National Park Service

U.S. Department of the Interior

Natural Resource Program Center

Data Management Plan for the North Coast and

Cascades Network Inventory and Monitoring 

Program (2005)

Natural Resource Report NPS/NCCN/NRR—2009/078
ON THE COVER

Royal Basin, Olympic National Park.

Photograph by: Courtesy of NPS files
Data Management Plan for the North Coast 

and Cascades Network Inventory and 

Monitoring Program (2005)

Natural Resource Report NPS/NCCN/NRR—2009/078

John R. Boetsch

National Park Service

Olympic National Park

600 East Park Avenue

Port Angeles, WA 98362-6757

Bret Christoe

National Park Service

Mount Rainier National Park

55210 238th Ave. South

Ashford, WA 98304-9751

Ronald E. Holmes

National Park Service

North Cascades National Park Service Complex

810 State Route 20

Sedro Woolley, WA 98284-1239

February 2009

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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North Coast and Cascades Network Inventory and Monitoring website 

(http://science.nature.nps.gov/im/units/NCCN) on the Internet, or by sending a request to the 

address on the back cover.

Please cite this publication as:

Boetsch, J. R., B. Christoe, and R. E. Holmes. 2009. Data management plan for the North Coast 

and Cascades Network Inventory and Monitoring Program (2005). Natural Resource Report 

NPS/NCCN/NRR—2009/078. National Park Service, Fort Collins, Colorado.

The content of this document has not changed since the original version (2005); only styles and 

formatting have been updated to meet the Natural Resource Publication Series standards (2009).

NPS D-48, February 2009
iii

Contents

Page

Contents.........................................................................................................................................iii

Figures............................................................................................................................................ ix

Executive Summary....................................................................................................................... xi

Acknowledgements...................................................................................................................... xiii

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

1.1 – Inventory and Monitoring Program Overview .......................................................... 1

1.2 – Data Management Goals and Objectives................................................................... 2

1.3 – Data and Data Management – Providing Context ..................................................... 3

1.4 – Sources of Natural Resource Data ............................................................................. 4

1.5 – Makeup of the North Coast and Cascades Network .................................................. 5

1.6 – Scope and Organization of the Data Management Plan ............................................ 8

1.7 – Guidance Documentation Associated with this Plan................................................. 9

1.8 – Review and Revisions.............................................................................................. 10

Credits............................................................................................................................... 10

Chapter 2 – Data Stewardship Roles and Responsibilities........................................................... 11

2.1 – Roles and Responsibilities....................................................................................... 11

2.2 – Data Management Coordination.............................................................................. 13

2.3 – Cooperation with Other Networks........................................................................... 14

2.4 – Project Stewardship ................................................................................................. 14

2.4.1 – Data Stewardship: Sharing Responsibilities............................................. 14

2.4.2 – Documentation is Key............................................................................... 14

2.4.3 – The Hub of Data Stewardship................................................................... 15

2.4.4 – Project Stewards........................................................................................ 15
iv

2.4.5 – Data Managers.......................................................................................... 16

2.4.6 – GIS Specialists.......................................................................................... 16

Credits............................................................................................................................... 17

Chapter 3 – Data Management Resources: Infrastructure and Systems Architecture .................. 19

3.1 – Computer Resources Infrastructure ......................................................................... 19

3.1.1 – Maintaining Digital Files.......................................................................... 20

3.2 – National Information Management Systems ........................................................... 21

3.3 – Network Systems Architecture ................................................................................ 24

3.3.1 – Project Database Standards....................................................................... 25

3.3.2 – Client-Server Database Architecture ........................................................ 27

3.3.3 – Project Tracking Application.................................................................... 28

Credits............................................................................................................................... 29

Chapter 4 – Data Management Process and Work Flow .............................................................. 31

4.1 – Project Work Flow................................................................................................... 31

4.1.1 – Planning and Approval ............................................................................. 31

4.1.2 – Design and Testing ................................................................................... 32

4.1.3 – Implementation ......................................................................................... 32

4.1.4 – Product Integration.................................................................................... 33

4.1.5 – Evaluation and Closure ............................................................................. 33

4.2 – Data Life Cycle ........................................................................................................ 34

4.3 – Integrating and Sharing Data Products.................................................................... 38

4.3.1 – Data Distribution....................................................................................... 39

4.3.2 – Integration with National Databases......................................................... 40

Credits............................................................................................................................... 40

Chapter 5 – Data Acquisition and Processing............................................................................... 41
v

5.1 – Programmatic Data .................................................................................................. 41

5.1.1 – Data Discovery and Data Mining ............................................................. 42

5.1.2 – Field Studies.............................................................................................. 43

5.1.3 – Data entry.................................................................................................. 45

5.1.4 – NPSpecies and Biodiversity Data ............................................................. 45

5.1.5 – Changes to Data Collection Procedures/Protocols ................................... 46

5.2 – Other NPS Data........................................................................................................ 46

5.2.1 – Park Data................................................................................................... 46

5.2.2 – Regional and National Programs.............................................................. 47

5.3 – External Data ........................................................................................................... 48

Credits............................................................................................................................... 50

Chapter 6 – Quality Assurance ..................................................................................................... 51

6.1 – NPS Mandate for Quality......................................................................................... 51

6.2 – Quality Assurance and Quality Control Mechanisms.............................................. 52

6.3 – Roles and Responsibilities....................................................................................... 53

6.4 – Goals and Objectives ............................................................................................... 53

6.5 – Data Collection ........................................................................................................ 54

6.5.1 – Methods for Reducing Collection Errors.................................................. 55

6.6 – Data Entry ................................................................................................................ 56

6.6.1 – Methods for Reducing Data Entry Errors................................................. 57

6.7 – Verification and Validation Procedures................................................................... 58

6.7.1 – Methods for Data Verification .................................................................. 59

6.7.2 – Methods for Data Validation .................................................................... 60

6.8 – Version Control........................................................................................................ 61

6.8.1 – Version Control Options........................................................................... 62
vi

6.9 – Data Quality Review and Communication .............................................................. 63

6.9.1 – Data Quality Review Methods.................................................................. 63

6.9.2 – Value of Feedback from QA/QC Procedures ........................................... 63

6.9.3 – Monitoring Conformance to Plans and Standards.................................... 64

6.9.4 – Communicating Data Quality ................................................................... 64

Credits............................................................................................................................... 65

Chapter 7 – Data Set Documentation............................................................................................ 67

7.1 – Purpose of Metadata ................................................................................................ 67

7.2 – NPS Integrated Metadata System Plan and Tools ................................................... 67

7.3 – Metadata Process and Workflow ............................................................................. 69

7.3.1 – Identify Relevant Data Sets and Compile Pertinent Metadata.................. 69

7.3.2 – Create Dataset Catalog Record ................................................................. 70

7.3.3 – Select Metadata Tool and Complete Record ............................................ 70

7.3.4 – Make Information Available..................................................................... 70

Credits............................................................................................................................... 71

Chapter 8 – Data Analysis and Reporting..................................................................................... 73

8.1 – Types of Reports...................................................................................................... 73

8.1.1 – Annual Reports ......................................................................................... 73

8.1.2 Analysis and Synthesis Reports................................................................... 74

8.1.3 – Scientific Journal Articles and Book Chapters......................................... 74

8.1.4 – Interpretation and Outreach ...................................................................... 75

8.2 – Identifying Project Objectives and Data Products................................................... 75

8.3 – Automated Reporting and Data Summaries ............................................................ 76

8.3.1 – Annual Report as Template ...................................................................... 76

8.3.2 – Project Leader Interviews......................................................................... 76
vii

8.3.3 – Automated Report Generation .................................................................. 76

8.4 – Exporting Data for Analysis and Exploration.......................................................... 77

8.5 – Making Data Available for Third-Party Analysis.................................................... 77

Credits............................................................................................................................... 77

Chapter 9 – Data Distribution and Dissemination ........................................................................ 79

9.1 – Data Ownership ....................................................................................................... 79

9.1.1 – National Park Service Policy on Data Ownership .................................... 79

9.1.2 – Establishing Data Ownership Guidelines ................................................. 80

9.2 – Data Distribution...................................................................................................... 81

9.2.1 – Data Distribution Mechanisms ................................................................. 81

9.2.2 – Data Classification: Protected vs. Public .................................................. 83

9.2.3 – Access Restrictions on Sensitive Data...................................................... 85

9.2.4 – Public Access to Network Inventory and Monitoring Data...................... 86

9.2.5 – Data Availability....................................................................................... 86

9.2.6 – Data Acquisition Policy ............................................................................ 87

9.3 – Data Feedback Mechanisms .................................................................................... 87

9.3.1 – Data Error Feedback Procedures .............................................................. 87

Credits............................................................................................................................... 88

Chapter 10 – Data Maintenance, Storage and Archiving ............................................................. 89

10.1 – Digital Data Maintenance ...................................................................................... 89

10.1.1 – Short-term Data Sets............................................................................... 89

10.1.2 – Long-term Monitoring Data Sets............................................................ 90

10.1.3 – Quality Control of Converted Data......................................................... 90

10.1.4 – Version Control....................................................................................... 90

10.1.5 – Spatial Data............................................................................................. 91
viii

10.2 – Storage and Archiving Procedures – Digital Data................................................. 91

10.2.1 – Directory Structure for Digital Archives ................................................ 91

10.2.2 – Directory Structure for Individual Projects............................................. 92

10.2.3 – Backup Procedures for Digital Data ....................................................... 92

10.2.4 – Testing Backup Files............................................................................... 93

10.2.5 – Data and Network Security..................................................................... 93

10.3 – Storage and Archiving Procedures – Documents and Objects .............................. 94

10.3.1 – Documents .............................................................................................. 94

10.3.2 – Specimens............................................................................................... 95

10.3.3 – Photographs............................................................................................. 95

Credits............................................................................................................................... 96

Glossary ........................................................................................................................................ 97

References................................................................................................................................... 101
ix

Figures

Page

Figure 1.1. Parks with significant natural resources in the North Coast and Cascades Network . 6

Figure 1.2. Hierarchy of NCCN data management documents .................................................... 9

Figure 2.1. Core project data stewardship duties of project leaders and data managers ............ 13

Figure 3.1. Schematic representing the logical layout and connectivity of computer resources 19

Figure 3.2. Model of the national-level application architecture ................................................ 22

Figure 3.3. Common lookup tables and satellite databases ........................................................ 26

Figure 3.4. Different levels of data standards and their corresponding degree of implementation 

variability .................................................................................................................................... 27

Figure 3.5. Client-server database architecture for NCCN ......................................................... 28

Figure 4.1. Conceptual model of project work flow ................................................................... 34

Figure 4.2. Diagram of the typical project data life cycle .......................................................... 37

Figure 4.3. Storing and disseminating project information ........................................................ 38

Figure 4.4. Steps involved in product distribution ...................................................................... 39

Figure 4.5. Data flow diagram for water quality data ................................................................. 40

Figure 10.1. Schematic of proposed data components of the NCCN servers ............................. 91

xi

Executive Summary

The central mission of the National Park Service (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 work done 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 instead is the means of 

maximizing the quality and utility of our natural resource information. A robust system for data 

management is particularly important for long-term programs where the lifespan of a data set 

will span the careers of several scientists. Seen in this way, it becomes obvious that data 

management is vital to the success of any long-term monitoring initiative.

The overarching goal of our data management system is to provide timely and usable scientific 

information about the status and trends of park resources to park managers. The success of our 

program hinges upon our ability to produce, manage and deliver this information to its intended 

audience. Our strategy for achieving this goal can be summarized as follows: Ensure the quality, 

interpretability, security, longevity and availability of our natural resource data. In implementing 

a data management system we will strive for the following:

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 natural resource culture which views data not as a commodity but as the lifeblood of 

our work

The North Coast and Cascades Network (NCCN) Data Management Plan outlines how we intend 

to implement and maintain a system that will serve the data and information management needs 

of our Inventory and Monitoring Program. This plan reflects our commitment to establishing and 

maintaining a robust system for data management to ensure the availability and usability of highquality natural resource information.

