National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center

Sierra Nevada Network 

Data Management Plan 

Natural Resource Report NPS/NRPC/NRR—2008/070 
ON THE COVER 

Upper Rock Creek, Sequoia National Park 

NPS Photo 
Sierra Nevada Network 

Data Management Plan 

Natural Resource Report NPS/NRPC/NRR—2008/070 

National Park Service 

Natural Resource Program Center 

1201 Oak Ridge Drive, Suite 150 

Fort Collins, CO 80525 

November 2008 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado 
ii

The Natural Resource Publication series addresses natural resource topics that are of interest and 

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Natural Resource Reports are the designated medium for disseminating high priority, current 

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general, diverse audience and may contain NPS policy considerations or address sensitive issues 

of management applicability. Examples of the diverse array of reports published in this series 

include vital signs monitoring plans; "how to" resource management papers; proceedings of 

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regularly-published newsletters. 

Views, statements, findings, conclusions, recommendations, and data in this report are solely 

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Printed copies of reports in these series may be produced in a limited quantity and they are only 

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Internet, or by sending a request to the address on the back cover. 

Please cite this publication as: 

Cook, R. R., and P. Lineback. 2008. Sierra Nevada Network data management plan. Natural 

Resource Report NPS/NRPC/NRR—2008/070. National Park Service, Fort Collins, Colorado 

NPS D-45, November 2008 
iii 

Contents 

List of Figures................................................................................................................................ ix 

List of Tables ................................................................................................................................. xi 

Acknowledgements....................................................................................................................xxiii 

Change History ........................................................................................................................... xvii 

Acronyms used in this document................................................................................................. xix 

Executive Summary..................................................................................................................... xxi 

Chapter 1. Introduction ................................................................................................................... 1 

1.1. The Sierra Nevada Network..................................................................................................... 1 

1.2. Purpose and Scope ................................................................................................................... 1 

1.3. Goals and Priorities.................................................................................................................. 3 

1.4. Organization............................................................................................................................. 3 

1.5. Key Terms and Definitions...................................................................................................... 3 

1.6. Making it Work........................................................................................................................ 5 

1.7. References................................................................................................................................ 6 

1.8. Appendixes .............................................................................................................................. 6 

1.9. Credits...................................................................................................................................... 6 

Chapter 2. Infrastructure and Systems Architecture....................................................................... 7 

2.1. Objectives ................................................................................................................................ 7 

2.2. Laws and Policies .................................................................................................................... 7 

2.3. General Standards and Guidelines........................................................................................... 8 

2.3.1. Network Computer Resources ....................................................................................... 8 

2.3.2. Network Security............................................................................................................ 9 

2.3.3. National Applications and Tools.................................................................................... 9 

2.3.4. SIEN I&M Web Site .................................................................................................... 10 

2.3.5. Collaboration Technologies ......................................................................................... 10 

2.3.5.1. Web Meetings ...................................................................................................... 11 

2.3.5.2. Content Management........................................................................................... 11 

2.3.5.3. Personal Communications ................................................................................... 12 

2.3.6. Geographic Information Systems................................................................................. 12 

2.3.6.1. Devils Postpile National Monument.................................................................... 12 

2.3.6.2. Sequoia and Kings Canyon National Parks ......................................................... 12 

2.3.6.3. Yosemite National Park ....................................................................................... 13 

2.3.6.4. Recommendations................................................................................................ 13 

2.3.7. Database Applications Development ........................................................................... 14 

2.3.9. Digital Data Formats .................................................................................................... 14 

2.3.9.1. Databases ............................................................................................................. 14 

2.3.9.2. Spatial data........................................................................................................... 15 
iv 

2.4. References.............................................................................................................................. 15 

2.5. Appendixes ............................................................................................................................ 16 

2.6. Credits.................................................................................................................................... 16 

Chapter 3. Project Development and Data Management Workflow ............................................ 17 

3.1. Five Stages of Project Development...................................................................................... 17

3.2. The Data Life Cycle............................................................................................................... 20 

3.3. References.............................................................................................................................. 23 

3.4. Appendixes ............................................................................................................................ 23 

3.5. Credits.................................................................................................................................... 23 

Chapter 4. Data Management Roles and Responsibilities............................................................ 25 

4.1. General Standards and Guidelines......................................................................................... 25 

4.2. Collaboration.......................................................................................................................... 27 

4.3. References.............................................................................................................................. 28 

4.4. Appendixes ............................................................................................................................ 28 

4.5. Credits.................................................................................................................................... 28 

Chapter 5. Databases..................................................................................................................... 29 

5.1. Objectives .............................................................................................................................. 29 

5.2. Laws and Policies .................................................................................................................. 29 

5.3. General Standards and Guidelines......................................................................................... 29 

5.3.1. The Natural Resource Database Template ................................................................... 29 

5.3.2. Database Development................................................................................................. 29 

5.3.2.1. Conceptual Data Models...................................................................................... 29 

5.3.2.2. Logical Data Models............................................................................................ 30 

5.3.2.3. Physical Data Models .......................................................................................... 33 

5.4. Quality Assurance/Quality Control........................................................................................ 34 

5.5. Database Documentation ....................................................................................................... 35 

5.5.1. Basic Requirements...................................................................................................... 35 

5.5.2 Revisions ....................................................................................................................... 36 

5.6. References.............................................................................................................................. 36 

5.7. Appendixes ............................................................................................................................ 36 

5.8. Credits.................................................................................................................................... 36 

Chapter 6. Acquisition, Processing and Reporting ....................................................................... 37 

6.1. Objectives .............................................................................................................................. 37 

6.2. Laws and Policies .................................................................................................................. 37 

6.3. General Standards and Guidelines......................................................................................... 37 

6.3.1. Programmatic Data....................................................................................................... 37 

6.3.1.1. Data Collection .................................................................................................... 38 

6.3.1.2. Data Processing.................................................................................................... 41 

6.3.1.2.1. Spatial data.................................................................................................... 41 

6.3.1.2.2. Photographs................................................................................................... 41 

6.3.1.2.3. Remote Sensing Data.................................................................................... 41 

6.3.1.2.4. Vouchers ....................................................................................................... 42 
v 

6.3.2. Non-programmatic Data............................................................................................... 42 

6.3.2.1. Data Collection .................................................................................................... 44 

6.3.2.2. Data Processing.................................................................................................... 45 

6.3.3. Analysis and Reporting ................................................................................................ 45

6.3.4. Changes to Procedures ................................................................................................. 45

6.3.5. Maintaining an Edit Log .............................................................................................. 46 

6.4. References.............................................................................................................................. 46 

6.5. Appendixes ............................................................................................................................ 46 

6.6. Credits.................................................................................................................................... 46 

Chapter 7. Quality Assurance and Quality Control (QA/QC) ...................................................... 47 

7.1. Objectives .............................................................................................................................. 47 

7.2. Laws and Policies .................................................................................................................. 47 

7.3. General Standards and Guidelines......................................................................................... 48 

7.3.1. Project Planning and Data Design................................................................................ 50 

7.3.2. Data Collection............................................................................................................. 50 

7.3.3. Data Entry .................................................................................................................... 51 

7.3.4. Data Verification.......................................................................................................... 52 

7.3.4.1. Methods for Data Verification............................................................................. 52 

7.3.5. Data Validation ............................................................................................................ 53 

7.3.6. Methods for Data Validation........................................................................................ 54 

7.3.7. Review, Conformance and Communication ................................................................ 54 

7.3.8. Roles and Responsibilities ........................................................................................... 55 

7.4. References.............................................................................................................................. 56 

7.5. Appendixes ............................................................................................................................ 56 

7.6. Credits.................................................................................................................................... 56 

Chapter 8. Dataset Documentation ............................................................................................... 57 

8.1. Objectives .............................................................................................................................. 57 

8.2. Laws and Policies .................................................................................................................. 57 

8.3. General Standards and Guidelines......................................................................................... 58 

8.3.1. Content Standards ........................................................................................................ 58 

8.3.2. Format and Storage Standards...................................................................................... 60 

8.3.3. Metadata Tools............................................................................................................. 61 

8.3.4. Work Flow Process ...................................................................................................... 62 

8.3.5. Maintenance and Access .............................................................................................. 62 

8.4. References.............................................................................................................................. 63 

8.5. Appendixes ............................................................................................................................ 63 

8.6. Credits.................................................................................................................................... 63 

Chapter 9. Data Ownership and Sharing....................................................................................... 65 

9.1. Objectives .............................................................................................................................. 65 

9.2. Laws and Policies .................................................................................................................. 65 

9.3. General Standards and Guidelines......................................................................................... 66 

9.3.1. Collaborative Agreements............................................................................................ 66 

9.3.2. Due Notification........................................................................................................... 67 
vi 

9.3.3. Access Restrictions on Sensitive Data ......................................................................... 67 

9.3.4. Answering Requests under the Freedom of Information Act ...................................... 68 

9.4. References.............................................................................................................................. 68 

9.5. Appendixes ............................................................................................................................ 68 

9.6. Credits.................................................................................................................................... 69 

Chapter 10. Data Dissemination ................................................................................................... 71 

10.1. Objectives ............................................................................................................................ 71 

10.2. Laws and Policies ................................................................................................................ 71 

10.3. General Standards and Guidelines....................................................................................... 72

10.3.1. Data and Information Distribution Mechanisms........................................................ 72 

10.3.1.1. The SIEN Website ............................................................................................. 73 

10.3.1.2. National Databases............................................................................................. 73 

10.3.2. Sensitive Data............................................................................................................. 75 

10.3.4. Feedback Mechanisms ............................................................................................... 75 

10.4. References............................................................................................................................ 75 

10.5. Appendixes .......................................................................................................................... 76 

10.6. Credits.................................................................................................................................. 76 

Chapter 11. Records Management and Object Curation .............................................................. 77 

11.1. Objectives ............................................................................................................................ 78 

11.2. Laws and Policies ................................................................................................................ 79 

11.2.1. Records....................................................................................................................... 79 

11.2.2. Collections.................................................................................................................. 80 

11.2.3. Local Network Policies .............................................................................................. 80 

11.3. General Standards and Guidelines....................................................................................... 80

11.3.1. Records Management................................................................................................. 80 

11.3.1.1. Electronic File Naming Guidelines and Standards ............................................ 80 

11.3.1.2. Directory Structure............................................................................................. 81 

11.3.2. Archival, Backup, and Storage................................................................................... 82 

11.3.2.1. Project Process and Workflow........................................................................... 82 

11.3.2.2. Electronic Files .................................................................................................. 83 

11.3.2.2.1. Individual Employee Responsibility........................................................... 83 

11.3.2.2.2. IT Staff Responsibility................................................................................ 84 

11.3.2.2.3. Short-term Projects ..................................................................................... 85 

11.3.2.2.4. Long-term Projects...................................................................................... 85 

11.3.2.2.5. Spatial Data................................................................................................. 86 

11.3.2.3. Physical Objects and Records............................................................................ 86 

11.3.2.3.1. Documents .................................................................................................. 86 

11.3.2.3.2. Natural History Specimens ......................................................................... 87 

11.3.2.3.3. Photographs................................................................................................. 87 

11.3.2.3.4. Role of Curators.......................................................................................... 88 

11.4. References............................................................................................................................ 88 

11.5. Appendixes .......................................................................................................................... 89 

11.6. Credits.................................................................................................................................. 90 
vii 

Chapter 12. Project Tracking and Documentation........................................................................ 91 

12.1. Objectives ............................................................................................................................ 91 

12.2. General Standards and Guidelines....................................................................................... 91

12.2.1. Project Organization................................................................................................... 91 

12.2.2. Project Tracking ......................................................................................................... 91 

12.2.3. Project Documentation............................................................................................... 92 

12.2.3.1. Protocol and SOP Version Changes .................................................................. 92 

12.2.3.2. Additional Project Metadata .............................................................................. 92 

12.3. References............................................................................................................................ 92 

12.4. Appendixes .......................................................................................................................... 92 

12.5. Credits.................................................................................................................................. 92 

Chapter 13. Implementation.......................................................................................................... 93 

13.1. Education and Training........................................................................................................ 93 

13.2. Milestone Goals ................................................................................................................... 93 

13.3. Revisions.............................................................................................................................. 94 

13.4. Credits.................................................................................................................................. 94 

Glossary ........................................................................................................................................ 95 

ix

Figures 

Figure 1. Sierra Nevada Network Parks and Ecoregion. ................................................................ 2 

Figure 2. Data management steps during the five stages of project development with data 

management activities involved in each phase. Core activities are in bold.................................. 18 

Figure 3. The data life cycle. ........................................................................................................ 22 

Figure 4. Schematic of a conceptual data model for monitoring the status of mountain yellowlegged frog populations following removal of non-native fish in high mountain lakes in Sequoia 

and Kings Canyon National Parks. ............................................................................................... 31 

Figure 5. Schematic of a logical data model for monitoring the status of mountain yellow-legged 

frog populations following removal of non-native fish in high mountain lakes in Sequoia and 

Kings Canyon National Parks....................................................................................................... 32 

Figure 6. Schematic of the physical database used for monitoring the status of mountain yellowlegged frog populations following removal of non-native fish in high mountain lakes in Sequoia 

and Kings Canyon National Parks. Note: this diagram does not include data type and length 

information, or domain specifications, which should be declared as part of a physical database 

model............................................................................................................................................. 34 

Figure 7. General course of data and associated Quality Assurance/Quality Control procedures. 

Quality Control with regards to data analysis is specific to each project and addressed in 

appropriate standard operating procedures. .................................................................................. 49 

Figure 8. Some common information management elements influencing the amount of QA/QC 

needed. .......................................................................................................................................... 50 

Figure 9. The NPS integrated metadata management system....................................................... 62 

Figure 10. Flow diagram for water quality data from I&M Networks to the National Data 

Warehouse..................................................................................................................................... 74 

Figure 11. General workflow for storage and archival of electronic (in blue) and hardcopy (in 

green) scientific research products................................................................................................ 84 

xi

Tables 

Table 1. Categories of data stewardship involving Sierra Nevada Network personnel................ 25 

Table 2. Summary of Roles and Responsibilities for a Network Inventory and Monitoring 

Program......................................................................................................................................... 26 

Table 3. Satellite resolution, swath width, area, cost, and history of various remote-sensing 

imagery sources (sorted by cost)................................................................................................... 40 

Table 4. Summary of possible data sources for different types of information and repositories 

where they are maintained. ........................................................................................................... 44 

Table 5. Repositories of SIEN public and sensitive data and information. .................................. 73 

xiii

Appendixes available on the SIEN Intranet Site 

http://www1.nrintra.nps.gov/im/units/sien/) 

1A) 1998 National Parks Omnibus Act

2A) Computer Network Framework for SIEN Inventory and Monitoring 

2B) National Repositories for Digital Data and Information 

2C) Collaboration Technologies – Evaluated Applications 

2D) Sequoia and Kings Canyon GIS History and Current Issues 

2E) Yosemite GIS Development and History Before 1992 

2F) Director’s Order #70: Internet and Intranet Publishing

2H) SIEN Directory Structure 

3A) Project Data Certification Form 

4A) Sierra Nevada Network Inventory and Monitoring Program, Data Management 

Responsibilities 

4B) Sierra Nevada Network Inventory and Monitoring Program, Coordination of Data 

Management Roles and Responsibilities 

5A) Sierra Nevada Network Database Documentation Template 

6A) Sierra Nevada Network I&M Program Checklist of Project Specifications 

6B) Field Data Collection with Global Positioning Systems 

6C) Spatial Data Standards 

6D) Sierra Nevada Digital Photo Management Strategy 

6E) Sierra Nevada Network Inventory and Monitoring Program Voucher Collection and 

Documentation Standards 

6F) Data Dictionary and Formatting Specifications for ANCS+ Voucher Records 

6G) Field Definitions for NPSpecies Voucher and Observation Records 

6H) Sierra Nevada Network Guidance for Data Mining and Discovery 
xiv

Appendixes (continued) 

7A) Director's Order #11B: Ensuring Quality of Information Disseminated by the National Park 

Service 

8A) Executive Order 12906: Coordinating Geographic Data Acquisition and Access, the 

National Spatial Data Infrastructure 

8B) NR GIS Data Store: Metadata Authoring Guidance 

8C) Migrating Metadata from SMMS to XML 

8D) NPS Metadata Tools and Editor: A Comprehensive Editing Tool for National Park Service 

Metadata 

8E) Tools for Creation of Formal Metadata: Metadata in Plain Language 

8F) Sierra Nevada Network Guidelines for developing FGDC compliant metadata for both 

spatial and biological data 

9A) The Freedom of Information Act 5 U.S.C. § 552, As Amended By Public Law No. 104-231, 

110 Stat. 3048 

9B) Sierra Nevada Network FOIA and Sensitive Data Guidelines 

9C) Synopsis of Laws and Policies Governing Public Dissemination of Natural Resources Data 

10A) NPSpecies Data Standards Manual 

10B) NatureBib Data Standards Manual 

10C) Transferring Vital Signs Water Quality Data to NPSTORET 

11A) Electronic File Naming Guidelines and Standards 

11B) Sierra Nevada Network Inventory and Monitoring Program Guidance and Rules for File 

Directory Structure 

11C) Sierra Nevada Network Project Template Directory Structure 

11D) Coversheet for Non-Digital Data listing Key Information 

11E) National Park Service Standards and Guidelines for Backup, Storage, and Recovery 
xv

Appendixes (continued) 

11F) Sequoia and Kings Canyon and Devils Postpile Network General Support Services Backup 

Activity Plan 

11G) Yosemite Network General Support Services Backup Activity Plan 

11H) Sierra Nevada Network Archive and Storage Locations 

12A) NPS Service-wide Project Tracking Systems 

xvii

Change History 

Titles of updated or revised sections of the plan will be followed by brackets containing the date 

of the latest version of that section and a brief update history. For example, the title of Section 

2.2 may be followed by “[06/02/2006; Update History: 1/15/2005, 10/23/2004]”. This indicates 

the current wording of that section was last updated on June 2, 2006 and it was previously 

revised on January 15, 2005 and October 23, 2004. 

The following revisions have occurred: 

Original 

Version # 

Date of 

Revision Revised By Changes Justification New Version #

1.0 

 
i
xix

Acronyms used in this document

ANCS+ Automated National Catalog System 

CDM Conceptual Data Model 

CESU Cooperative Ecosystems Studies Unit 

CSDGM Concept Standards for Digital Geospatial Metadata 

DEPO Devils Postpile National Monument 

DMP Data Management Plan 

EPA U.S. Environmental Protection Agency 

ERD Entity Relationship Diagram (for databases) 

ESRI Environmental Systems Research institute 

FGDC Federal Geographic Data Committee 

FOIA Freedom of Information Act 

FTP File Transfer Protocol 

GIS Geographic Information System 

GPS Geographic Positioning System 

HTML Hypertext Markup Language 

I&M Inventory & Monitoring (Program) 

IMS Internet Mapping Server 

IT Information Technology 

ITIS Integrated Taxonomic Information System 

JPEG Joint Photographic Experts Group 

LAN Local Area Network 

LDM Logical Data Model 

LIDAR Light Detection and Ranging 

MOU Memorandum of Understanding 

mp Metadata Parser 

MS Microsoft – change in text to Microsoft 

PWR Pacific West Region(al Office) 

NBII National Biological Inventory Infrastructure 

NOAA National Oceanic and Atmospheric Administration 

NPS National Park Service 

NRDT Natural Resource Database Template 

NRTS Natural Resource Table Standards 

OMB Office of Management and Budget 

PC Personal Computer 

PDA Portable Digital Assistant 

PDF Adobe Portable Document Format 

PDM Physical Data Model 

QA/QC Quality Assurance/Quality Control 

RDMS/RDBMS Relational Database Management System 

RFID Radio Frequency Identification 

SDE Spatial Database Engine 

SEKI Sequoia and Kings Canyon National Parks 

SIEN Sierra Nevada Network 

SOP Standard Operating Procedure 

SQL Structured Query Language 

TIFF Tagged Image File Format 
xx

Acronyms (continued)

 

USGS U.S. Geological Survey 

WAN Wide Area Network 

WASO Washington Support Office 

XML Extensible Markup Language 

YOSE Yosemite National Park 
xxi

Executive Summary 

The central mission of the NPS Inventory and Monitoring Program is to provide timely and 

usable scientific information about the status and trends of park resources to park managers. To 

meet this challenge, we need an information management system that can effectively produce, 

maintain and distribute the products of scientific investigation conducted in our parks. 

Good data management is the means by which a thorough understanding of the value of 

scientific information about our natural resources can become a part of our National Park Service 

heritage. Data management refers to the framework by which data are acquired, maintained, and 

made available. Data management is not an end unto itself, but a means of maximizing the 

quality and utility of our natural resource information. A robust data management system is 

particularly important for long-term programs where the lifespan of a data set will span the 

careers of several scientists. Viewed in this way, it becomes obvious that data management is 

vital to the success of any long-term research program. 

The purpose of the Sierra Nevada Data Management Plan is to provide I&M and other park staff 

with a conceptual framework for a system of data management that will ensure the production 

and dissemination of timely and usable scientific information about the status and trends of park 

resources to park managers. Our strategy for achieving this goal can be summarized as follows: 

Ensure the quality, interpretability, security, longevity and availability of our natural resource 

data. Our objectives include: 

 Confidence in the security and availability of natural resource data and related 

information 

 Easy access to most information, and appropriate safeguards for sensitive information 

 Awareness of the intended use and limitations of each data set 

 Infrastructure and documentation that encourages data exploration 

 Compatibility of data sets for exploration and analysis at larger scales and across 

disciplines 

 Implementation of standards and procedures that facilitate information management, and 

that reinforce good habits among staff at all levels of project implementation – project 

leaders, technicians, and volunteer data collectors 

 A proper balance between the standards needed to ensure quality and usability, and the 

flexibility to meet specific needs and encourage innovation 

 A team of natural resource professionals who view data not as a commodity but as the 

lifeblood of our work 

This plan describes how our Network will: 

 Support Inventory and Monitoring Program objectives 

 Acquire and process data 

 Assure data quality 

 Document, analyze, summarize and disseminate data and information 

 Maintain nationally developed data management systems 

 Maintain, store and archive natural resources data and information 

xxiii

Acknowledgements 

The Sierra Nevada Network Data Management Plan is truly a product of many efforts. The 

community of data managers in the I&M Program and parks within the 32 I&M Networks 

fosters a collaborative environment with the free sharing of information. Credit is due to all who 

have preceded SIEN in the completion of network data management plans, and for making these 

plans available to the rest of us. Special credit and gratitude are extended to the original Data 

Management Planning Workgroup, coordinated by John Boetsch, who laid the groundwork for 

much of this work, and to the “Tier 2” and “Tier 3” data managers who deftly extended these 

early efforts under the leadership of Margaret Beer and Dorothy Mortenson. Specific credits are 

included at the end of each chapter. We sincerely apologize for any omissions that may have 

occurred. 

We would also like to thank the members of the SIEN Data Management Steering Team for their 

enthusiastic support of, and contributions to, this plan. We thank Bob Basham, Anne Birkholz, 

Ward Eldredge, Paul Gallez, Anne Pfaff, Dan Sohn who contributed written material to the first 

draft; Leona Svancara who coauthored Chapters 6 and 7; and Ginger Bradshaw for research that 

contributed to the development of Chapters 8 and 10. We thank also: Margaret Beer and Chris 

Dietrich who graciously offered enthusiastic support and assistance throughout the development 

of this plan; Leona Svancara, Andi Heard, Meryl Rose, Patrick Flaherty and Ward Eldredge who 

provided comments on all or portions of the draft; and to Steve Fancy, Lisa Nelson, Joe Gregson, 

Alison Loar, Mark Wotawa, and all of the other remarkable data management professionals who 

have contributed so much to the advancement of data and information management within the 

National Park Service. 

1

Chapter 1. Introduction 

The National Park Service (NPS) Inventory and Monitoring (I&M) Program was the catalyst for 

developing the Sierra Nevada Network (SIEN) Data Management Plan (DMP). The I&M 

Program represents a long-term commitment by the NPS to assess and document the status and 

trends of park resources (Appendix 1A – National Park Omnibus Act 1998). To effectively 

assess and document park resource status and trends, the Network must develop and implement a 

plan that outlines a sound long-term road map to guide the development, management, and 

dissemination of data and information. 

1.1. The Sierra Nevada Network 

The Sierra Nevada Network includes four National Park units in the southern and central Sierra 

Nevada range of California, including: 

 Devils Postpile National Monument 

 Kings Canyon National Park 

 Sequoia National Park 

 Yosemite National Park 



Collectively, these parks contain about 1.57 million acres of awe-inspiring grandeur and 

diversity (Figure 1). 

1.2. Purpose and Scope 

The targeted audience of this plan is the primarily the SIEN I&M program, but the document has 

been developed to more broadly meet the needs of other park programs. In addition to I&M, this 

plan is intended to provide guidance to all SIEN natural resource management divisions and 

United States Geological Survey (USGS) field stations located in Yosemite National Park 

(YOSE) and Sequoia and Kings Canyon National Parks (SEKI). These programs work closely 

with the parks and I&M, and have similar lines of business. We use the term Network throughout 

this document to refer collectively to these programs. The practices and procedures outlined will 

also apply to cooperators who have fiscal or formal agreements with Network programs. 

This plan offers a guide for Network programs and outlines how we intend to implement and 

maintain data management systems and best practices that optimize the data and information 

needs of selected programs. It reflects a commitment to ensure the quality, interpretability, 

security, longevity, and long-term availability of high-quality natural resource data and 

information. This is accomplished through standards and guidelines outlined for: 

 Proper work flow and management of data through a projects life cycle 

 The data management responsibilities of each person involved with a project 

 Quality assurance and quality control measures that should become standard practice 

 Documentation of projects (project development summaries and SOPs) and data sets 

(formal metadata) 

 Handling and protection of sensitive data and information 

 Dissemination to the public of non-sensitive data and information 
2

 Proper management, archiving, and storage of all records and objects associated with 

projects 

 Project organization and tracking 

Figure 1. Sierra Nevada Network Parks and Ecoregion. 
3

1.3. Goals and Priorities 

The data management goals of the Sierra Nevada Network have developed and evolved over 

several years and are intended to apply broadly to all Network programs. Adoption of the 

standards and guidelines contained in this plan can be used as general indicators of program 

success. I&M staff will be responsible for application to I&M projects; park and USGS staff to 

their own research and management programs. Park IT staff will be responsible for 

implementing many of the infrastructure and systems-architecture requirements, including data 

backup and archiving. 

