA TO INFORMATION

Generating knowledge from data begins with the careful construction of lasting context. To be truly useful, the subject of the data must 

be indisputable. Ambiguity can lead to doubt and assumption. For example, a statement like “the geothermal potential of Paris is X” 

can create uncertainty. Those familiar with this paper’s origins in the United States might assume the statement refers to Paris, Texas as 

opposed to Paris, France. Careful attribution of metadata is a reliable way to avoid opportunity for error in reuse.

2.1 Establishing permanence

To be useful time and time again, data must be reliably available. The DOE GDR assigns a Uniform Resource Identifier (URI) for each 

dataset and resource. A URI is a web address, similar to a URL, but more precise in that it leads to a single, specific (and ideally 

permanent) identifier for that resource. In the case of the DOE GDR, all resources are given a unique and permanent address derived 

from their submission number. For example, the following URI will always point to the file Silver Peak TGH Drilling Map Sep 

2009.pdf, part of submission 268:

http://gdr.openei.org/files/268/Silver%20Peak%20TGH%20Drilling%20Map%20Sep%202009.pdf

2.2 Semantic identity

The URI assigned to each resource in the DOE GDR also serves as a permanent reference point for the resource. This identifier appears 

in the metadata catalog broadcast by the DOE GDR, so that other nodes on the NGDS can positively identify this resource. This is an 

essential step in linking the data to other relevant information on the NGDS and on any system capable of harvesting the DOE GDR 

metadata catalog, including OpenEI. OpenEI.org is the open data platform where the DOE GDR lives and it utilizes linked open data to 

provide worldwide access to energy data. 

The DOE GTO is working closely with OSTI and the office of Energy Efficiency and Renewable Energy within DOE (EERE) to link 

the data submitted by geothermal researchers to program goals and project results. Establishing a semantic identity and is critical to 

associating key pieces of information with each other, especially when they reside at different locations. To accomplish this, plans for 

future DOE GDR development include integration with the U.S. Office of Science and Technical Information (OSTI) DataCite platform 

for the automated generation of Digital Object Identifiers (DOIs). A DOI is commonly used and respected unique identifier for 

electronic assets (Wikimedia Foundation 2014). Assigning a DOI to a GDR resource would further define its semantic identity and

would be linked to its individual metadata and URI. Anyone looking to cite, discuss, or otherwise reference a specific DOE GDR 

resource could do so without ambiguity by using the appropriate DOI.

2.3 Common ground

Data that conform to commonly used structures or organizational patterns often prove more useful than data that do not. Another 

important step in tethering data to information is the definition and use of data content models. To date, the NGDS has defined more 

than 30 data content models (NGDS 2014). The models are a common framework for the organization of data and encourage the 

submission of data in similar, standardized structures. This allows submitted data to be easily merged, combined, or queried with other 

data submissions. Storing data in a common format increases the usefulness of the data. As a content model becomes more widely 

adopted, data conforming to the model become more valuable. Whenever possible, the DOE GDR encourages formatting data in one of 

the NGDS content models located here: http://geothermaldata.org/content-models/data-interchange-content-models (NGDS 2014).

2.3.1 Three tiers of data

Figure 3: The three tiers of data as described on the GDR FAQ page (GDR FAQ 2014).
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Data submitted to the DOE GDR that conforms to an NGDS content model is considered to be “Tier 3,” or standardized, structured 

data. Not all submissions will be able to conform to these standards, but those that do will be more reusable (Weers and Anderson 

2013).

By contrast, “tier 1” data is unstructured and represents the least useful type of data commonly submitted to the GDR. This tier 

describes data in formats typically optimized for display or printing. It includes PDFs, presentations, and images. Extracting usable data 

from an image, especially if that image is a scanned document, can be tedious and prone to error. 

The second tier is reserved for structured data. This is typically data in spreadsheets, databases, or structured text formats such as 

comma-delimited (CSV) files. While the structure may be common, it is often not standardized and will likely require some sort of 

translation before it can be used with other, similar data. For example, two data submissions might both be structured in spreadsheets, 

but one describes the location of a resource as “Coordinates” while the other might assign location by “County,” mentioning location in 

a “Description” field, or omit location altogether. Such inconsistencies may make it difficult to compare or combine different data 

submissions.

Because “tier 3” data submissions adhere to standardized content models, they can be easily combined with other data submissions that 

use the same model. In a sense, “tier 3” submissions are “plug and play” and can easily be combined with like submissions for crosscutting analysis or merged into a central database.

3. BECOMING A NODE ON THE NGDS

To become a node on the NGDS, one need only download the NGDS “Node in a Box” software and install it on an appropriate server. 

The open source software and installation instructions can be found on the NGDS GitHub page: https://github.com/ngds/ckanext-ngds.

For the DOE GDR, this process was complicated by additional security requirements. Fortunately, the NGDS “Node in a Box” 

software utilizes a common and standard method for data interchange called a Catalog of Services for the Web (CSW), which the DOE 

GDR development team was able to adopt to marry DOE GDR data to the communication methods required of the “Node in a Box” 

solution.

The team then resolved discrepancies between the metadata models used by the two sites. Many of these inconsistencies were resolved 

in the CSW endpoint by translating the DOE GDR metadata from its native format to the NGDS-specific CSW format. In some cases, 

the DOE GDR metadata identified flaws in the NGDS CSW harvesting tools. In other cases, a change to the DOE GDR metadata 

collection process was necessary. The location selector featured in Figure 2 is an example of an improvement designed to better marry

DOE GDR metadata to NGDS standards.

While numerous improvements have already been made, the DOE GDR team plans to continue to improve the discoverability of GDR 

data on the NDGS through metadata enhancements and CSW endpoint reconfigurations.

4. TYING IT ALL TOGETHER

The DOE GTO is working closely with OSTI and the office of Energy Efficiency and Renewable Energy within DOE (EERE) to link 

the data submitted by geothermal researchers to program goals and project results. Tethering GDR data to over-arching goals allows 

DOE to measure the success of the Geothermal Program as a whole. More importantly, data submitted with proper context is easier to 

use and easier link with other information, making it more reusable. 

Linking information from different sources is key for further innovation. If data can be placed in a new context, more and 

more valuable applications – and therefore knowledge – will be generated. (Bauer and Kaltenböck, 2012)

Providing the proper context for data is critical to creating these links, which can fuel innovation in the geothermal sector. Careful 

attribution of metadata can help insure the reusability of the data and advance understanding of geothermal sciences.

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