The NCCN Data Management Plan describes how our Network will:
xii

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 data
xiii

Acknowledgements

Much of the material in this plan was developed during a lively and productive period of 

collaboration among data managers from across the National Park Service. This level of 

collaboration makes it difficult to give proper credit to those who made significant contributions, 

and whose material appears in this document in one form or another. Nevertheless, we owe a 

debt of gratitude for the hard work and insight of those who participated in this process: Sara 

Stevens, Margaret Beer, Deborah Angell, Dorothy Mortenson, Doug Wilder, Gareth Rowell, 

Michael Williams, Geoffrey Sanders, Rob Daley, Teresa Leibfreid, Bill Moore, Patrick Flaherty, 

Fred Dieffenbach, and Velma Potash. We would also like to acknowledge Gary Entsminger for 

providing editorial revisions of some of the chapters drafted by this work group, and Joe Gregson 

and Steve Fancy for their comments and review of early drafts of the collaborative material. 

Additional credits are provided at the end of each chapter. 

Finally, thanks to Roger Hoffman, Darin Swinney, Katherine Beirne, Anne Braaten, Lise Grace, 

Natalya Antonova and Ruth Jenkins for their many contributions to this plan.

1

Chapter 1 – Introduction

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 work done 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 instead is the means of 

maximizing the quality and utility of our natural resource information. A robust system for data 

management is particularly important for long-term programs where the lifespan of a data set 

will span the careers of several scientists. Seen in this way, it becomes obvious that data 

management is vital to the success of any long-term monitoring initiative.

This chapter summarizes the data management goals and objectives for our NCCN data 

management system. This Data Management Plan (DMP) documents the overarching strategy 

for ensuring that program data are documented, secure, and remain accessible and useful for 

future generations. The DMP, in turn, refers to other guidance documents and standard operating 

procedures which convey the specific standards and steps for achieving our data management 

goals.

1.1 – Inventory and Monitoring Program Overview

The Inventory and Monitoring (I&M) Program represents a long-term commitment by the 

National Park Service (NPS) to assess and document the status and trends of park ecological 

resources. In 1998, the National Parks Omnibus Management Act established the framework for 

the I&M Program to fully integrate natural resource monitoring and other scientific activities 

into the management processes of the National Park System. 

The Act charges the Secretary of the Interior to ―continually improve the ability of the National 

Park Service to provide state-of-the-art management, protection, and interpretation of and 

research on the resources of the National Park System‖, and to ―... assure the full and proper 

utilization of the results of scientific studies for park management decisions.‖ Section 5934 of 

the Act requires the Secretary of the Interior to develop a program of ―inventory and monitoring 

of National Park System resources to establish baseline 

information and to provide information on the longterm trends in the condition of National Park System 

resources.‖

To carry out this mission, the National Park Service 

initiated a service-wide, natural resource Inventory and 

Monitoring Program encompassing 270 parks with 

significant natural resources. Ecologically similar parks among the 270 chosen were grouped 

into 32 networks. Each I&M Network has been tasked with documenting existing park 

National Park Service Mission

The National Park Service preserves 

unimpaired the natural and cultural 

resources and values of the national 

park system for the enjoyment, 

education and inspiration of this and 

future generations...
2

vertebrates and vascular plants, developing a management based, ecological monitoring program 

with a written plan and protocols, and a Data Management Plan that describes our approach to 

network information management.

1.2 – Data Management Goals and Objectives

The overarching goal of our data management system is to provide timely and usable scientific 

information about the status and trends of park resources to park managers. The success of our 

program hinges upon our ability to produce, manage and deliver this information to its intended 

audience. Specifically, our goal is to develop and maintain a system for producing value-added 

natural resource data. This system should ensure that natural resource information are available, 

usable, sharable, integrated and interpreted:

Available – We have the means of knowing that the data exist and where the data can be 

found.

Usable – Data are stored in a stable, reliable, and interpretable data retrieval system.

Shareable – Data products are complete, certified for quality assurance, screened for 

sensitive information, formatted for use and documented for interpretation by others.

Integrated – Data products are consistent with data exchange standards, interoperable 

with related natural resource data sets, and are collected and stored in a way that 

optimizes the tradeoff between meeting local needs and achieving compatibility with 

other agencies and partners.

Interpreted – The data have been reviewed, summarized and transformed into useful 

information.

Although our primary responsibility is for ecological data and related information derived from 

Inventory and Monitoring Program activities, it is anticipated that the scope of our data 

management efforts will expand to include all significant natural resource data specific to the 

North Coast and Cascades Network and the parks of which it is composed.

In implementing a data management system, we will strive for the following:

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 natural resource culture which views data not as a commodity but as the lifeblood of 

our work
3

1.3 – Data and Data Management – Providing Context

Collection of natural resource data is our first step toward understanding the ecosystems within 

our national parks. These ecosystems are evolving, as is our knowledge of them and how they 

function. We use these ―raw‖ data to analyze, synthesize, and model aspects of ecosystems. In 

turn, we use our results and interpretations to make decisions about the Park‘s natural resources. 

Thus, data collected and maintained by the North Coast and Cascades Network will become 

information through analysis, synthesis, and modeling. Information is the common currency 

among those involved in stewardship projects throughout our National Park System.

Any good set of data – whether collected last week or 20 years ago – must provide enough 

information about itself that it can be preserved and used appropriately. Anyone using these data 

will need to know as much as possible about how and why these data were collected. Therefore, 

our data management system cannot simply attend to the tables, fields, and values that make up a 

data set. It must also provide a process for developing, preserving, and integrating the context 

that makes data valuable and interpretable. Although this means more time will be spent 

documenting data sets, it will result in better preservation and presentation of data.

We sometimes use the term ―data‖ in a way that also encompasses other products that are 

generated alongside primary tabular and spatial data. These products fall into five general 

categories: raw data, derived data, documentation, reports, and administrative records (Table 

1.1). To meet I&M Program goals, and to ensure adequate context for the primary data products, 

these categories of project deliverables all require some level of management to ensure their 

quality and availability. We intend to integrate the manner in which our network creates, 

manages, and makes available the products of our scientific efforts. Thus, we will take a holistic 

view of how natural resource data are generated, processed, finalized, and provided to others. All 

phases of data and information processing are integrated, and information about each phase is 

shared through good documentation.

Table 1.1. Categories of data products and project deliverables

Category Examples

Raw data GPS rover files, field forms and notebooks, photographs and sound/video 

recordings, telemetry or remote-sensed data files, biological voucher 

specimens

Compiled/derived data Relational databases, tabular data files, GIS layers, maps, species checklists

Documentation Data collection protocols, data processing/analysis protocols, record of 

protocol changes, data dictionary, FGDC/NBII metadata, data design 

documentation, quality assurance report, catalog of specimens/photographs

Reports Annual progress report, final report (technical or general audience), 

periodic trend analysis report, publication

Administrative records Contracts and agreements, study plans, research permits/applications, other 

critical administrative correspondence
4

1.4 – Sources of Natural Resource Data

There are many potential sources of important 

information about the condition of natural 

resources in our parks. The types of work that 

may generate natural resource data about park 

resources include:

Inventories

Monitoring

Protocol development pilot studies

Special focus studies done by internal staff, 

contractors or cooperators

External research projects

Monitoring or research studies done by other 

agencies on park or adjacent lands

Resource impact evaluations related to park 

planning and compliance with regulations

Resource management and restoration work

Because the I&M Program focuses on long-term monitoring and natural resource inventories, 

our first priority should be toward the data and information that we derive from these primary 

efforts. However, we can easily apply the same standards, procedures, infrastructure and 

attitudes about data management to other natural resource data sources. One challenge will be to 

prioritize and manage workload and other resources. Naturally, high-profile data sets that 

provide crucial information to park management will be prioritized for data management 

regardless of funding source.

This plan covers four major categories of data that are coordinated or managed by NCCN. These 

are:

1. Data managed in service-wide databases. NCCN uses three data systems developed by 

the I&M WASO office. NatureBib is used as a bibliographic tool for cataloging reports, 

publications, or other documents that relate to natural resources in park units. Dataset 

Catalog is used to document primarily non-spatial databases or other data assemblages. 

NPSpecies is used to develop and maintain lists of vertebrates and vascular plants in 

network parks, along with associated supporting evidence.

2. Data developed or acquired directly by the network as a result of inventory, monitoring, 

or other projects. This category includes project-related protocols, reports, spatial data, 

and associated materials such as field notes and photographs provided to NCCN by 

contractors or developed by NCCN staff. Projects can be short-term (i.e., one to two 

years duration) or long-term (e.g., ongoing monitoring).

3. Data that, while not developed or maintained by NCCN, are used as data sources or 

provide context to other data sets. Examples of this category include: base GIS data 

Prioritizing data management efforts in 

a sea of unmanaged data

Our highest priority is to produce and curate 

high-quality, well-documented data originating 

from the Inventory and Monitoring Program. As 

time and resources permit, we will work toward 

raising the level of data management for current 

projects, legacy data, and data originating 

outside the Inventory and Monitoring Program. 

1. We will place greatest emphasis on projects 

that are just beginning to be developed and 

implemented, because inserting good data

management practices from the very 

beginning is most effective.

2. Updating data management for ongoing 

projects and conversion of legacy data will 

be prioritized according to the value of the 

information they provide.
5

developed by parks, other agencies or organizations; national or international taxonomic 

classification systems; climate or hydrologic data collected by regional or national 

entities; data and products developed under the auspices of a research permit.

4. Data acquired and maintained by network parks that NCCN assists in managing. 

Because of the lack of data management expertise in many network parks, NCCN

provides data management assistance for high-priority data sets or those that may benefit 

from standardized procedures. Examples include: park observations databases for wildlife 

and rare plant data; legacy natural resource monitoring data sets; and data on exotic 

invasive plant species. 

These categories can contain one or more of the following data formats:

hard-copy documents (e.g., reports, field notes, survey forms, maps, references, 

administrative documents)

objects (e.g., specimens, samples, photographs, slides)

tabular data (e.g., databases, spreadsheets, tables, delimited files)

spatial data (e.g., shapefiles, coverages, remote-sensing data)

other digital files (e.g., Word files, email, websites, digital images)

Each of these data formats has specific requirements for ongoing management and maintenance, 

which we address in this plan.

1.5 – Makeup of the North Coast and Cascades Network

The North Coast and Cascades Network (NCCN or Network) is composed of eight park units in 

western Washington and the northwestern corner of Oregon. They include five small, historically 

based parks and three larger, mountainous parks that are coastal, continental, or both. Seven of 

these parks contain significant natural resources, and thus are the subject of this plan (Figure 

1.1):

Ebey‘s Landing National Historical Reserve (EBLA)

Fort Vancouver National Historic Site (FOVA)

Lewis and Clark National Historical Park (LEWI), formerly Fort Clatsop National 

Memorial

Mount Rainier National Park (MORA)

North Cascades National Park Service Complex (NOCA)

Olympic National Park (OLYM)

San Juan Island National Historical Park (SAJH)
6

Figure 1.1. Parks with significant natural resources in the North Coast and Cascades Network
7

NCCN is unique in being the only NPS network that contains two parks with prototype I&M 

programs – one at Olympic National Park and another at North Cascades National Park Service 

Complex. Although these parks were competitively selected as prototypes in 1993, they did not 

receive prototype funding until 2000, simultaneous with network funding. Subsequent to 

funding as separate entities, the management of these prototype programs was united in that 

funding and implementation decisions are made collectively. Funding from the prototype 

programs supports scientific and data management personnel stationed at each prototype, which 

augments the Network Coordinator and Data Manager positions funded by the network. The 

mission of the prototype programs is to function in support of the network and its constituent 

parks by providing guidance and expertise related to monitoring design, data management, 

analysis, and reporting – while still meeting their individual prototype responsibilities as national 

centers of innovation and excellence.

Board of Directors

The Board of Directors is composed of park superintendents of the seven park units that 

comprise NCCN. The Pacific West Region I&M Coordinator and the Network Coordinator are 

ex-officio members of the Board. The primary role of the Board is to promote accountability and 

effectiveness for the I&M Program by reviewing progress and quality control for the network, 

and to oversee spending of network funds. The Board also provides guidance and oversight for 

the Technical Committee in the design and implementation of vital signs monitoring and other 

activities related to the Natural Resource Challenge. An important role of the Board is to seek 

strategies for leveraging network funds and personnel to best accomplish the inventory and 

monitoring and other natural resource needs of network parks, and for finding additional funding 

sources. As the governing body of the network, the Board is consulted on hiring of new 

personnel using funds provided to the network in combination with base funds and other sources. 