Priority must be given to new projects as these will most easily be able to incorporate the 

practices we describe. Priority for legacy data should be given to: 

1. Data sets needed for current project development 

2. Data sets used frequently by park staff or cooperators 

3. Historic data sets archived for possible future use 

1.4. Organization 

The main body of this document addresses broadly these principal subject areas, but directs most 

of the details, including: descriptions of laws and policies; detailed standards and guidelines; 

descriptions of Network data management tools and applications, and standard operating 

procedures into chapter appendices. It has been designed with a consistent modular format to 

maximize ease of locating specific information on each general topic. The SIEN Intranet site 

(http://www1.nrintra.nps.gov/im/units/sien/) will contain appendices, guidance documents and 

standard operating procedures to all Network staff and interested NPS users. 

Most chapters include the following sections: 

Objectives – Specific statements describing the results to be achieved by the programs 

subscribing to this plan. 

Laws and Policies – Requirements and sideboards established through laws, policies, and 

mandates at different levels of governance, ranging from broad federal law to NPS and 

USGS policies, to policies developed specifically for the Sierra Nevada Network. 

General Standards and Guidelines – Relevant Network standards and guidelines applicable 

to specific chapter topic(s). These reference more detailed information in chapter appendices. 

At the end of each chapter is a list of references to literature cited, a list of appendices, and 

chapter credits. 

1.5. Key Terms and Definitions 

Since the principal purpose of this plan is to provide standards and guidelines for the 

development, management, and dissemination of data, we strive to apply consistent terminology 

when discussing key concepts and terms. 

Data are distinct pieces of information, usually formatted in a special way. This includes 

symbols or representations of facts or ideas that can be communicated, interpreted, or processed 

by manual or automated means. 
4

Digital natural resources data generally occur either in tabular form or as files specially 

formatted for use in Geographic Information Systems (GIS). GIS-formatted data include, but are 

not limited to, shapefiles, coverages, or georeferenced Tagged Image File Formats (TIFFS). 

GIS data contain information about the location and shape of, and relationships among, features 

on the surface of the earth and are usually stored as geographic coordinates and topology. 

Topology is used to compare the geographic locations of features relative to one another (e.g., 

roads connected to a highway, two vegetation polygons adjacent to one another). 

Tabular data are usually organized into logical tables of records and fields, arranged in a matrix 

of rows and columns. Tabular data can be displayed, manipulated, and stored as simple text files 

or in applications software (e.g., spreadsheets, relational databases). Tabular data can contain 

reference to a geographic coordinate system and when they do, can also be displayed with GIS 

software. 

Spatial data are any data that reference geographic coordinates. GIS data always contain these 

references. Tabular data that contain spatial references are also considered spatial. For example, 

a table of wildlife observations might include the x and y coordinates of the location of 

observation, along with other information, such as species identity, observer name, and date of 

observation. These data can be imported and displayed graphically with other spatial data layers, 

such as roads and highways, or vegetation types. For this reason, the terms GIS data and spatial 

data are often used interchangeably. We use spatial data to refer to any data set containing 

reference to a geographic coordinate system. 

Tabular data (spatial or not) can be organized within a Relational Database Management System

(RDBMS). RDBMS allow data to be structurally organized for maximum efficiency of storage 

and retrieval. If properly designed, they enable data to be recombined in any way desired, and 

therefore provide powerful vehicles for synthesis, analysis, and reporting. 

Raw data are data in their original form, i.e., data that have not been altered, summarized, or 

grouped into broader categories. Raw data can exist in many forms: as hand-written information 

on field data forms and in notebooks, as unaltered photographs, sound and video recordings, 

remote sensing imagery, and Global Positioning System (GPS) files. They may be typed or 

uploaded directly into a computer upon acquisition, or transcribed later from data sheets. 

Derived data are raw data that have been processed, or converted to another form using some 

automated or manual process. Raw natural resources data are often processed and packaged for 

summation, statistical analysis, and graphical display, or the production of maps and other 

information products. 

Legacy data are data existing prior to the adoption of this data management plan, and at risk of 

becoming obsolete due to software or metadata limitations. 

Programmatic data are data developed specifically for a project with well-defined objectives and 

specific requirements describing data use and application. As an example, satellite imagery may 
5

be acquired for a geographic area to provide an analytical data framework for monitoring 

landscape change over a period of time. 

Non-programmatic data are data developed for other programs, but are of value to meeting 

current objectives. For example, the same data acquired for monitoring landscape change might 

be reclassified and used to evaluate the severity of historical wildfires. 

Sensitive data are data that through loss, unauthorized access, or modification, could be used in 

such as way as to adversely affect valuable resources, the national interest, the conduct of federal 

programs, or individual privacy. Examples of sensitive natural resources data might include the 

locations of rare flora or fauna species, caves, or cultural sites. 

Metadata is information about data. A complete set of metadata describes all aspects of the data 

in question, including the ‘who, what, where, when, how, and why the data were collected, along 

with any processing that has occurred. Metadata are considered an essential component of any 

good data set.

1.6. Making it Work 

This plan offers many practical guidelines and solutions, but successful implementation will 

require continuous formal and informal training and education of staff and cooperators. Good 

data management is ‘an approach and way of thinking’, rather than a technological smorgasbord 

(Michener and Brunt 2000). We assume that Network staff and cooperators are interested in 

sound data management practices. However, we also recognize that managers and researchers 

often want minimal intrusiveness and operate under limited time schedules and budgets. Support 

for data management can be offered by I&M and park technical staff. However, successful 

implementation will depend on the active participation of everyone who collects and manages 

data, and so we provide guidelines for data management responsibilities associated with the roles 

of individuals within park natural resources programs. 

To be successful, a significant paradigm shift will be required in how people think about, and 

work with, data and information. Project managers must be more cognizant of their data and 

information-related tasks and align to the changes prescribed throughout this plan. Programs will 

need to invest substantially more resources toward data management than they currently do; the 

SIEN I&M Program will be expending 33% of its overall budget to data and information 

management. We acknowledge that positive changes to existing programs will not happen 

quickly or easily. Hopefully, this plan will serve as a catalyst for incremental change in Network 

activities that eventually will lead to the adoption of best data management practices – the 

techniques, methodologies, and standard operating procedures that, through experience and 

research, have been proven to lead reliably to desired results). Adoption will ultimately improve 

the production of timely and usable scientific information about the status and trends of park 

resources. This information can then be made available to park managers and scientists, as well 

as outside researchers, who collectively seek to understand and conserve those resources. 
6

1.7. References 

Boetsch, J. R., B. Christoe, and R. E. Holmes. 2005. Data management plan for the North Coast 

and Cascades Network Inventory and Monitoring Program. United States National Park 

Service. Port Angeles, Washington. 88 pp. 

Michener W. and J. Brunt. 2000. Ecological Data: Design, Management and Processing. 

Blackwell Science, Oxford, England. 180 pp. 

Mortenson, D. 2005. Data Management Plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. United States National Park Service. Anchorage, Alaska. 234 pp. 

1.8. Appendixes 

Appendix 1A) 1998 National Parks Omnibus Act 

1.9. Credits 

This chapter was written by Pat Lineback and Rosamonde Cook, with assistance from Andi 

Heard and Meryl Rose. Portions were adapted from Boetsch et al. (2005) and Mortenson (2005). 
7

Chapter 2. Infrastructure and Systems Architecture 

Modern information management infrastructure and system architecture represent the foundation 

of network information management systems. Infrastructure refers to the system of computers 

and servers that are functionally or directly linked through computer networking services. System 

architecture refers to the applications, database systems, repositories, and software tools that 

make up the framework of our information management enterprise. 

Park and Network programs rely heavily on park, regional and national information technology 

(IT) personnel and resources to maintain its computer infrastructure and systems architecture. 

Local park program IT specialists are primarily responsible for development and management of 

local architecture with oversight and support from regional and national IT programs. 

Development and management of network architecture is more broadly distributed between 

local, regional and national IT staffs and direct participation by other local and national programs 

including GIS and I&M staffs. 

Ongoing maintenance of computer resources includes, but is not limited to, hardware 

replacement, software installation and support, security updates, virus-protection, 

telecommunications networking, and backup/archiving processes. As technology relating to 

computer hardware resources advances, and the Network’s computer infrastructure evolves, 

modifications will be made to meet information management objectives. Communication and 

collaboration with park IT specialists is essential to ensuring adequate resources and service 

continuity for SIEN computer systems architectures. 

2.1. Objectives 

2.2. Laws and Policies 

All Department of Interior (DOI) agencies are required to standardize on Microsoft Office 

products for desktop application and use of spreadsheets, documents, databases, and presentation 

software (DOI, Chief Information Officer, November 30, 2005). 

All Department of Interior (DOI) agencies are required to standardize on ESRITM products when 

using GIS software (DOI, Chief Information Officer, November 30, 2005) 

 Ensure staff have appropriate access to electronic files that are secure and 

protected from accidental and malicious loss 

 Simplify GIS software installation and management, and expedite access and 

retrieval of GIS data using automation tools. 

 Identify and implement appropriate collaborative technologies and tools that 

enhance networking and communications at both an intra and interagency level. 

 Develop a collective long-term strategy for SIEN parks towards identifying and 

developing a content management system and intranet portal for storing, 

managing, searching, and disseminating electronic files. 
8

In general, Network and park staffs will conform to any federal, Department, and Bureau data 

infrastructure and systems architecture policies and standards. Further, I&M staff will conform to 

national I&M Program standards and mandates to assure program integration and information 

sharing.

Specific infrastructure and systems related Network policies are listed below. 

 The network directory structure will have a security system allowing appropriate 

individuals the ability to manage and maintain restricted files. 

 Generally, computer users should not encrypt files and folders because of the potential 

for lost encryption keys and unrecoverable data. Park IT staffs are responsible for 

ensuring that file encryption capabilities have been disabled on individual and network 

computers. 

 Installation of ArcGIS software, updates, and tool extensions should be automated and 

delivered through software installation and management portals. 

 SIEN Parks should implement modern data structures and data models that optimize 

flexibility and scalability and integrate with other national applications. 

 SIEN Parks (DEPO excluded) should employ software tool(s) that simplify and 

streamline staff access to spatial data and metadata. 

 GIS software access will be delivered to remote users via a network-based application 

delivery system that utilizes master spatial data. 

2.3. General Standards and Guidelines 

These standards and guidelines expand and clarify local and national policies and offer 

accountability and metrics for measuring staff and program performance. 

2.3.1. Network Computer Resources 

An important element of an information management program is a reliable and secure network of 

computers and servers. Other than personal computers, Network programs such as I&M do not 

have separate computer infrastructure including file or application servers. To improve 

collaboration and cooperation, any new or existing Network programs will be integrated into 

existing park computer and network infrastructures. This includes integration and management 

of electronic files on existing or planned park server architectures. 

There are multiple components associated with the NPS infrastructure including park level, 

regional, and national systems. Each hosts different parts of information systems that are 

maintained and secured by various park, Network, regional, and national IT and resource 

specialists and staff. IT duties for Network programs such as I&M are provided mostly by IT 

staff at SEKI. This includes hosting and managing electronic files being created, managed, and 

disseminated by Network staff and cooperators. It is anticipated that YOSE IT staff will provide 

additional future specialized IT support as Network parks begin implementing this plan. As an 

example, YOSE IT has Citrix administration expertise that will enable park staff to utilize 

ArcGIS software and spatial data across wide area networks with lower bandwidth connections. 

Implementation of this application will broaden GIS availability and simplify spatial data 

administration lowering costs and leveraging the use of GIS. 
9

A basic network framework for the SIEN I&M program is described in Appendix 2A - Network 

Framework for SIEN Inventory and Monitoring. 

2.3.2. Network Security 

Local and wide area networks currently conform to Department of Interior security guidelines. 

All sensitive electronic files should be placed in protected folders with limited read and write 

access. Electronic file and directory permissions administration will be partially decentralized 

with file and folder administrative rights controlled by limited Network staff including IT and a 

few trained program staff. A database of file permissions will be developed and maintained. 

Deployment of a Microsoft utility will enable the management and restoration of New 

Technology File System (NTFS) file and folder permissions on network servers. YOSE staff 

needing read or write access to the SEKI directory structure or SEKI staff needing read or write 

access to the YOSE directory structure will be enabled through Microsoft Active Directory and 

NTFS permissions. 

There are very rare circumstances where encryption of sensitive electronic files and/or folders 

may be necessary. As an example, files on removable media or portable devices may rarely need 

additional protection from unauthorized access or use. Appendix 11F and 11G, General Support 

Services Backup Activity Plans for SIEN Parks, provide specific guidance to Network staff who 

must implement electronic file encryption or other security measures (encryption policy and 

procedures are under development). 

2.3.3. National Applications and Tools 

Various national offices, including the NPS Natural Resource Program Center (NRPC) and the 

national I&M program actively develop and implement national-level, program-wide 

information management systems. Other programs such as cultural resources and maintenance 

have their own distributed applications that often intersect and also have broader program 

application and use. Principal applications and data repositories developed for the I&M and 

Service-wide programs, and referenced throughout this document, are summarized below (see 

Appendix 2B for more details). 

The NPS Data Store – an Internet-based graphical search interface that links data and 

metadata to a searchable server on which data are organized by NPS units, offices and 

programs. Access permission is required. 

NPS Focus Digital Library and Research Station – a decentralized digital imagery and 

data management system, implemented through a central public Internet portal sponsored by 

the NPS Office of the Chief Information Officer. Includes to the NPS Data Clearinghouse. 

NPS Data Clearinghouse – the central repository for NPS GIS data available to the public. 

Implemented through the NPS Focus gateway. 

NPS Automated National Catalog System (ANCS+) – the official curatorial cataloging 

system of the NPS. 
10

NatureBib – the master database for natural resources bibliographic references for the NPS. 

Implemented on a dual system of public and secure servers to protect sensitive information. 

NPSpecies – the master species database for the NPS. Includes park-specific lists of the plant 

and animal species that occur in or near the parks along with records (vouchers, observations, 

and literature citations) of physical or written evidence of their occurrence and status. 

Implemented on a dual system of public and secure servers to protect sensitive information. 

Biodiversity Data Store – an Internet-based repository of documents, GIS and other data 

sets that contain information of plant and animal species that contribute to knowledge of the 

biodiversity in the National Parks 

NPSTORET (also known as NPS Water Quality Database Templates) – a NPS database 

designed to facilitate park-level standardized reporting for STORET, an Internet-based 

interagency water quality database developed and supported by the Environmental Protection 

Agency (EPA) to house local, state, and federal water quality data collected in support of 

managing the nation’s water resources under the Clean Water Act. 

2.3.4. SIEN I&M Web Site 

The SIEN I&M web site provides to the public, general information about the Network and its 

parks, and the I&M Program, including information on inventories, monitoring, data 

management, and reports & publications as directed by WASO’s general webpage templates. 

The website will also be used to serve products such as results of I&M vital signs monitoring 

(e.g., executive briefs, progress reports, trend reports, etc.) and the SIEN Data Management Plan. 

Future plans include the inclusion of a linked blog site, for updating park staff, partners, and 

interested parties on recent program highlights and newsworthy happenings. An Intranet portal is 

also planned, where park and Network staff can share information and data that are not ready for 

public posting. 

Structure and content of the SIEN I&M web site comply with National Park Service standards, 

FOIA requirements for accessibility of public information, and Section 508 of the Rehabilitation 

Act which ensures access to electronic information by people with disabilities (http://www. 

section508.gov/). Web development adhered to the national I&M program’s Web Author 

Instructions, Practices, and Tools (http://www1.nrintra.nps.gov/im/datamgmt/webdev/index. 

cfm), and to policies and guidance on natural resources web publishing provided in the following 

documents: (1) Director's Order 70: Internet and Intranet Publications (Appendix 2F); (2) NPS 

Internet/Intranet Publications Manual (http://inside.nps.gov/helpdesk/manual.htm); and (3) NPS 

Natural Resource Web Manual (http://www1.nrintra.nps.gov/webnotes/NRWebManual/index. 

cfm). Sample Network page models were provided by WASO I&M support staff, along with 

security coding and support during development in quality assurance and quality control. 

2.3.5. Collaboration Technologies 

A small group of Network park staff – the Collaborative Technologies Work Group – evaluated 

current collaborative technologies and developed recommendations aimed at improving remote 

collaboration between groups and/or individuals at physically separate locations. Effective and 

efficient communications are essential and staffs must consider using both emerging and existing 
11

technologies as a means to improve communications. The I&M program is an good example of a 

program requiring collaboration between employees at four national parks spread across a large 

geographic area making face-to-face meetings difficult. 

This workgroup completed a needs assessment and reviewed technologies relating to meeting 

facilitation, audio conferencing, content management, file versioning, and personal 

communications. One of their most significant findings was that NPS security policy limits the 

availability of various collaboration technology tools. NPS security policies are becoming 

increasingly restrictive leaving parks with less flexibility for adopting some collaborative 

technology. Under current policy, NPS programs are limited to technologies within the NPS 

intranet. Specifically, NPS employees are prohibited from using any application hosted on 

external websites. 

A brief summary of some of the evaluated collaborative tools and recommendations are listed 

below. A more thorough detailed review and assessment is included in Appendix 2C, 

Collaboration Technologies Needs Assessment, Sierra Nevada Network National Parks. 

2.3.5.1. Web Meetings: There are many web and video conferencing applications available 

commercially, however, all but Lotus Sametime Meeting are hosted on external servers and, 

therefore, prohibited by current NPS IT policy. Network staffs have not tested the Sametime 

application. 

2.3.5.2. Content Management: An important objective of the Sierra Nevada Network is the 

development and implementation of a strategy for a content management system and local 

intranet portal for storing, managing, and retrieving electronic files. A content management 

system is a computer software system for organizing and facilitating collaborative creation of 

documents and other content. Further, this system should enable efficient searching and retrieval 

of needed electronic files. This Network creates thousands of electronic files each year in an 

increasingly complex environment. It is essential to begin developing strategies for improving 

the management and dissemination of these files using consistent and structured methods. 

Adoption of a content management system should be preceded by a formal needs assessment that 

considers comprehensive staff needs. Unfortunately, current NPS policy limits the Network from 

selecting and implementing most content management systems regardless of how effective or 

efficient any particular application might be. 

A limited needs assessment was completed to better understand the technology and its use. 

Several applications were evaluated which provide content management services. These 

included: 

 Wiki – An open source content manager supporting the FAQ’s of the Santa Monica 

Mountains Recreation Area website. 

 Drupal – An example of a Wiki open source content manager. 

 Microsoft Sharepoint Services – Commercial content manager. 

 Microsoft Sharepoint Portal Server - Commercial content manager. 

 WebEx WebOffice – Commercial content manager. 
12

The collaborations technologies work group recommended adopting Microsoft Sharepoint 

Services, which is hosted by the PWR regional office. 

2.3.5.3. Personal Communications: The use of email and telephones to conduct personal 

communication between individuals at separate locations is ubiquitous. Instant messaging 

compliments these and adds additional functionality. Email is comparatively slow and a 

telephone call limits access to an individual’s desk or cell phone availability. Instant messaging 

is near real-time and offers additional flexibility for communicating with one or more employees. 

Lotus Notes Sametime Instant Messaging is currently available to all NPS employees with a NPS 

computer network login profile. The collaborative technologies work group’s recommendation 

was to better utilize and take advantage of Lotus Notes Sametime Instant Messaging. 

2.3.6. Geographic Information Systems 

ESRI GIS applications and products are regularly used by Network staff. This includes use of 

map products or analyses generated from GIS software. Additionally, about 75 staff across SIEN 

and local USGS programs have technical ArcGIS skills and abilities. Skill levels range from 

infrequent users with beginning skills to advanced users who can complete complex analyses. 

A brief overview and current GIS issues at each of the four Network parks are described below. 

2.3.6.1. Devils Postpile National Monument: Currently, SEKI staff provide GIS support to 

DEPO and manage their spatial data. This small 800 acre park unit has limited and infrequent 

needs for GIS support at the local level. 

2.3.6.2. Sequoia and Kings Canyon National Parks: Over a period of 20 years, the SEKI GIS 

program has evolved from a single Unix workstation located in a closet and running public 

domain GRASS software to a GIS program today that includes about 30 staff who are using GIS 

software on their desktop computers. Additional history on the evolution of the SEKI GIS 

program is described in Appendix 2D. 

ArcGIS license manager software runs on two servers (both USGS and NPS have their own 

license allocations) at Ash Mountain Headquarters and distributes client software licenses to 

local area network staff. Recent monitoring has shown that it is not unusual for ten staff to be 

simultaneously using ArcGIS software within the Ash Mountain LAN. ArcGIS software 

installation, updates, and extensions are available using the SEKI intranet, but require user 

interactions. 

To run ArcGIS off network, individual hardware dongle keys must be installed and configured 

on each computer. 

A larger direct and indirect GIS user base has created challenges that are partially addressed 

throughout this plan. This rapid growth and change in use and application of GIS at SEKI has 

caused severe growing pains including: the inability to adequately develop and maintain essential 

spatial data, inadequate technical support and training for staff, and inadequate planning and 

adapting to changes in data architectures and changing technology. Flat budgets make it 

increasingly difficult to support increasing demands and requirements. Users need more support 
13

with their projects due to GIS integration including data development, mapping, and 

consultation. Creating the data storage and archival infrastructure that allows distributed access 

to data has become more difficult as more individuals tap into the wealth of GIS data and have 

changed the underlying data architecture requirements. Federal policy requires federal agencies 

to make GIS data and metadata available for sharing with the public and other organizations. 

There are not enough resources to adequately complete these tasks. Coordination requirements 

across SEKI programs and at an interagency level have dramatically increased with the growing 

GIS user base. Development and migration of existing spatial data to new data models that are 

flexible and support multiple programs requires a new level of commitment requiring more staff 

participation and engagement by multiple divisions. This is difficult given the SEKI traditional 

hierarchical management structure that has promoted autonomy within each division. The longterm GIS budget outlook is grim and we expect these issues will continue to worsen. 

2.3.6.3. Yosemite National Park: Appendix 2E, Early GIS History at YOSE, describes the roots 

and pioneering deployment and development of GIS up to 1992. YOSE staff will need to 

develop a narrative for this section of document. 

2.3.6.4. Recommendations: At the park level, the GIS programs in this Network run 

independently of one another, but the nature and complexity of GIS issues are similar for both 

SEKI and YOSE. There are some common infrastructure solutions that are applicable to these 

parks: 

 Automate GIS Software Installations and Updates – Because of the increasing numbers 

of GIS software users, it is most efficient to implement ESRI software installation 

protocols that streamline installation and updates including silent updates for existing 

ArcGIS software users. Initial software installations should be completed through a 

flexible intranet portal, but future updates conducted through silent and bulk updates. 

 NPS Theme Manager - There are hundreds of spatial data layers available to staff. They 

are hosted in multiple locations and require intimate familiarity with spatial data to find, 

understand, and use these data. It is inefficient, particularly for infrequent users to find 

and use the data they need. Therefore, both SEKI and YOSE should deploy and manage 

NPS Theme Manager to deliver data and metadata to Park staffs. This ArcGIS extension, 

developed by the Alaska Regional Office, provides a front end for spatial data and makes 

it easier for staff to locate and access spatial data. The installation and management can 

be semi-automated and updates roll out to users each time they start a new ArcGIS 

project. 

 Provide ArcGIS software access to park GIS users with low bandwidth connections – 

There are some GIS users in remote park locations that have lower bandwidth 

connections in wide area network environments. Without replicating master spatial data 

to their personal computers, it is difficult to provide these users with access to 

contemporary data and updated ArcGIS software. A recommended strategy for providing 

access to master spatial data and ArcGIS software is deployment of server-based ArcGIS 

through a Citrix application interface. Citrix is operational at both YOSE and SEKI. 

Preliminary testing of ArcGIS software indicates broader deployment of Citrix-served 

ArcGIS is an effective solution. 
14

2.3.7. Database Applications Development 

Desktop versions of SIEN databases will be in the latest Microsoft Access format unless 

otherwise specified in the project study plan. Desktop databases produced under contract will 

also use Microsoft Access unless otherwise specified in advance. 

SIEN projects will invest in modular, standalone project databases that share design standards 

and links to centralized lookup tables, and can be developed, maintained, and archived 

separately. There are numerous advantages to this strategy: 

 Data sets are modular, allowing greater flexibility in accommodating the needs of each 

project area. 

 Individual project databases and protocols can be developed at different rates without a 

significant cost to data integration. 

 Any project database can be modified without affecting the functionality of other project 

databases. 

 We avoid a large initial investment in a centralized database and the concomitant 

difficulties of integrating among project areas with very different – and often unforeseen 

– structural requirements. 

 Potentially greater efficiency for interdisciplinary use. 

The SIEN will develop a set of master look-up tables or an Integrated Data Dictionary (also 

referred to as a Buffet of Fields and Tables [BOFAT] by WASO), that will allow databases 

across projects and across disciplines to remain as consistent as possible. Likely lists of standard 

attributes and values to be developed include park codes, place names, watershed codes, species 

identifiers, and vegetation or land type classifications (See Chapter 5 – Database Design for 

more details). To the extent possible, SIEN will work with WASO and other Networks to 

develop and utilize shared look-up tables for data consistency and inter-operability at a higher 

level across programs. 

As database applications dictate, SIEN and park staffs should evaluate the need for a more robust 

enterprise relational database management system such as Microsoft SQL Server. 

2.3.8. Word Processing 

All reports and other textual documents will be finalized in editable electronic format in the NPS 

standard current version Word Processor or Adobe Acrobat, unless otherwise specified in the 

project study plan. Distribution copies will be converted to the latest version of Adobe Acrobat. 

2.3.9. Digital Data Formats 

2.3.9.1. Databases: Well thought-out database design standards are necessary to promote 

compatibility among data sets that will be aggregated and summarized, encourage sound 

database design and facilitate interpretability of data sets. As much as possible, SIEN standards 

for database design will mirror those conveyed through the Natural Resource Database Template 

(NRDT). The NRDT is a flexible, relational database template, designed for storing inventory, 

monitoring, and research data (including raw data collected during field studies). The template 

was designed to be used in the development of standalone databases and databases that interact 
15

with GIS software (e.g., ArcView or ArcGIS). A description of the NRDT and a working 

database is presented in Chapter 5 – Database Design. Additional information can be found at 

(http://science.nature.nps.gov/im/ apps/template/index.cfm). 

2.3.9.2. Spatial data: The coordinate system standard for SIEN parks is Universal Transverse 

Mercator with North American Datum 1983. Generally, existing spatial data should be migrated 

to this data projection and it should be used for any new spatial data development. Most 

significant data for all four Network parks are already in this modern coordinate system format. 

Currently, Network and park staff use many different raster and vector data formats for storing 

and managing spatial data. Vector data formats commonly include Arc/Info coverages, 

shapefiles, and personal geodatabases. Raster data formats commonly include MRSID, TIFF, 

GEOTIFF, and Grid structures. ESRI software products have historically demonstrated excellent 

backward compatibility with older data structures and there is no SIEN requirement that data 

formats be migrated to the more modern data structures. 

