U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2013–1265

The United States Geological Survey Science Data 

Lifecycle Model
Cover. Diagram showing USGS Science Data Lifecycle Model. 
The United States Geological Survey 

Science Data Lifecycle Model

By John L. Faundeen, Thomas E. Burley, Jennifer A. Carlino, David L. Govoni, 

Heather S. Henkel, Sally L. Holl, Vivian B. Hutchison, Elizabeth Martín, 

Ellyn T. Montgomery, Cassandra C. Ladino, Steven Tessler, and Lisa S. Zolly

Open-File Report 2013–1265

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Faundeen, J.L., Burley, T.E., Carlino, J.A., Govoni, D.L., Henkel, H.S., Holl, S.L., Hutchison, V.B., Martín, Elizabeth, 

Montgomery, E.T., Ladino, C.C., Tessler, Steven, and Zolly, L.S., 2013, The United States Geological Survey Science Data 

Lifecycle Model: U.S. Geological Survey Open-File Report 2013–1265, 4 p., http://dx.doi.org/10.3133/ofr20131265.
iii

Contents

Abstract...........................................................................................................................................................1

Background.....................................................................................................................................................1

The Data Lifecycle Model ...................................................................................................................2

Primary Model Elements......................................................................................................................2

Cross-Cutting Model Elements...........................................................................................................3

Data Management Roles and Responsibilities in Research Projects .........................................3

Summary..........................................................................................................................................................4

Acknowledgments.........................................................................................................................................4

References Cited............................................................................................................................................4

Figure

1. Diagram showing USGS Science Data Lifecycle Model .......................................................2
iv
The United States Geological Survey Science Data 

Lifecycle Model

By John L. Faundeen, Thomas E. Burley, Jennifer A. Carlino, David L. Govoni, Heather S. Henkel, Sally L. Holl, 

Vivian B. Hutchison, Elizabeth Martín, Ellyn T. Montgomery, Cassandra C. Ladino, Steven Tessler, and 

Lisa S. Zolly

Abstract

U.S. Geological Survey (USGS) data represent corporate 

assets with potential value beyond any immediate research 

use, and therefore need to be accounted for and properly managed throughout their lifecycle. Recognizing these motives, 

a USGS team developed a Science Data Lifecycle Model 

(SDLM) as a high-level view of data—from conception 

through preservation and sharing—to illustrate how data management activities relate to project workflows, and to assist 

with understanding the expectations of proper data management. In applying the Model to research activities, USGS 

scientists can ensure that data products will be well-described, 

preserved, accessible, and fit for re-use. The Model also serves 

as a structure to help the USGS evaluate and improve policies and practices for managing scientific data, and to identify 

areas in which new tools and standards are needed.

Background

The U.S. Geological Survey (USGS) Community for 

Data Integration (CDI) was established in 2009 (see https://

my.usgs.gov/confluence/display/cdi/CDI+Charter) to address 

data and information management issues affecting the 

Bureau’s scientific research. The CDI brings together expertise from external partners and representatives across USGS 

who are involved in research, data management, and information technology, and provides a forum for collaboration and 

brainstorming. Through partnerships and working groups, 

the CDI leads the development of data management tools 

and practices, cyber infrastructure, collaboration tools, and 

training in support of scientists and technology specialists. 

The CDI represents a dynamic community of practice focused 

on advancing science data and information management and 

integration capabilities across the USGS.

In 2010, the CDI established a Data Management 

Working Group (DMWG) to develop, enhance, and recommend best practices and policies that would assist USGS in 

effectively handling, documenting, preserving, and providing 

access to the Bureau’s science data. To provide a conceptual foundation for these activities that encapsulates the best 

practices of data management in a model that resonates with 

USGS research scientists, a working group sub-team investigated existing data lifecycle models. That sub-team investigation resulted in a compilation of more than 50 data lifecycle 

models from academia, other Federal agencies, and international organizations. The models differed widely in granularity, complexity, presentation, and community perspective; 

however, none of the reviewed models captured traditional 

USGS processes or the level of detail and clarity desired. A 

simplified, custom model, informed by the reviewed models, 

was needed to clearly connect data management activities with 

research project plans and to provide a useful structure for 

organizing data management resources.