There will be at least two formal board meetings annually, though any member can call a 

meeting as needed.

Technical Committee

The network Technical Committee is composed of the chiefs of natural resources for the member 

parks, key natural resource staff and subject area specialists, a science advisor, a liaison from 

USGS-Biological Resources Division, and the GIS and data management staff for the member 

parks. This committee meets monthly to coordinate inventory and monitoring activities and to 

propose decision points to the Board of Directors. The Network Coordinator convenes and 

chairs meetings of the Technical Committee. The Technical Committee also provides general 

oversight and guidance for work groups that are responsible for topic-specific activities.

Discipline work groups

Discipline work groups are made up of scientists and resource managers from network parks 

who are working together to manage and coordinate inventory and monitoring within the 

network. These work groups are defined by the broad topical categories for which they are 

responsible: Aquatic (water quality, aquatic invertebrates, amphibians, fish), Marine/Coastal

(estuarine/intertidal communities), Geology (bedrock, landforms, glaciers, erosion), Terrestrial 

Wildlife (mammals, birds, reptiles, terrestrial invertebrates), Vegetation (vascular, non-vascular 
8

plants, exotic plant management, rare plants), and Atmospherics (climate, atmospheric 

deposition). Each work group meets as needed and meetings are coordinated by a chairperson 

appointed by each group. Meeting agendas and minutes are posted on the network's intranet 

along with any other resulting documentation such as budgets, assignments, and reports.

Data management work group

This work group consists of the three data managers of the network (two prototype data 

managers and one network data manager), and the GIS staff of the network parks and regional 

support office. This group meets to develop and evaluate strategies for maintaining our data 

management system. Because good data management is a collaborative endeavor, the work 

group will work toward increasing the awareness and competency for good data management 

among park scientists, resource managers, field technicians, and others who produce, maintain or 

use natural resource data. The group meets at least quarterly to communicate new methods, 

procedures and standards, and to set priorities for data management within the network. The 

group also designates a rotating chairperson for 4-month terms to represent the data management 

group at technical committee meetings and discipline work group meetings.

1.6 – Scope and Organization of the Data Management Plan

The North Coast and Cascades Network Data Management Plan outlines how we intend to 

implement and maintain a system that will serve the data and information management needs of 

our Inventory and Monitoring Program. This plan reflects our commitment to establishing and 

maintaining a robust system for data management to ensure the availability and usability of highquality natural resource information.

The NCCN Data Management Plan describes how our Network will:

support I&M 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 data

We have developed this data management plan in modular format in the hope of increasing 

readability and usability among a wider audience. This format allows users to more easily locate 

and access subdocuments pertaining to a particular element of information management 

guidance or process. Individual procedural documents within the plan can be provided as 

standalone documents to park and regional data management staff, cooperators and project 

leaders. This format also allows for a more efficient review as technical experts can be provided 

with applicable portions of the plan based on their expertise (GIS, databases, metadata, GPS, 

etc.). There are three document types that make up this plan:

NCCN Data Management Plan

Guidance Documents
9

Standard Operating Procedures

Note: These documents describe the data management system and practices as a whole, whereas 

project-specific details are described in the standard operating procedures included in project 

protocols.

This Data Management Plan is composed of chapters that provide overview information that is 

more stable, whereas information that is more subject to change is more typically found in 

Guidance Documents and Standard Operating Procedures (SOPs). The conceptual model in 

Figure 1.2 shows the hierarchy of documentation related to NCCN information management.

Figure 1.2. Hierarchy of NCCN data management documents

Standard Operating Procedures (SOP) describe work processes that are to be followed by 

network staff and cooperators. SOPs are useful in that they document the way activities are to be 

performed to facilitate consistent conformance to technical and quality assurance requirements.

The development and use of SOPs promotes quality through consistent implementation of a 

process or procedure within the organization, even if there are temporary or permanent personnel 

changes. 

1.7 – Guidance Documentation Associated with this Plan

Once completed, the following documents will be made available on the NCCN website under 

Data Management (http://science.nature.nps.gov/im/units/nccn/datamgmt_guide.cfm). Specific 

procedures and SOPs will typically be included in these documents as appendices.

Tracking Project Information

Naming Conventions Files and Data Objects

Data Design Guidelines and Database Specifications

Reporting Guidelines and Templates

Specifications for Data Products and Project Deliverables

GPS Data Collection and Processing

GIS Development and Management Guidelines

Template Language for Contracts and Agreements

Guidelines for Managing Third-party Data

Standards for Voucher Collection and Cataloguing

Photographic Image Management Strategy

NCCN Data Management Plan

Main Document

(Overview document)

Guidance Documents

(Network specifications and topicspecific guidance)

Standard Operating Procedures

(Specific procedures – e.g., metadata 

creation, QA/QC procedures)
10

Quality Assurance Guidelines

Metadata Procedures and Specifications

NPSpecies Database Management Procedures

NatureBib Database Management Procedures

Guidelines for Data Distribution and Data Discovery

Sensitive Information Management Guidelines

Archival Procedures for Project Information

Webpage Development and Management Guidelines

1.8 – Review and Revisions

The NCCN Data Management Plan covers I&M Program needs based on the most current 

information systems technology relevant through 2005. This document will undergo an initial 

peer review by members of the NPS data management community, the NCCN Technical 

Committee, and informal review by individuals from other agencies. Following peer review, 

maintenance of this plan will be incremental as changes are needed to reflect best practices and 

current information. 

Revisions to this plan and associated data management documents will be made as needed, and 

the whole plan will be reevaluated every 3 to 5 years. The data management work group for the 

network will be responsible for ongoing maintenance of data management documents. 

Recommendations for changes can be forwarded to one of the data managers by any interested 

party or consumer of network inventory and monitoring data – park resource managers, project 

managers, technicians, superintendents, external users, etc. These recommendations will be 

brought up and discussed at the regular meetings of the data management work group. Revisions

will be tracked using the revision log at the beginning of the document. Simple changes can be 

made immediately in the document, and more complex changes and decision points can be 

recorded for subsequent plan updates.

Credits

The content of this chapter was adapted from material developed by Sara Stevens (Northeast 

Coast and Barrier Network) and Doug Wilder (Central Alaska Network).
11

Chapter 2 – Data Stewardship Roles and Responsibilities

Data management is a complex process characterized as much by attitudes and habits as it is by 

infrastructure, standards and procedures. Although primary responsibility resides with the data 

managers, good data stewardship could not possibly be accomplished by data managers alone –

it is truly a collaborative endeavor that involves many people with a broad range of tasks and 

responsibilities. As such, a valid data management system must be developed and continually 

modified to meet the needs of everyone with a role in coordinating, generating, maintaining, and 

using natural resource information in its many forms. This is a diverse group made up of park 

managers, scientists, technical specialists, and interpretive staff. A successful data management 

system is maintained by reinforcing communication and awareness among those responsible for

the origin, quality, disposition, and use of the data.

The demand for detailed, quality information about natural resources requires a group of people 

working together to steward data and information assets. The complex and technical character of 

ecological data, combined with the diverse and constantly evolving information technology, 

requires knowledgeable individuals from many areas to come together to ensure that data are 

collected using appropriate methods, and that resulting data products are well managed. Natural 

resource information must be credible, representative, and available for current and future needs. 

2.1 – Roles and Responsibilities

A role is a function or position (e.g., Project Leader)

A responsibility is a duty or obligation (e.g., review data records)

Table 2.1 summarizes the roles and responsibilities of various personnel. These 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. Also, the network coordinator ensures that the network data manager and 

ecologist achieve the required performance level. 
12

Table 2.1. Roles and responsibilities for data stewardship

Role Data Stewardship Responsibilities

Project Crew Member Collect, record, and verify data

Project Crew Leader Supervise crew and organize data

GIS Specialist or Data Technician Process and manage data

Information Technology Specialist Provide IT support for hardware, software, networking

Project Leader Oversee project operations, including data management

Resource Specialist/Ecologist Validate and make decisions about data. Integrate science in 

park and network activities.

GIS Coordinator Support park management objectives with GIS and resource 

information management

Data Manager Develop and support data management system. Ensure 

project data are organized, compliant, safe, and available.

Database Application Developer Know and use database software and database applications

Curator Oversee all aspects of specimen acquisition, documentation, 

and preservation, and manage the park collections

Statistician or Biometrician Analyze data and/or consult on analysis

Network/Prototype Coordinator Coordinate and oversee all network activities

Park Research Coordinator Facilitate data acquisition by external researchers. 

Communicate NPS requirements to permit holders.

I&M Data Manager (National Level) Provide Service-wide database availability and support

End Users (managers, scientists, 

interpreters, public)

Inform the scope and direction of science information needs 

and activities. Interpret information and apply to decisions.

Chief personnel involved with data management include Project Leaders and Data Managers. 

The Network Coordinator interacts with Project Leaders to ensure that timelines for data entry, 

validation, verification, summarization/analysis and reporting are met. Figure 2.1 illustrates the 

core data management duties of the Project Leader and Data Manager and where those duties

overlap.
13

Figure 2.1. Core project data stewardship duties of project leaders and data managers

2.2 – Data Management Coordination

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. The I&M

Program encourages coordination among participants at all levels to help ensure that data 

collected in parks are properly developed, maintained, and made available for management 

decision-making, research, and education. 

The network data managers work with I&M Program data management staff and regional 

resource information management personnel to maintain a high-level of involvement in servicewide and regional databases and data management policy. The data managers also work locally 

with network personnel, park staff, and cooperators to promote and develop workable standards 

and procedures that result in the compatibility and availability of data sets.

Key contacts include park GIS specialists and the project leaders for each monitoring or 

inventory project. Involvement and input from park scientists and resource management staff is 

also essential. We rely on everyone in the network for the successful development of planning 

materials, inventory study plans, and monitoring protocols. Consistent and productive 

communication among these individuals leads to common understanding and better 

synchronization of data management activities. This communication may take the form of

personal visits, phone calls, email, joint meetings and training sessions, and participation in 

meetings and work of NCCN‘s Technical Committee and Board of Directors. 
14

2.3 – Cooperation with Other Networks

Data managers throughout NPS regularly coordinate with each other and national program staff 

via annual meetings, conference calls, workgroups, a listserv, web sites, and informal 

communication. Data managers have already demonstrated effective cooperation in the ways that 

we share ideas and technology, and collaborate to develop data management standards and 

documentation. NCCN data managers will maintain an active role in promoting practical levels 

of compatibility among protocols and data sets involving other networks and organizations.

2.4 – Project Stewardship

Since the implementation of each project will normally require the expertise and involvement of 

several people over a period of months or years, it makes sense that one person is charged with 

keeping track of the objectives, requirements, and progress for each project. This project leader 

is usually a park scientist or resource management specialist with training and experience in the 

field of science related to the inventory or monitoring project. This expertise helps the project 

leader to oversee data acquisition and processing, coordinate with GIS and data managers on 

information management needs, and communicate project objectives and results. A project 

leader who cannot act as an authority for the data should work with the appropriate specialists to 

account for those aspects of data stewardship. 

To ensure the quality of each project, including data requirements, project leaders should be 

assigned only those projects they can effectively oversee based on workload and other relevant 

factors. Unless the project is of a very short duration (3 months or less), each project should have 

at least one alternate project leader to provide continuity in case the principal project leader 

becomes unavailable. 

2.4.1 – Data Stewardship: Sharing Responsibilities

Keeping track of data from the time of acquisition until it is no longer useful is the shared 

responsibility of everyone involved with data – producers, analysts, managers, and end users. 

This, in essence, is data stewardship. It is a principle of mutual accountability rather than one 

particular job for one individual. Successful data stewardship requires that people involved in 

NCCN activities learn and understand the expectations for continuous data management. This is 

equally important for NCCN staff, park employees, and contractors or cooperators. All project 

participants should receive training, briefings, materials, and additional regular communication 

about data stewardship from project leaders, and data managers. The purpose is to promote the 

appropriate level of understanding about how their efforts relate to park and network 

management objectives, NPS policies, and other federal government requirements.