With the imminent release of ArcGIS 9.2, a new data structure called a file geodatabase will be 

released. Beginning in 2007, the file geodatabase will be the recommended data structure for 

significant new spatial data development in the SIEN. The file geodatabase is a spatial 

“container” used to hold a collection of datasets. Each dataset is held as a file that can scale up to 

1 terabyte in size. Network and park staffs should develop strategies for migrating existing data 

and developing new spatial data into the file geodatabase format. This data format has numerous 

advantages including: a) providing a relatively simple and scalable solution for GIS users; b) 

works across operating systems; c) scales to handle very large datasets; d) has an efficient data 

structure optimized for performance and storage and has feature geometry storage requirements 

one-third of what is required by shapefiles and personal geodatabases; and e) offers more 

flexibility for multi-user editing and use of the same spatial data layer. 

The national NPS GIS office (WASO-GIS) is facilitating the development of standardized data 

models for spatial data. These data models are designed to be flexible, scalable, and integrate 

with other national applications. GIS tools are being developed to simplify development and 

management of this data. Current WASO-GIS spatial standardization efforts include buildings, 

trails, and vegetation. As these new data models are released, Network parks should implement 

these new data models and convert legacy data into these new data models. 

2.4. References 

Boetsch, J. R., B. Christoe, and R. E. Holmes. 2005. Data management Plan for the North Coast 

and Cascades Network Inventory and Monitoring Program. USDI National Park Service. 

Port Angeles, Washington. 88 pp. 

Daley, R. 2005. Data and Information Management Plan, Greater Yellowstone Network 

Inventory and Monitoring Network. United States National Park Service, Bozeman, 

Montana. 82 pp. 
16

Department of Interior, Chief Information Officer. November 30, 2005. Office of Chief 

Information Officer, Directive 2006-003, Distribution of the Department of the Interior 

Technology Reference Model Version 3.0. 

Mortenson, D. 2005. Data Management Plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. United States National Park Service. Anchorage, Alaska. 234 pp. 

National Park Service. 2001. Director's Order 70: Internet and Intranet Publishing. 

Svancara, L. K. 2006. Upper Columbia Basin Network, Information Management Plan. Natural 

Resource Report NPS/PWR/UCBN/NRR—2006/000. National Park Service, Moscow, 

Idaho. 

2.5. Appendixes 

2A) Computer Network Framework for SIEN Inventory and Monitoring 

2B) National Repositories for Digital Data and Information 

2C) Collaboration Technologies – Evaluated Applications 

2D) Sequoia and Kings Canyon GIS History and Current Issues 

2E) Yosemite GIS Development and History Before 1992 

2F) Director’s Order #70: Internet and Intranet Publishing

2.6. Credits 

This chapter was written by Pat Lineback with contributions from Anne Birkholz (Southern 

Sierra Interagency Data Analyst), Paul Gallez (Yosemite NP), Bob Basham (Information 

Technology, Sequoia and Kings Canyon NP), Dan Sohn (Information Technology, Yosemite 

NP), and Rosamonde Cook, and with information provided by Boetsch et al. (2005), Daley 

(2005), Mortenson (2005), Svancara, (2006). 
17

Chapter 3. Project Development and Data Management 

Workflow 

To better understand the information management needs of the SIEN, it is useful to 

understand the general work flow of project development and the information management tasks 

associated with each stage. There are two main types of projects handled by Network natural 

resources staff and the Inventory and Monitoring Program: 

1. short-term, which may include individual park research projects, inventories, or pilot 

work done in preparation for long-term monitoring or research. 

2. long-term, including Network vital-signs monitoring projects central to the I&M program 

and multi-year research projects and monitoring performed by other park programs, 

agencies and cooperators. Long-term projects will often require a higher level of 

documentation, peer review, and program support. 

For information management, the primary difference between short- and long-term projects is an 

increased need to adhere to and maintain standards for long-term projects. Maintaining 

standardization from year-to-year will be necessary when comparing data over an extended 

period of time (decades for long term monitoring). 

3.1. Five Stages of Project Development 

To ensure the development of high quality scientific information, data management must be a 

component of all aspects of project development. Projects can be divided into five primary 

stages: 

 planning and approval 

 design and testing 

 implementation 

 product integration 

 evaluation and closure 

Each stage is characterized by a set of activities carried out by staff involved in the project 

(Figure 2). Primary responsibility for these activities rests with different individuals according to 

the different phases of a project. Additional discussion of the different roles and responsibilities 

of park and Network staff can be found in Chapter 4 of this plan. Specific project tracking 

methods and documentation are presented in Chapter 12. 

1. Planning and Approval – This is the stage at which many of the preliminary decisions are 

made regarding project scope and objectives. Funding sources, permits and compliance 

are also addressed at this time. Primary responsibility rests with project leaders and 

program administrators. Although this phase lacks specific data management activities, it 

is important that data managers remain informed. This is especially true as timelines for 

deliverables are finalized. All contracts, agreements, and permits should include standard 

language that describes the formats, specifications, and timelines for project deliverables. 
18

Figure 2. Data management steps during the five stages of project development with data 

management activities involved in each phase. Core activities are in bold. 

Long-term 

monitoring and 

other multi-year 

projects 

Revisions to 

protocols and 

databases 

Administrative 

reporting & 

work plan 

 Project initiation 

Planning and 

approval 

Design and 

testing 

Implementation

Preparation 

Data 

acquisition & 

processing 

Product 

development, 

delivery & 

review 

Product 

integration 

Evaluation and 

closure 

Project conclusion 

 Yes 

 No 

Background review of related, existing information 

Identify measurable objectives and target populations 

Develop proposal and budget, solicit and secure funding 

Complete contracts/agreements, permits, compliance* 

Develop study plan 

Identify project deliverables 

Develop methodology or adapt existing methods* 

Define SOPs and guidelines 

Create/revise data sheets and data dictionaries 

Complete Data design and modifications 

Initiate work development 

Identify destinations for deliverables

Conduct Peer review 

Logistics, planning, hiring, contracting, and training 

Installation of equipment and monitoring protocols 

Equipment purchase and maintenance 

Data collection, acquisition of external data sets 

Data entry, processing, quality assurance 

Data validation, and certification 

Summarization, analysis, and map productions 

Interpretation and reporting 

Metadata development 

Product review and revision 

Finalize and distribute metadata & data products 

Integrate project data with national databases 

Catalog products 

Archive and store digital files and other products

Evaluate how well project met objectives and what 

modifications need to be made to methods, procedures, data 

design, etc. 

Sign off - project requirements have been met and 

deliverables are complete and available 

* Determination of permit and compliance requirements often requires detailed knowledge of project methods, which are often not fully developed 

until after the planning and approval stage; therefore, methods development and compliance is an iterative process. 

Data Management Activities 

Changes 

needed? 
19

2. Design and Testing – All details regarding how data will be acquired, processed, 

documented, analyzed, reported and made available to others are worked out. The project 

leader is responsible for developing and testing project methodology, or for modifying 

existing methods to meet project objectives. It is critical that project leaders and data 

managers work together during this phase. The dialog between these individuals will help 

to build and reinforce good data management throughout the project. By beginning 

collaborative development as soon as possible after project approval, data integrity and 

quality can most easily be assured. 

An important part of this collaboration is the development and detailed documentation 

(Chapter 10) of project databases, including relational diagrams, data dictionaries, 

business rules, and front-end programming, along with the formal metadata. Devoting 

adequate attention to this aspect of a project is the single most important part of assuring 

the quality, integrity and usability of the resulting data. Although it is possible that posthoc changes will occasionally be required, it is important to minimize these through 

careful initial design work. 

3. Implementation – Data are acquired, processed, error-checked (Chapter 6) and further 

documented (Chapter 8), and products such as reports, maps, GIS themes, and other 

products are developed. The project leader oversees all aspects of implementation – from 

logistics planning, contracting, training, and equipment procurement to data acquisition, 

documentation, report preparation and final delivery. Data management staff function 

primarily as facilitators – providing training and support for database applications, GIS, 

GPS and other data processing applications; facilitation of data summarization, validation 

and analysis; and assistance with the technical aspects of documentation and product 

development. The specific roles of data management staff will depend primarily on the 

technical capabilities of the project staff. As much as is possible, these roles should be 

worked out in advance of implementation. Toward the end of this phase, project staff 

members work to develop and finalize the deliverables that were identified in the project 

planning documents (i.e., protocol, study plan, contracts, agreements or permits). 

4. Product Integration and Distribution – Data are merged from the working database to a 

master database maintained on the Network server, certified data sets and their metadata 

are finalized and posted in national repositories, and products are distributed or otherwise 

made available to their intended audience (Chapter 10). Data are merged to the master 

only after the annual working data set has been certified for quality by the project leader. 

Product integration may include creating records for reports and other project documents 

in NatureBib, posting imaged documents to NPFocus, and updating NPSpecies and 

ANCS+ to reflect any new species occurrence information or specimen vouchers derived 

from the project. These updates allow the information from the project to be searchable 

and available to others via service-wide search engines. Certain projects may also have 

additional integration needs, such as when working jointly with other agencies. 

In general, all raw and derived data products, metadata, reports and other documentation 

should be delivered to the data steward assigned to the project. Administrative records 
20

should be delivered to appropriate park and Network staff as specified. All project 

deliverables should be distributed according to specifications, which should be stipulated 

in all protocols, contracts, agreements, and permits. Products that do not meet program 

requirements should be returned for revision. 

5. Evaluation and Closure – Records are updated to reflect the status of the project and its 

associated deliverables in a Network project tracking application (Chapter 12). For longterm monitoring and other cyclic projects, this phase occurs at the end of each field 

season, and leads to an annual review of the project. For non-cyclic projects, this phase 

represents the completion of the project. After products are catalogued and made 

available, program administrators, project leaders, and data managers should work 

together to assess how well the project met its objectives, and to determine what might be 

done to improve various aspects of the methodology, implementation, and formats of the 

resulting information. For monitoring protocols, careful documentation of all changes is 

required. Changes to methods, Standard Operating Procedures (SOPs), and other 

procedures are maintained in a tracking table associated with each document. Major 

revisions may require additional peer review. 

3.2. The Data Life Cycle 

Data take on different forms during various phases of a project, and are maintained in different 

places as they are acquired, processed, documented, analyzed, reported, and distributed. What we 

refer to as the "data life cycle" is characterized by a series of events that we can model to 

facilitate communication (Figure 3) as follows: 

1. Acquire data – Data are acquired in digital or analog form. Digital data can be recorded 

on handheld computers and PDAs, tablets, or laptop computers. Analog data are entered 

on field data sheets. 

2. Archive raw data – Copies of all raw data files are archived intact. Digital files are copied 

to the digital library (the set of LAN folders created for the project); hard copy forms are 

either scanned and placed in the digital library or are copied and placed in the archives. 

Archiving or scanning of hard copy data forms may occur at the end of a season as a 

means of retaining all marks and edits made during the verification and validation steps. 

3. Enter/import data – Analog data are entered manually and digital data files are uploaded 

to the working database. 

4. Verify, process, and validate – Accurate transcription of the raw data is verified; data are 

processed to remove missing values and other flaws; and data are validated through 

visual inspection and queries to capture missing data, out-of-range values, and logical 

errors. 

5. Documentation and certification – Develop or update project metadata and certify the 

data set. Certification is a confirmation by the project leader that the data have passed all 

quality assurance requirements and are complete and documented. It also means that data 

and metadata are ready to be posted and delivered. 
21

6. Upload data – Certified data are uploaded from the working database to the master 

project database. This step might be skipped for short-term projects where there is no 

need to distinguish working data for the current season from the full set of certified 

project data. 

7. Archive versioned data set – The SIEN Project Data Certification Form is completed 

(Appendix 3A). Copies of the certified data and metadata are placed in the digital library. 

This can be accomplished by storing a compressed copy of the working database or by 

exporting data to a more software-independent format (e.g., ASCII text). 

8. Disseminate data and update national databases– Certified data and metadata, and 

digital image products are posted to national repositories (the NPS Data Store, 

Biodiversity Data Store, NPS Focus) to make them more broadly available to others. 

National databases, including NPSpecies, NPSTORET, and ANCS+ are updated with 

data obtained from certified data sets. 

9. Reporting and analysis – Certified data are used to generate data products, analyses, and 

reports, including semi-automated annual summary reports for monitoring projects. 

Depending on project needs, data might be exported for analysis or summarized within 

the database. 

10. Distribute information products – Information products such as reports, maps, and 

checklists are disseminated to the public through the SIEN website and NPS Focus, and 

catalogued in NatureBib. 

11. Share data and information – Data, metadata, reports and other information products can 

be shared in a variety of ways – by FTP or mailing in response to specific requests, or by 

providing direct access to project records to park staff and cooperators. 

12. Track changes – All subsequent changes to certified data are documented in an edit log, 

which accompanies project data and metadata upon distribution. Significant edits will 

trigger reposting of the data and products to national databases and repositories. 

13. Store products – Reports and other data products are stored according to format and 

likely demand, either in the digital library, on off-line media, or in the document archives. 

14. Catalog project products – Catalog products and all information associated with a 

project, including results of analyses and paths of dissemination. Project tracking 

databases can be useful tools for this purpose. 
22

Figure 3. The data life cycle. 

 Raw 

data

 Acquire Data 

Archive raw data 

Archives & Digital 

Library 

Network server 

 Working 

 database 

Verification, 

processing, and 

validation 

Certified 

data and 

metadata

Archive 

versioned 

National datasets 

Databases 

(NPS Data Store 

NPSpecies, 

ANCS+, 

NPSTORET) 

 Disseminate 

data 

 Data entry/import 

Documentation and 

certification 

Short-term 

projects 

Products 

(e.g., reports, 

maps, checklists) 

Master 

database 

Reporting & 

analysis 

Distribute 

Information, 

Update 

SIEN Website, 

Cataloging 

System, 

National 

Databases

Share and deliver data 

and information. Respond 

to requests (e.g., ftp, 

mail) 

Track 

changes 

Update 

Store 

product

Edit Log 
23

3.3. References 

Boetsch, J. R., B. Christoe, and R. E. Holmes. 2005. Data management Plan for the North Coast 

and Cascades Network Inventory and Monitoring Program. USDI National Park Service. 

Port Angeles, Washington. 88 pp. 

3.4. Appendixes 

3A) Project Data Certification Form 

3.5. Credits 

This chapter was written by Rosamonde Cook adapted from concepts and material developed by 

Boetsch et al. (2005). 

25 

Chapter 4. Data Management Roles and Responsibilities 

4.1. General Standards and Guidelines 

An increasing demand for more detailed, high quality data and information about natural 

resources and ecosystem functions requires a group of people working together to steward data 

and information assets. Knowledgeable individuals from many disciplines must come together to 

ensure that data are collected using appropriate methods, and that resulting datasets, reports, 

maps, models, and other derived products are well managed. Datasets and the presentations of 

these data must be credible, representative, and available for current and future needs. Table 1 

summarizes general types of data stewardship activities and the roles typically associated with 

them. 

Table 1. Categories of data stewardship involving Sierra Nevada Network personnel 

Stewardship Activity Description of Activities Principal Roles 

Production Creating data or information from any original or 

derived source. This includes recording locations, 

images, measurements, and observations in the 

field, digitizing source maps, keying in data from 

a hardcopy source, converting existing data 

sources, image processing, and preparing and 

delivering informative products, such as summary 

tables, maps, charts, and reports. 

Project Crew Leader, 

Project Crew Member, 

Data/GIS Specialist or 

Technician 

Analysis Using data to predict, qualify, and quantify 

ecosystem elements, structure, and function as 

part of the effort to understand these components, 

address monitoring objectives, and inform park 

and ecosystem management. 

Ecologists, 

Resource Specialists 

Management Preparing and executing policies, procedures, and 

activities that keep data and information resources 

organized, available, useful, compliant, and safe. 

Project Leader, 

Data Manager, 

GIS Manager, 

IT Specialist, 

Database Manager, 

National-level Data and 

Information Managers 

End Use Obtaining and applying available information to 

develop knowledge that contributes to 

understanding and managing park resources. 

Project Leader, 

Park Managers and 

Superintendents, 

Others 

Although each position is associated with only one category in the table according to overriding 

responsibilities, many positions contribute to multiple categories. The degree to which 

specialization can occur with regard to data management responsibilities will depend on program 
26 

resources, staff and funding. For a very small program, all of these activities might be 

accomplished by a single individual. For a large program, such as the SIEN I&M Program, a 

much higher degree of diversification and specialization are required. Table 2 summarizes the 

roles and responsibilities of personnel that may participate in a program such as this. Roles are 

listed ‘from the ground up’ to help demonstrate the hierarchy of responsibilities. For example, a 

project leader is ultimately responsible for the activities listed in the field level roles of crew 

leader and crew member. It is possible that one person/position has more than one role. For 

example, the Ecologist and Project Leader roles and responsibilities may fall under one position. 

See Appendix 4A for a much more detailed definition of individual responsibilities, and 

Appendix 4B for a discussion of coordination among roles and responsibilities. 

Table 2. Summary of Roles and Responsibilities for a Network Inventory and Monitoring 

Program. 

Role Primary responsibilities related to data management 

Project Crew Member Record and verify measurements and observations based on project 

objectives and protocols. 

Document methods, procedures and anomalies. 

Project Crew Leader Supervise crew members to ensure their data collection and management 

obligations are met, including data verification and documentation. 

Data/GIS Specialist or 

Technician 

Perform assigned level of technical data management and/or GIS 

activities, including data entry, data conversion, and documentation. 

Work on overall data quality and stewardship with project leaders, 

resource specialists, and the Network data manager. 

Information 

Technology/Systems 

Specialist (park IT support 

staff) 

Maintain local area network, establish and maintain system security, and 

keep software and hardware systems up to date. Maintain connections 

between the LAN and the Internet. Work with the Network Data Manager 

and GIS liaisons to establish a directory structure that provides local 

access and security for natural resource data. Manage the infrastructure 

for digital data backups for the local area network. 

Network Coordinator Interface with project leaders to ensure that timelines for data entry, 

validation, verification, summarization/analysis and reporting are met. 

Review and approve proposed changes to project protocols prior to 

implementation. 

Project Leader Oversee and direct operations for one or more Network projects. 

Maintain communication with project staff, Network Data Manager, and 

resource specialists regarding data management. 

Resource Specialist Understand the objectives of the project, the resulting data, and their 

scientific and management relevance. Make decisions with regard to 

validity, utility, sensitivity, and availability of program data. Describe, 

publish, release, and discuss the data and associated information 

products. 
27 

Table 2. Summary of Roles and Responsibilities for a Network Inventory and Monitoring 

Program. (continued) 

Role Primary responsibilities related to data management 

GIS Manager Support park management objectives. Coordinate and integrate local GIS 

and resource information management with Network, regional, and 

national standards and guidelines. 

Data Manager Provide planning, training, and operational support for the awareness, 

coordination, and integration of data and information management 

activities, including people, information needs, data, software, and 

hardware. Coordinate internal and external data management activities. 

Database Manager Apply particular knowledge and abilities related to database software and 

associated application(s). 

Ecologist Ensure useful data are collected and managed by integrating natural 

resource science in Network activities and products, including objective 

setting, sample design, data analysis, synthesis, and reporting. 

National-level data 

management support staff 

Provide service-wide database design, support, and services, including 

receiving and processing to convert, store, and archive data in servicewide databases. 

End Users (e.g., park 

managers, researchers, other 

agency staff, members of the 

public) 

Appropriate use and application of data and derived products and for 

providing feedback for improvements. 

Data management is about people and organizations as much as it is about information 

technology, database theory, and applications. For large park resources programs, such as the 

SIEN Inventory and Monitoring program to work effectively, everyone within the program, and 

all those in collaboration with it, must have stewardship responsibilities for the production, 

analysis, management, and/or end use of data produced by the program. In order to meet new 

data management goals and standards developed by the National Park Service and its 

constituents, program staff must understand how data and information flow (discussed in 

Chapter 3) and what their roles and responsibilities are in this process. A role is a function or 

position (e.g., Data Manager). A responsibility is a duty or obligation (e.g., review data records). 

4.2. Collaboration 

The Natural Resource Challenge states that collaboration among the National Park Service, other 

public agencies, universities, and non-governmental organizations is necessary to effectively 

acquire, apply, and promulgate the scientific knowledge gained in National Parks. A detailed 

approach to collaboration in data management should be developed for all Network natural 

resources programs. 
28 

4.3. References 

Beer, M., Nance, E., Wright, A, Powell, M. and R. DenBleyker. 2005. Northern Colorado 

Plateau Network, Data Management Plan. National Park Service, Inventory and Monitoring 

Program, Northern Colorado Plateau Network, Moab, Utah. 86 pp. plus appendices.

Daley, R. 2005. Data and Information Management Plan, Greater Yellowstone Network 

Inventory and Monitoring Network. United States National Park Service, Bozeman, 

Montana. 82 pp. 

Mortenson, D. 2005. Data Management Plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. United States National Park Service. Anchorage, Alaska. 234 pp. 

Stevens, S. M. and G. Entsminger. 2004. Information Management Plan for the Inventory and 

Monitoring Program, Northeast Coastal and Barrier Network. United States National Park 

Service. Kingston, Rhode Island. 99 pp. 

Wilder, D. 2005. Data Management Plan, Central Alaska Network. United States National Park 

Service. Inventory and Monitoring Program, Fairbanks, AK. 86 pp. 

4.4. Appendixes 

4A) Sierra Nevada Network Inventory and Monitoring Program, Data Management 

Responsibilities 

4B) Sierra Nevada Network Inventory and Monitoring Program, Coordination of Data 

Management Roles and Responsibilities 

4.5. Credits 

This chapter was written by Rosamonde Cook, adapted from concepts and material developed by 

Stevens and Entsminger (2004), Beer et al. (2005), Daley (2005), Mortenson (2005), and Wilder 

(2005).
29 

Chapter 5. Databases 

5.1. Objectives 

Adopting sound database design principles that will help facilitate interpretability, ensure 

integrity, and promote compatibility of natural resources data collected in the parks. 

5.2. Laws and Policies

To help meet these objectives, the NPS I&M Program has developed a standardized database 

template for inventory and monitoring projects, the Natural Resource Database Template 

(NRDT) (http://science.nature.nps.gov/im/apps/template/index.cfm). I&M Network programs are 

expected to design databases used for natural resource inventories and vital signs monitoring on 

this template. 

5.3. General Standards and Guidelines 

5.3.1. The Natural Resource Database Template 

The National Park Service I&M Program’s Natural Resource Database Template (NRDT) is a 

set of Microsoft Access relational database tables that parks and networks can use to develop 

applications for capturing natural resource inventory and monitoring data. 

The NRDT: 

 Provides both a data interchange standard and a standard MS Access database core that 

allows flexibility in application design. 

 Serves as a starting point for application development that can be extended as necessary 

to accommodate any inventory or monitoring field sampling protocol. 

 Standardizes location and observation data to facilitate the integration of data sets. 

 Acts as a design platform for developing database applications in MS Access allowing 

users to enter, edit, display, summarize, and generate reports for inventory or monitoring 

data sets. 

 Integrates with other I&M data management systems and data standards including the 

NR-GIS Metadata Database, Geographic Information System (GIS) tools and data, the 

NPS GIS Committee Data Layers Standard and the NPS Metadata Profile. 

5.3.2. Database Development 

Communication is a vital part of developing a suitable database design for individual projects. 

One mechanism for this communication is collaborative development of data models by data 

managers and project leaders. Data models combine diagrams with associated descriptions and 

are completed in three stages: conceptual, logical and physical. Each is outlined below, using as 

an example, a database created for monitoring the status of mountain yellow-legged frog (Rana 

muscosa) populations following removal of non-native fish from high mountain lakes in Sequoia 

and Kings Canyon National Parks. 

5.3.2.1. Conceptual Data Models : Conceptual data models are constructed to graphically 

portray processes specifically related to the implementation phase of a project – especially those 

involving acquisition, processing, and QA/QC of data (Figure 4). They are software-
30 

independent, free of details and focus on capturing enough information needed to accurately 

depict project data design. Conceptual data models contain the following: 

 An overview of the sampling scheme in layman’s terms. 

 A flow diagram of procedures, and when and what information is being collected or 

produced. 

 A description or mock-up illustration of how data should be presented. 

5.3.2.2. Logical Data Models: A logical data model is an abstract representation of database 

entities, their relationships and key attributes (Figure 5). Logical data models are intended to 

provide a conceptual framework for the database developer as well as a schematic that can be 

easily interpreted by the layman. They are not intended to provide full representation of the 

physical database, but rather to facilitate communication and understanding between developers 

and users. A logical data model should be produced early in project development (ideally 

beginning with early protocol development) and serve as a guide to the physical data model that 

documents the actual implementation of the database. 

A logical data model should include the following five components: 

1. Database tables – sufficient to model the sampling scheme. Enough information should 

be provided to allow users to understand how key information and data are distributed 

among tables in the database (e.g., sampling location, frequency, and measurements to be 

taken. 

2. Field attributes – data types, units of measure, acceptable value ranges, and other 

business rules. 

3. Logical relationships – how data tables are logically related (e.g., each location will be 

visited numerous times, one or more species might be observed at each location per visit) 

4. Structural hierarchies – the structure and order of relationships between data tables, 

which can be determined once the logical relationships are known, e.g., 

• Site (e.g., watershed) 

° sampling event (location id, date, time) 

i. number of frogs per lifestage counted 

ii. water temperature, air temperature, presence of fish 

5. Views – how the data will be viewed or what operations will be performed (e.g., queries 

to search for out of bound values, summary reports of individuals counted per lake). 
31 

Figure 4. Schematic of a conceptual data model for monitoring the status of mountain yellowlegged frog populations following removal of non-native fish in high mountain lakes in Sequoia 

and Kings Canyon National Parks. 

Weather Conditions: 

 Water temp at start 

 Water temp at end 

Time spent searching 

Establish sampling 

station. Assign 

location id

Return for repeat 

observations over time 

Frog counts Gill netting 

Species 

Lifestage 

Number of individuals 

Net number 

Set date 

Set time 

Location id 

Date 

Start time 

End time 

Location id, coordinates, 

physical attributes (e.g., 

vegetation type). 

Species 

Lifestage 

Number of individuals 

Access 

Database 

Queries for quality 

control 

Automated annual 

reports 

Data sheets Data sheets 

 Protocol 
32 

Figure 5. Schematic of a logical data model for monitoring the status of mountain yellow-legged 

frog populations following removal of non-native fish in high mountain lakes in Sequoia and 

Kings Canyon National Parks.

 Integrated data dictionary 

Observer id 

Last name 

First name 

Title 

Organization 

Location id 

Date 

Start time 

End time 

Observers

Location id 

Location name 

Park code 

x-coord 

y-coord 

Locations 

TSN 

Scientific name 

Common name 

Species

 Sampling Events

Observer id 

Observers

Water temp 

Air temp 

Conditions

TSN 

Lifestage 

Count

Counts of Frogs

Net number 

Date set 

Time set

Gill net sets

TSN 

Lifestage 

Count

Fish captures 

TSN 

Lifestage 

Count

Incidental captures 
33 

SIEN will develop a set of master look-up tables or an Integrated Data Dictionary (also referred 

to as a Buffet of Fields and Tables [BOFAT] by WASO), that will allow databases across 

projects and disciplines to remain as consistent as possible. Likely lists of standard attributes and 

values to be developed include park codes, place names, watershed codes, species identifiers, 

and vegetation or land type classifications. 