The sub-team analyzed each model for its relevance to 

existing USGS processes for creation and management of 

science data. Common characteristics among all models were 

identified, and the resulting information was synthesized and 

analyzed over several months by a review team with diverse 

perspectives on USGS science data; these efforts culminated 

in a two-day workshop to develop a first rendition of a model. 

Circular, spiral, linear, and decision-tree type models were 

posited and thoroughly discussed for their relevance to USGS 

processes and their effectiveness in communicating the stages 

of the data lifecycle.

The sub-team ultimately chose a linear presentation for 

the model graphic (fig. 1, hereafter referred to as the Model) 

having an intuitive, left-to-right flow of its narrative and a 

clearly defined starting point that aligns with the inception 

of the research project. Careful attention was paid to making 

effective semantic choices, with the firm goal of providing 

clear communication of the Model’s components. Explanatory text accompanies the Model and is intended to help 

users better understand each element. The Model’s supplementary documentation and discussion are available on the 

USGS Data Management Web site (http://www.usgs.gov/

datamanagement/).

The sub-team made drafts of the Model available for 

review through multiple venues, including hosting a poster at 

the 2011 CDI Workshop, briefings to the CDI during monthly 
2 The United States Geological Survey Science Data Lifecycle Model

meetings, and posting on the CDI Web site (https://my.usgs.

gov/confluence/display/cdi/Home). USGS scientists, data 

managers, and policy analysts provided valuable feedback 

and suggested changes that were incorporated into subsequent iterations. The final draft of the Model was reviewed 

and accepted in November 2012 by CDI executive sponsors 

Kevin T. Gallagher (Associate Director for Core Science 

Systems) and Linda C. Gundersen (Emeritus; then-Director, 

Office of Science Quality and Integrity).

The Data Lifecycle Model

The USGS serves the Nation by providing scientific 

information; therefore, data are a core asset to the Bureau. 

The February 22, 2013, Memorandum from the White House 

Office of Science and Technology Policy (Holdren, 2013) 

observes that “Scientific research supported by the Federal 

Government catalyzes innovative breakthroughs that drive our 

economy. The results of that research become the grist for new 

insights and are assets for progress in areas such as health, 

energy, the environment, agriculture, and national security.”

The Bureau’s Fundamental Science Practices encourage 

“USGS scientists...to publish their data and findings in ways 

that contribute to the most effective release of USGS science and 

best enhance the Bureau’s reputation for reliable science” (U.S. 

Geological Survey, 2011). Proper management of USGS science 

data is the responsibility of USGS scientists and the staff who 

support them. Data management and data integration have been 

identified in the USGS Science Strategy as critical areas that are 

essential to the success of future understanding and application 

of USGS science (U.S. Geological Survey, 2007). Implementing 

the Model helps USGS scientists understand and manage for the 

lifecycle of data and information products to ensure that the data 

are discoverable, well described, and preserved for access and 

use beyond the life of research projects.

Incorporating data management principles and practices 

is an essential component of the larger process of planning 

activities, resources, and techniques that will be required by 

the research project. A data lifecycle model offers a high-level 

overview of the individual actions, operations, or processes 

that must be undertaken at different stages. The Model as a 

visual tool can, in turn, assist scientists in anticipating and 

planning for specific actions that need to be taken at each stage 

to manage the data, and thus help to ensure timely, comprehensive, and secure approaches to data curation. The resulting 

well-curated data resources, which researchers can re-use, 

are critical to integrated science and extend the value of the 

data. This Model, developed for USGS science data, is one 

way to facilitate shared recognition and understanding of the 

necessary steps to document, protect, and make available the 

Bureau’s valued data assets.

Primary Model Elements

Plan, the first Model element, is intended to assist scientists in assuring consideration of all activities related to the 

handling of the project’s data assets, from project inception 

to publication and archiving. During this stage, all elements 

of the Model should be evaluated, addressed, and documented. The project team should consider approaches, needed 

resources (including funding and personnel), and intended outputs for each stage of the data lifecycle. A data management 

plan is the recommended output of this element of the Model.