2.4.2 – Documentation is Key

If one shared responsibility stands above the pack in importance and value, it is the careful 

documentation of data sets, the data source(s), and the methodology by which the data were 

collected or acquired. This documentation establishes the basis for the appropriate use of the data 

in resulting analysis and products, both in the short-term and long-term. Network monitoring 
15

protocols contain key elements of data documentation, although additional documentation will 

need to be provided periodically. See the Chapter 7 of this plan for important guidance and 

reference for documentation and metadata.

2.4.3 – The Hub of Data Stewardship

Project leaders, data managers, and GIS specialists comprise the central data management team 

for inventory and monitoring projects. Each is responsible for certain aspects of project data, and 

all share responsibility for some overlapping tasks. Because of the collaborative nature of project 

data management, good communication among project leaders, data managers, and GIS 

specialists is essential to meeting program goals. 

2.4.4 – Project Stewards

The project leader is responsible for data quality during all phases of the project, including 

collecting, entering, handling, reviewing, summarizing, and reporting data. Developing project 

documentation and metadata are crucial elements of the project leader‘s role. Figure 2.1 

illustrates some of the overlap between project and data management responsibilities.

Specifically, a project leader is responsible for: 

project documentation that describes all aspects of project implementation

development and implementation of standard procedures for field data collection and 

handling

quality assurance and quality control measures, which include the supervision and 

certification of all field operations, staff training, equipment calibration, species 

identification, data collection, data entry, verification, and validation

maintaining concise explanatory documentation of all deviations from standard 

procedures

providing detailed documentation for each field data collection period

maintaining hard copies of data forms and archiving of original data forms

scheduling of regular project milestones such as data collection periods, data processing 

target dates, and reporting deadlines

regular summary reports and periodic trend analysis

acting as the main point of contact concerning data content

The project leader will also work closely with the data manager to:

develop quality assurance and quality control procedures specific to project operations

identify training needs for staff related to data management philosophy, database 

software use, and quality control procedures

coordinate changes to the field data forms and the user interface for the project database

document and maintain master data

identify sensitive information that requires special consideration prior to distribution

manage the archival process to ensure regular archiving of project documentation, 

original field data, databases, reports and summaries, and other products from the project
16

define the process for transforming raw data into meaningful information 

create data summary procedures to automate and standardize this transformation process

identify, prioritize, and convert legacy data sets to modern formats

increase the interpretability and accessibility of existing natural resource information

2.4.5 – Data Managers

The data manager is responsible for ensuring the compatibility of project data with program 

standards, for designing the infrastructure for project data, and for ensuring long-term data 

integrity and availability.

Data managers will:

develop and maintain the data management system for metadata creation and project 

data management 

create and support project databases in accordance with best practices and current 

standards

provide training in the theory and practice of data management as tailored to the needs of 

project personnel

develop ways to improve the accessibility and transparency of digital data

establish and implement procedures to protect sensitive data according to project needs

collaborate with GIS specialists to integrate tabular data with geospatial data in a GIS 

system in a manner that meets project objectives

Data managers will also work closely with the project leader to:

define the scope of the project data and create a data design that meets project needs

become familiar with how project data are collected, handled, and used

review quality assurance aspects of project protocols and standard procedure documents

identify elements that can be built into the database structure to facilitate quality control, 

such as required fields, range limits, pick-lists and conditional validation rules

create a user interface that streamlines the process of data entry, review, validation, and 

summarization that is consistent with the capabilities of the project staff

develop automated database procedures to improve the data summarization and reporting 

process

make sure that project documentation is complete, complies with metadata requirements, 

and enhances the interpretability and longevity of the project data

ensure regular archiving of project materials

inform project staff of changes and advances in data management practices

2.4.6 – GIS Specialists

Park GIS specialists play a crucial supporting role for NCCN projects. GIS support includes 

project planning to determine the GIS data and analysis needs for a project. GIS specialists will 

provide consultation to project leaders on field collection of spatial data including the use of GPS 
17

and other spatial data collection techniques. They will also coordinate importing of spatial data 

into the GIS, work with project leaders to analyze spatial data, and provide the results in map or 

tabular form. The GIS specialists will work with project leaders to properly document data in 

compliance with spatial metadata standards and will be responsible for stewardship of GIS data 

and products.

GIS specialists will also work directly with data managers to design databases and applications. 

GIS specialists will work closely with the data managers to create relationships between GIS and 

non-spatial data and create appropriate database and GIS applications to facilitate the analysis of 

both spatial and non-spatial data.

The Columbia Cascades Support Office (CCSO) in Seattle will continue their role as primary 

GIS support for EBLA, FOVA, LEWI, and SAJH since these parks do not have GIS Specialists 

on staff. However, GIS specialists from MORA, NOCA, and OLYM may also provide GIS 

support to NCCN projects taking place in these parks.

Credits

This chapter was adapted from concepts and material developed in collaboration with Rob Daley 

(Greater Yellowstone Network).

19

Chapter 3 – Data Management Resources: Infrastructure and 

Systems Architecture

Our computer resource infrastructure is composed of computers and servers that are functionally 

or directly linked through computer networking services. This infrastructure represents the 

foundation upon which our network information system is built. Systems architecture signifies 

the applications, database systems, repositories, and software tools that make up the framework 

of our data management enterprise. 

Our I&M program relies heavily on park, regional, and national IT personnel and resources to 

maintain the computer resource infrastructure. This includes but is not limited to hardware 

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

telecommunications networking, and backups of servers. Therefore communication with park 

and regional IT specialists is essential to ensure adequate resources and service continuity for our 

systems architecture. Rather than focusing on a detailed description of our current computer 

resources, this chapter will instead describe our infrastructure in more general terms and focus 

more specifically on the systems architecture that is central to data management.

3.1 – Computer Resources Infrastructure

An important element of a data management program is a reliable, secure network of computers 

and servers. Our digital infrastructure has three main components: park-based local area 

networks (LAN), network data servers, and servers maintained at the national level. This 

infrastructure is maintained by park, regional, and national IT specialists, who administer all 

aspects of system security and backups.

Figure 3.1. Schematic representing the logical layout and connectivity of computer resources
20

These components each host different parts of our natural resource information system.

National servers

Master applications – integrated client-server versions of NatureBib, NPSpecies, NR-GIS 

Metadata Database

Centralized repositories – NR-GIS Data Store, Protocol Clearinghouse

Public access sites – portals to NatureBib, NPSpecies, NPSFocus, and websites for 

monitoring networks

Network data servers

Master project databases – compiled data sets for monitoring projects and other multi-year 

efforts that have been certified for data quality

Common lookup tables – e.g., parks, projects, personnel, species

Project tracking application – used to track project status, contact information, product due 

dates

Network digital library – network repository for finished versions of project deliverables for 

network projects (e.g., reports, methods documentation, data files, metadata, etc.)

Park LAN

Local applications – desktop versions of national applications such as NPSpecies and Dataset 

Catalog

Working files – working databases, draft geospatial themes, drafts of reports, administrative 

records

Park digital library – base spatial data, imagery, and finished versions of park project 

deliverables

Park GIS files – base spatial data, imagery, and project-specific themes

3.1.1 – Maintaining Digital Files

Each of the NCCN park LANs accommodates hierarchical directory structures for storing digital 

files. There are five main categories of directory structure sections in which digital files can be 

maintained (also see Chapter 10):

Admin Documents related to program administration.

Databases Local copies of national databases (e.g., NPSpecies), databases for common 

lookup tables, and project back-end databases are maintained. This section may 

also have separate subfolders for database development.

Libraries Read-only storage of finished project deliverables, catalogued photographs, and 

other reference documents generated and maintained by the program.

Working Workspace where groups and individuals can maintain draft material and other 

files as arranged by group then by fiscal or calendar year. The layout of folders 

and subfolders is more flexible here than elsewhere. On an annual basis, project 

leaders and administrators should work to clean out these sections by identifying 

material that belongs in one of the Libraries or elsewhere, deleting unneeded 

files, and moving the remainder to offline media or to a read-only folder named 

by year.
21

GIS Base spatial data, imagery, and project-specific themes. The typical arrangement 

of folders and subfolders is park-specific. Some project-specific themes in 

development reside in working sections until they are migrated here upon 

completion. This section will typically be found on a separate drive due to 

different storage space requirements and backup issues, and because its scope is 

not confined to the I&M program.

The key aspects of this file management strategy are as follows:

1. Working files are kept separate from finished products.

2. Finished products are typically read-only, except for "inbox" folders where users can 

drop things off to be cataloged and filed.

3. Standards such as naming conventions and hierarchical filing are enforced within the 

Libraries, Database, and GIS sections. Although less stringent in other sections, these 

conventions are encouraged as good practice.

3.2 – National Information Management Systems

The need for effective natural resource information management cuts across NPS divisional 

boundaries. For this reason, information management strategies must be defined at the highest 

level possible. In this context, integrated inventory and monitoring of natural resources is 

multidisciplinary and requires national-level, programmatic data and information management 

strategies for success. 

The basic strategy of natural resource and therefore inventory and monitoring information 

management is to provide integrated natural resource databases and information systems that 

enhance NPS managers‘ and staff‘s access and use of timely and valid data and information for 

management decisions, resource protection, and interpretation. Inventory and monitoring 

information needs are broadly separated into two categories:

Detailed data and information needed for onsite resource management and protection. 

The information used to guide natural resource management decisions must be specific to 

inform and be useful to management staff at parks and central offices.

Summary information needed to describe the resources and their condition. This kind of 

information usually needs to be aggregated across the National Park Service for use by 

NPS and DOI managers and central office personnel to answer requests from Congress 

and for budget, program, and project planning. 

The NPS Natural Resource Program Center (NRPC) and the I&M Program actively develop and 

implement a national-level, program-wide information management framework. NRPC and 

I&M staff integrate desktop database applications with internet-based databases to serve both 

local and national-level data and information requirements. NRPC staff members work with 

regional and support office staff to develop extensible desktop GIS systems that integrate closely 

with the database systems. Centralized data archiving and distribution capabilities at the NRPC 

provide for long term data security and storage. NRPC sponsors training courses on data 

management, I&M techniques, and remote sensing to assist I&M data managers with developing 

and effectively utilizing natural resource information.
22

National-level application architecture

To achieve an integrated information management system, three of the national-level data 

management applications (NatureBib, NPSpecies, and NR-GIS Metadata Database) utilize a 

distributed application architecture with both desktop and internet-accessible (master) 

components (Figure 3.2).

Figure 3.2. Model of the national-level application architecture

NatureBib

NatureBib is the master database for bibliographic references that merges a number of previously 

separate databases such as Whitetail Deer Management Bibliography (DeerBib), Geologic 

Resource Bibliography (GRBib), and others. It also contains citation data from independent 

databases like NPSpecies and the Dataset Catalog and NR-GIS Metadata Database. It currently 

focuses on natural resource references but may eventually be linked to references on cultural 

resources and other park operations. As with NPSpecies and NR-GIS Metadata Database, it is 

possible to download data from the master web version into the MS Access desktop version that 

can be used locally on computers with limited internet connectivity 

(http://science.nature.nps.gov/im/apps/nrbib/).

NPSpecies

NPSpecies is the master species 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). Taxonomy and nomenclature are based on ITIS, the 

interagency Integrated Taxonomic Information System. The master version of NPSpecies for 

each park or network can be downloaded from the master website into an MS Access version of 

NPSpecies. The internet-based version is the master database, which can be accessed via 

password-protected logins administered by park, network and regional data stewards (points of 

contact or POCs) assigned for each park and network. The master database requires that species 

lists are certified by parks before any data will be available to the public. NPSpecies is linked to 

NatureBib for bibliographic references that provide written evidence of a species‘ occurrence in 

a park and will be linked to NR-GIS Metadata Database to document biological inventory 
23

products. The MS Access application and additional details can be found at the NPSpecies 

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

Dataset Catalog and NR-GIS Metadata Database

Dataset Catalog is a desktop metadata database application developed by the I&M Program to 

provide a tool that parks, networks, and cooperators can use to inventory and manage data set 

holdings. Although not designed as a comprehensive metadata tool, the Dataset Catalog is used 

for cataloging abbreviated metadata about a variety of digital and non-digital natural resource 

data sets. The Dataset Catalog helps parks and networks begin to meet Executive Order 12906 

mandating federal agencies to document all data collected after January 1995. It provides brief 

metadata and a comprehensive list of all resource data sets for use in data management, project 

planning, and more stringent metadata activities. As with other service-wide applications, the 

master metadata database (NR-GIS Metadata Database) is available through a website and will 

be linked to NPSpecies (the NPS species database) and NatureBib (the bibliographic database). 