5.3.2.3. Physical Data Models: The physical data model depicts the structure of the actual 

database, with all of its data tables, field definitions, and relationships (Figure 6). Although the 

logical and physical models might appear similar, the physical model provides enough detail to 

construct the physical database. The NRDT defines three levels of required or optional tables for 

the core database: 

 Mandatory – necessary components of all databases. Includes: tbl_Locations (for sample 

site location information), tbl_Events (sampling events: location, date, and time), 

tbl_DB_Meta (for metadata) and tbl_DB_Revisions (revision history) 

 Mandatory if Applicable – standardized tables that become part of a database as needed. 

Includes tbl_Event_Contacts (observers and others), tbl_Event_Group (for grouping 

sampling events), and tbl_Event_Details (information on who and when the event record 

was entered) 

 Optional tables and fields – contain results of sampling or laboratory analysis and are 

therefore project-specific. All inventory and monitoring databases will contain one or 

more of these tables. 

The mountain yellow-legged frog database was developed prior to the NRDT table and field 

naming standards. In the future, SIEN standards for names, fields, tables and other database 

objects will mirror those conveyed through the NRDT as much as possible. 

However, each database must ultimately meet the needs of the network scientists. Considerations 

for these needs may include interactions with other agencies and ease of use, maintenance, 

integration, and customization. Where there are differences between local and national standards, 

documentation of the rationale for these differences will be developed. In addition, 

documentation and database tools (e.g., queries that rename or reformat data) will be developed 

to ensure that data exports for integration are in a format compatible with current national 

standards. 

Not shown in Figure 6 are the links to the lookup tables in the Integrated Data Dictionary. This 

has yet to be created by the SIEN, although the Network already possesses a substantial table of 

place names which are already being used by several database applications. Tables will also be 

created with the names of park personnel involved in natural resources projects; vegetation 

types; wildlife habitat types; sampling methods, and others. Species will be identified by ITIS 

Taxonomic Serial Numbers, available in the Access file StdClass_Master.mdb which is part of 

the NPSpecies desktop database system, and updated annually from the ITIS database. 

To the extent possible, SIEN will work with WASO and other I&M networks to develop and 

utilize shared look-up tables for data consistency and inter-operability at a higher level within the 

I&M Program. 
34 

Figure 6. Schematic of the physical database used for monitoring the status of mountain yellowlegged frog populations following removal of non-native fish in high mountain lakes in Sequoia 

and Kings Canyon National Parks. Note: this diagram does not include data type and length 

information, or domain specifications, which should be declared as part of a physical database 

model. 

It is especially important to remember that not everything can be thought of from the start and, to 

support change, data modeling should be iterative and interactive. 

5.4. Quality Assurance/Quality Control

The success of any natural resources program is dependent on the quality of the data it collects, 

manages, and disseminates. The concepts of quality assurance (QA) and quality control (QC) 

were defined in Chapter 7 and discussed with respect to each phase of a project's lifecycle. 

Relational Database Management Systems can be designed to incorporate and automate many 

QA/QC procedures associated with data collection and processing. The following include some 

of the practices that will help ensure quality data: 

 Set unique constraints (or unique key indexes) on every table in a database. Unique 

constrains are multiple key indexes set to allow no duplication, so as to prevent the entry 

of the same data record more than once. The index is set on the fields that make the 

records in a table unique. For child tables, it always includes the foreign key from the 
35 

parent table. Duplication of entries is a common problem in databases without unique 

constraints. 

 Implement all value constraints and business rules on the backend of the database, not on 

the forms used for data entry. 

 Create separate forms for data entry/editing and review, or use mode control on single 

forms with both functions. 

 Populate lookup tables as much as possible before data entry begins, and restrict access to 

them by data entry personnel. 

 Set value ranges and validation rules on fields wherever possible to control the range of 

values that can be entered. 

 To the extent possible, set the limit to list property to True on fields that receive input 

from a value list or lookup table. 

 Include queries or programming modules that run checks for logical errors and out of 

bound values on calculations. 

 Create field forms that best match data entry forms. 

 When using hand-held computers in the field for data entry, the same set of controls 

should be placed on data fields as exist in the master database. 

5.5. Database Documentation 

5.5.1. Basic Requirements 

Relational databases will be documented according to the standards outlined above. Complete 

documentation will also include a narrative overview with a description of the business rules 

employed, entity relationship diagrams, and documented programming code. Relational 

databases will also utilize internal documentation such as table and field descriptions and will 

include a table to track modifications. 

MS Access databases should be documented with the built-in Database Documenter tool, and 

documentation should include all objects. This will produce a pre-formatted report containing 

complete specifications of properties, relationship types and attributes, table indexes, and user 

and group permissions on every object in the database. This information, combined with the 

programming code would enable any developer to recreate the structure and function of the 

database. 

The NPS Metadata Profile currently does not support all of this information, so it will need to be 

stored independently of the formal metadata, although, as with the formal metadata, it should be 

stored in a folder with the database. 
36 

Some metadata can now be extracted automatically from an MS Access database. The NPS 

Database Metadata Extractor MS Access add-in, Version 1.0 for MS Access 2003 2002 2000, 

automatically harvests entity (table) and attribute (field) metadata, including value ranges 

(domains) from MS Access databases. It further allows the user to edit and review the harvested 

metadata, make batch edits, and export the metadata to a FGDC-compliant XML file. Exported 

XML can be used in the Metadata Tools & Editor either by opening it to start a new metadata 

record or by updating a template to fill in section 5 of an existing record. 

Project databases developed for the I&M Program will include a mandatory table, tbl_Db_Meta, 

which will contain a description of the purpose of the database, and a link to metadata records in 

the NPS Data Store. 

5.5.2 Revisions 

Every alteration in a project database that occurs after data collection and entry have begun 

should be documented. A table of revision history can be included within the database itself or 

maintained as a separate log. For I&M project databases, the table tbl_DB_Revisions is 

mandatory. This table includes contact person; date, reason, and description of the revision; and 

a link to tbl_Db_Meta.

5.6. References 

Beer, M., Nance, E., Wright, A, Powell, M. and R. DenBleyker. 2005. Northern Colorado 

Plateau Network, Data Management Plan. National Park Service, Inventory and Monitoring 

Program, Northern Colorado Plateau Network, Moab, Utah. 86 pp. plus appendices. 

Boetsch, J. R., B. Christoe, and R. E. Holmes. 2005. Data management plan for the North Coast 

and Cascades Network Inventory and Monitoring Program. United States National Park 

Service. Port Angeles, Washington. 88 pp. 

5.7. Appendixes

5A) Sierra Nevada Network Database Documentation Template 

5.8. Credits 

This chapter was written by Rosamonde Cook, with some material adapted from Beer et al. 

(2005) and Boetsch et al. (2005). 
37 

Chapter 6. Acquisition, Processing and Reporting 

Large, multi-scale natural resources programs increasingly rely on data and information gathered 

from multiple sources. This chapter describes the general steps involved with acquiring, 

processing, and reporting data to meet standards established by the NPS I&M Program, although 

these standards could apply broadly to any of the programs covered by this Data Management 

Plan. Also included are guidelines for the acquisition and processing of physical objects 

(photographs, voucher specimens) which are often collected as part of resource management, 

inventory and monitoring, and other research projects. Instructions specific to particular projects 

should be developed and included with the protocols for those projects. 

6.1. Objectives 

6.2. Laws and Policies 

Current laws and policies encourage programs to make use of existing (or non-programmatic) 

spatial data before investing in the acquisition of new data sets. Specifically, Executive Order 

12906, Section 3(d) states that each agency must adopt internal procedures to ensure the agency 

accesses the National Geospatial Data Clearinghouse before it expends federal funds to collect or 

produce new spatial data, to determine whether the information has already been collected by 

others or whether cooperative efforts to obtain the data are possible. 

As part of any new project, the SIEN will use the National Geospatial Data Clearinghouse and 

NPS Data Store for searching existing natural resources data set. The SIEN will also use the 

Internet, agency contacts, and other means to find data that may benefit its programs. 

6.3. General Standards and Guidelines 

General standards and guidelines for the acquisition and processing of programmatic and nonprogrammatic data (as defined in Chapter 1) are provided below. Quality assurance and control 

procedures, which are essential for ensuring the production of quality data and information 

products, and which comprise critical components of all stages of the data life cycle (Chapter 3), 

are discussed in greater detail in Chapter 7. 

6.3.1. Programmatic Data 

Project leaders and data managers are responsible for ensuring data collection, data entry, 

verification, validation, storage, and archiving are consistent with Network standards. In addition 

to general Standard Operating Procedures (SOPs) that define Network-wide requirements, 

protocol-specific SOPs may be developed that detail procedures and/or methodologies. Leads 

 Acquire, through a variety of sources, data and information needed by SIEN 

Park managers to properly manage and maintain the natural resources of their 

parks. 

 Acquire data and information needed to understand broad-scale changes in the 

environment that impact ecosystems on a regional or national level in 

cooperation with NPS and collaborative monitoring efforts. 
38 

and cooperators on I&M projects should always receive a copy of Sierra Nevada Network I&M 

Program Checklist of Project Specifications (Appendix 6A)

6.3.1.1. Data Collection: Listed below are some of the tools available for field data collection. 

All methods involve some trade-offs in terms of expense, efficiency, and susceptibility to data 

entry/transcription errors. Project protocols should provide detailed specifications on how the 

following tools are used with individual projects and some justification for the choice of 

methods. 

Field Forms – the most common method of recording field data. Field forms are inexpensive 

but require neat, legible handwriting. There generally exists greater opportunities for error 

during the collection/data entry process compared with other methods because data entered 

on forms must later be key-entered into project databases. Thus, this method tends to require 

more data entry time and more rigorous QA/QC. 

Field Computers – increase data collection and data entry efficiency. Data can be directly 

downloaded to office desktops, eliminating the data entry step. QA/QC checks can be built 

directly into the database, further reducing data entry error and processing time. The 

drawback to field computers is usually greater expense and sometimes, training time for field 

crews. There are two types of computers used in the field. 

 Personal Data Assistants (PDAs) – the small size and relative low cost of these 

devices make them attractive options for collecting field data. PDAs can be 

weatherized fairly easily and inexpensively. However, they have limited internal 

memory and so data must be continually backed-up to larger storage devices. Most 

run either Windows CE or Palm operating systems which may require additional 

processing/programming to transfer/create the structure of the master database in the 

field units. Working with relational database structures can be difficult, or even a 

practical impossibility if the database is a complex one. In these cases, uploading 

field data can require great care and special skill. 

 Tablet PCs – possess the same properties as most laptops and provide the user with 

the convenience of a touch screen interface. They are bulkier, heavier, more 

expensive and harder to weatherize than the PDAs, but more powerful as well. They 

are the best choice for field projects that are very data-intensive or rely on complex 

data structures. Because they run Windows XP (Tablet Edition) and Microsoft Office 

software, MS Access databases can be directly transferred from the desktop to field 

units without additional programming. Data from the field units can in turn, be 

uploaded to the desktop with minimal effort. 

In some cases, the best choice is a combination of field forms and computers, for example, 

where large amounts of notes or comments need to be recorded in the field. 

Automated Data Loggers – are mainly used to collect ambient information such as weather 

data or water quality information. These units must be properly calibrated and maintained, 

which requires proper training of field crews and SOPs that outline these procedures. Two 

types of automated data loggers are available. 
39 

 Permanently deployed devices – provide continuous or more frequent data 

(compared to field staff), provide data during conditions unsafe for field staff (i.e., 

severe storms, flood events), and with proper equipment can provide near real-time 

data. Data still must be retrieved, although this can be accomplished remotely – via 

satellite, landline, or cell phone. Providing sufficient and reliable power may be an 

issue. Automated data-loggers require proximity to AC power, installation of solar 

power, or regular site visits to change batteries. Wilderness regulations may restrict 

the type of and location where a device is deployed. 

 Portable hand-held devices – are deployed for sampling only during site visits. 

GPS Units – Two types of GPS units are used during field work in SIEN parks to collect 

location (geographic coordinates, altitude) information. 

 Recreation Grade Units – include both self-contained units (e.g., Garmin 

Map76CSx) and companion units for PDAs (e.g., Holux). These work well for 

collecting general position information but are not recommended for high accuracy 

(sub-meter) location information. 

 Survey Grade Units – such as some Trimble GPS receivers. These are good for 

collecting accurate sub-meter location information but are expensive.

See Field Data Collection with Global Positioning Systems (Appendix 6B) and Spatial Data 

Standards (Appendix 6C) for details on methods of collection and processing of spatial data. 

Tape Recorders – Handheld micro-cassette tape recorders are useful for recording field 

data. Recorded observations are subsequently transcribed to paper or directly entered into 

computer files. As with other technological solutions, there are drawbacks including battery 

and tape maintenance, low environmental tolerance, and risk of failure. However, if a single 

data collector is in the field, tape recorders can provide an easily operated, high quality, 

efficient method of collecting data. All audio tapes used for recording field data should be 

labeled appropriately (e.g., date, site, project) and stored in the fire proof cabinet. Analog 

audio cassettes degrade over time and are a media that is quickly becoming outdated and 

obsolete. If this is a desired method for field data collection, efforts should be made to 

transfer the audio data to a more permanent audio format such as CDs or MP3 files. 

Photographs – provide an excellent visual record of field visits and are useful for capturing 

point records of long-term study sites. They also serve well for automated data collection by 

remotely recording information using web cams or trip cameras. Digital photo management 

will follow the procedures outlined in Sierra Nevada Network Digital Photo Management 

Strategy (Appendix 6D). Slides and photographs should be stored and archived according to 

guidelines outlined in Chapter 11. 
40 

Remotely Sensed Data – includes satellite imagery and aerial photography. Remote sensing 

can be a powerful tool for characterizing and analyzing landscape data, as well as readily 

capturing data within areas of low accessibility. Considerations for selecting remote sensing 

imagery are as follows: 

 Accuracy and resolution needed 

 Frequency of measurement 

 Costs 

 Licensing for public use 

 Ortho-rectification standards 

Each remote sensing product is unique. It is imperative that users fully understand the 

product being used and that products be accompanied by well-documented metadata. Any 

projects involving remote sensing should include consultation with a professional remotesensing specialist, and project managers should consider the trade-offs between accuracy and 

cost among different imagery sources (Table 3). 

The SIEN GIS library currently includes satellite imagery coverage for most of the parks, 

along with base cartographic layers, digital orthophoto quadrangles, and natural resource 

theme layers. 

Table 3. Satellite resolution, swath width, area, cost, and history of various remote-sensing 

imagery sources (sorted by cost)

Sensor Multi-spectral 

Resolution (m) 

Panchromatic 

Resolution (m) 

Swath 

(km) 

Area (sq 

km) 

Cost/sq 

km ($)* 

History 

Quickbird T 2.4 0.7 16 272 29.73 2001-present 

Ikonos T 4 1.0 11 121 27.03 1999-present 

Spot5 T

 10 2.5 and 5 60 3599 0.77 2002-present 

Spot 66 10 60 3599 0.05** 1986-present 

ETM+ 30 15 185 34221 0.03 1999-2003 

TM 30 NA 185 34221 0.03 1982-1999 

ASTER 15 NA 60 3599 0.02 1999-present 

MSS (ESTS) 79 NA 185 34221 NA 1972-1997 

AVHRR 1100 NA 2700 7290138 NA 1978-present 

MODIS 250 NA 2330 5429053 NA 1999-present 

* cost uses the multi-spectral unit specifications 

** cost for post-1998 imagery (pre-1998 imagery is half-price) 

T

 requires tasking 

Any additional remote sensing data acquired will likely require some spatial or spectral 

processing depending on how they are received. All such data sets should be received in a georeferenced format although some may need to be transformed to the SIEN standard (UTM NAD 

83). The extent of any additional processing will depend on how the data are to be used and what 
41 

type of information is to be extracted from them. Specific requirements and procedures should be 

outlined in project protocols or SOPs. 

6.3.1.2. Data Processing: Ideally, each project will have a database developed prior to the 

collection of any data in the field. SIEN I&M program databases will be developed in 

conjunction with project protocols and will be based on the I&M Natural Resources Database 

Template (NRDT) (Chapter 5) and will include built-in procedures for QA/QC. Ideally, data 

processing should proceed as follows, and as soon as possible after data are collected, if using 

field forms. 

 Field crews enter all data into an approved project database, under the supervision of the 

project manager. 

 Field crews periodically forward project data files to the project leader and/or data 

manager (refer to individual protocols for specific requirements). 

 All data undergo QA/QC procedures (see Chapter 7 for more specifics on data 

verification and validation). 

 The data manager maintains the master copy of the database and updates it with certified 

data files received from the project leader. 

 National databases are updated as per the procedures outlined in Chapter 10. 

6.3.1.2.1. Spatial data: See Field Data Collection with Global Positioning Systems (Appendix 

6B) and Spatial Data Standards (Appendix 6C) for complete details on methods for data 

collection and processing of spatial data.

6.3.1.2.2. Photographs: Photos taken as part of a project’s data collection protocol constitute 

data and need to be organized, documented and preserved in conjunction with all other project 

data. The processing, documentation, and storage of photographs, including digital photos is 

detailed in Appendix 6D. In general, the level of processing required will depend on the purpose 

of the photograph. Photos are regularly used by many park programs such as resource 

management, maintenance, fire, and research.

Editing of digital photos may be done to improve orientation or correct for lighting conditions 

(e.g., rotation to best orientation and removal of 'red eye'), but should never include alterations 

that change the original content of the photo. Photos may be cropped to remove edge areas that 

grossly distract from the subject. Poor quality photos can be deleted or destroyed, except where 

the subject is unique. Photos of medium quality should be assessed against existing photos of the 

same subject; if they duplicate a subject with no enhancement of quality or perspective, they may 

be deleted or destroyed. Working photos should be stored either in a workspace within a specific 

project or within a user's photo library. Digital aerial photos and imagery should be 

georeferenced. All digital data photos should have geographic coordinates associated with them. 

6.3.1.2.3. Remote Sensing Data: The processing of remotely-sensed data is often project specific. 

Therefore, project plans, protocols, and SOPs should document these procedures. Some steps 

will be common to all data sets, including: 
42 

 Images are geometrically registered using nearest-neighbor resampling methods and coregistered to UTM NAD83. Registration accuracy is assessed. 

 Images are radiometrically corrected and converted to exo-atmospheric reflectance. 

 Atmospheric corrections are made, if applicable. 

 All iterations (raw, intermediate corrections, and final) are maintained until project 

completion when raw and final products are archived. 

6.3.1.2.4. Vouchers: The acquisition and processing of biological specimen vouchers will be 

guided by the policies outlined in Sierra Nevada Network Inventory and Monitoring Program 

Voucher Collection and Documentation Standards (Appendix 6E). 

Project leaders will provide parks with material and data, in MS Excel format or commadelimited ASCII files (.csv) for automated uploading into ANCS+ (Appendix 6F) and input to 

NPSpecies (Appendix 6G). Data provided to non-NPS curators will be in MS Excel format as 

well. Specimens are owned by the parks in which they were collected and are curated by park 

staff unless agreements have been made with outside institutions. See Chapter 11 for more 

details. 

6.3.2. Non-programmatic Data 

A large amount of the data used to manage the natural resources of SIEN parks is collected by 

entities outside of the Network (universities and other parks, agencies, and NPS programs). 

These kinds of data collection efforts are referred to as data discovery or data mining. Data 

collected and products produced by such efforts provide a great deal of information about park 

natural resources and are therefore relevant to many of its programs. These data can be classified 

as follows: 

Current or ongoing – These are data that are currently being acquired or were recently 

acquired, but are known to be of value and are targeted for incorporation into existing or 

planned Network projects. Their acquisition and use should follow very specific guidelines 

identified by project protocols. Sources can be other Network park programs or external NPS 

sources. These data might be used to complement program data collection, to fill in gaps of 

missing data, or as a basis for comparison. 

Legacy - Those data found and compiled through the data mining process. These may include 

vertebrate and vascular plant species data, other important natural resource inventory data, 

specimen or voucher data, bibliographic data, and existing monitoring datasets. 

Data discovery is the process of locating and identifying these useful data sets. Nonprogrammatic data can be obtained from academia, private organizations and non-profit groups, 

as well as local, state, and federal government agencies. Within the NPS, park, regional and 

national programs are all potential sources of information. 
43 

Parks within the SIEN may use base funds or receive funding through Natural Resources 

Protection and Preservation (NRPP) programs to support park-level projects. Typically, these 

include: 

 Park-based biological inventories - Network parks often conduct their own park-based 

inventory projects. 

 Park-based monitoring projects - Parks also engage in monitoring (such as vegetation 

or water quality) which can produce information valuable to other programs, such as 

I&M. 

 Park and multi-park based projects - include other studies or projects conducted at the 

park or regional level that do not fall into one of the previous two categories (e.g., 

restoration projects). 

NPS regional and national programs support all parks within the Pacific West Region and are 

good resources for natural resources information. These include: 

 Air – National-level programs collect data, maintain databases, assure data quality, and 

perform the trend analyses relevant to SIEN air quality issues. 

 EPMT - Exotic Plant Management Teams (EPMT) collect and maintain data regarding 

the presence of exotic species in many parks, and develop and document the methods 

used to treat these species. Data are stored in the Alien Plant Control and Monitoring 

Database (APCAM) and maintained by the EPMT data manager. 

 Fire Program – Data on fire occurrence within the SIEN are maintained both at 

individual park and regional levels. National databases such as Fire-Pro, SACS and Fire 

Program Analysis (FPA) have been, and will be, used to maintain information regarding 

fire incidence and the resources dedicated to fire management. 

 GIS – The SIEN is supported by regional GIS specialists to help ensure regional GIS 

data are available and accurate. Much of these data are also available through the NPS 

GIS Clearinghouse. 

 Wildlife Management - The regional Threatened and Endangered (T&E) Species 

Specialist provides management support and coordinates reporting of T&E species 

populations. 

Additional data sources are listed in Table 4 and Appendix G of Appendix 10B. 

Future data mining efforts within the SIEN should adhere to the guidelines and protocols 

outlined in Sierra Nevada Network Guidance for Data Mining and Discovery (Appendix 6H). 

Information collected during the data discovery process should be maintained either 

electronically or in hard copy format depending on how it was collected, and should be 

documented as fully as possible (Chapter 8). Data should be disseminated to the appropriate 

repository as summarized in Table 4 and discussed in Chapter 10. 

Data discovery is an integral part of project development, but efforts should not be limited solely 

to project development needs. This should be an ongoing process requiring regular data searches 

and visits to Network parks to ensure that Network parks maintain as much material relevant to 
44 

managing their natural resources as possible. Encouraging data sharing among parks will assist 

in this process and may alleviate the need for regular searches of park records. 

6.3.2.1. Data Collection: The collection of data from non-programmatic sources should follow 

program specifications as outlined by project protocols or SOPs. Procedures should be 

standardized as much as possible and include: 

 Contacting data stewards and informing them of program needs. 

 Establishing Memoranda of Understanding (MOUs) if needed. 

 Developing a contingency plan in case the data source is no longer available. 

 Determining whether data can be consistently exported/imported and establishing a 

schedule. 

 Determining how the data will be stored and integrated into the program. 

 Determining how errors will be addressed. 

 Determining if documentation is adequate and if not, completing where necessary. 

 Identifying any interest in the exchange of program data and information with the outside 

program. 

Agency or organizational stewards of these data often have the expertise to conduct proper 

quality control procedures and the capability to function as a repository and clearinghouse for 

validated data. In some cases, portions of external databases may be incorporated into SIEN 

databases and thereby made more accessible to staff. 

Table 4. Summary of possible data sources for different types of information and 

repositories where they are maintained. 

Type of Data Possible Source Repository 

Bibliographic / 

Literature 

 Online literature databases (e.g., First 

Search or Biosis) 

 Library catalogs (e.g., academic or research 

institutions) 

 Park archives through ANCS+ 

 NatureBib 

 Reference cabinets for hard 

copy materials 

 Digital archive for electronic 

materials 

Geographic Data  Regional centralized GIS data 

 Federal and state geographic data 

clearinghouses 

 Local, state, and federal government offices 

 Regional and park GIS specialists 

 NPS Data Store 

 Digital archive 

Biologic / Natural 

Resource Data 

 Voucher collections (museums, parks, 

universities) 

 Network parks 

 NPSpecies 

 Digital archive 
45 

6.3.2.2. Data Processing: Much of the data identified during the discovery process are likely to 

be the legacy type. As time and resources permit, legacy data should be converted to file formats 

compatible with current software standards. Hardcopy references and other materials containing 

legacy data can be scanned and saved as .pdf files and stored in a program's digital library 

(Chapter 11). All legacy data sets should be reviewed and cataloged as follows: 

 Enter all biodiversity data into NPSpecies (this is especially important for park-based 

biological inventories). 

 Enter all natural resource reports and publications related to SIEN parks into NatureBib. 

Hard copies should be stored in the appropriate park collections and electronic copies 

archived in the proper directory on the Network file servers (Chapter 11). 

All GIS data should be stored in the proper projection (UTM NAD83) and accompanied by 

FGDC-compliant metadata (Chapter 8). 

6.3.3. Analysis and Reporting 

Obtaining meaningful results from data summary and analysis is essential to providing useful 

information for natural resource managers and scientists. Thus it is incumbent on data managers 

and stewards to provide valid data in formats that support scheduled and ad hoc display, query, 

analysis, summary, and reporting. Routine and scheduled data summary, analysis and reporting 

requirements and procedures should be identified in project protocols. 

The development of data products should be guided by project objectives, protocols and data 

management SOPs. Products defined in advance, such as routine summaries and output formats 

can be automated through the use of queries and reports stored in a project's database. Project 

leaders should work with data managers to create, and test these queries. Queries can be used to 

view raw or summarized data, or to output either type as custom-formatted reports or as files that 

can be imported by other analytical tools (e.g., statistics software). Other queries can be built to 

facilitate data exploration and unscheduled analysis. Specific needs should be determined by 

individual projects. 

Most likely, data will be exported from project databases for most statistical analyses beyond 

means, standard deviations, and other descriptive statistics. SIEN will use third party statistical 

software (i.e., R, SAS, and SPSS) for generating frequency distribution plots, tests for normality 

analysis of variance, time series analysis, and others. 