Acquire, the second Model element, represents the activities through which new or existing data are collected, generated, or considered and evaluated for re-use. Streamgage data, 

historical maps, seismology motion sensor outputs, biological 

records, and satellite observations are examples of acquired 

data and information that represent the diverse and robust 

variety of science data inputs to USGS research. Scientists 

are skillful in designing data acquisition techniques to address 

research questions; in the USGS context, this element emphasizes the importance of considering relevant USGS policies 

and best practices that maintain the provenance and integrity 

of the data as a USGS information product. The outputs of this 

element are the project’s data inputs.

Figure 1. USGS Science Data Lifecycle Model. Boxes indicate the main Model elements, and the shaded arrows below 

represent cross-cutting elements.
Background 3

Process, the third Model element, represents various 

activities associated with preparation of new or previously collected data inputs. Processing of input data may entail definition of data elements; integration of disparate datasets; extraction, transformation, and load operations; and application of 

calibrations to prepare the data for analysis. The Process element in the Model reminds scientists that USGS standards and 

tools are available that can meet project requirements while 

also building a Bureau-wide foundation of data for integrated 

science. The outputs of this element are datasets that are ready 

for integration and analysis.

Analyze, the fourth Model element, represents the 

activities associated with the exploration and interpretation 

of processed data, where hypotheses are tested, discoveries 

are made, and conclusions are drawn. Analytical activities 

include summarization, graphing, statistical analysis, spatial 

analysis, and modeling, and are used to produce scientific 

results and information. In this element new data are generated, versions are tracked, and processes are documented. 

Data management during analysis improves the efficiency of 

data analysis activities, preserves documentation that is critical for scientific integrity, and creates a foundation for future 

research. The outputs of this element are interpretations or 

new datasets, which often are published in written reports or 

machine-readable formats such as map layers or numerical 

modeling results.

Preserve, the fifth Model element, represents the 

activities associated with storing data for long-term use and 

accessibility. Preservation often is not considered until the end 

stage of a project, when it might be neglected because of the 

pressure of project budgets and timetables. The intentional 

placement of this element ahead of Publish/Share in the Model 

is a reminder that federally funded scientists must plan for the 

long-term preservation of data, metadata, ancillary products, 

application-neutral storage formats, and any additional documentation, to ensure availability and re-use.

Publish/Share, the sixth element in the Model, combines 

the Bureau’s concepts of traditional peer-reviewed publication 

with the distribution of data through Web sites, data catalogs, 

social media, and other venues. Publication and dissemination of USGS data and information are critical components of 

the USGS Mission (U.S. Geological Survey, 1989), and are 

a focal point of recent Federal directives to increase access 

to the results of federally funded research (Holdren, 2013). 

This element reminds scientists that data, as well as traditional 

publications, are research products.

Cross-Cutting Model Elements

Each of the primary elements of the Model addresses 

discrete activities and outputs unique to that stage; however, 

other critical activities must be performed continually across 

all stages of the lifecycle to help support effective data management (fig. 1).

Describe (metadata, documentation), the first crosscutting element, highlights the importance of step-wise documentation throughout the data lifecycle. Beginning with the 

data management plan, this element emphasizes documentation 

of every lifecycle stage in sufficient detail that other scientists 

can validate research outputs through replication, evaluate the 

validity of the results, and determine the usefulness of the data 

for future research. Recording this information at each stage of 

the data lifecycle—rather than at the concluding stages of the 

project—helps to ensure accuracy and comprehension of the 

science data created, compiled, processed, and shared. Standards-based metadata and documentation such as software code 

comments, data models, and work flows facilitate indexing, 

accession, understanding, and future uses of the data. Although 

misinterpretation or misuse of data cannot be prevented, welldocumented data can help to expose and correct such errors 

when they occur. Finally, the Describe element communicates 

the provenance and authority of USGS data and information.