A Microsoft Access version may be downloaded from the website (Dataset Catalog: 

http://science.nature.nps.gov/im/apps/datacat/index.htm and NR-GIS Metadata Database: 

http://science.nature.nps.gov/nrdata). 

Other National-Level Inventory and Monitoring Information Management and GIS Applications

NPSTORET

STORET is an 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. 

STORET is used by NPS as a repository of physical, chemical, biological, and other monitoring 

data collected in and around national park units by park staff, contractors, and cooperators. The 

NPS operates its own service-wide copy of STORET and makes periodic uploads to the EPA 

STORET National Data Warehouse so that data collected by and for parks will be accessible to 

the public. NPS Director‘s Order 77 indicates that the NPS should archive water quality data in 

STORET, and the NPS Water Resources Division (WRD) requires that any data collected as part 

of a funded WRD project be archived in STORET. NPSTORET (also known as Water Quality 

Database Templates) the NPS master database designed to facilitate park-level standardized 

reporting for STORET. The database is still in development, but metadata, protocols, data 

dictionaries, and reporting capabilities are available through a front-end form. Upon 

implementation, network staff and cooperators will be able to use the MS Access version of 

NPSTORET either as a direct database for data entry and management, or as a means of

submitting data for upload to STORET by WRD staff. The MS Access application and 

additional details can be found at: http://www.nature.nps.gov/water/infodata.htm. Additional 

information on STORET can be found at: http://www.epa.gov/storet.

Natural Resource Database Template

The Natural Resource Database Template (NRDT) is a flexible, relational database in MS 

Access for storing inventory and monitoring data (including raw data collected during field 

studies). This relational database can be used as a standalone database or in conjunction with the 

GIS software (e.g., ArcView or ArcGIS) to enter, store, retrieve, and otherwise manage natural 

resource information. The template has a core database structure that can be modified and 

extended by different parks and networks depending on the components of their inventory and 
24

monitoring program and the specific sampling protocols they use. Natural Resource Database 

Template is a key component of the I&M program‘s standardized monitoring protocols. These 

monitoring protocols include separate modules detailing different aspects of monitoring project 

implementation, from sampling design to data analysis and reporting, and include data 

management components that describe database table structure, data entry forms and quality 

checking routines. Approved monitoring protocols, including the databases that are based on the 

Database Template, are made available through a web-based protocol clearinghouse (see below). 

A description of the Database Template application, a data dictionary, and example 

implementations are located on the NR Database Template website 

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

Natural Resource Monitoring Protocols Clearinghouse

The Natural Resource Monitoring Protocol Clearinghouse (i.e., Protocol Database) is a webbased clearinghouse of sampling protocols used in national parks to monitor the condition of 

selected natural resources. The database provides a summary of, and in many cases allows the 

user to download a digital copy of, sampling protocols that have been developed by the prototype 

monitoring parks or other well-established protocols used in national parks. The Protocol 

Database also makes it possible to download database components (e.g., tables, queries, data 

entry forms) in MS Access that are consistent with the Natural Resource Database Template that 

have been developed for a particular protocol. See the Protocol Database website for available 

protocols (http://science.nature.nps.gov/im/monitor/VitalSigns/BrowseProtocol.aspx).

NR-GIS Data Store

The NR-GIS Data Store is a key component of the data dissemination strategy employed by the 

I&M Program. The NR-GIS Data Store is a graphical search interface that links metadata to a 

searchable data server on which data sets are organized by NPS units, offices and programs. The 

interface allows customized public or protected searches of natural resource data sets, inventory 

products and GIS data produced by the I&M and Natural Resource GIS Programs. Each park or 

network is able to post and curate its data on the server. The NR-GIS Data Store will be 

integrated with the master NR-GIS Metadata Database application to streamline programmatic 

data documentation and dissemination processes. The simple browse function of this server can 

be accessed at: http://nrdata.nps.gov/. 

See the NR-GIS Data Store website for further information (http://science.nature.nps.gov/nrdata).

3.3 – Network Systems Architecture

Rather than developing a single, integrated database system, our design relies upon modular, 

standalone project databases that share design standards and links to centralized data tables. 

Individual project databases are 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. In addition, one project database can be 

modified without affecting the functionality of other project databases.

By working up from modular data sets, we avoid a large initial investment in a centralized 

database and the concomitant difficulties of integrating among project areas with very 
25

different – and often unforeseen – structural requirements. Furthermore, the payoff for this 

initial investment may not be realized down the road by greater efficiency for 

interdisciplinary use.

3.3.1 – Project Database Standards

Project database standards are necessary for ensuring compatibility among data sets, which is 

vital given the often unpredictable ways in which data sets will be aggregated and summarized. 

When well thought out, standards also help to encourage sound database design and facilitate 

interpretability of data sets. Databases that are developed for park and network projects will all 

contain the following main components:

Common lookup tables – These are accessed 

via links to tables that reside in a centralized 

database, rather than storing redundant 

information in each database. These tables 

typically contain information that is not 

project-specific (e.g., lists of parks,

personnel, and species).

Core tables and fields based on network and 

national templates – These tables and fields 

are used to manage the information 

describing the ―who, where and when‖ of 

project data. Core tables are distinguished 

from common lookup tables in that they 

reside in each individual project database 

and are populated locally. These core tables contain critical data fields that are standardized 

with regard to data types, field names, and domain ranges.

Project-specific fields and tables – The remainder of database objects can be considered 

project-specific, although there will typically be a large amount of overlap among projects. 

This is true even among projects that may not seem related – for example, a temperature field 

will require similar data types and domain values. As much as is possible, efforts will be 

made to develop these project-specific objects to be compatible with those maintained by 

other networks and cooperators managing similar data sets – especially if integration with 

other data sets is important for meeting project objectives.

Centralized database components: common tables

Certain key information is not only common to multiple data sets, but to the organization as a 

whole – lists of contacts, projects, parks, species are often complex and dynamic. It is a good 

strategy to centralize this information so that users have access to the most updated versions in a 

single, known place. Centralizing also avoids redundancy and versioning issues among multiple 

copies because there is only one copy to maintain. Centralized information is maintained in 

database tables that can be linked or referred to from several distinct project databases. Network 

applications – for project tracking, administrative reporting, or budget management – can also 

Compatibility with national standards

As much as possible, NCCN standards for fields, 

tables and other database objects will mirror 

those conveyed through the Natural Resource 

Database Template. 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.
26

link to the same tables so that all users in the network have instantaneous access to edits made by 

other users (Figure 3.3).

Figure 3.3. Common lookup tables and satellite databases

At present, these common tables are grouped and maintained in separate Access (.mdb) files 

according to Table 3.1. Separating these tables by functional groupings is done primarily to 

reduce conflicts and performance losses associated with multiple users in MS Access. Databases 

associated with individual projects each access the common tables via links established in each 

project back-end data file. As our network completes its transition to a client-server database 

system, these components will be placed together in a database that is replicated among data 

nodes hosted at MORA, OLYM and NOCA. Individual project databases will continue to access 

these common tables in the same manner.

Table 3.1. Groupings for common lookup tables

Grouping Description

Parks list of park units and networks

Projects list of park and network projects, including inventories, monitoring, parksponsored initiative projects, and external research projects

People comprehensive list of contacts for parks and network, project-specific crew 

lists, lists of groups and users for tracking and managing access privileges

Species comprehensive list of taxa for the park, linkage to NPSpecies taxonomic 

module, project-specific species lists

Other Lookups lists of watersheds, drainages, place names, weather conditions, habitat 

attributes

Different levels of data standards

The three types of database objects also correspond to three putative levels of data standards. 

Because common lookup tables are stored in one place and are referred to by multiple databases, 
27

they represent the highest level of data standard because they are implemented identically among 

data sets. The second level of standards is implied by the core template fields and tables, which 

are standardized where possible, but project-specific objectives and needs could lead to varied 

implementations among projects. The third level of standards is applied most flexibly to 

accommodate the range of needs and possibilities for each project, yet always with compatibility 

and integrity in mind. The following figure presents the resulting variation in implementation of 

these differing levels as a ―bull‘s eye‖, with the common lookup tables providing the most 

consistent implementation and hence the smallest range of variation.

Figure 3.4. Different levels of data standards and their corresponding degree of implementation 

variability

3.3.2 – Client-Server Database Architecture

Our data management program is in the process of completing a transition to a client-server 

database system. Our strategy is to manage common tables and high-value, long-term project 

databases within this system as a means of maximizing performance in a distributed, multi-user 

environment. There are three data servers that comprise the network infrastructure – one located 

at MORA, and one at each of the prototype parks (NOCA and OLYM). These three servers 

function as independent data nodes that can be accessed from any park location so long as it is 

within the wide area network maintained by the Pacific West Region. They are also integrated in 

that common tables are replicated regularly among data nodes, backups for one node are stored 

on a separate node, and network databases are distributed across the three nodes.
28

Figure 3.5. Client-server database architecture for NCCN

The advantages of this strategy include:

Data are fully backed up on an off-site data node, which is crucial for information recovery 

in case of a local catastrophe at one of the host sites. Backups can be automated through 

scheduled services.

Common tables are replicated for continuous access during server down time. Also, by 

hosting common tables at each node, locally hosted databases can refer to the local copy of 

the common tables, which maximizes performance and server node autonomy.

Parallelism in data node setup simplifies system maintenance, which is important given our 

collaborative model for data management.

Distribution of data services allows for program growth and balanced access loads.

Park databases can be hosted and managed locally.

3.3.3 – Project Tracking Application

To support program coordination and annual reporting, and to improve accountability for the 

products of our natural resource inventory and monitoring efforts, our network will develop and 

implement a project tracking database. The primary functions of this application include:

Maintaining the list of projects – By maintaining a single list of natural resource data 

projects, it is much easier to quickly find project-related information (e.g., status, funding 

sources and amounts, objectives, contact information) and summarize that information for 

administrative reports.

Tracking product deliverables – For each project a comprehensive list is maintained of what 

deliverables are expected and when. Once they are delivered and posted or archived, this
29

function shifts to being a search tool (finding aid) for available products. Deliverables are 

first specified at project initiation and information is updated at various project milestones 

(e.g., contracting, product delivery, archival).

Managing project codes – These are intelligent alphanumeric codes used to tie together 

digital information in various, minimally connected 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.

This application will be hosted by each network data node (Figure 3.4), and will be replicated 

among nodes to ensure uninterrupted access to NCCN users. Although primarily maintained by 

NCCN data managers, the database will be available to project leaders, GIS staff, the network 

coordinator, and NCCN park administrators. Each of these staff will be able to make certain 

changes to update information about project status, deliverable details, etc. Certain database 

views will be created to help project leaders keep on schedule and to facilitate quick reporting on 

project status, accomplishments and delivered products.

This section will be updated once the application is implemented and additional details are 

available.

Credits

Section 3.2 (National Information Management Systems) was adapted from material written by 

Lisa Nelson (WASO). Thanks to Patrick Flaherty for ideas and material related to Section 3.1 

(Computer Resources).

31

Chapter 4 – Data Management Process and Work Flow

This chapter considers the general work flow characteristics of projects that produce natural 

resource data and then gives an overview of how natural resource data are generated, processed, 

finalized and made available. Data management activities that relate to the various stages of a 

project are highlighted. By describing the progressive stages of a project and the life cycle of the 

resulting data, we can more easily communicate the overall objectives and specific steps of the 

data management process. In addition, this awareness helps us to manage the staffing resources 

needed to produce, maintain, and deliver quality data and information. More details about data 

acquisition, quality assurance, documentation, dissemination and maintenance can be found in 

later chapters of this plan.