6.3.4. Changes to Procedures 

Changes to established data collection procedures are discouraged unless there are acceptable, 

valid reasons for altering the methodologies. Ideally, all problems should be identified during the 

design and testing stages of the project and changes implemented prior to the collection of any 

field data. Protocols should attempt to identify any foreseeable issues that might occur as well as 

contingencies to address them. Inevitably, unforeseen problems may occur which require 

procedure/protocol revision after data collection has begun. Significant changes to protocols 

should be approved by the principal investigator, project leader and data manager. The key 

official should evaluate the proposed changes and determine if additional peer review is required 
46 

before accepting them. All changes should be carefully documented within project SOPs and any 

associated databases. 

6.3.5. Maintaining an Edit Log 

Any changes that occur as a result of a change in a project's protocol should be documented in 

the formal documentation developed for the database (Chapter 5). 

6.4. References 

Boetsch, J. R., B. Christoe, and R. E. Holmes. 2005. Data management plan for the North Coast 

and Cascades Network Inventory and Monitoring Program. United States National Park 

Service. Port Angeles, Washington. 88 pp. 

Daley, R. 2005. Data and Information Management Plan, Greater Yellowstone Network 

Inventory and Monitoring Network. United States National Park Service, Bozeman, 

Montana. 82 pp. 

Mortenson, D. 2005. Data Management Plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. United States National Park Service. Anchorage, Alaska. 234 pp. 

Stevens, S. M. and G. Entsminger. 2004. Information Management Plan for the Inventory and 

Monitoring Program, Northeast Coastal and Barrier Network. United States National Park 

Service. Kingston, Rhode Island. 99 pp. 

6.5. Appendixes 

6A) Sierra Nevada Network I&M Program Checklist of Project Specifications 

6B) Field Data Collection with Global Positioning Systems 

6C) Spatial Data Standards 

6D) Sierra Nevada Digital Photo Management Strategy 

6E) Sierra Nevada Network Inventory and Monitoring Program Voucher Collection and 

Documentation Standards 

6F) Data Dictionary and Formatting Specifications for ANCS+ Voucher Records 

6G) Field Definitions for NPSpecies Voucher and Observation Records 

6H) Sierra Nevada Network Guidance for Data Mining and Discovery 

10B) NatureBib Standards Manual 

6.6. Credits 

This chapter was written by Rosamonde Cook and Leona Svancara, adapted from concepts and 

material developed by Stevens and Entsminger (2004), Boetsch et al. (2005), Daley (2005), and 

Mortenson (2005). 
47 

Chapter 7. Quality Assurance and Quality Control (QA/QC) 

The success of any natural resources program is dependent on the quality of the data it collects, 

manages, and disseminates. Analyses performed to detect ecological trends or patterns require 

data that are recorded properly and have acceptable precision and minimal bias. Poor quality data 

can limit detection of subtle changes in patterns and processes, can lead to incorrect 

interpretations and conclusions, and could greatly compromise the credibility of the program 

managing it. 

Quality assurance (QA) can be defined as an integrated system of management activities 

involving planning, implementation, documentation, assessment, reporting, and quality 

improvement to ensure that a process, item, or service is of the type and quality needed and 

expected by the consumer. Quality control (QC) is a system of technical activities that measure 

the attributes and performance of a process, item, or service relative to defined standards (Palmer 

2003). While QA procedures maintain quality throughout all stages of data development, QC 

procedures monitor or evaluate the resulting data products. 

7.1. Objectives 

7.2. Laws and Policies 

Director’s Order #11B, “Ensuring Quality of Information Disseminated by the National Park 

Service,” was issued in order to promote data quality (Appendix 7A). It defines ‘quality’ as 

incorporating three key components: objectivity, utility, and integrity. 

Objectivity consists of: 1) presentation: whether disseminated information is being presented 

in an accurate, clear, complete, and unbiased manner within a proper context; and 2) 

substance: ensuring accurate, usable, and reliable information. 

Utility refers to the usefulness of the information to its intended users. 

Integrity refers to the security of information, meaning protection from unauthorized access 

or revision is ensured that information is not compromised through corruption or 

falsification. 

Director’s Order #11B also specifies that information must be based on reliable data sources that 

are accurate, timely, and representative of the most current information available. These 

standards apply not only to NPS-generated information, but also to information provided by 

other parties to the NPS if the NPS disseminates or relies upon this information. 

 Ensure natural resources projects produce high quality and credible data that 

can be confidently used by managers, researchers and the public 

 Implement standard quality assurance and quality control procedures to meet 

the first objective 
48 

The SIEN holds as policy that techniques and procedures chosen for a project will maximize data 

quality. 

7.3. General Standards and Guidelines 

To ensure that the SIEN produces and maintains data of the highest possible quality, procedures 

have been established to identify and minimize errors at each project stage associated with the 

data life cycle (Figure 7). QA/QC procedures specific to any project should be specified in a 

project's protocols and SOPs. However, some general concepts apply to all Network projects. 

The general guidelines presented below were primarily adapted from the I&M Program's Draft 

Data Management Protocol (Tessler & Gregson 1997) and ideas contained in Michener and 

Brunt (2000). 

Although a data set containing no errors would be ideal, the cost of attaining 95%-100% 

accuracy may outweigh the benefit. Therefore, at least two factors are considered when setting 

data quality expectations: 

 frequency of incorrect data fields or records, and 

 significance of error within a data field. 

Errors are more likely to be detected when data sets are clearly documented and what constitutes 

a ‘significant’ error within that data set is understood. The significance of an error can vary both 

among data sets and within a single data set. For example, a two-digit number with a misplaced 

decimal point (e.g., 99 vs. 9.9) may be a significant error while a six-digit number with an 

incorrect decimal value (e.g., 9999.99 vs. 9999.98), may not. However, one incorrect digit in a 

six-digit species' Taxonomic Serial Number could indicate a different species. QA/QC 

mechanisms are designed to prevent data contamination, which occurs when a process or event 

introduces either of two fundamental types of errors into a data set: 

 Errors of commission include those caused by data entry or transcription, or 

malfunctioning equipment. They are common, fairly easy to identify, and can be 

effectively reduced up front with appropriate QA mechanisms built into the data 

acquisition process, as well as QC procedures applied after the data have been acquired. 

 Errors of omission often include insufficient documentation of legitimate data values, 

which could affect the interpretation of those values. These errors may be harder to detect 

and correct, but many of these errors should be revealed by rigorous QC procedures. 

Selected QA/QC procedures relative to the amount of planning and quality control necessary to 

have confidence in the data are illustrated in Figure 8. The most effective mechanism for 

ensuring that a project produces high-quality data is to determine procedures that direct project 

staff through accurate data collection, entry, and validation, and adhere to them. 
49 

Figure 7. General course of data and associated Quality Assurance/Quality Control procedures. 

Quality Control with regards to data analysis is specific to each project and addressed in 

appropriate standard operating procedures. 

Data Entry 

Data Acquisition 

Project planning 

Verification 

Validation 

Dissemination 

Documentation 

Archiving 

Analysis and 

Reporting 

Data Stages QA/QC Activities 

 Database design 

 Collection procedures 

 Equipment maintenance/calibration 

 Data collection training 

 Data entry method planning 

 Verification/validation method planning

 Backup/archive raw data 

 Obtain data results from lab/other 

processing 

 Log what was collected 

 Data entry/training 

 Adherence to data entry procedures 

 Check records to ensure that data 

are entered and match originals 

 Review by project manager 

 Check that data make sense 

 Check for misspellings, out of range 

values, incorrect codes, and logic 

errors 

 Note any corrections on originals 

 Formal certification by project manager 

 Quality statement by project manager 

 Sensitivity statement for each project 

 Follow project close-out procedures 

 Maintain, store, and archive data 

 Disseminate data and metadata 

QA/QC Procedure 

review by: 

 project manager 

 data manager 
50 

Figure 8. Some common information management elements influencing the amount of QA/QC 

needed. 

7.3.1. Project Planning and Data Design 

Quality assurance begins with choice of project methods and information to be gathered. Quality 

assurance will be achieved by stipulating that: 

 Common lookup tables are created for parameter values recorded by multiple projects 

(e.g., weather variables, standard equipment, field personnel, etc). 

 Each vital sign protocol has SOPs that address core information management practices 

(e.g., field crew training, use of handheld computers, equipment maintenance, and data 

backup, entry, verification, and validation, etc.). 

 Each project uses handheld computers for data collection if possible. If not, at a 

minimum, standardized field sheets are used. 

 Databases adhere to the standards outlined in this plan (Chapter 5) 

 Database data entry forms, if necessary, resemble the field sheets. 

 Automated error checking features will be included in database applications. 

 Database application design will maximize the use of auto-fill, auto-correct, range limits, 

pick lists, and other constraints specific to projects. 

 Database applications will include a means to track the date a record is created or 

modified, the name of the person creating/modifying the record, and errors reported on 

the data after dissemination. 

 Database maintenance logs will be maintained for each SIEN database and housed in 

association with database files. 

7.3.2. Data Collection 

Chapter 6 addressed general data acquisition and initial handling as well as changes to data 

collection protocols. Attention to detail during the data collection phase, however, is crucial to 

overall data quality. Unlike a typographical error that occurs during data entry, an incorrect 

observation in the field is not easily corrected. 

 Complex data model 

 Multi-stage data acquisition 

 No use of field sheets and/or data ranges 

 Data entry long after collection 

 Data entry by someone not familiar with 

data collection 

 Simple data model 

 Direct from field to database (handhelds, 

data logger, etc.) 

 Immediate data entry after collection 

 Data entry by personnel familiar with 

collection methods 

 Database control (ranges, common 

lookup tables, etc.) 

more planning/control needed less planning/control needed 
51 

The SIEN adopts the following guidelines regarding data collection that affect data quality: 

 Field crews will receive proper training. 

 Any project using field equipment will include a calibration and maintenance SOP that 

will also specify establishment of an equipment maintenance log. 

 Wherever possible and appropriate, data loggers or field-based computers will be used to 

collect data. When this is not possible, data will be recorded on formatted, projectspecific data sheets that reflect the overall design of the project and are designed to 

minimize the amount of writing necessary to effectively record observations. 

 When field sheets are necessary, or more appropriate, the format will reflect the computer 

data entry interface to help ensure all relevant information is recorded and subsequent 

data entry errors are minimized. 

 Completed field forms will be proofed for errors each day in the field. Edits will be made 

by drawing a single line through the information to be changed, adding any replacement 

information in clear writing next to the original entry, and initializing the changes. 

 Separate SOPs will describe the use of data loggers or field-based computers and will 

include direction for daily review and back-up. 

7.3.3. Data Entry 

Data entry is the process whereby raw data are transferred from paper field forms into an 

electronic data format. When data are gathered or stored digitally in the field (e.g., on a data 

logger), data entry consists of the transfer of data (downloading) to a file in an office computer 

where they can be further manipulated. The goal of data entry is to transcribe field observations 

into a computer database with 100% accuracy, although errors are unavoidable. Subsequent data 

verification is conducted to ensure that raw data matches entered data. Following verification, 

data validation may result in changes to the entered data. Data entry is a separate operation from 

data validation and care must be taken to not impose validation (beyond that automatically 

imposed by programming rules in a database) during data entry. 

The SIEN adopts the following guidelines regarding data entry: 

 To the extent possible, data entry will be automated. This may simply entail downloading 

data from field-based computers but may include the application of new technology to 

allow for machine-driven data entry (e.g., voice-recognition software). 

 Data will be entered as soon as reasonably possible after collection. 

 Data entry will be completed by someone familiar with data collection. The project leader 

(with assistance from the data manager if needed) must ensure that data entry staff are 

familiar with the database software, database structure, and any standard codes used by 

the Network. At a minimum, data entry technicians should know how to open a data 

entry form, create a new record, and exit the database properly. They must also learn how 

to correct mistakes made while typing. 

 If feasible, data will be entered by two qualified persons; one person will read the 

observations and the other will enter the data. 

 Data will be entered into pre-designed database forms that resemble field sheets and 

maximize error control. 
52 

7.3.4. Data Verification 

Data quality is appraised by applying verification and validation procedures as part of the quality 

control process. These procedures are more successful when preceded by effective quality 

assurance practices (i.e., planning). Data verification checks that the digitized data match the 

source data, while data validation checks that the data make sense. Although data entry and 

verification can be handled by personnel who are less familiar with the data, validation requires 

in-depth knowledge about the data. 

The SIEN adopts the following guidelines regarding data verification: 

 Project leaders are responsible for specifying in the project protocol one or more of the 

data verification methods available and ensuring proper execution. At the discretion of 

the project leader, additional verification methods may be applied. 

 Data verification should be carried out by staff familiar with data collection and entry. 

 All records (100%) will be verified against original source data. 

 A subset of randomly selected records (10%) will be reviewed after initial verification by 

the project leader. If errors are found, the entire data set will be verified again. 

 A record of the verification process for each dataset, including number of iterations and 

results, will be prepared by the project leader as part of formal metadata generation (see 

Chapter 8 for more details). 

 Spatial data collected as part of the project will be viewed in a GIS and visually inspected 

for accuracy (e.g., points located outside park boundaries, upland locations occurring in 

water). 

7.3.4.1. Methods for Data Verification: Each of the following methods has a direct correlation 

between effectiveness and effort. The methods that eliminate the most errors can be very time 

consuming while the simplest and cheapest methods will not be as efficient at detecting errors. 

1) Visual review at data entry. The data entry technician verifies each record after input and 

immediately corrects any errors. This method is the least complicated since it requires no 

additional personnel or software. Its reliability depends entirely upon the person keying data 

and thus, is probably the least reliable data verification method. 

2) Visual review after data entry. Upon completion of data entry, all records are printed and 

compared with the original values from the hard copy. Errors are clearly marked and 

corrected in the database as soon after data entry as possible. Reliability increases if someone 

other than the person keying the data performs the review. Alternatively, two technicians 

(one reading from the original data and one checking the entered data) can perform this 

review. 

3) Duplicate data entry. The data entry person completes all data entry as normal. Random 

records are selected (e.g., every nth record) and entered into an empty replica of the 

permanent database, preferably by someone other than the person keying the permanent data. 

A database query is then used to automatically compare the duplicate records from the two 

datasets and report on any mismatches. Disparities are manually reviewed and corrected if 

necessary. This method adds the overhead of retyping the selected records, as well as the 
53 

creation of a comparison query. However, it becomes increasingly successful as the value of 

n decreases. Professional data entry services frequently use this method. 

Data verification can be improved by calculating summary statistics and identifying duplicate or 

omitted records. For example, the number of known constant elements, such as the number of 

sampling sites, plots per site, or dates per sample can be evaluated. Databases can also be built 

with controls to prevent duplicate records (Chapter 5), although tests must be performed to 

identify missing records. The more checks that are devised to test for completeness, the greater 

will be one's confidence in the quality of the data set data. 

7.3.5. Data Validation

Validation is the process of reviewing computerized data for range and logic errors and may 

accompany data verification only if the operator has comprehensive knowledge of the data and 

subject. More often, validation is a separate operation carried out after verification by a project 

specialist who can identify generic and specific errors in particular data types. It is essential that 

we validate all data as truthful and do not misrepresent the circumstances and limitations of 

collection. 

General step-by-step instructions are not possible for data validation because each data set has 

unique measurement ranges, sampling precision, and accuracy. Specific guidelines should be 

written into all project protocols and SOPS. Invalid data commonly consist of misspelled species 

names or site codes, wrong dates, or out-of-range errors in parameters with well defined limits 

(e.g., pH). More interesting and often puzzling errors are detected as unreasonable metrics (e.g., 

stream temperature of 70°C) or impossible associations (e.g., a tree 2 feet in diameter and only 3 

feet high). These types of erroneous data are called logic errors because they produce illogical 

(and incorrect) results. The discovery of logic errors has direct, positive consequences for data 

quality and provides important feedback to the methods and data forms used in the field. 

Histograms, line plots, and basic statistics can reveal possible logic and range errors. 

The SIEN adopts the following guidelines regarding data validation: 

 Project protocols will address a process for data validation that includes at least one of 

the available methods. 

 Corrections or deletions as a result of data validation require notations in the original 

paper field records about how and why the data were changed, with the editor’s initials. 

 Modifications of the field data will be clear and concise while preserving the original data 

entries or notes (i.e., no erasing). 

 Validation efforts will also include a check for the completeness of a data set since field 

sheets or other sources of data could easily be overlooked. 

 Use of automated routines and/or data summary and visualization (e.g., histograms, line 

plots, and basic statistics) will be maximized to identify possible logic and range errors. 

 Use of database programming will be maximized to control data entry. This will be 

achieved via the use of lookup tables and/or field-type design in a database (e.g., yes/no 

field-types). 
54 

7.3.6. Methods for Data Validation 

The following general methods can be used as guidelines: 

1) Data entry application programming. Certain components of data validation are built into 

data entry forms. This method is essentially part of the database design and is discussed 

earlier in this chapter. Not all fields, however, have appropriate ranges known in advance. 

Caution must be exercised when using lookup tables to constrain variable values. Values 

occurring outside the range set by a lookup table (established during database design) may 

not always be invalid. As part of data validation procedures, the project leader is responsible 

for correct use of lookup tables or other automated value range control (see Chapter 5 for 

more detail). 

2) Outlier Detection. According to Edwards (2000), “the term outlier is not (and should not 

be) formally defined. An outlier is simply an unusually extreme value for a variable, given 

the statistical model in use.” Any data set will undoubtedly contain some extreme values, so 

the meaning of ‘unusually extreme’ is subjective. The challenge in detecting outliers is in 

deciding how unusual a value must be before it can (with confidence) be considered 

‘unusually’ extreme. 

Data quality assurance procedures should not try to eliminate outliers. Extreme values 

naturally occur in many ecological phenomena; eliminating these values simply because they 

are extreme is equivalent to pretending the phenomenon is ‘well-behaved’ when it is not. 

Eliminating data contamination is perhaps a better way to explain this quality assurance goal. 

When an outlier is detected (via GIS, database, graphic, and statistical tools for ad-hoc 

queries and displays), the possibility of contamination will be evaluated and noted. 

3) Other exploratory data analyses. Palmer and Landis (2002) suggest calculations for 

assessments of precision, bias, representativeness, completeness, and comparability may be 

applicable and, for certain types of measurements, evaluation of detection limits may also be 

warranted. Normal probability plots, Grubb’s test, and simple and multiple linear regression 

techniques may also be used (Edwards 2000). 

7.3.7. Review, Conformance and Communication 

The National Park Service requires QA/QC review and approval prior to communicating or 

disseminating data and information. Documentation of the QA/QC standards used in producing 

the information and that substantiate the quality of the information must be formally certified and 

distributed with the related data and information. Mechanisms must also be in place for receiving 

and addressing comments or complaints pertaining to data quality (see also Chapter 10). 

As part of the close-out and evaluation stage of each SIEN project, QA/QC procedures will be 

reviewed by the project leader and recommendations for change will be included in the annual 

report. Similarly, SIEN data management staff will review and revise the QA/QC procedures 

included in this information management plan and/or SOPs as needed. 

To ensure the highest quality, data custodians should conduct periodic audits to ensure 

compliance with the information management plan and protocol QA/QC procedures. Such 
55 

quality checks promote a cyclic process of continuous feedback and improvement of both the 

data and quality planning process. Audits may include verification of the following: 

 Data collection and reporting requirements are being met 

 Data collection and reporting procedures are being followed 

 Verification and validation procedures are being followed 

 Data file structures and maintenance are clear, accurate and according to plan 

 Revision control of program documents and field sheets are adequate 

 Calibration and maintenance procedures are being followed 

 Seasonal and temporary staff have been trained in data management practice 

 Metadata collection and construction for the program proceeds in a timely manner 

 Data are being archived and catalogued appropriately for long term storage 

The final step in the QA/QC process is preparation of summary documentation that assesses 

overall data quality. The statement of data quality is composed by the project leader and 

incorporated into formal metadata for the data set. Metadata should also provide information on 

the specific QA/QC procedures applied and the results of review. Typically, data quality 

information will be conveyed as part of FGDC-compliant metadata (Chapter 8) and will be 

available via the NPS Data Store (Chapter 10). 

7.3.8. Roles and Responsibilities 

Producing and maintaining high quality data is the responsibility of everyone involved with the 

handling of project data. It is essential that each member of the team have a stake in data quality, 

and is responsible for the quality of the results generated from his or her tasks. While Chapter 4

discusses data management roles and responsibilities, selected QA/QC duties are emphasized 

here. 

Project leaders need to: 

 be aware of QA/QC procedures in protocols and convey their importance to technicians 

and field crews 

 ensure compliance with the protocols 

 plan for and ensure proper execution of data verification and validation 

 review all final reports and information products. 

Project technicians must: 

 follow established protocols for data collection, data entry, and verification 

 inform the project leader or data manager of quality-related problems or difficulties. 

The data manager is responsible for: 

 developing Network-wide SOPs to ensure data quality 

 making project leaders, technicians, and others involved aware of the established 

procedures and enforcing adherence to them 
56 

 evaluating the quality of all data and information against NPS standards before 

dissemination outside the Network 

 performing periodic data audits and quality control checks to monitor and improve 

quality control operations. 

7.4. References 

Angell, D. L. 2005. Sonoran Desert Network Data Management Plan. National Park Service, Inventory 

and Monitoring Program, Sonoran Desert Network, Tucson, Arizona. 80 pp. 

Beer, M., Nance, E., Wright, A, Powell, M. and R. DenBleyker. 2005. Northern Colorado 

Plateau Network, Data Management Plan. National Park Service, Inventory and Monitoring 

Program, Northern Colorado Plateau Network, Moab, Utah. 86 pp. plus appendices.

Edwards, D. 2000. Data quality assurance. Pages 70-91 in Michener, W. K., and J. W. Brunt 

(eds), Ecological Data: Design, Management and Processing, Methods in Ecology Series. 

Blackwell Science Ltd., Malden, Massachusetts. 

Hart, M. and U. Gafvert. 2005. Data Management Plan: Great Lakes Inventory and Monitoring 

Network. National Park Service Great Lakes Inventory and Monitoring Network Report 

GLKN/2005/20. 

Michener, W. K., J. W. Brunt, J. J. Helly, T. B. Kirchner, and S. G. Stafford. 1997. 

Nongeospatial metadata for the ecological sciences. Ecological Applications 7:1, 330- 342. 

Palmer, C. J. 2003. Approaches to quality assurance and information management for regional 

ecological monitoring programs. Pp. 211-225 in Busch, D. E. and J. C. Trexler (eds) 

Monitoring Ecosystems: Interdisciplinary Approaches for Evaluating Ecoregional Initiatives. 

Island Press, Washington, DC. 

Palmer, C. J., and E. B. Landis. 2002. Draft. Lake Mead National Recreational Area Resource 

Management Division: Quality System Management Plan for Environmental Data Collection 

Projects. 

Tessler, S., and J. Gregson. 1997. Draft Data Management Protocol. NPS I&M Program. 

http://www1.nrintra.nps.gov/im/dmproto/joe40001.htm. 

7.5. Appendixes 

7A) Director's Order #11B: Ensuring Quality of Information Disseminated by the National Park 

Service 

7.6. Credits 

This chapter was written by Rosamonde Cook and Leona Svancara, with concepts and material 

developed by Angell (2005), Beer et al. (2005), and Hart and Gafvert (2005). 
57 

Chapter 8. Dataset Documentation 

Documenting data is a time-consuming task for NPS staff and cooperators. Thorough 

documentation however, is essential for preserving the integrity and longevity of data and the 

products of its analysis, and is therefore, an essential component of sound data management. 

8.1. Objectives 

8.2. Laws and Policies 

Executive Order 12906 (Appendix 8A) requires all federal agencies to fully document all spatial 

data collected or produced, either directly or indirectly, since 1995 with metadata that meet 

specific standards developed by the Federal Geographic Data Committee (FGDC) as outlined by 

its Content Standard for Digital Geospatial Metadata (CSDGM) (Federal Geographic Data 

Committee 1994). Executive Order 12906 also directs agencies to plan for legacy data 

documentation and provide metadata and data to the public. 

The NPS GIS Committee requires all new spatial data sets be described according to FGDC 

standards and the NPS Metadata Profile. The NPS Metadata Profile

(http://science.nature.nps.gov/nrdata/docs/npsprofile.cfm) extends the original seven sections of 

the CSDGM to include the FGDC Biological Data Profile, ESRI Metadata Profile (ESRI 2003), 

and the NPS Profile. Profiles are simply formatted sections of information that are included in a 

formal metadata document. The NPS Metadata Profile is utilized by the Natural Resource and 

NPS Data Store for managing GIS and other data for both internal use and publication to the 

NPS GIS Clearinghouse within the NPS Focus Digital Library and Research Station. 

The FGDC Biological Data Profile (FGDC Biological Data Working Group, and USGS 

Biological Resources Division 1999) contains elements (i.e., specific information sections) for 

describing biological data sets (both spatial and non-spatial). Metadata that contain these 

elements can be disseminated to the National Biological Information Infrastructure (NBII, 

www.nbii.gov/datainfo/metadata) Clearinghouse and the NPS Data Store. 

The ESRI Metadata Profile (ESRI 2003) contains elements to better document aspects of ESRI 

data formats, particularly objects stored in geodatabases, and allow ArcCatalog© to maintain 

metadata automatically. 

The NPS Profile contains information about the park units to which the data pertain. 

 Document all significant geographic (GIS) and tabular datasets to SIEN 

standards described in this chapter. 

 Maintain and leverage the investment made by NPS staff and cooperators in 

producing quality data. 

 Aid the discovery of relevant data by NPS staff, collaborators, and the public. 

 Preserve integrity and longevity of data and its documentation indefinitely. 
58 

8.3. General Standards and Guidelines 

From project development through final delivery of information products, the Network Data 

Manger and project leaders will place a high priority on documenting the aims, quality, and 

meaning of data, and allow for the time this will take when scheduling. Detailed protocols 

provide an important source of information about the data produced by a project and therefore 

project leaders will track and document protocol versions (Chapter 12). Metadata will adhere to 

strict standards, and their development will be guided by the practices described below. Crews 

will be trained to record decisions made in the field that affect data quality or meaning. Project 

leaders will start metadata at the onset of the project and set the tone for record-keeping 

throughout the course of the project. 

8.3.1. Content Standards 

Network metadata content standards will adhere to those set by FGDC and NPS policy as 

described above. The CSDGM consists of seven sections (Sections 1-7), all of which contain 

elements required for spatial data. The NPS Metadata Profile adds another section (Section 0). 

The Biological and ESRI Profiles are added as elements to Sections 1-7. A summary of the 

CSDGM and NPS Metadata Profile sections is provided below. 

Section 0, NPS Information –purpose of the metadata, relevant park unit(s), and data steward. 

Section 1, Identification Information – who produced the data set, when and why it was 

produced, and where it is from. Constraints on access (e.g., for sensitive data) and use are also 

recorded in this section. Also includes the geographic extent, bounding altitudes, taxonomy, and 

analytical tools used in processing (for biological data). 

Section 2, Data Quality – the accuracy of attributes and geographic positions and the procedures 

used to ascertain accuracy. This section also documents the completeness and lineage of the data 

set. Lineage includes source(s) of the data and processing steps, and methodologies used (for 

biological data). 