Manage quality, the second crosscutting element, 

reminds scientists to plan quality-assurance measures for data 

at the project’s inception, and then undertake ongoing qualitycontrol monitoring and adjustment at subsequent lifecycle 

stages to verify that those measures perform as expected as the 

project proceeds. This element aligns with the Bureau’s emphasis on releasing only products of the highest quality science.

Backup and secure, the third crosscutting element, 

involves managing physical risks to the data throughout the 

data lifecycle while also ensuring that the data are accessible. 

This element reminds scientists that routine backups are critical to prevent the physical loss of data because of hardware or 

software failure, natural disasters, or human error before the 

final Preservation of the data. Loss-prevention measures apply 

to the raw and processed research data, original science plan, 

data management plan, data acquisition strategy, processing 

procedures, versioning, analysis methods, published products, 

and associated metadata. This element also encourages scientists to plan for secure data sharing services, particularly when 

the project’s scientists work at multiple facilities.

Data Management Roles and Responsibilities in 

Research Projects

The USGS Science Data Lifecycle Model includes data 

management activities that require specialized knowledge 

and skills, as well as ongoing education about methods and 

standards. The Model encourages researchers to plan project 

teams that recognize two different roles: researchers and data 

stewards. Depending on staff expertise, the researcher and data 

steward roles may fall to one person for one or more lifecycle 

stages or may be divided among multiple individuals with 

discrete responsibilities. Although multiple personnel may 

oversee the various data lifecycle elements, the project manager is responsible for ensuring that each element is addressed 

throughout the life of the project.
4 The United States Geological Survey Science Data Lifecycle Model

Summary

The U.S. Geological Survey (USGS) Science Data 

Lifecycle Model represents a high-level view of USGS data 

collection, handling, and dissemination activities. In applying 

the Model to research activities, USGS scientists can ensure 

that data products will be well described, preserved, accessible, and fit for re-use. The Model also serves as a structure 

to help evaluate and improve USGS policies and practices for 

managing scientific data, and to identify areas in which new 

tools and standards are needed.

Acknowledgments

This work benefited from USGS staff contributions 

provided by Richard Huffine, Tim Kern, Qi Tong, and USGS 

Volunteer Trent Faust, at various times through the development of the Model. The authors recognize and thank them for 

their efforts.

References Cited

Executive Order No. 13642, Federal Register Vol. 48, 

No. 93, pg. 28111 (May 14, 2013), accessed September 2013 at https://www.federalregister.gov/

articles/2013/05/14/2013-11533/making-open-andmachine-readable-the-new-default-for-governmentinformation#p-4. 

Holdren, J.P., 2013, Memorandum for the heads of executive 

departments and agencies—Increasing access to the results 

of federally funded scientific research: Executive Office 

of the President, Office of Science and Technology Policy, 

6 p., accessed February 26, 2013, at http://www.whitehouse.

gov/sites/default/files/microsites/ostp/ostp_public_access_

memo_2013.pdf.

U.S. Geological Survey, 1989, U.S. Geological Survey 

Manual—Organization—Creation, Mission, and Functions: U.S. Geological Survey Manual Section 120.1, 

accessed September 2012 at http://www.usgs.gov/usgsmanual/120/120-1.html.

U.S. Geological Survey, 2007, Facing tomorrow’s challenges—U.S. Geological Survey science in the decade 

2007–2017: U.S. Geological Survey Circular 1309, x + 

70 p. Also available at http://pubs.usgs.gov/circ/2007/1309/.

U.S. Geological Survey, 2011, U.S. Geological Survey 

Manual Chapter 502.4—Fundamental Science Practices—

Review, Approval, and Release of Information Products, 

accessed September 2013 at http://www.usgs.gov/usgsmanual/500/502-4.html.

“Government information shall be managed as an asset throughout its lifecycle 

to promote interoperability and openness, and, wherever possible and legally 

permissible, to ensure that data are 

released to the public in ways that make 

the data easy to find, accessible, and 

usable.” — Executive Order — Making 

Open and Machine Readable the New 

Default for Government Information 

(May 9, 2013)

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Faundeen and others— The United States Geological Survey Science Data Lifecycle Model—Open-File Report 2013–1265

ISSN 2331–1258 (online)

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