4.1 – Project Work Flow

From the perspective of managing workflow, there are two main types of projects: 

Short-term projects, which may include individual park research projects, inventories, or 

pilot work done in preparation for long-term monitoring.

Long-term projects, which will mainly be the implemented monitoring projects central to the 

I&M program but which may also include multi-year research projects and monitoring 

performed by other agencies and cooperators. Long-term projects will often require a higher 

level of documentation, peer review and program support.

From a data management standpoint, a primary difference between short- and long-term projects 

is an increased need to adhere to standards for long-term projects to ensure internal consistency 

over time. While the need to follow standards is still present for short-term projects, sometimes 

the cost of compliance will outweigh the benefits due to the scope, budget, and level of NPS 

influence over the project details. Nevertheless, both short-term and long-term projects share 

many work flow characteristics, and both generate data products that must be managed and made 

available.

Projects can be divided into five primary stages: planning and approval; design and testing; 

implementation; product integration; evaluation and closure (Figure 4.1). Each stage is 

characterized by a set of activities carried out by staff involved in the project. 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 2 of this plan.

4.1.1 – Planning and Approval

Here many of the preliminary decisions are made regarding project scope and objectives. In 

addition, funding sources, permits and compliance are all addressed in this phase. 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 of 

projects in this phase. 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. 
32

4.1.2 – Design and Testing

During this phase, all of the details are worked out regarding how data will be acquired, 

processed, analyzed, reported and made available to others. The project leader is responsible for 

developing and testing project methodology, or for modifying existing methods to meet project 

objectives. It is critical that the project leader and the data manager work together throughout 

this phase. The dialog between these two will help to build and reinforce good data management 

throughout the project – especially during the crucial stages of data acquisition, processing, and 

retrieval. By beginning collaborative development as soon after project approval as possible, data 

integrity and quality can most easily be assured. An important part of this collaboration is the 

development of the data design and data dictionary, where the specifics of database 

implementation and parameters that will be collected are defined in detail. Devoting adequate 

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

quality, integrity and usability of the resulting data. Although it is likely that additional changes 

will need to be made once implementation has begun, it is important to minimize these post-hoc 

changes through careful design work. Once the project methods, data design, and data dictionary 

have been developed and documented, a database can be constructed to meet project 

requirements.

4.1.3 – Implementation

During the implementation phase, data are acquired, processed, error-checked and documented. 

This is also when products such as reports, maps, GIS themes, and other products are developed 

and delivered. The project leader oversees all aspects of implementation – from logistics 

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

preparation and final delivery. Throughout this phase, 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 during this phase 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, 

contract, agreement or permit). 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 should be delivered to appropriate park and network staff as specified. 

All project deliverables should be developed and delivered according to product specifications, 

which should be stipulated in all protocols, contracts, agreements, and permits. Products that do 

not meet program requirements will be returned for revision. 
33

4.1.4 – Product Integration

During this phase, data products and other deliverables are integrated into national and network 

databases, metadata records are finalized and posted in clearinghouses, and products are 

distributed or otherwise made available to their intended audience. Another aspect of integration 

is merging data from a working database to a master database maintained on the network server. 

This occurs only after the annual working data set has been certified for quality by the project 

leader. Certain projects may also have additional integration needs, such as when working 

jointly with other agencies for a common database.

Product integration includes creating records for reports and other project documents in 

NatureBib, posting imaged documents to the appropriate repository, posting metadata records 

that have been completed and submitted by project leaders, and updating NPSpecies to reflect 

any new species occurrence information derived from the project. This will allow the 

information from the project to be searchable and available to others via service-wide search 

engines.

4.1.5 – Evaluation and Closure

Upon project closure, records are updated to reflect the status of the project and its associated 

deliverables in a network project tracking application. For long-term 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, SOPs and other procedures are maintained in a 

tracking table associated with each document. Major revisions may require additional peer 

review.
34

Figure 4.1. Conceptual model of project work flow

4.2 – Data Life Cycle

During various phases of a project, project data take on different forms and are maintained in 

different places as they are acquired, processed, documented and archived. This data life cycle is 
35

characterized by a sequence of events that we can model to facilitate communication. These 

events involve interactions with the following objects:

Raw data – Analog data recorded by hand on hard-copy forms and digital files from 

handheld computers, GPS receivers, telemetry data loggers, etc.

Working database – A project-specific database for entering and processing data for the 

current season (or other logical period of time). This might be the only database for 

short-term projects where there is no need to distinguish working data for the current 

season from the full set of validated project data.

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 the 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 (see 

Chapter 7).

Master database – Project-specific database for storing the full project data set, used for 

viewing, summarizing, and analysis. Only used to store data that have passed all quality 

assurance steps.

Reports and data products – Information that is derived from certified project data.

Edit log – A means of tracking changes to certified data.

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. 

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.

Although the data life cycle may vary depending on specific project needs and objectives, the 

typical life cycle for NCCN proceeds as follows (Figure 4.2):

1. Acquire data – For data recorded by hand in the field, data forms should be reviewed 

regularly (at least daily) for completeness and validity in order to capture errors as close 

to their origin as possible.

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

copied to the digital library section 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. Data entry / import – Analog data are entered manually and digital data files are

uploaded to the working database.

4. Verification, processing and validation – Verify accurate transcription of raw data; 

process data to remove missing values and other data flaws; and validate data using 

database queries to capture missing data, out-of-range values, and logical errors.
36

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.

6. Archive versioned data set – 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; 

see Chapter 10).

7. Extract data, post data, and update national databases – To make data available to 

others, certified data and metadata are posted to national repositories such as NR-GIS 

Data Store. In addition, national databases such as NPSpecies, NPSTORET, and NR-GIS 

Metadata Database are updated with data extracted from the certified data set. Note: 

Data and data products may not be posted if they contain protected information about the 

nature or location of rare, threatened or endangered species, or other natural resources of 

management concern (see Chapter 9).

8. 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.

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. 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.

11. Post products and update national databases – To make data available to others, reports 

and other products are posted to national repositories such as NR-GIS Data Store or the 

NR Data Image Server. In addition, products are catalogued in NatureBib. Data 

products may not be posted if they contain protected information about the nature or 

location of rare, threatened or endangered species, or other natural resources of 

management concern (see Chapter 9).

12. Distribute data and information – Data, metadata, reports and products can be shared and 

distributed in a variety of ways – especially via the web-based national databases and 

repositories, by FTP or mailing in response to specific requests, or by providing direct 

access to project records to cooperators. In all cases, distribution will follow legal 

requirements under the Freedom of Information Act and limitations established to protect 

information about sensitive resources (see Chapter 9).
37

13. 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.

Figure 4.2. Diagram of the typical project data life cycle

This sequence of events occurs in an iterative fashion for long-term monitoring projects, whereas 

this sequence is followed only once for short-term projects. For projects spanning multiple 

years, decision points include whether or not a separate working database is desirable, and the 

extent to which product development and delivery is repeated year after year. 
38

4.3 – Integrating and Sharing Data Products

Once project data and data products have been finalized, they need to be secured in long-term 

storage and made available to others. To accomplish this requires that we use a range of 

information systems such as product repositories, clearinghouses, and web applications. Each of 

these systems has a different purpose and function, as shown in Figure 4.3.

Figure 4.3. Storing and disseminating project information

The specific repositories for most NCCN products are indicated in Table 4.1.

Table 4.1. Repositories for NCCN products

* Biological specimens can also be retained at other facilities (e.g., University of Washington Herbarium) 

with an appropriate agreement.

Repository Item

NCCN Digital Library Project data, metadata, and other products

Raw and certified data sets

Metadata, protocols, SOPs

Reports and administrative records

Digital photographs, derived products

NCCN Project Databases Comprehensive data for multi-year projects

Park Collections and/or National Archives Administrative records, voucher specimens*, raw 

data forms, hard copy reports

National Databases 

- NPSTORET, NPSpecies, NatureBib

Compiled information about water quality, park 

species lists and taxonomic documentation, park 

resource bibliographies

NR Data Image Server Copies of digital reports and other documents 

(catalogued in NatureBib)

NR-GIS Data Store Copies of digital data sets (non-sensitive) and 

metadata
39

4.3.1 – Data Distribution

The process of product distribution involves several steps (Figure 4.4). As products are 

finalized, they can be sent to the appropriate person for integration, posting and distribution. In 

most cases it will be either the Data Manager or GIS specialist who reviews the product for 

conformance with format standards, then stores the product in the appropriate repository. Note 

that it is expected that all products will have already been reviewed for completeness and 

accuracy prior to delivery. After storing the products, their existence is documented by posting 

metadata and by updating records in the NCCN project tracking application. At this point, data 

discovery is accomplished as metadata are then indexed by the clearinghouse function of the NRGIS Metadata Database. These metadata records provide pointers to data and data products. 

Distribution then follows as data discovery allows potential users to find and either request or 

download the data sets from their repositories.

Figure 4.4. Steps involved in product distribution
40

4.3.2 – Integration with National Databases

In addition to storing and distributing data products, product integration also involves updates to 

national databases such as NPSpecies and NPSTORET. Both of these databases have local 

desktop databases which can be updated with data collected during the course of a project. 

Desktop databases are then uploaded and synchronized with the national databases on a regular 

basis. 

To update NPSpecies, data on the distribution and occurrence of species in NCCN parks will be 

compiled and added to the database upon delivery of data and data products. Synchronization 

with the master version of NPSpecies will occur at least twice annually, or more frequently 

depending on the timing and amount of updates.

For NPSTORET, any project collecting water quality data will be flagged in the project tracking 

application so that water quality data can be either extracted and uploaded or directly entered 

into NPSTORET. All water quality data collected by our network will be managed according to 

guidelines from the NPS Water Resources Division. We will implement and maintain a desktop 

copy of NPSTORET and transfer its contents at least annually to NPS Water Resource Division 

for upload to the STORET database (Figure 4.5).

Figure 4.5. Data flow diagram for water quality data

Credits

This chapter was adapted from concepts and material developed in collaboration with Dorothy 

Mortenson (Southwest Alaska Network), Velma Potash (Cape Code National Seashore), Sara 

Stevens (Northeast Coastal and Barrier Network), and Doug Wilder (Central Alaska Network). 

Special thanks to Dorothy Mortenson and Doug Wilder for significant contributions and ideas.
41

Chapter 5 – Data Acquisition and Processing

The NPS I&M Program, in support of the Natural Resource Challenge, is responsible for 

acquiring the necessary information required by park managers to properly manage and maintain 

the natural resources of their park. To successfully accomplish this task, information from 

multiple sources is collected and appropriately processed so that it meets NPS data standards. 

This chapter of the Data Management Plan describes the steps involved with acquiring data as 

well as the initial stages of data processing. These steps are integral to strengthening the 

scientific foundation of the I&M program and providing the park managers with high quality 

data necessary to properly manage park resources.

There are three general classifications for the types of data handled by the I&M Program:

Programmatic Data – any data produced from projects that are initiated (funded) by the 

I&M Program or projects that in some way involve the I&M Program.

Non-programmatic NPS Data – any data produced by the NPS that did not involve the 

inventory and monitoring program.

Non-programmatic External Data – any data produced by agencies or institutions other 

than the National Parks Service.

These definitions do not in any way indicate or rank the importance of the different data types to 

the I&M Program. This is only a distinction between data gathered from different sources. The 

importance or value placed on a data set will be based on the quality or usefulness of data itself 

and the impact it has on the NCCN I&M Program and parks. 

The following sections outline in more detail the manner in which the different data types 

described above are acquired and processed.

5.1 – Programmatic Data

Data collection initiated by the Inventory and Monitoring Program usually takes place as part of 

either natural resources inventories or vital signs monitoring projects but can also include 

collaborative efforts between the NCCN I&M Program and other regional programs or 

institutions. Data for these projects are typically collected by I&M personnel, park staff or by 

cooperators/contractors. 

Natural Resources Inventories

Resource inventories were designed to identify the primary resources of each park and as such 

they represent an important phase in the management of park resources.