Section 3, Spatial Data Organization – methods of spatial reference. Mandatory for spatial data. 

Section 4, Spatial Reference Information – coordinate system definitions. Mandatory for spatial 

data. 

Section 5, Entity and Attribute Information – attribute names, definitions, codes and their 

meanings and other information essential to a basic understanding of the data. 

Section 6, Distribution Information – methods and contacts used for obtaining data. Also 

documents information critical for using biological data formatted in ASCII. 

Section 7, Metadata Reference Information – includes who created the metadata, when it was 

created, the profile used, and the frequency of update. 

Metadata that are fully compliant with FGDC and NPS standards have entries in Section 0 and all 

element fields in Sections 1-7 where the Optionality field contains the term ‘mandatory’ or 

‘mandatory if applicable’. A mandatory element is one which must be populated for every data 
59 

set. A mandatory if applicable element is one which must be populated if the data set exhibits the 

characteristic being documented by the metadata element. For example, the element defining the 

vertical coordinate system would be mandatory if a data set contains elevation data. 

Metadata that are minimally compliant with FGDC and NPS standards have entries in Section 0 

and all ‘mandatory’ and ‘mandatory if applicable’ element fields in Sections 1, 6, and 7, and 

Section 2 for biological data. These include the fields used by the NPS Data Store. Data sets 

documented to this extent can be distributed via the Data Store's online upload utility. See 

Appendix 8B for guidance on creating metadata for the NPS Data Store. 

Different types of data and information require different kinds and levels of documentation. 

Standards for documentation of SIEN data sets are as follows: 

Spatial Data will contain, at a minimum, all of Section 0 and the required elements of Sections 

1-7. 

Non-Spatial Data will include, at a minimum, all elements of Section 0, the required elements of 

Sections 1, 6, and 7, and Section 2 for biological data. The minimum requirements for nonspatial data therefore meet the requirements for minimum compliance with the FGDC and NPS 

standards. 

Relational Databases will be documented according to the standards outlined above. Complete 

documentation will also include entity relationship diagrams, business rules, and programming 

code. The NPS Metadata Profile currently does not support this type of documentation, so it will 

be stored separately from the formal metadata, in a folder with the database. Relational databases 

will also utilize internal documentation such as table and field descriptions and will include a 

table to track modifications. See Chapter 5 for more information on documenting relational 

databases. 

Legacy Data will be documented to the extent possible according to the standards outlined 

above. Metadata that accompany legacy spatial and non-spatial data sets are suitable for upload 

to the NPS Data Store if they include entries sufficient for minimum compliance with FGDC and 

NPS standards. Priority for documentation will be: 

 Data sets needed for current project development 

 Data sets used frequently by park staff or cooperators 

 Historic data sets archived for possible future use 

Any contracts entered into by the SIEN with data miners will stipulate the submission of FGDC 

and NPS-compliant metadata. The Network Data Manager or project leader will assist with 

metadata acquisition by providing tools, format protocols and file transfer services. 

Legacy metadata in SIMMS databases should be converted to Extensible Markup Language 

(XML) as outlined in SIEN Standard Operating Procedures: Migrating metadata from SIMMS to 

XML (Appendix 8C). 
60 

Data from Outside the Network are data that are generated and/or managed outside of SIEN 

programs but used in analysis with SIEN data or distributed in any manner by the NPS. These 

data require the same level of documentation produced for SIEN-generated data, including but 

not limited to, data produced under contract with the NPS. Metadata will be requested from the 

originating entity by the Network Data Manager or project lead. 

Generally, external data will not be posted on Network or park local or wide area networks 

(LAN/WAN) without accompanying metadata. This will include any metadata downloaded with 

the data, plus additional information regarding date of download and any alterations made to the 

data by NPS staff. Staff posting data to the SIEN LAN or WAN will make a reasonable effort to 

make up for any deficiency in the original metadata, but should not create new metadata for data 

from well-known sources such as USGS digital line graph (DLG) data. NPS staff will 

occasionally post ‘value added’ external data (e.g., a digital elevation model clipped to park 

boundaries and converted to the standard projection) to the NPS Data Store, and associated 

metadata will reflect the source data as well as processing prior to upload. 

Sensitive Data. Metadata documentation (Section 1, Constraints on Access) provides one means 

of labeling sensitive data in order to ensure their protection and integrity over time. Sensitive 

data therefore will be documented according to the standards outlined above, although these data 

and their metadata will not be uploaded to the NPS Data Store. Instead, they will be archived in 

secure locations on Park and/or Network servers. 

Digital Photos. A national metadata standard for digital photos is currently under development 

by WASO. In the meantime, digital photos will be documented according to the specifications 

outlined in Sierra Nevada Network Digital Photo Management Strategy: Goals, Policies, and 

Guidelines (Appendix 6C) and will include at a minimum:

 Author – who took the photo, with agency affiliation 

 Title – who or what is in the image 

 Location – where the image was taken 

 Date – when the image was taken 

 Park code 

 Access constraints – who may view the image 

 Copyright information – restrictions on using the image 

 Contact information – who to contact for further information 

8.3.2. Format and Storage Standards 

To enable uploading to the NPS Data Store and further public distribution by NPS servers, 

metadata must be formatted in a manner that will allow parsing with the USGS Metadata Parser 

(see below). Proper formatting can be most easily accomplished using the metadata tools 

described below. 

Metadata and other documentation will be stored as close to their associated data files as 

possible, with formal metadata files saved in XML. Metadata may be exported in text, Hypertext 

Markup Language (HTML) or other formats as needed but the original XML file will be 

maintained with the data as the master copy. 
61 

Hypertext links within the metadata document will point to accompanying datasets stored on 

Network servers. In general, a single metadata document will apply to both raw and certified 

versions of the data.

8.3.3. Metadata Tools 

ESRI’s ArcCatalog© is a multifunction application for managing spatial data and for editing 

FGDC compliant metadata. ArcCatalog© uses stylesheets to display XML metadata in a format 

for easy viewing and editing. Because ArcCatalog© metadata are linked to the datasets they 

describe, certain inherent properties of datasets, such as bounds, coordinate system, feature 

count, and attribute names, can be automatically populated and maintained in the metadata. 

NPS Metadata Tools and Editor (Appendix 8D) is a custom software application for authoring 

and editing NPS metadata. It extends the basic functionality of ArcCatalog© for managing 

spatial metadata and provides a stand-alone tool for creating and manipulating non-spatial 

metadata outside of ArcCatalog©. The Metadata Editor formats metadata according to the NPS 

Metadata Profile. Like ArcCatalog©, editing is done with stylesheets. A variety of stylesheets 

are offered, each tailored to a specific type of metadata and displaying only the elements or 

sections pertinent to that type. NPS-specific stylesheets are based on the NPS Metadata Profile 

extension and contain all elements in the FGDC, ESRI, and Biological metadata standards and 

profiles plus NPS-specific elements. Metadata can be parsed with mp, the USGS MetaParser 

which comes bundled with the Metadata Tools. Output format is XML, suitable for upload to the 

NPS Data Store. Metadata Tools includes utilities for searching, cataloging, parsing, and spellchecking metadata records. 

NPS Database Metadata Extractor is an add-in for MS Access 2000-2003. The extractor 

automatically harvests entity (table) and attribute (field) metadata from MS Access databases, 

including domains. It further allows the user to edit and review the harvested metadata and make 

batch edits and to export metadata to a FGDC-compliant XML file. Exported XML can be used 

in the Metadata Tools & Editor either by opening it to start a new metadata record or by updating 

with template to fill section 5 of an existing record. This tool will eventually become part of the 

NPS Metadata Tools & Editor. 

Metadata Parser (mp) is a program developed by the USGS that comes bundled with both 

ArcCatalog© and the NPS Metadata Tools and Editor. It is used to validate metadata records by 

checking the syntax against the CSDGM and to generate compliant output files for posting to 

clearinghouses. The parser generates a textual report indicating errors in the metadata, primarily 

in the structure, but also in the values of some of the scalar elements where values are restricted 

by the standard. 

The “Metadata in Plain Language” questionnaire (Schweitzer 2006, Appendix 8E) is a userfriendly information sheet (MS Word© document) that can be used to elicit metadata needed to 

complete all FGDC required elements for data sets not formatted for geographic information 

systems (e.g., shapefiles, coverages, and geotiffs). It currently does not include questions specific 

to the Biological Data Profile nor the NPS Profile and therefore, does not alone, meet the content 

standards specified by the NPS Metadata Profile. Additional information must be obtained to 

comply with these specifications.
62 

8.3.4. Work Flow Process 

The Network will establish a standard operating procedure for metadata generation and 

maintenance (Appendix 8F). In general, metadata development begins with project design and 

planning (Chapter 3). Metadata documents should be created with the tools recommended above 

to ensure standardization and the ability to upload to the NPS Data Store. Other tools may be 

used as long as the metadata produced meet the appropriate requirements for content, format and 

organization. Initially, some sections may be populated by SIEN staff with information from 

formal written project protocols. As the project progresses, the document will be completed with 

information such as field season dates, protocol changes, and taxonomy information. Completion 

will occur after the dataset is certified by the Network Data Manager or project leader. The 

Network Data Manager or project leader ensures that updated metadata are uploaded to the NPS 

Data Store. The relationship between local applications and tools, and national-level storage and 

delivery system is illustrated in Figure 9. See details for data dissemination in (Chapter 10). 

Figure 9. The NPS integrated metadata management system. 

8.3.5. Maintenance and Access 

Once metadata are complete, the Network Data Manager or project leader saves one copy with 

the primary dataset at the local level, and uploads an XML-formatted copy to the NPS Data 

Store. 

Repository: NPS GIS 

Clearinghouse 

Access: public 

Repository: NPS Data Store 

(Oracle database) 

Access: Secure server. 

Login required. 

Automatic 

posting of 

non-sensitive 

data 

Tools: Arc Catalog©, 

Metadata Tools & Editor 

Purpose: Create/edit 

metadata 

Tool: Metadata catalog 

(MS Access database) 

Purpose: Organizes 

metadata files 

Local 

level 

National 

level 

Fully compliant 

meta-data 
63 

Metadata can easily be searched with Metadata Catalog, one of the tools in the NPS Metadata 

Tools and Editor. Metadata Catalog creates a Microsoft Access database that stores the location, 

filename and other information about all metadata files in a selected directory. 

8.4. References 

ESRI. 2003. ESRI Profile of the Content Standard for Digital Geospatial Metadata. 

Environmental Systems Research Institute, Redlands, California. 

Federal Geographic Data Committee. 1994. Content standards for digital spatial metadata (June 

8 draft). Federal Geographic Data Committee. Washington, D.C. 

FGDC Biological Data Working Group, and USGS Biological Resources Division. 1999. 

Content Standard for Digital Geospatial Metadata - Biological Data Profile, FGDC-STD001.1-1999 Federal Geographic Data Committee. Washington, D.C. 

Mortenson, D. 2005. Data Management Plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. United States National Park Service. Anchorage, Alaska. 234 pp. 

Schweitzer, P. 2006. Metadata in Plain Language. United States Geological Survey, Reston, 

Virginia. http://geology.usgs.gov/tools/metadata/tools/doc/ctc/.

8.5. Appendixes 

6C) Sierra Nevada Network Digital Photo Management Strategy 

8A) Executive Order 12906: Coordinating Geographic Data Acquisition and Access, the 

National Spatial Data Infrastructure 

8B) NR GIS Data Store: Metadata Authoring Guidance 

8C) Migrating Metadata from SMMS to XML 

8D) NPS Metadata Tools and Editor: A Comprehensive Editing Tool for National Park Service 

Metadata 

8E) Tools for Creation of Formal Metadata: Metadata in Plain Language 

8F) Sierra Nevada Network Guidelines for developing FGDC compliant metadata for both 

spatial and biological data 

8.6. Credits 

This chapter was written by Rosamonde Cook, with concepts and material developed by 

Mortenson (2005) and information provided by Ginger Bradshaw and Chris Dietrich. 

65 

Chapter 9. Data Ownership and Sharing 

For the purposes of releasing data, the term sharing means releasing to an individual or entity 

upon request or as part of an agreement to do work that benefits the parks. The term 

dissemination includes releases to the public via publications or other standard routes of delivery 

such as the NPS Data Store. Chapter 10 provides detailed standards and guidelines for data 

dissemination. Guidelines for data sharing, including sensitive data procedures, are described in 

this chapter. 

9.1. Objectives 

9.2. Laws and Policies 

The National Park Service defines conditions for the ownership and sharing of data, natural 

resource collections, and results based on research funded by the United States government in 

two principal documents - the Freedom of Information Act, 5 U.S.C. § 552, referred to as FOIA 

(Appendix 9A), and Office of Management and Budget (OMB) Circular A-110 (Office of 

Management and the Budget, 1999). 

FOIA stipulates that the United States government, including the National Park Service, must 

provide access to data and information of interest to the public, regardless of whether or not they 

were created by the federal government. FOIA is intended to establish a right for any person to 

access federal agency records that are not protected from disclosure by exemption or by special 

law enforcement record exclusions. Under the terms of FOIA, agencies must make non-protected 

data and information products available for inspection and copying in public reading rooms 

and/or from the Internet. 

The NPS is directed to protect information about the nature and location of sensitive park 

resources under one Executive Order and four resource confidentiality laws: 

 Executive Order No. 13007: Indian Sacred Sites 

 National Parks Omnibus Management Act (16 U.S.C. 5937) 

 National Historic Preservation Act (16 U.S.C. 470w-3) 

 Federal Cave Resources Protection Act (16 U.S.C. 4304) 

 Archaeological Resources Protection Act (16 U.S.C. 470hh) 

The OMB ensures that grants and cooperative agreements are managed properly. Federal 

funding must be disbursed in accordance with applicable laws and regulations. OMB circulars 

establish some degree of standardization government-wide to achieve consistency and uniformity 

in the development and administration of grants and cooperative agreements. OMB Circular A110 establishes property standards within cooperative agreements with higher institutions and 

non-profit organizations. Section 36, “Intangible Property” describes the administrative 

 Establish clear guidelines for the ownership and sharing of natural resources 

data and information. 

 Protect sensitive data from unauthorized access and inappropriate use. 
66 

requirements pertinent to data and ownership, stipulating that all data and materials collected or 

generated using National Park Service personnel and funds become the property of the National 

Park from which they were collected. 

For additional information, see: 

 The Freedom of Information Act 5 U.S.C. § 552, As Amended By Public Law No. 104-

231, 110 Stat. 3048 (Appendix 9A). 

 Sierra Nevada Network FOIA and Sensitive Data Guidelines (Appendix 9B). 

 A Synopsis of Laws and Policies Governing Public Dissemination of Natural Resources 

Data (Appendix 9C). Also contains the text to Section 36 of OMB Circular A-110. 

 U.S. Department of the Interior FOIA regulations, instructions, and general information 

at http://www.doi.gov/foia/. 

 Draft Director’s Order #66: Freedom of Information Act and the Protection of Exempted 

Information (National Park Service, 2004a). 

 Reference Manual RM-66A: FOIA processing (National Park Service, 2004b). 

 Draft Reference Manual RM-66B: Handling protected information (National Park 

Service, 2004c). 

9.3. General Standards and Guidelines 

Data and information will be shared internally through Network channels, externally through 

online databases and repositories, and by special request as appropriate, and only after quality 

control procedures have been fully implemented. 

9.3.1. Collaborative Agreements 

To ensure that proper ownership, format, and development of Network products are maintained, 

all cooperative or interagency work must be conducted as part of a signed collaborative 

agreement. Every cooperative or interagency agreement or contract involving the Network must 

cite OMB Circular A-110 under the Reports and Deliverables Section. The following shows 

appropriate language to use when citing Circular A-110: 

“As the performing organization of this agreement, [institution or organization 

name] shall follow the procedures and policies set forth in OMB Circular A-110.” 

Every cooperative or interagency agreement or contract must include a list of deliverables and 

products clearly defined within each agreement or contract. Details on formatting and media 

types that will be required for final submission must be included. Agreements and contracts must 

list all products expected to result from the project. These include, but are not limited to, field 

notebooks, photographs (hardcopy and digital), specimens, raw data, and reports. 

The following statement must be included in the Reports and Deliverables section of all 

agreements and contracts made by the SIEN Inventory and Monitoring Program: 

“All reports and deliverables must follow Sierra Nevada Network Product 

Specifications". 
67 

See Sierra Nevada Network I&M Program Checklist of Project Specifications (Appendix 6A) 

and the documents referenced therein for further details on data acquisition, documentation, and 

reporting standards and requirements. 

Project leaders should provide a schedule of deliverables that includes sufficient time for the 

NPS to review drafts before scheduled final submissions.

9.3.2. Due Notification 

SIEN staff will notify investigators prior to making datasets available to the public. This will 

allow each investigator the opportunity to make a request in writing to further restrict access to 

the dataset by the public. Network staff will review the investigator’s request and determine 

whether the request will be granted and for how long the dataset will remain restricted. Details 

on how data and information products will be made available to the public are provided in 

Chapter 10.

9.3.3. Access Restrictions on Sensitive Data 

With regard to natural and cultural resources, sensitive data and information are those that could 

be used to harm, remove, or destroy sensitive resources protected by units of the National Park 

System. Network staff members are responsible for managing access to sensitive data handled by 

their programs. These responsibilities include: 

 Identify and classify all potentially sensitive park resources and information. 

 Provide to superintendents, a complete list of potentially sensitive park resources for 

which data may be collected as part of any project. The superintendent then determines 

what information should be protected. 

 Ensure that all protected information, digital and hardcopy, is properly identified and 

marked. 

 Ensure that all references to protected information are removed or obscured in any 

reports, publications, maps, data, or other form made available to the public.

 Inform investigators working on Network projects that: 

o All data and associated information must be made available for review by Network 

staff prior to release in any format. 

o Any information classified as protected should not be released in any format except 

as approved in advance by the National Park Service. 

o The Network Coordinator, NPS project liaison, or Data Manager will identify all 

potentially sensitive park resources to the principal investigator for each project. 

Reciprocally, the principal investigators for each project must identify any known 

references to potentially sensitive park resources. 

Agreements with partners will include a standard confidentiality agreement which states that, 

among other things, the researcher will not share protected data or information with any other 

party, public, private, or academic, without channeling requests through the NPS project lead. 

Example language is provided in Appendix 9B. Researchers may fulfill such requests, but there 

will be a confidentiality agreement between NPS and the new holder of the data. 
68 

When sharing data and information on sensitive resources with other federal agencies, the other 

agency will be told of the need to channel any requests for the data or information through the 

NPS, as per NPS Director’s Order #66 (National Park Service 2004a) and accompanying 

Reference Manuals 66A (National Park Service 2004b) and 66B (National Park Service 2004c). 

9.3.4. Answering Requests under the Freedom of Information Act 

Data stewards will forward records requested pursuant to the FOIA to the requester via the Park 

FOIA Officer in a timely manner. Data stewards will maintain records subject to the FOIA in a 

manner so as to make them rapidly available and to leave no doubt that the most recent version 

of accurate, non-sensitive information is provided to requesters. Data stewards are responsible 

for reading, understanding, and following applicable laws, policy, and procedural guides. 

If information requested under the FOIA has not already undergone sensitivity classification, it 

will be thoroughly but rapidly assessed for presence of sensitive information and, if such 

information is found, the data steward will work with the appropriate Park and regional FOIA 

Officers to formulate a response to the requester, which may include complete denial, complete 

fulfillment, or partial fulfillment once sensitive information have been cleared from the requested 

records. 

9.4. References 

Hart, M. and U. Gafvert. 2005. Data Management Plan: Great Lakes Inventory and Monitoring 

Network. National Park Service Great Lakes Inventory and Monitoring Network Report 

GLKN/2005/20. 

National Park Service. 2004a. Draft Director’s Order #66: Freedom of Information Act and the 

Protection of Exempted Information. http://www1.nrintra.nps.gov/DO66/ DO_66_Final_ 

Draft.doc. 

National Park Service. 2004b. Draft Reference Manual RM-66A: FOIA processing. 

http://www1. nrintra.nps.gov/DO66/RM-66A-Draft.doc. 

National Park Service. 2004c. Draft Reference Manual RM-66B: Handling protected 

information. http://www1.nrintra.nps.gov/DO66/RM-66B-Draft.doc. 

Office of Management and the Budget. 1999. Circular A-110. http://www.whitehouse.gov/omb/ 

circulars/a110/a110.html#1 

Stevens, S. M. and G. Entsminger. 2004. Information Management Plan for the Inventory and 

Monitoring Program, Northeast Coastal and Barrier Network. United States National Park 

Service. Kingston, Rhode Island. 99 pp. 

9.5. Appendixes 

6A) Sierra Nevada Network I&M Program Checklist of Project Specifications 

9A) The Freedom of Information Act 5 U.S.C. § 552, As Amended By Public Law No. 104-231, 

110 Stat. 3048 
69 

9B) Sierra Nevada Network FOIA and Sensitive Data Guidelines 

9C) Synopsis of Laws and Policies Governing Public Dissemination of Natural Resources Data 

9.6. Credits 

This chapter was written by Rosamonde Cook, adapted from material developed by Stevens and 

Entsminger (2004) and Hart and Gafvert (2005). 

71 

Chapter 10. Data Dissemination 

Under the terms of Freedom of Information Act (5 U.S.C. § 552), public agencies must make 

non-protected data (described in Chapter 9) and information available for inspection and copying 

in public reading rooms, the Internet, or via requests through a specified process. Providing welldocumented data in a timely manner is one of the most important goals of the I&M Program, and 

critical to the success of the program. This chapter describes the methods by which quality 

natural resource data and information collected by the SIEN are made available to park 

managers, researchers, educators, and the general public. 

10.1. Objectives 

10.2. Laws and Policies 

Three major pieces of legislation set the tone for information dissemination and protection by 

federal agencies. They are the Freedom of Information Act, 5 USC Section 552 (FOIA), the 

National Parks Omnibus Management Act of 1998, 16 USC Section 5937 (NPOMA), and OMB 

Circular A-110 (Office of Management and the Budget, 1999). Each provides for the wide and 

proactive sharing of information and each recognizes instances where such sharing may be 

harmful. 

The FOIA requires that federal agencies provide access to most data and information holdings, 

whether developed by employees or by cooperators. Agencies are directed to post commonly 

requested items in public reading rooms and/or on public websites. Nine exemptions provide for 

withholding information, such as that relating to law enforcement actions and confidential 

personnel records that are protected. Of these, the third exemption allows withholding 

information that is protected under other statutes. These are summarized in Appendix 9C and 

include: 

 The Archaeological Resources Protection Act, which requires information on the 

location and nature of archaeological resources to be protected. 

 The National Historic Preservation Act, which directs that information regarding 

historic resources should be withheld if disclosure would cause significant 

invasion of privacy or risk harm to historic resources. 

 The Federal Cave Resources Protection Act, which prohibits release of 

information regarding the nature and location of caves. 

 Ensure non-sensitive data are easily discoverable and obtainable. 

 Ensure data that have not yet been subjected to substantial quality control are 

not released to the public, unless necessary in response to a FOIA request. 

 Distribute data with complete and accurate metadata that clearly identify who 

collected the data, what data were collected, and where, when and how the 

data were collected. 

 Identify and protect sensitive data from unauthorized access. 

 Maintain a complete record of data distribution/dissemination.
72 

 Executive Order 13007, Protection and Accommodation of Access to Indian 

Sacred Sites, which allows for maintaining the confidentiality of Indian Sacred 

Sites. 

 The National Parks Omnibus Management Act, which provides protection of 

sensitive natural and cultural resource information. 

The NPOMA, heralded as the NPS “research mandate” (Harmon 1999), established the 

Cooperative Ecosystem Studies Unit and I&M programs. While NPOMA clearly requires a 

higher level of information sharing with the research community and the interested public, as 

well as use in management decisions, the exemptions in Section 207 allow for protection of 

information about resources not previously specified in other legislation. 

Another key policy statement, OMB Circular A-110 establishes federal government rights to use 

and share information gathered with federal support. The Circular also specifies that such 

information is subject to the FOIA and its exemptions. See Chapter 9 for details. 

10.3. General Standards and Guidelines 

The SIEN will employ a number of distribution methods that ensure information collected and 

developed as part of Network programs are made widely available to park employees and the 

public. 

SIEN parks develop and implement a map center concept that focuses on delivering both 

electronic and hardcopy maps. Map delivery should be efficient and meet the primary and 

contemporary mapping needs of all park program staffs. 

Data and information will be disseminated principally through the Internet and only after quality 

control procedures have been fully implemented, unless a request is made pursuant to the FOIA 

with consideration to sensitivity and ownership classification (Chapter 9). 

SIEN staff will notify investigators prior to making datasets available to the public. This will 

allow each investigator the opportunity to request in writing to further restrict access to the 

dataset by the public. Network staff will review the Investigator’s request and determine whether 

the request will be granted and for how long the dataset will remain restricted.

10.3.1. Data and Information Distribution Mechanisms 

According to FOIA (specifically the 1996 amendments), all information routinely requested must 

be made available to the public via reading rooms and/or the Internet. The Network’s principal 

means of distributing inventory and monitoring data will be the Internet (Table 5). A number of 

Internet-based databases and repositories have been developed as part of the NPS I&M Program 

to store and disseminate a variety of natural resource information. They include: 

 The Sierra Nevada Network public website 

 National public applications, NPSpecies and NatureBib 

 The NPS Data Store 

 The Biodiversity Data Store 
73 

Table 5. Repositories of SIEN public and sensitive data and information. 

Item Repository 

Reports (public) - digital SIEN LAN servers, SIEN public website, NPS Data 

Store, NPS Focus 

 - hard copy SIEN I&M library, YOSE Library, USGS Libraries 

 - bibliographic NatureBib 

Network-generated digital datasets and data 

products (public, non-sensitive) 

 Certified data and data products (including 

photographs) 

 Metadata 

SIEN LAN servers, NPS Data Store, Biodiversity 

Data Store, NPSpecies, EPA STORET, the 

California Data Exchange Center (weather data), and 

the California Data Exchange Network (water 

resources data) 

Network-generated digital datasets and data 

products (NPS staff, sensitive) 

 Raw, validated data 

 Analytical products 

 Metadata 

 Reports 

 Digital photos 

 Digital presentations, etc. 

SIEN LAN servers 

Project products 

 Specimen vouchers 

 Photographic film 

SEKI and YOSE Museums or other curation 

facilities, according to project protocol 

10.3.1.1. The SIEN Website: The Network will regularly provide updated information about 

inventories and monitoring projects, including annual reports and detailed project reports through 

its public web site (http://www1.nature.nps.gov/im/units/sien/index.htm). Links, and a brief 

description, will be provided to each of the national databases and repositories described below. 