Vital Signs Monitoring Projects
42

Vital signs are measurable, early warning signals that indicate changes that could impair the 

long-term health of natural systems. Early detection of potential problems allows park managers 

to take steps to restore ecological health of park resources before serious damage can happen. 

Each Network, working in conjunction with park resource managers, was given the task of 

prioritizing the natural resources vital signs that were most important to its parks and to the 

network as a whole.

5.1.1 – Data Discovery and Data Mining

Data discovery or data mining is the process of searching for existing data/information that may 

be useful to the I&M Program mission and that is related to the natural resources of the network 

parks. For more information on I&M data mining practices refer to:

http://science.nature.nps.gov/im/inventory/index.cfm

A large percentage of data discovery occurs at the onset of new projects or during the 

development of new protocols. The process involves reviewing many different sources for 

varying types of information. Many of the following data sources are accessible via the internet,

but some require visiting local research or academic institutions, museums or local parks to 

conduct the searches. 

Bibliographic/Literature 

National NPS Databases (e.g. NatureBib)

Online literature databases (e.g. First Search or Biosis)

Local document library (e.g. NCCN Library)

Library catalogs (e.g. academic or research institutions)

Park archives

Geographic Data

Regional GIS Specialists

Park GIS Specialists

Federal and State Geographic data clearinghouses 

Biological/Natural Resources Data

NPSpecies

Voucher collections (museums, parks, universities)

Network Parks

All information collected during the data discovery process is maintained either electronically or 

in hard copy format, depending on how it was collected. Any geographic data sets collected 

during this process should be accompanied by FGDC compliant metadata, and all data sets found 

– geographic or otherwise – should be entered in Dataset Catalog. 
43

Dataset Catalog – new data sets (spatial or non-spatial) should be entered into Dataset 

Catalog.

NatureBib – the desktop version of NatureBib has been adopted by the NCCN to function as 

the internal literature catalog for the Network. All natural resource reference information 

collected during the data discovery process is entered into NatureBib.

NPSpecies/NatureBib – Information relating to the biodiversity of Network parks is entered 

into NPSpecies and linked to the associated reference (assuming the data came from a report 

or publication) in NatureBib.

Reference Cabinets – Hard copy materials are stored in three locations depending on how 

they relate to the Network parks and the NCCN I&M Program.

All original data sheets, final reports, and contracts are stored in a fire proof cabinet.

General references as well as those references linked to NPSpecies records in NatureBib are 

stored in file cabinets in the NCCN I&M Office. 

Much of the information collected during the data mining process is likely to be legacy data, or 

data collected prior to the inception of the NCCN I&M Program. If legacy data are collected in a 

digital format the information should be converted to current file formats compatible with the 

current software standards. In the past, hard copy materials were maintained as such, but in the 

future, the NCCN intends to scan hardcopy references and materials and saving them as .pdf files 

in order to create a digital library. 

Data discovery is an integral part of project development, but data discovery 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 the NCCN I&M Program 

maintains as much relevant material pertaining to the parks as possible. Encouraging data 

sharing among Network parks will assist in this process and may alleviate the need for regular 

searches of park records. Our goal is to conduct general data discovery efforts on an annual 

basis. (Guidelines in development)

5.1.2 – Field Studies

Biological inventories and monitoring projects are the most common examples of field studies 

conducted by the NCCN I&M Program. The Data Manager is responsible for ensuring that data 

collection, entry, verification, storage, and archiving for all field projects are consistent with the 

NCCN I&M Program standards. In addition to general standard operating procedures (SOPs) 

that define network-wide requirements, protocol specific SOPs are developed detailing 

procedures and/or methodologies. The data manager will work closely with the principal 

investigator and Network staff to develop these guidelines and methodologies connected with 

data collection, storage and maintenance. This may range from detailing the proper usage of 

data entry forms or databases to outlining calibration procedures for automated data loggers. 

Refer to the individual protocols for protocol specific SOPs. For general data management 

guidance, refer to:
44

http://science.nature.nps.gov/im/units/nccn/datamgmt_guide.cfm

Field Data Acquisition Methods

Listed below are some of the tools available for field data collection. Refer to the protocol 

specific SOPs for details on how the following tools may apply to individual projects.

Field Forms – the most common method of recording field data. Inexpensive but more 

opportunities for errors during the collection/data entry process. Requires neat, legible 

handwriting and rigorous QA/QC. 

 

Field Computers – increase data collection and data entry efficiency. Data can be directly 

dumped from the field computers to the office desktops thereby eliminating the data entry step. 

Fewer chances for error as QA/QC checks can be built into the database. Could be inefficient if 

copious amounts of notes or comments need to be recorded in the field. 

Palm-top computers (PDAs) – the small size and relative low cost of these devices 

make them attractive options for collecting field data. Good for small field projects but 

not powerful enough for large, data intensive field projects. PDAs can be weatherized

fairly easily and inexpensively. Most run either Windows CE or Palm operating systems 

which may require additional processing/programming to transfer/create the database 

structure in the field units.

Tablet PCs – same properties as most laptops and provide the user with the convenience 

of a touch screen interface. They are bulkier, more expensive and harder to weatherize

than the PDAs but are more powerful as well. Good for field projects that are very data 

intensive. Because these units run Windows XP (Tablet Edition), the project database 

can be directly transferred from desktop units to field units without additional 

programming steps. 

Automated Data Loggers – mainly used to collect ambient information such as weather data or 

water quality information. Must be properly calibrated and maintained so field crews must 

receive proper training and SOPs outlining these procedures.

Permanently deployed devices – often cost prohibitive. Data from these devices must 

be retrieved and batteries changed on a regular basis. These intervals should be defined 

in the protocol.

Portable hand-held devices – deployed for sampling only during site visits. Generally 

less expensive than units that are permanently deployed in the field.

GPS Units – Two types of GPS units are often used during field work in NCCN parks to collect 

location information. (Guidelines in development)
45

Handheld Garmin Units – good for collecting general position information. Not 

recommended for obtaining high accuracy location information. 

Trimble and GeoExplorer GPS Receivers – good for collecting highly accurate (submeter) location information. Due to the small size of certain of the NCCN parks, field 

crews are encouraged to use these units when collecting GPS data. 

5.1.3 – Data entry

All field studies funded by the NCCN I&M Program have a Microsoft Access database 

associated with them. The NCCN I&M Program has adopted the Natural Resources Database 

Template (NRDT) as the foundation for its databases and will provide assistance in database 

development for any programmatic project. The database template is highly flexible and can be 

modified and customized for each project to meet the needs and requirements of the researcher. 

The database incorporates mechanisms such pick lists and validation rules for quality assurance 

purposes. (Guidelines in development)

Field crews/project staff enter all data into the database provided to them.

Field crews are required to periodically forward the data manager the project data files. 

Refer to individual protocols for the requirements regarding forwarding data.

The data manager maintains the master copy of the database and updates it with data files 

received from the field crews. 

All data must undergo QA/QC procedures (see Chapter 6 of this document for more 

specifics relating to data verification and validation).

5.1.4 – NPSpecies and Biodiversity Data

NPSpecies is a National Park Service database developed by WASO to store, manage and 

disseminate scientific information on the biodiversity of organisms in National Park Service 

units throughout the United States and its territories. The database is available in an on-line form 

(Oracle) or a desktop version (MS Access). For more specifics on NPSpecies please refer to the 

following web page:

http://science.nature.nps.gov/im/apps/npspp/index.cfm

Three forms of evidence used to document a species existence in NPS units are:

References

o All written reports which document species presence in a park unit are entered 

into the NPSpecies and NatureBib databases.

Observations

o All observation data collected during biological inventories are entered into the 

NPSpecies database. 

Vouchers
46

o Information relating to all voucher specimens collected during a biological 

inventory must be entered into NPSpecies.

o Once catalog and accession numbers are obtained, all voucher information must 

also be entered into ANCS+. 

o The parks where the vouchers were collected technically own the specimens and 

have the right to decide where the specimens will be stored. 

5.1.5 – Changes to Data Collection Procedures/Protocols

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 

procedures/protocols revision after data collection has begun. Significant changes to the 

protocols must be approved by the principal investigator, project leader and data manager. The 

key official must evaluate the proposed changes and determine if additional peer review is 

required before accepting them. 

Altering data collection procedures or protocols may also occur as a result of the comprehensive 

review that all monitoring protocols undergo every five years. During the review, data are

evaluated to determine if the current protocol accomplished its goal. If it is concluded that the 

protocol in its present form has not achieved the desired results, changes could be recommended. 

Once again, all changes must be approved by the principal investigator as well as the key official 

and data manager.

5.2 – Other NPS Data

A large percentage of data collected in Network parks are collected by the park personnel 

involved in projects initiated at the individual park level or by other NPS regional or national 

programs. The data collected and products produced by such efforts provide a great deal of 

information about park natural resources and are therefore relevant to the mission of the I&M 

Program.

5.2.1 – Park Data

Parks in the NCCN often use base funding or receive funding through NRPP (Natural Resources 

Protection and Preservation) programs to support park-level projects.

Park-based biological inventories - Network parks often conduct their own park-based 

inventory projects, the data from which can be used to supplement Network-level 

inventories conducted by the I&M Program. 

Park-based monitoring projects - Parks also engage in park-level monitoring projects 

(such as vegetation and water quality) which produce information that is valuable when 

developing Network-level monitoring protocols. 
47

Park and multi-park based projects - 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).

5.2.2 – Regional and National Programs

NPS regional and national programs support all of the parks within the Pacific West Region and 

are good resources for natural resources information. Table 5.1 lists the regional contact 

information for the personnel in charge of the following programs in the NCCN.

Air – National-scale programs collect data, maintain databases, assure data quality, and 

perform the trend analyses relevant to NCCN air quality issues. The NCCN I&M 

program will rely on the data analyses from these national scale monitoring networks to 

obtain trends for many of its air vital signs.

EPMT - Exotic Plant Management Teams (EPMT) collect and maintain data regarding 

the presence of exotic species in regional parks, and develop and document the methods 

used to treat these species. This information is stored in the Alien Plant Control and 

Monitoring Database (APCAM) which is maintained by the EPMT data manager or 

EPMT liaison. http://www1.nrintra.nps.gov/brmd/invasivespecies/exoticplants/index.cfm

Fire Program – Data concerning the occurrence of fires within the NCCN are 

maintained both at the individual park and regional level. National databases such as 

Fire-Pro, SACS and the soon-to-be implemented Fire Program Analysis (FPA) package 

(http://fpa.nifc.gov/) have been and will be used to maintain information regarding fire 

incidence and the resources dedicated to fire management. The NPS is also involved in 

efforts such as the Joint Fire Science Program (http://www.firescience.gov/) that provide 

scientific information and support for fuel and fire management programs.

GIS – The NCCN I&M Program is supported by regional GIS specialists to help ensure 

that regional GIS data are available and accurate. Much of this data is also available 

through the Spatial Data Clearinghouse.

Inventory and Monitoring – The regional and network coordinators provide support 

and guidance for I&M related tasks and activities and communicate national program 

directives. 

Water - Almost all of the field data collection by the regional water resources program is 

done in support of the water resources vital signs monitoring projects. The program also 

synthesizes, analyzes and interprets water resources data collected by parks. 

Wildlife Management - The regional Threatened and Endangered (T&E) Species 

Specialist provides management support to the parks in the Pacific West Region. The 

T&E Species Specialist coordinates reporting of T&E species populations.
48

Table 5.1. NPS Pacific West Region program contacts

Program Contact Title E-Mail

Air Judy Rocchio

Elizabeth Waddell

PWR Air Resource Lead

Air Resource Specialist

judy_rocchio@nps.gov

elizabeth_waddell@nps.gov

EPMT Judy Daniels

Todd Neel

EPMT Data Manager

NCCN EPMT Coordinator

judy_daniels@nps.gov

todd_neel@nps.gov

Fire Jeff Manley Fire Pro Core Team jeff_manley@nps.gov

GIS Craig Dalby

Allen McCoy

Regional GIS Coordinator

Regional GIS Specialist

craig_dalby@nps.gov

allen_mccoy@nps.gov

Inventory & 

Monitoring

Penny Latham 

Mark Huff

PWR Reg. Coordinator 

NCCN Coordinator

penny_latham@nps.gov

mark_huff@nps.gov

Water Mietek Kolipinski

Marie Denn

PWR Water Res. Lead

Regional Aquatic Ecol.

mietek_kolipinski@nps.gov

marie_denn@nps.gov

Wildlife Steve Gibbons T & E Specialist steve_gibbons@nps.gov

Data Processing

These data often do not require a great deal of processing because the NCCN I&M Program 

shares many of the file standards with Network parks and regional programs. Some basic 

processing steps include:

Enter all new park biodiversity data into NPSpecies (this is especially important for parkbased biological inventories) and enter all associated references into NatureBib.