10.3.1.2. National Databases: NPSpecies will be updated with observations and vouchers 

collected during the course of SIEN projects, if these data are deemed important to the further 

documentation of species already present on certified park species lists, or constitute additions to 

these lists (Appendix 10A). Data will either be manually entered into the online databases by 

SIEN or Park staff, or if present in sufficient quantity (e.g., more than 100 records), sent to the 

WASO office for upload after first ensuring proper formatting. NatureBib will be updated as new 

documents become available (Appendix 10B). These databases are available via both a secure 

server and a public server. The public can not access records flagged ‘sensitive’ as NPS or ParkOnly. 

Water quality data collected to meet federal regulatory requirements are managed according to 

guidelines from the NPS Water Resources Division (WRD), which also oversees the integrated 

water quality monitoring portion of the I&M Program. NPS requires all I&M data to be uploaded 

to EPA’s STORET National Data Warehouse. WRD developed NPSTORET – a desktop 

database application for I&M networks to help manage data entry, documentation, and transfer. 

Data from NPSTORET are transferred periodically STORET (Figure 10). Individual networks 

are free to use NPSTORET for data entry and maintenance, or to develop a customized database 

compatible for data exchange and delivery. The SIEN may choose the latter and build a desktop 

application that would also interface with the California Environmental Data Exchange Network 

(CEDEN) client-server database, a central repository for water resources data collected within 
74 

California and operated through the State's Department of Water Resources. In that case, formal 

procedures, in accordance with WRD’s Electronic Data Deliverable (NPSEDD) file 

specifications, will be developed for transferring data into NPSTORET for delivery to WRD 

(Appendix 10C). 

Figure 10. Flow diagram for water quality data from I&M Networks to the National Data 

Warehouse. 

Data from short-term studies (such as biological inventories) will be provided to the public 

through the NPS Data Store and/or the Biodiversity Data Store within two years after data 

collection was completed, or following publication of the investigator’s results (whichever 

comes first). 

Data from long-term air quality monitoring conducted by Network parks or by cooperators 

working with the parks, including visibility, gaseous pollutants (mainly ozone), atmospheric 

deposition (wet and dry), and meteorology are available through the NPS Air Resources Division 

website at: http://www2.nature.nps.gov/air/monitoring. 

Long-term monitoring data collected as part of the I&M Vital Signs Monitoring Program will be 

made available on the NPS Data Store at four or five year intervals, or when trend analyses have 

been completed and reported on by the Network. The NPS Data Store is available on a secure 

server and open to individuals approved for access. All data distributed to the Data Store and not 

defined as sensitive will be available to the public through the NPS GIS Clearinghouse. Before 

data are posted, the investigator or project leader will be asked to verify the final dataset and 

metadata, and to identify any sensitive data or information. Sensitivity will be documented in the 

metadata which will accompany all data sets (Chapter 8). 

Non-sensitive digital images, including photos, drawings, maps, text, and GIS DOQ/DRG 

images will be made available to the public through the NPS Focus digital library. 

Data from the Basic Natural Resources Inventories conducted through the NPS Natural Resource 

Program Center and the I&M Program are stored in a variety of locations (Appendix 2B). 

NPSTORET 

(desktop version) 

or NPSEDD files 

Networks 

NPS STORET 

(Oracle) STORET National Data 

Warehouse 

EPA 

Washington, D. C. 

WRD 

Fort Collins 

Edits or changes post 

 quality assurance 

Annually Monthly 
75 

10.3.2. Sensitive Data 

Data released outside of the federal government without a confidentiality agreement is 

considered a release of information under the Freedom of Information Act and triggers the 

application of the “release to one, release to all” principle. There are many venues for release of 

sensitive data that constitute release to the public. Therefore, information should be reviewed for 

sensitivity by qualified NPS staff prior to inclusion in these media. They include: 

 Internet posting 

 Interpretive programs, media, and activities 

 News releases 

 Resource management storing and sharing activities 

 NEPA compliance documents 

Inadvertent access may be deemed a release depending on the circumstances. Network staff will 

institute quality control and quality assurance measures to ensure that any person uploading 

records to public databases is familiar with the procedures for identifying and entering protected 

sensitive information (Appendix 10A, Appendix 10B). See Appendix 9B on classifying and 

handling sensitive natural resources data. 

10.3.4. Feedback Mechanisms 

The SIEN website will provide an opportunity for NPS staff, cooperators and the public to 

provide feedback on data and information gathered as part of Network programs. A “comments 

and questions” link will be provided on the main page of the site for general questions and 

comments. A more specific “data error feedback” link will direct comments to SIEN staff 

pertaining to errors found in website-accessible data. 

The following procedure describes the process that the SIEN will use to receive, verify, and 

correct data errors identified by web site users. 

 Users send a notification through the SIEN website. 

 An acknowledgment of receipt is sent to the user. 

 Information is input into an error log table incorporated either in the project 

database or a specific error tracking database developed for the Network. 

 Network staff determine whether the data in question are correct or incorrect. If 

correct, the user is informed that no corrections are to be made and the 

information stands. If incorrect, Network staff make the appropriate corrections 

and notify the user and original data collectors. 

 The Network website is refreshed with the corrected information. 

10.4. References 

Harmon, D. 1999. The New Research Mandate for America's National Park System: Where It 

Came From and What It Could Mean. George Wright Forum 16(1):8-23. 

Mortenson, D. 2005. Data Management Plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. United States National Park Service. Anchorage, Alaska. 234 pp. 
76 

Office of Management and the Budget. 1999. Circular A-110. http://www.whitehouse.gov/omb/ 

circulars/a110/a110.html#1. 

Stevens, S. M., and G. Entsminger. 2004. Information Management Plan for the Inventory and 

Monitoring Program, Northeast Coastal and Barrier Network. United States National Park 

Service. Kingston, Rhode Island. 99 pp. 

10.5. Appendixes 

2B) National Repositories for Data and Information 

9B) Sierra Nevada Network FOIA and Sensitive Data Guidelines 

9C) A Synopsis of Laws and Policies Governing Public Dissemination of Natural Resources 

Data 

10A) NPSpecies Data Standards Manual 

10B) NatureBib Data Standards Manual 

10C) Transferring Vital Signs Water Quality Data to NPSTORET 

10.6. Credits 

This chapter was written by Rosamonde Cook, with information provided by Ginger Bradshaw 

and material developed Stevens and Entsminger (2004) and Mortenson (2005). 
77 

Chapter 11. Records Management and Object Curation 

Documenting park resources and their management is an essential part of National Park Service 

administration. The potential for loss of data, documents, or objects can come from a variety of 

sources, including catastrophic events (e.g., fire, flood, and earthquake), user error, hardware 

failure, software failure or data corruption, theft, and intentional acts of vandalism. The 

responsibility for the preservation of park records begins with each employee and thorough 

implementation of effective best practices is crucial to ensuring long-term preservation and 

accessibility of park records. 

The Federal Records Act (44 U.S.C. 3301) defines records as: 

"... all books, papers, maps, photographs, machine readable materials, or other 

documentary materials, made or received by an agency of the United States 

Government under Federal law or in connection with the transaction of public 

business and preserved as evidence of the organization, functions, policies, decisions, 

procedures, operations, or other activities of the Government or because of the 

informational value of data in them". 

The law includes records created under contracts or other agreements with other agencies, 

organizations, or individuals. 

Documents most necessary for fulfillment of the NPS mission are considered mission critical

records. The Records Management Handbook (National Park Service 2005) defines such records 

as resource management records – “the permanently valuable, substantive program records 

documenting the actual work of the Park Service in managing critical cultural, natural, and 

informational resources.” Records documenting natural and cultural resources and their 

management contain information that may affect the future management of these resources and 

are irreplaceable. They should receive the highest priority in records management activities and 

resources and should receive archival care as soon as practical after their creation. 

Resource management records may include inventories, drawings, field notes, maps, 

photographic negatives, prints and slides, audio and video recordings, and electronic files and 

media. All are considered records and their management and preservation are addressed in this 

chapter. 

This chapter also provides guidelines and standards for the curation of natural history specimens 

and other non-record objects. Such collections, and their associated records, are by definition 

museum objects, but have much in common with resource management records. Most 

significantly, both result from park studies as defined in Management Directive 4 of the Records 

Management Handbook: 

"The term studies means short- or long-term scientific or scholarly investigations or 

educational activities that may involve natural resource surveys, inventories, 

monitoring, and research, including data and specimen collection. Studies include 

projects conducted by researchers and scholars in universities, foundations and other 
78 

institutions; tribal colleges and organizations; other federal, tribal, and state 

agencies; and NPS staff. The data and information acquired through studies 

conducted in parks will be made publicly available".

Digital data pose particular challenges to long-term preservation. Technological obsolescence is 

a significant cause of information loss, and data can quickly become inaccessible to users if 

stored in out-of-date software programs or on outmoded media. Effective maintenance of digital 

files depends on proper management of a continuously changing infrastructure of hardware, 

software, file formats, and storage media. Major changes in hardware can be expected every 1-2 

years, and in software every 1-5 years (Vogt-O’Connor 2000). As software and hardware evolve, 

data sets must be consistently migrated to new platforms, or they must be saved in formats that 

are independent of specific platforms or software (e.g., ASCII delimited files). This chapter 

describes procedures for both short and long-term preservation and management of electronic 

files storage. 

An essential part of any archival holding (digital or non-digital) is its accompanying explanatory 

materials (Olson and McCord 1998). Thus, effective long-term maintenance of these products 

depends on thoughtful and appropriate data documentation. Chapter 6 provides detailed policies, 

guidelines, and practices for documenting data. Chapter 12 provides detailed policies on project 

tracking and organization. 

11.1. Objectives 

 Adopt and implement robust standard file directory structure specifications. 

 Where practical, maintain mission critical and permanent paper records in 

both hardcopy and electronic file format, and protect and preserve them 

indefinitely. 

 Maintain datasets no more than two versions behind current software versions, 

or store in American Standard Code for Information Interchange (ASCII) 

format, complete with data and file documentation. 

 Ensure information can be easily obtained, shared, and properly interpreted by 

a broad range of users. 

 Ensure backup, storage, and recovery practices for electronic files equal or 

exceed the minimum standards established by the NPS Office of Chief 

Information Officer. 

 Store all electronic files on servers in a networked environment using 

approved file-naming standards and file directory structures. 

 Maintain all data, programmatic, and administrative electronic files 

indefinitely. 

 Ensure all short and long term projects are well-documented, organized, and 

protected according to local and national standards and guidelines. 

 Ensure all collected natural history specimens and objects are welldocumented and preserved in perpetuity. 
79 

11.2. Laws and Policies 

The most relevant guidance for managing NPS records and object curation are found in the 

following references: 

 The NPS Records Management Handbook (National Park Service 2005a) 

 Management Directive 4 of the NPS Records Management Handbook: Studies and 

Collections. Provides direction for approving and managing scientific research 

 NPS Director’s Order 19: Records Management (National Park Service 2001a). Provides 

direction for managing and preserving records 

 NPS Director’s Order 19, Appendix B: Records Disposition Schedule (National Park 

Service 2001b). Determines the long term disposition of specific NPS records 

 NPS Director’s Order 24: Museum Collections Management (National Park Service 

2000). Provides direction for managing and preserving museum collections 

 36 CFR 2.5: Research Specimens (Code of Federal Regulations, 2001). Provides 

direction for managing natural history collections 

 NPS Management Policies (National Park Service 2006). Describes requirements for the 

long term housing of records and objects 

 NPS Museum Handbook (National Park Service 2005b). Provides the overarching 

guidance for archival procedures 

11.2.1. Records 

NPS Records Disposition Schedule (National Park Service 2001b) defines the appropriate 

disposition of specific record categories. The term “disposition” applies to records no longer 

needed for active, on-going business; it can mean permanent archiving, transfer to a Federal 

Records Center, or destruction. Section N of this Appendix (Natural and Social Sciences) states 

that all natural resource records are considered permanent and need to be retained in an 

appropriate park museum facility or at the National Archives. It also states that even nonarchival copies of natural resource-related materials are “potentially important for the ongoing 

management of NPS resources” and should not, in any instance, be destroyed.

Directors Orders 19, Section 6.2 provides specific guidelines and standards for electronic 

records. Under these guidelines, an electronic or digital recordkeeping system may be used as 

the primary recordkeeping system only under the following circumstances: 

 When the records in the system are, or can be made, software and hardware independent, 

and meet current NARA format standards for electronic records. 

 When the records to be stored are temporary, with a retention period of 5 years or less. 

This includes most records typically created during routine administrative functions in 

offices on electronic mail and word processing programs. 

 Inherently electronic records where there is little practical choice, (generally geo-spatial 

data and web pages). 

 If the records to be stored are temporary, with a retention period of more than 5 years. 

In all other circumstances, traditional media records of paper, photography, film, microfiche and 

magnetic recordings must be used for archival storage. 
80 

11.2.2. Collections 

Regarding the collection of natural history specimens, NPS Management Policies states the 

following: 

"Field data, objects, specimens, and features obtained for preservation during 

inventory, monitoring, research, and study projects, together with associated records 

and reports, will be managed over the long term within the museum collection. 

Specimens that are not authorized for consumptive analysis remain federal property 

and will be labeled and cataloged into the NPS cataloging system (ANCS+, or its 

successor) in accordance with applicable regulations". 

The overarching technical guidance for archival procedures is found in the NPS Museum 

Handbook. In particular, Part II, Appendix A – Mandates and Standards for NPS Museum 

Collections lists the cultural and natural history laws, regulations and conventions for NPS 

museum collections and should be reviewed prior to object collections. 

11.2.3. Local Network Policies 

These policies supplement or reinforce certain national policies deemed very important to the 

Network. 

 To the extent practicable and consistent with the other goals and policies identified in this 

plan, backup and archival storage of electronic files will comply with relevant existing 

federal, department, and bureau policy, rules and regulations. 

 Park IT staffs are responsible and accountable for maintaining, backing-up, and restoring 

electronic files on all Network servers. 

 Regular and complete backups of electronic files on personal computers are the 

responsibility of each staff member. 

 The rotating configuration for media backups will be five years duration with at least 

quarterly offsite media backups supporting long-term storage. 

 To the extent practicable, any electronic file that meets the definition of both mission 

critical or a permanent record will be printed out and managed as a hardcopy record. 

 Local area network temporary, permanent, and disposition-suspended federal records and 

objects will meet the minimum physical and security conditions required by federal, 

department, and bureau laws, mandates, and policies. 

11.3. General Standards and Guidelines 

11.3.1. Records Management 

11.3.1.1. Electronic File Naming Guidelines and Standards: All electronic files will be named 

according to guidelines and standards outlined in Appendix 11A – Electronic File Naming 

Guidelines and Standards. These will apply to all SIEN electronic files created or maintained by 

staff or cooperators. Network programs that are adopting them will be clearly identified in Part 2 

of the appendix. 
81 

Highlights of SIEN file naming standards: 

 File names should be as short as possible, but include all mandatory information. 

 The electronic file name should be unique, such that the chances for another electronic 

file having the same name on a local area network are unlikely. 

 Version numbers (e.g., version1, version2, final, etc.) are not to be used on documents. 

The date of file creation (yyyymmdd) will represent the version. 

 Generally, use lower case characters. However, it is an acceptable practice to mix upper 

and lower case letters in a filename to separate a concatenation of words, such as words 

in a title. 

 Do not use spaces in file names. 

 Do not use special characters, other than the underscore (_), and periods. 

 Use the underscore to separate categories in an electronic file name. 

 Use generally recognizable abbreviations or spell words out. 

 Avoid uncommon acronyms, abbreviations and codes. Generally, avoid codes that 

require users to refer to another source for the code description. NPS codes such as park 

code (e.g., YOSE, SEKI) are acceptable. 

 Use leading zeroes for numbers 1 through 9. 

 File extensions: Use the default extension recommended by the software application 

associated with the document. (Examples: doc, xls, mdb, rtf) 

11.3.1.2. Directory Structure: Directory file structures already exist on Network servers but are 

generally inadequate. Electronic files will be managed within a hierarchical set of file directory 

structures. Appendix 11B provides an example of a very detailed layout of the directory structure 

for the SIEN I&M program which includes folders for administrative files, data, publications, 

and reports. All Network programs will shift toward a file directory structure like this one. The 

SEKI local area network will be the primary repository for all Network I&M electronic files, but 

will provide access to YOSE and DEPO employees and I&M staff. Local area network file 

servers will remain the repositories for individual park programs. 

Key aspects of the SIEN file management strategy include the following: 

 To the extent possible, all applications work (e.g., spreadsheets, GIS, documents, 

databases) will be conducted on a SIEN server. 

 The local area network copy of all electronic files will be the primary version. Any local 

electronic file versions on personal computers should be considered backup files. 

 Generally, only one copy of an electronic file will be maintained on a Network server. 

One exception to this situation would be a master database that is read-only, but may also 

have a working database copy that is being actively updated. 

 Generally, all electronic files will be maintained in a networked environment that enables 

immediate retrieval. Although there are some exceptions, generally offline storage and 

retrieval of electronic files from media will be discouraged. 

 Working files are kept separate from finished products. 

 Finished products are typically read-only, except for "inbox" folders where users can 

drop things off to be cataloged and filed. 
82 

 Standards, such as naming conventions and hierarchical filing, are enforced within 

shared and archived sections (e.g., libraries, GIS, databases). Although less stringent in 

other sections, these conventions are encouraged as good practice. 

 Version control is implemented in both active and archived directories. Generally,

previous versions of databases will be saved in their native format and archived using 

the same best practices as used for current versions. Documentation of version updates 

and associated details will be part of the archive metadata document, and revision 

information and history will also be included in tables within the database files 

themselves. 

A Project Template Directory Structure for short-term projects has been created (Appendix 

11C). The data manager or designated staff will create a working directory based on this 

template for each project, which project leaders can then modify as needed (e.g., deleting 

directories not being used). Upon completion, projects and products are certified by project 

leaders and moved if needed.

11.3.2. Archival, Backup, and Storage 

11.3.2.1. Project Process and Workflow: Both electronic and hardcopy files should be 

consolidated and packaged for archival when a project is complete or when milestones are 

reached. Project leaders are responsible for packaging electronic files and data and for preparing 

materials for the curator. Project protocols should designate who will be responsible for product 

archiving, integration, backup, and distribution. The workflow diagram below illustrates the 

paths that product archival and storage follow (Figure 11). Museum staff will follow a much 

more detailed procedure for actual curation. 

For digital data, the project leader should prepare files as follows: 

 Comply with SIEN electronic file naming standards 

 Verify that all electronic files are organized on an NPS server according to SIEN file 

directory hierarchy standards. 

 Comply with SIEN data documentation standards (Chapter 8). 

 Include all related documents listed in the SIEN project deliverables guidance document 

(Appendix 6A). 

 Assure that hardcopy records are printed for all mission critical or permanent records. 

 Make a clear distinction between public and sensitive information. 

 For I&M Projects, ensure that the I&M Project Tracking Database (Chapter 12) has been 

updated and is complete. 

For digital data, the data manager or other designated staff is responsible for the following: 

 Archive project information on an SIEN server according to SIEN file directory hierarchy 

standards. 

 Assure that proper securities are assigned to electronic files. 

 Complete a project backup on CD or DVD that is placed in an approved location. 
83 

 Integrate deliverables, such as final reports, into appropriate catalogs, electronic libraries 

and NPS clearinghouses, as described in this plan or specific project protocols.

For non-digital records and objects, the project leader should package information as follows: 

 Complete a coversheet listing contact information, project abstract and purpose, 

sensitivity, use of materials, and all materials included in the package (Appendix 11D). 

 Clearly label materials with park acronym, date(s), accession number, project number. 

 Comply with paper and folder type requirements (e.g., acid free paper). 

 Print and document selected photos. 

 Label voucher specimens according to the Museum Handbook and complete loan paper 

work if necessary. Specimens not residing in NPS repositories should be stored in a 

selected facility that meets NPS museum collection standards. 

 Address specimen curation in project protocols and specify name and address of facility, 

Memorandum of Agreement (if applicable), list of items to be curated or loaned. 

 Create a CD or DVD of all related, electronic materials and label with park acronym, date 

created, date(s) of information, accession number, project number, list of contents 

For non-digital records and objects, museum staff are responsible for the following: 

 Catalog information into ANCS+ and properly store materials according to the Museum 

Handbook. 

11.3.2.2. Electronic Files: Performing regular backups of data and arranging for offsite storage 

of backup data sets are important safeguards against data loss. Recently, the NPS developed 

general standards and guidelines for backup, storage, and retrieval of electronic files (Appendix 

11E). This document describes high level procedures at the bureau level that provide minimum

standards for data backup, storage, and recovery. It was produced by the NPS Office of the Chief 

Information Officer to provide high-level procedures for the NPS General Support System. 

These efforts center on providing the NPS with the ability to quickly reinstate information 

systems and the data required to run those systems. 

A primary requirement of the NPS General Standards and Guidelines for Backup, Storage, and 

Retrieval of Electronic Files is that each NPS organizational unit creates a formal set of backup 

procedures for critical system-level and user-level information. The procedures should address 

documenting backups and enforcing backup policies. These requirements have been met through 

the development of Network General Support Services Backup Activity Plans for SEKI & DEPO 

(Appendix 11F) and YOSE (Appendix 11G). These plans outline specific and consistent backup 

routines for Network parks, including: frequency and types of backups; media rotation 

schedules; hardware, software and media specifications; offsite storage locations; testing 

procedures; staff responsibility; and data classification on individual servers. 

11.3.2.2.1. Individual Employee Responsibility: Backups of electronic files on personal 

computers are the responsibility of each staff member. SIEN policy discourages electronic file 

storage on personal computers and, to the extent possible, electronic files should be managed on 

a Network server. Most SIEN employees are used to managing and maintaining their electronic 
84 

files on a personal computer, rather than storing and managing on a Network server. The primary 

advantage for shifting from personal computer to local area network storage is that backup 

management and security are shifted to the IT staff who use well-defined protocols and optimize 

protection and backup of electronic files. The IT staff will provide an individual work directory 

on a Network server for those staff who request personal workspace. Other than redundant 

backup to a personal computer’s hard drive, the use of individual work directories eliminates 

electronic file storage on local hard drives. 

In some situations, employees work in geographic locations that are not networked and are 

unable to develop and manage electronic files on a Network server. In situations where an 

employee works with a computer that is off-network, each employee will be responsible for 

archiving and storing electronic files before they are transferred to a SIEN server. Off-network 

electronic files should be regularly transferred to a networked server for permanent archiving, 

storage, and final disposition. Appendix 11F and Appendix 11G provide specific guidance for 

SIEN staff who use personal computers off network including best practices for backing up and 

copying electronic files to SIEN servers.

Figure 11. General workflow for storage and archival of electronic (in blue) and hardcopy (in 

green) scientific research products.

Products Backup

Electronic files backedup onto media 

Integration

Like products integrated 

into product library 

* Reports with other 

reports 

* Seasonal data with 

master data. 

Long-term Archive 

* Associated hardcopy 

documents filed in 

Network central files 

* Associated objects and 

hardcopy documents 

archived and cataloged by 

park and/or regional staff 

Distribution 

Copy of reports distributed 

Are there to park and public libraries 

specimens? 

Stored 

at NPS?

NPS Curatorial 

Facility 

Specimen collections 

stored in respective 

park archival facility 

Non-NPS 

Curatorial Facility

* Facility approved by 

NPS curator 

* MOU in place 

* Loan paperwork 

complete 

Delivery Electronic Storage Hardcopy Storage 

Specimen Curation 

Products Delivered 

* Reports 

* Photos 

* Field Notes 

* etc. 

Project Archive

Electronic files stored in 

project archive 

electronic library 

Specimens Collected 

* Specimens collected in field 

following NPS Network and park 

procedures 

* Accession number assigned 

* Specimens correctly identified 

by expert 

* Specimens properly mounted 

according to Museum Handbook 

* ASCII (.csv) files created with 

specimen information 

* Document archival complete or 

concurrently underway 

Specimen 

Catalog 

Park curator 

catalogs 

specimens 

into ANCS+ 

No 

Yes 
85 

11.3.2.2.2:. IT Staff Responsibility: IT staff will adhere to their Network General Support 

Services Backup Activity Plans (described above). 

11.3.2.2.3.: Short-term Projects: Short-term projects usually last from one to three years and 

include individual research projects, inventories, or pilot work done in preparation for long-term 

research or monitoring. 

Upon project finalization, a set of ASCII comma-delimited text files will be created for each data 

table comprising the dataset. These files will be accompanied by a readme.txt file that explains 

the contents of each file, file relationships, and field definitions. The ASCII files are in addition 

to the native version of the dataset (typically in database format) and will help ensure the data are 

usable in a wide range of applications or platforms. 

The SIEN will employ a utility created by the Gulf Coast Network called Access_to_ ASCII.mdb,

which automates the creation of these ASCII files from Access databases. All ASCII files 

created from databases will undergo quality control to ensure the number of records and fields 

correspond to the source data set and that conversion has not created errors or data loss. A 

second reviewer (preferably the project leader) will evaluate the ASCII files and documentation 

to verify that tables, fields, and relations are fully explained and presented in a way that is useful 

to secondary users. All finalized files will be stored in the archive section of the project directory 

folders. 

In addition to creating ASCII files, SIEN will also update completed and archived data sets that 

may be in older versions of MS Access, with the goal of having no dataset more than two 

versions behind the current version used by the Network. Converted databases will require 

thorough quality control if they are actively being used for data entry or analysis and if no 

documentation of prior quality assurance/quality control measures is available. Forms, queries, 

reports, and data entry will be thoroughly tested, and corrected where any problems occur. All 

previous versions of the data set will be saved. 

11.3.2.2.4.: Long-term Projects: Long-term data are projects include vital sign monitoring and 

other multi-year research and monitoring projects performed by the parks, other agencies and 

cooperators and for which data acquisition and entry will continue indefinitely. 

Long-term monitoring databases will require occasional conversion to current database formats. 

All active or long-term databases will conform to the current NPS software version standards. 

Long-term projects also will have variable long-term data archiving requirements. Raw data 

should always be stored in perpetuity. Processed data sets should be archived complete before 

any modifications are made to project protocols. Depending on the project, it might also be 

necessary to preserve interim data sets (data “milestones”) over the long term. Archived data sets 

or subsets destined for long-term archival will be saved, whenever possible, in their native 

formats in addition to ASCII text files. Specific data archiving requirements for ongoing projects 

should be spelled out in the Standard Operating Procedures for each project. 
86 

11.3.2.2.5.: Spatial Data: Spatial data sets essential to the SIEN will be maintained in a format 

that remains fully-accessible by the current ArcGIS version. ArcGIS has maintained 

compatibility with previous data formats and, while shapefiles have retained functionality in 

ArcGIS, coverages may require conversion if they are no longer supported. At this time there is 

no practical way to save GIS data in a software or platform-independent format. 

Both uncorrected and corrected GPS data (e.g., .ssf and .cor files) will be archived in their native 

format in addition to the corresponding GIS files that are created. 

11.3.2.3.: Physical Objects and Records: The guidelines in this section apply to physical objects 

and records (e.g., draft and final reports, field notes, data collection forms, maps, photographs, 

audio and video recordings, natural and cultural history specimens) collected or produced by 

staff and cooperators. 

The long term management of physical objects and records will require the close cooperation of 

the individual collectors (whether NPS personnel, contractors, or permitted researchers) and 

local park museum personnel. Much of the responsibility will rest with the collector who remains 

the sole source for all curated documentation and the principal authority for essential collection 

information. 

Appendix 11H – Sierra Nevada Network Archive and Storage Locations identifies repositories 

for paper documents, and other archival objects. Each item will be stored in a suitable physical 

location appropriate to the object or document, which will depending on where it is in its 

lifecycle (Chapter 3). Each location cited in Appendix 11H is classified according to whether it 

can house temporary, permanent, or disposition suspended records or objects. A temporary 

record has value only for a specified period of time. A permanent record or object has enduring 

value and must be preserved forever. A disposition-suspended object or record should not be 

destroyed or transferred to the NARA. Depending on the classification, locations must meet 

certain physical and security conditions as described by the Records Management Handbook and 

Museum Handbook. 

11.3.2.3.1.: Documents: NPS Directors Order 19, Section 4.1.1 states that anything classified as 

a federal record must be printed and managed in paper form. Such records include certain emails, 

reports, publications, maps, or other documentary materials. It is important for Network staff to 

understand that the NPS does not currently have an approved system for archiving electronic 

records. At a practical level, this means that with only few exceptions (see above), permanent 

records must be printed and organized in paper files. 

A formal process is needed for ensuring that federal hardcopy records needing permanent 

preservation are handled properly. This is part of the disposition process. These records cannot 

simply stay in the office of creation or use for two reasons: 1) there are mandatory preservation 

requirements that should be handled by professionals; and 2) federal records of enduring value 

must, by law, be available to the public for research use. Given the existence of active local 

museum programs at SEKI and YOSE, the majority of collections derived from SIEN projects 

can be managed by their respective parks. DEPO collections will continue to be curated by 

SEKI. 
87 

The Technical Information Center (TIC) in Denver, Colorado, is the principal repository for 

planning documents for the NPS. The TIC provides temperature and humidity-controlled 

facilities, a professional archival staff, and meets all museum standards set by NPS. This 

repository may be used for technical reports for which off-site storage is considered worthwhile. 

NPS Director's Order 19, Appendix B – Records Disposition Schedule has 40 pages devoted to 

classifying and filing NPS federal records and disposition specifications for each NPS federal 

record category. Generally, permanent federal records should be transferred to the NARA when 

they are 30 years old. To the extent possible, the SIEN will comply with the most current NPS 

disposition schedule. 

11.3.2.3.2.: Natural History Specimens: The most explicit direction for the management of 

natural history collections is found in NPS Management Policies, Directive 4.2. It reads: 

"Specimens that are not authorized for consumptive analysis remain federal property and will be 

labeled and cataloged into the NPS cataloging system (ANCS+, or its successor) in accordance 

with applicable regulations (36 CFR 2.5)". 

36 CFR 2.5 further states that collected specimens “placed in displays or collections will bear 

official National Park Service museum labels and their catalog numbers will be registered in the 

National Park Service National Catalog” and that the specimens and any derived data be made 

available to the public. 

Natural history specimens collected under the auspices of the SIEN will be transferred to the 

Network park in which they were collected for either inclusion in the parks’ museum collections 

or for transfer to a repository approved by the park. In this latter case, the park will enter into a 

formal, documented relationship with the repository and the specimens will be considered to be 

“on loan” with clearly stated terms and responsibilities. All loans are subject to the approval of 

the park superintendent. 

Ideally, park curators will be involved at an early stage in the permitting or agreements process; 

early cooperation between curators and collectors will facilitate compliance with the current NPS 

Management Policies. Park curators can provide the collector with a pre-formatted Access 

database template, the use of which will greatly simplify eventual importation of catalog data 

into the Automated National Catalog System (ANCS+). At a minimum, parks will need such 

data in comma-delimited format (.cvs) for automated uploading into ANCS+. 

11.3.2.3.3.: Photographs: While the SIEN is prepared to accept both digital and analog 

photographs, the particular photographic methods to be used by any given project will be 

developed jointly by the project leader and Network staff or cooperators. Early involvement of 

curatorial staff will insure that the specific preservation considerations of particular photographic 

methods are understood and considered. 

For projects using analog methods, SIEN strongly encourages the use of Kodakchrome 35mm 

slide film which has the longest dark storage dye stability of any color film (Wilhelm and 

Brower 1993). 
88 

Slides should be labeled using indelible pigment, pH-neutral ink (e.g., Pigma Micron), or using 

laser-printed archival-quality slide labels. Labels must include the following information: 

 Title – who or what is in the image 

 Location – where the image was taken 

 Date – when the image was taken 

 Park Code 

 Access Constraints – who may view the image 

 Copyright information – restrictions on using the image 

 Contact Information – who to contact for further information 

Optional, but important information includes the geographic location with UTM NAD83 

coordinates (SIEN mapping standard) and datum. All slides should be placed in archival, 

polypropylene slide sleeves and stored in the dark, preferably in an archival box. 

Additionally, slides may be scanned and saved digitally in Tagged Image File Format (TIFF). If 

scans are used as the primary means of distributing or reproducing the images, the lifespan of the 

original slides will be greatly extended. Further recommendations regarding management of 

digital imagery can be found in Chapter 6 and Appendix 6D. 

If photographic prints are desired, staff and cooperators are strongly encouraged to use true 

black-and-white films (e.g., Ilford Delta, Kodak T-Max or Tri-X). Prints and negatives will be 

stored in individual polypropylene sleeves and within archival boxes. Each print will be labeled 

on the back, using archival-quality labels that are either laser-printed or hand-labeled in pencil 

with the same information elements required for slides. If a contractor is submitting photographs, 

corresponding TIFF files may also be submitted. 

Every image, regardless of format, will be entered into the SIEN photo database (guidance 

documents in development) where attributes such as electronic file name, keywords, project, 

photo description, photographer, date, and location are cataloged. All photo files and the 

associated photo database are housed on the archive portion of the SIEN server. 

11.3.2.3.4.: Role of Curators: Park curators are a source of expertise, advice, and guidance on 

archiving and curatorial issues and have a role in almost all projects undertaken by the Network. 

Project leaders will involve park curators when projects are in the planning stage, to ensure all 

aspects of specimen preservation or document archiving are considered and any associated 

expenses are included in project budgets. Section 4.3.16 of Director's Order 24 requires 

"...project budgets to include funding for the basic management of collections that are projectgenerated". The costs for curation include cataloging, labeling, specimen preparation, initial 

storage of objects and specimens, and the organization and storage of project documentation. 

11.4. References 

Code of Federal Regulations. 2001. Title 36, Volume 1, Chapter 1, Part 2, Section 2.5 (36 CFR 

2.5), U.S. Government Printing Office, Washington, D.C. 

Mortenson, D. 2005. Data management plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. U.S. National Park Service. Anchorage, Alaska. 234 pp. 
89 

National Park Service. 2000. Director’s Order 24: NPS Museum Collections Management, U.S. 

Department of the Interior, National Park Service, Washington, D.C. 

National Park Service. 2001a. Director’s Order 19: Records Management. U.S. Department of 

the Interior, National Park Service, Washington, D.C. 

National Park Service. 2001b. Director's Order 19, Appendix B: Records Disposition Schedule. 

Revised May, 2003. U.S. Department of the Interior, National Park Service, Washington, 

D.C. 

National Park Service. 2005a (revised). Museum Handbook, Part II, Museum Records. U.S. 

Department of the Interior, National Park Service, Washington, D.C. 

National Park Service. 2005b. Records Management Handbook. U.S. Department of Interior, 

National Park Service, Washington, D.C. 

National Park Service. 2006. Management Policies 2006. U.S. Department of the Interior, 

National Park Service, Washington, D.C. 

Olson, R. J., and R. A. McCord. 1998. Data Archival. Pages 53-57 in Michener, W. K., Porter, J. 

H., and S. G. Stafford. Data and information management in the ecological sciences: a 

resource guide. LTER Network Office, University of New Mexico, Albuquerque, NM. 

(available http://intranet.lternet.edu/archives/documents/data-informationmanagement 

/DIMES/html/ frame.htm. 

Vogt-O’Connor, D. 2000. Planning digital projects for preservation and access. National Park 

Service Conserve O Gram 14:4. NPS Museum Management Program, Washington, DC. 

(available http://www.cr.nps.gov/museum/publications/conserveogram/19-21.pdf) 

Wilhelm, H., and C. Brower. 1993. The permanence and care of color photographs: traditional 

and digital color prints, color negatives, slides and motion pictures. Preservation Publishing 

Company, Grinnell, Iowa. 

11.5. Appendixes 

6A) Sierra Nevada Network I&M Program Checklist of Project Specifications 

6D) Sierra Nevada Digital Photo Management Strategy 

11A) Electronic File Naming Guidelines and Standards 

11B) Sierra Nevada Network Inventory and Monitoring Program Guidance and Rules for File 

Directory Structure 

11C) Sierra Nevada Network Project Template Directory Structure 

11D) Coversheet for Non-Digital Data listing Key Information 

11E) National Park Service Standards and Guidelines for Backup, Storage, and Recovery 

11F) Sequoia and Kings Canyon and Devils Postpile Network General Support Services Backup 

Activity Plan 
90 

11G) Yosemite Network General Support Services Backup Activity Plan 

11H) Sierra Nevada Network Archive and Storage Locations 

11.6. Credits 

This chapter was written by Pat Lineback and Ward Eldredge (SEKI Archivist), with 

contributions from Dan Sohn (YOSE IT), Paul Gallez (YOSE Database Specialist), Bob Basham 

(SEKI IT), Mortenson (2005), Michael Grimes (NPS Records Officer), and Rosamonde Cook. 
91 

Chapter 12. Project Tracking and Documentation 

12.1. Objectives 

12.2. General Standards and Guidelines 

12.2.1. Project Organization 

All projects electronic files should be well organized in a project directory (Appendix 2H). The 

SIEN digital directory structure is organized at the project level, such that most or all digital files 

associated with a project are filed under a common root directory. Project file names will adhere 

to the naming conventions established for the Network (Chapter 11). Physical objects acquired as 

part of a project will be stored according to specification in Chapter 11 and its appendices. 

12.2.2. Project Tracking 

The SIEN I&M Project Tracking Database will be developed and will serve as the primary 

organizational tool for cataloging and searching information on I&M projects. Specifically, the 

purpose of this database is to: 

 Maintain a list of projects – project managers and other users will be able to locate 

rapidly most project-related information (e.g., objectives, contact information, status, 

funding sources, and more) and to easily summarize this information for administrative 

reports. 

 Track product deliverables – a comprehensive list of project deliverables will be 

maintained with information on permitting contracting, content and formatting 

specifications, delivery schedules, disposition schedules, expected due dates, and storage 

location. This information is generally specified at project initiation and updated 

throughout the course of a project. 

 Manage project codes – These are intelligent alphanumeric codes used to tie together 

digital information in various, minimally connected NPS project tracking systems (e.g., 

RPRS, PMIS), along with analog materials that cannot otherwise be linked to an 

integrated information system. These codes are also used to link to databases and GIS 

themes, especially where information from multiple sources is stored together. See 

Appendix 12A for a list of Service-wide Project Tracking Systems. 

The project tracking database will be hosted on a SIEN data server. Although primarily 

maintained by the SIEN Data Manager, the database will be available to project leaders and the 

Network Coordinator who will be able to update information as needed. The Data Manager will 

create custom query views and reports to help administrators manage projects, and to facilitate 

reporting on project status, accomplishments and delivered products. 

Develop and implement a comprehensive and cohesive procedure for tracking 

projects, including project status, data, and the products of analysis to support 

program coordination and annual reporting, and to improve accountability for 

Sierra Nevada Network natural resource inventory and monitoring efforts and 

products. 
92 

12.2.3. Project Documentation 

Project-specific protocol narratives and SOPs are the principle means by which I&M projects 

will be documented. Standards for these documents have been developed by the national I&M 

Program and are discussed for the SIEN in Mutch et al. (2006). These documents must always 

accompany the distribution of monitoring data. 

12.2.3.1. Protocol and SOP Version Changes: Over time there will be instances when protocol 

narratives and SOPs need to be updated, and these updates may occur independently of each 

other. That is, a change in one SOP will not necessarily invoke changes in other SOPs; a 

narrative update may not require SOP modifications. The SIEN Project Tracking Database will 

track the project narrative and SOPs by version number. This information will be updated 

whenever any narrative or SOP document is modified. The protocol narrative and SOPs will not 

be distributed without a log of changes from the Project Tracking Database. 

12.2.3.2. Additional Project Metadata: Long-term monitoring projects may require 

documentation beyond the scope of established FGDC standards and the Sierra Nevada Network 

Database Documentation Template (Appendix 5A). Documentation should be provided for 

algorithms, output files, and analytical products which may reside in different systems and 

formats, and could potentially be overlooked when distributing or applying project data. Data use 

and data request histories, and information on secondary research or publications resulting from 

long-term monitoring projects, should also be maintained. The Project Tracking database 

potentially could be used to organize this information as well. Specific methods for documenting 

this information will be tested for ease of use and are likely to evolve over time as needs become 

apparent and new solutions are developed. 

12.3. References 

Mortenson, D. 2005. Data management plan for the Inventory and Monitoring Program, 

Southwest Alaska Network. U.S. National Park Service. Anchorage, Alaska. 234 pp. 

Mutch, L., Rose, M., Heard, A., Cook, R. R., and G. Entsminger. 2006. Sierra Nevada Network 

Vital Signs Monitoring Plan, Draft Phase III Report, National Park Service Inventory & 

Monitoring Program. U.S. National Park Service, Three Rivers, California. 

12.4. Appendixes 

2H) SIEN Directory Structure 

5A) Sierra Nevada Network Database Documentation Template 

12A) NPS Service-wide Project Tracking Systems 

12.5. Credits 

This chapter was written by Rosamonde Cook, with information provided by Mortenson (2005). 
93 

Chapter 13. Implementation 

The data management plans for each of the 32 I&M Networks are the first comprehensive 

documents of their kind in the NPS and contain practices that may be new to staff and 

cooperators. However, almost every requirement stems from federal law, Executive Orders, 

Director’s Orders, or national I&M Program guidance. The DMP helps put these requirements 

into context, and provides operational guidance for achieving them. 

A DMP ad hoc steering team of 15 members was established during the summer of 2006 to help 

guide plan development. It comprises staff from Sequoia and Kings Canyon 

National Parks, Yosemite National Park, and U.S. Geological Survey field stations in Yosemite 

and Sequoia. This team will assist in the development of goals, roles and responsibilities,

sideboards, and best data management practices, and aid development of specific DMP policies. 

Team members are expected to be the primary advocates for implementation of this plan and, in 

some cases, will help transform existing business practices for certain programs. 

13.1. Education and Training 

Implementation will require education and training in order to familiarize park staff and 

cooperators with the tools, procedures, and guidelines outlined in the plan. Formal (training 

sessions) and informal (one on one communication and assistance) methods will be used. These 

efforts will begin in 2007 and be led, at least initially, by I&M data management staff and the 

SEKI GIS coordinator, with participation by interested parties at all parks actively encouraged. 

13.2. Milestone Goals 

Goals for the first 3 years will include: 

 Acceptance and understanding by all staff of targeted programs and their cooperators of 

the fundamentals of data and information management, including 

o File management 

o Documentation 

o Quality assurance and quality control 

o Electronic storage 

o Archive storage 

 Improvement of data management practices by implementing 

o accepted database design standards 

o thorough testing of databases, data collection methods, and their integration prior 

to field work 

o quality assurance and control procedures at every stage of project development 

 Common SOPs and guidance documents for multiple protocols 

 Inclusion of detailed specifications for data management consistent with the DMP in all 

protocols developed for the Vital Signs Monitoring Program 

 Development of procedures and outlets for communication within and among Network 

parks and with the public as described in this plan 
94 

Beyond the first three years, goals should include the development and assessment of: 

 Methods for improving file management (e.g., a content management system), database 

administration and security (e.g., migration to SQL-Server), integration into the network 

of off-site users, and other needs identified in the DMP 

 Procedures to facilitate the summarization and reporting of monitoring data to staff, 

cooperators, and the public in formats appropriate to each audience 

 Framework and gateway for integration of monitoring data with other initiatives both 

within the NPS and with outside agencies

 Methods for improving information management (e.g., basic content management 

services including library services, search tools, and decentralized web administration), 

database administration and security (e.g., migration to SQL-Server), integration into the 

network of off-site users, and other needs identified in the DMP 

Full implementation of this plan for SIEN budgeted projects will be achieved by 2010. 

Implementation at the broader park level is expected to be an ongoing process and will most 

likely occur in stages. Realistically, it will not be possible for all programs to fully adopt the 

practices and procedures recommended without an increase in current levels of funding and staff 

support. Currently, none of the Network park resources divisions have a data manager on staff, 

although Yosemite may add a position in the future. Programs wishing to participate should 

identify those areas that can be implemented given existing resources. For example, it should be 

relatively easy for most programs to begin implementing standards and guidelines for organizing 

and managing electronic files, including digital photos. Pragmatism should dictate prioritization 

and implementation. Critically important to successful implementation at the broader park level 

will be the active support of park resource management teams. 

13.3. Revisions 

The next plan revision should be completed within three years of plan approval, or by October 1, 

2010, and then every five years afterward. Plan appendices, including SOPs, detailed guidelines, 

reference manuals, policy statements, etc., will likely require more frequent updates to account 

e.g., for changes in technology or availability of better information. These updates should be 

completed as needed. 

13.4. Credits 

This chapter was written by Rosamonde Cook and Pat Lineback. 
95 

Glossary 

ArcIMS, ArcSDE, (Arc Internet Map Server and Arc Spatial Database Engine) are ESRI GIS 

software applications that provide tools for managing large, complex data sets within a relational 

database management system – ArcSDE, and serving the data over the Internet - ArcIMS. 

Best practices - A technique, methodology, or standard operating procedure that, through 

experience and research, has been proven to reliably lead to a desired result. 

Biodiversity Data Store is a digital online repository of documents, GIS and other data sets that 

contribute to the knowledge of biodiversity in National Park units, including presence/absence, 

distribution and abundance of animal and plant species. Along with NatureBib and NPSpecies, 

comprises the NPS Biodiversity Information System. 

Certified data and metadata – Completed data and documentation for short-term projects, or 

one season of completed data for long-term monitoring projects. Certification is a confirmation 

by the project leader that data have passed all quality assurance requirements and are complete 

and ready for distribution. Metadata records include the detailed information about project data 

needed for proper use and interpretation. 

Conceptual data model is a detailed model that shows the overall structure of organizational 

data, independent of any particular database management system or other implementation 

considerations. Conceptual data models are typically produced as the first step in system design, 

often presented as entity relationship diagrams, and frequently a precursor to the logical data 

model. 

Data refers to observational information gathered directly (in the field) from a natural resource 

via specific protocols and organized for analysis, summary or reporting. Data may either in 

analog or digital form (generally stored either on paper or a variety of computer-compatible 

media), though the latter is encouraged where ever feasible. Data may exist in several states 

(conditions) including ‘raw’, ‘validated’ and ‘analyzed’. ‘Analyzed’ data includes ‘reported’ or 

‘summarized’ data and may represent ‘information’ as a final form of the data from which 

decisions or conclusions may be made. Ultimately, data are intended to contribute to the 

knowledge and decisions regarding the conditions, processes, and changes within the ecosystem. 

Data set can best be considered a convenient grouping of data, or individual observations, such 

that the summary of the information will be meaningful to prospective users. 

Edit log – A means of tracking changes to certified data. 

Document – Recorded information regardless of physical form or characteristics. Often used 

interchangeably with record (From DO19 glossary). 

Ecological indicators are a subset of the physical, chemical, and biological elements and 

processes of natural systems selected to represent the overall health or condition of the system. 
96 

Glossary (continued) 

Entity relationship diagrams (ERDs) are high-level data models that are useful in developing 

conceptual designs for databases. Creation of an ER diagram, which is one of the first steps in 

designing a database, helps the designer(s) to understand and to specify the desired components 

of the database and the relationships among those components. An ER model is a diagram 

containing entities or "items", relationships among them, and attributes of the entities and the 

relationships. 

Goal - The general ends toward which this data management plan are directed. 

Inventories are “an extensive point-in-time effort to determine location or condition of a 

resource, including the presence, class, distribution, and status of plants, animals, and abiotic 

components such as water, soils, landforms, and climate. Inventories contribute to a statement of 

park resources, which is best described in relation to a standard condition such as the natural or 

unimpaired state. Inventories may involve both the compilation of existing information and the 

acquisition of new information. They may be relative either to a particular point in space 

(synoptic) or time (temporal).” (Source: http://www .nature.nps.gov/im/monitor/index.htm ) 

Legacy data existing data that has been acquired by a park unit and still has importance, but 

which is at risk of becoming obsolete because of software limitations or available metadata. 

Local archives and digital library – Local storage of copies of data, metadata and other 

products generated by projects. Archives are for hard-copy items and off-line storage media, 

whereas the digital library is maintained live on a server. 

Logical data model (LDM) is an abstract representation of a set of data entities and their 

relationships, usually including their key attributes. It may omit non-key attributes or use 

abstractions of actual types, and may include physical tables that are represented by relationships 

or as aggregates within a larger entity, depending on the notation used and the level of 

abstraction. The logical data model is intended to facilitate analysis of the function of the data 

design, and is not intended to be a full representation of the physical database. It is typically 

produced early in system design, and it is frequently a precursor to the physical data model that 

documents the actual implementation of the database. 

Master database – Central repository for project data, used for viewing, summarizing, and 

analysis. Contains only data that have passed all quality assurance/quality control. 

Metadata is information about data. A metadata document contains specific and detailed 

information about a data set, including who, what where, when, why and how the data were 

collected, analyzed, or manipulated. 

NatureBib is the National Park Service bibliographic database. NatureBib is designed to work 

with NPS Focus to help staff identify, locate, and obtain NPS natural resources documents. 
97 

Glossary (continued) 

National databases and repositories – Applications and repositories maintained at the national 

level, primarily for the purpose of integration among NPS units and for sharing information with 

cooperators and the public. 

Natural Resource Database Template (NRDT) is a core set of database tables that serves as a 

foundation for building relational databases for the NPS Inventory and Monitoring Program. 

Nonspatial data are data that do not include reference to geographic location and shape of, and 

relationships among, geographic features. 

NPS Data Store an online graphical search interface that links dataset metadata to a searchable 

data server on which datasets are organized by NPS units, offices and programs.

NPS Focus is the National Park Service online digital library designed to provide a venue for 

researching and downloading natural resources documents and other digital imagery. 

NPS Data Clearinghouse – the central repository for NPS GIS data available to the public. 

Implemented through the NPS Focus gateway. 

NPS Focus (Digital Library and Research Station) – a decentralized digital imagery and data 

management system, implemented through a central Internet portal sponsored by the NPS Office 

of the Chief Information Officer. Includes to the NPS Data Clearinghouse. 

NPSpecies is the master species biodiversity database for the NPS. The database lists the species 

that occur in or near each park, and the physical or written evidence for the occurrence of the 

species (e.g., references, vouchers, and observations). NPSpecies is implemented online through 

secure and public servers. 

NPSTORET is the NPS version of the U.S. EPA STORET database, used for transferring water 

quality data collected by NPS programs to STORET. 

Object – Physical items such as natural history specimens, cultural artifacts, photographs, audio, 

and video tapes. 

Objective - Specific statements describing the results to be achieved. 

Ortho-rectification is the process of correcting an image for distortions due to camera lens, 

sensor non-verticality, and terrain, a process collectively included in photogrammetry. 

Orthophoto is a digital image in which the pixels have been corrected to an orthogonal 

projection, produced by removing errors due to tilt and relief displacement. 
98 

Glossary (continued) 

Physical data model is a representation of a data design which takes into account the facilities 

and constraints of a given database management system. In the lifecycle of a project it is 

typically derived from a logical data model, though it may be reverse-engineered from a given 

database implementation. 

Protocol (or Monitoring Protocol) refers to the formal documents and sampling processes that 

describe how a vital sign will be monitored; composed of a narrative section, standard operating 

procedures and supplementary information (databases, reports, tools, hardcopy materials). 

Raw Data are environmental measurements that have been recorded but not subjected to any 

quality assurance or control beyond those applied during field work. Raw data are typically 

recorded on field sheets or in handheld computers, but may also include the products of remote 

sensing (aerial photographs and spectrophotometry). 

Relational Database Management System (RDMS/RDBMS) is a type of database management 

system (DBMS) that stores data in the form of related tables. 

Sensitive data - Any information, which through loss, unauthorized access, or modification 

could adversely affect the national interest, the conduct of federal programs, or the privacy of 

individuals. 

Spatial data – Information about the location and shape of, and relationships among, geographic 

features, usually stored as coordinates and topology. Can be stored in tabular or GIS file format. 

Standard Operating Procedures (SOPs) are detailed step-by-step instructions that outline a 

formal set of procedures for performing specific tasks. 

STORET is a database application maintained by the U.S. Environmental Protection Agency 

that contains raw biological, chemical and physical data on surface and ground water quality 

collected by federal, state and local agencies, Indian Tribes, volunteer groups, academics, and 

others. 

Structured Query Language (SQL) is a language used to interrogate and process data in a 

relational database. Originally developed for mainframes, all database systems designed for 

client/server environments support SQL. SQL commands can be used to interactively work with 

a database or can be embedded within a programming language to interface to a database. 

Programming extensions to SQL have turned it into a full-blown database programming 

language, and all major database management systems (RDBMS s) support the language. There 

is an ANSI standardized SQL, but most database management systems (RDBMS s) have some 

proprietary enhancement, which if used, makes SQL non-standard. 

Tabular Data – Data organized into logical tables. Refers to the format of data sets rather than 

content. Tables usually contain information arranged in rows and columns. 
99 

Glossary (continued) 

Validated data are data that have been verified according to the standard operating procedure 

under which the data were gathered (typically the protocol for a given project) and are deemed 

ready for reporting and/or analysis. 

Vital Signs are ecological elements or processes chosen to represent the overall health or 

condition of park ecosystems, known or hypothesized effects of environmental stressors, or 

elements of value to humans, and are the subject of long-term monitoring by the NPS I&M 

Program. 

Working database – A project-specific database for entering and processing data for the current 

season (or other logical period of time). May also constitute the master database for very shortterm projects 

The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and 

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities. 

NPS D-45, November 2008 
National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

1201 Oakridge Drive, Suite 150 

Fort Collins, CO 80525 

www.nature.nps.gov 

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