Enter all natural resource reports and publications related to NCCN parks into NatureBib. 

Hard copies should be stored in the appropriate park collections and electronic copies 

archived in the proper directory on the NCCN file servers.

Ensure that all GIS data are in the proper projection and accompanied by FGDCcompliant metadata.

All data sets should be entered into and tracked using Dataset Catalog.

It is important that park, regional and network staff work closely together to ensure that 

information is maintained in a manner that promotes data sharing. Accordingly, the NCCN Data 

Manager will:

Work closely with park and regional personnel to ensure that high quality data are 

available

Provide training to park staff interested in learning to use NPSpecies or NatureBib

Offer to enter any information collected/maintained by the parks into NPSpecies or 

NatureBib

Provide assistance with the processing of voucher specimens collected in Network parks

Develop databases based on the NRDT that meet the needs of park resource managers

5.3 – External Data

As was the case in the previous section, external data sources often provide information relevant 

to the mission of the I&M program. It should be noted that such sources need not be directly 
49

connected to NCCN parks but may instead pertain to methodologies or protocols that could assist 

NCCN staff with the development of a more productive program. 

There are a myriad of possible data sources available to provide information pertinent to the 

NCCN I&M Program.

Government Agencies - Federal, State and local

Academia

Private organizations/non-profit groups

Some NPS units in the NCCN collect these data and contribute them to the larger regional or 

national data sets maintained by the other agencies. The agencies or organizations that compile

these data have the expertise to conduct the proper quality control procedures and the capability 

to function as a repository and clearinghouse for the validated data. 

When the data are not kept in-house, there can be two modes of acquisition: downloads from 

online databases or requests for data on CD or other media. In some cases, portions of external 

databases may be incorporated into NCCN databases and thereby made more accessible to 

NCCN staff.

Data Processing

Unlike the data from NPS sources, much of the data collected from external sources must 

undergo some degree of processing to meet the standards of the NCCN I&M Program; however 

some of the basic processing steps are very similar. 

All GIS data obtained from other entities are stored in the proper format, have the correct 

spatial reference information and have FGDC compliant metadata. This is especially 

applicable to data collected in support of the vital signs relating to land use/land cover 

change.

All biodiversity data received from other entities should be entered into NPSpecies. This 

would include data sets like the Breeding Bird Survey. Also, if the data were taken from 

a report or published document, the reference must be entered into NatureBib.

Certain data sets will require more than the basic processing steps described above. The level of 

data procession required for external data sets such as those used in the vital signs monitoring 

program depends on the desired output. If more intensive analyses are intended than are 

provided through these means – such as conducting in depth analyses at a specific location rather 

than monitoring regional trends – the data may require additional processing steps. In such 

cases, the specific protocols should provide the necessary data processing requirements. 

Remote sensing data sets such as satellite imagery or aerial photography will require varying 

levels of processing depending on how they are received. These may include geospatial 

processing or spectral processing. Ideally, all spatial data sets will be received in a georeferenced format and may only require geographic transformations to meet NCCN standards. 
50

Varying degrees of spatial and spectral processing may be necessary to adequately answer the 

proposed questions. The individual monitoring protocols outline the necessary processing steps.

Credits

This chapter was adapted from material developed by Geoffrey Sanders (National Capital 

Region).
51

Chapter 6 – Quality Assurance

We must have confidence in the data we use. Our analyses to detect trends and patterns in 

ecosystem processes require data of documented quality to minimize error and bias. Data of 

inconsistent or poor quality can result in loss of sensitivity and incorrect interpretations and 

conclusions. The potential for problems with data quality increases dramatically with the size 

and complexity of the data set (Chapal & Edwards 1994). 

Our network data management system must ensure that our projects produce and maintain data 

of the highest possible quality. NCCN will establish and document protocols for the 

identification and reduction of error at all stages in the data lifecycle. These stages include 

project planning, data collection, data entry, verification and validation, processing, and 

archiving. Although a data set containing no errors would be ideal, the cost of attaining 95%-

100% accuracy may outweigh the benefit. Therefore, we consider at least two factors when 

setting data quality expectations: 

 frequency of incorrect data fields or records

 significance of error within a data field

We are more likely to detect an error when we work with clearly documented data sets and 

understand what a ―significant‖ error is within a given data set. The significance of an error can 

vary with data sets and depends on where it occurs. For example, a two-digit number off by one 

decimal place is a significant error. A six-digit number, with the sixth digit off by one decimal 

place, is not a significant error. But one incorrect digit in a six-digit species number could 

indicate a different species. That is a significant error. 

6.1 – NPS Mandate for Quality

Not long ago, maintaining data meant filling filing cabinets full of notebooks and paper. Now we 

are more likely to use computer hardware and software – technology that changes rapidly and 

sometimes unpredictably. If we expect our current data to be useful to future generations, the 

data must survive changes in technology. We can promote data longevity through high-quality 

documentation and maintenance during all phases of data management: during data collection, 

entry, verification, and validation. Well-documented data sets are especially important when 

sharing data. 

NPS Director‘s Order #11B: ―Ensuring Quality of Information Disseminated by the National 

Park Service,‖ issued in 2002, promotes information and data quality. It defines ‘quality’ as 

incorporating three key components–objectivity, utility, and integrity. 

Objectivity consists of: 1) presentation, which focuses on whether disseminated 

information is being presented in an accurate, clear, complete, and unbiased manner 

within a proper context, and 2) substance, which focuses on ensuring accurate, usable, 

and reliable information.
52

Utility refers to the usefulness of the information to its intended users, from the 

perspectives of both the Network and the general public. 

Integrity refers to the security of information, e.g., protection from unauthorized access or 

revision to ensure that the information is not compromised through corruption or 

falsification. 

Order #11B also specifies that information must be based on reliable data sources, which 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. 

High quality data and information are mandated by directives and orders, and they are vital to the 

credibility and success of the I&M program. Data need to meet national-level quality standards 

and need to be accessible to be used for wise and defensible decision-making at all levels. Data 

need to be able to be shared and aggregated with data from other parks and from adjacent lands 

to support landscape-level and national planning and decision-making.

6.2 – Quality Assurance and Quality Control Mechanisms

Palmer and Landis (2002) define Quality Assurance (QA) 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.‖ They define Quality Control (QC) as ―a system 

of technical activities to measure the attributes and performance of a process, item, or service 

relative to defined standards.‖ Quality Assurance procedures maintain quality throughout all 

stages of data development. Quality Control procedures monitor or evaluate the resulting data 

products.

QA/QC mechanisms are designed to prevent data contamination, which occurs when a process or 

event other than the one of interest affects the value of a variable and can introduce two 

fundamental types of errors into a data set:

Errors of commission include those caused by data entry and transcription errors or 

malfunctioning equipment. They are common, fairly easy to identify, and can be 

effectively reduced upfront 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.

QA/QC procedures applied to ecological data include four procedural areas (or activities), 

ranging from simple to sophisticated, and inexpensive to costly: 
53

1) defining and enforcing standards for electronic formats, locally defined codes, 

measurement units, and metadata 

2) checking for unusual or unreasonable patterns in data 

3) checking for comparability of values between data sets 

4) assessing overall data quality

Much QA/QC work involves the first activity (defining and enforcing standards), which begins 

with data design and continues through acquisition, entry, metadata development, and archiving. 

The progression from raw data to certified data implies increasing confidence in the quality of 

the data through time.

6.3 – Roles and Responsibilities

Quality assurance methods should be in place at the inception of any project and continue 

through all project stages to final archiving of the data set. It is critical that each member of the 

data management group work to ensure data quality. Everyone plays a part in producing and 

maintaining high quality data. Anyone assigned to a project is responsible for the quality of the 

results generated from his or her task(s).

The data manager is responsible for:

 developing protocols and SOPs to ensure data quality

 making project leaders, technicians, etc., aware of the established procedures and 

enforcing adherence to them

 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 

the data quality program

Project leaders must:

 be aware of quality protocols and convey their importance to technicians and field 

crews

 ensure compliance with the protocols

 validate data after the verification process is complete

 review all final reports and information products

Technicians must follow established protocols for data collection, entry, and verification 

established in the inventory and monitoring protocol data management SOPs.

6.4 – Goals and Objectives

We must ensure that a project produces data of the right type, quality, and quantity to meet 

project objectives and user needs. Quality criteria should be set at a level proportionate to 

project-specific objectives. The EPA (2003) defines data quality objectives as qualitative and 

quantitative statements that:
54

 clarify the intended use of the data

 define the type of data needed to support management decisions

 identify the conditions under which the data are to be collected

 specify tolerable limits on the probability of making a decision error due to 

uncertainty in the data

The most effective mechanism for ensuring that a project produces data of the right type, quality, 

and quantity is to provide procedures and guidelines to assist the researcher in accurate data 

collection, entry, and validation. We will initiate a comprehensive set of SOPs and datacollecting protocols for quality control, field methodologies, field forms, and data entry 

applications with some built-in validation mechanisms.

Although specific QA/QC procedures will depend upon the individual vital signs being 

monitored and must be specified in the protocols for each monitoring vital sign, some general 

concepts apply to all network projects. The general QA/QC procedures presented in this plan 

were primarily adapted from the Draft Data Management Protocol (Tessler & Gregson 1997) and 

the ideas contained in Michener and Brunt (2000). These general guidelines will ensure that all 

data collected are checked for integrity before being integrated into the monitoring program 

databases. Refer to SOPs and monitoring protocols for specific QA/QC procedures.

6.5 – Data Collection

Careful, accurate recording of field observations in the data collection phase of a project will 

help reduce the incidence of invalid data in the resulting data set. Unlike a typographical error 

that occurs when a recorded observation is incorrectly transferred from a paper field form to a 

digital database, an incorrect entry in the field cannot be easily corrected. Therefore, attention to 

detail during data collection is crucial to overall data quality.

Paper field notebooks or data forms have been the primary methods for ecological data collection 

for many years. Although paper has advantages in terms of longevity and ease of use, it does not 

work well under some environmental conditions, and processing options are limited until the 

data are transferred to digital format. As an alternative to paper, several options for electronic 

data collection in the field are now available, including handheld computers, automated data 

collection instruments, and tape recorders. 

Before the data collection phase of a project begins, the data manager is responsible for 

providing the protocols/SOPs for data collection and storage to the project leader. All field sheets 

and field data recording procedures must be reviewed and approved by the data manager and 

documented in the protocol SOPs. The project leader, in turn, will ensure that field crews 

understand the procedures and closely follow them in the field. If training is needed, the data 

manager will work with the project leader to provide that training. Field technicians are 

responsible for proofing raw data forms prior to data entry, ensuring their readability and 

legibility, and verifying and explaining any unusual entries. They are expected to understand the 

data collection forms, know how to take measurements, and follow the protocols.
55

6.5.1 – Methods for Reducing Collection Errors

Use a formatted, project-specific data sheet as opposed to a field notebook. When electronic data 

collection devices are not used, data should be recorded on paper data forms. We strongly 

recommend acid-free paper to prevent fading and subsequent data loss. Some circumstances may 

warrant the use of paper and writing implements that can withstand moisture, dust, and other 

extreme environmental conditions.

Standardized data sheets that identify the necessary information to be recorded and forms that 

reflect the design of the computer data entry interface will help ensure that all relevant 

information is recorded and subsequent data entry errors are minimized. Data sheets should 

contain as much basic preprinted project information as possible and sufficient space for 

recording relevant metadata such as date, collectors, weather conditions, etc.. They should 

clearly specify all required information, using examples where needed to ensure that the proper 

data are recorded. Data recorders should adhere to the following guidelines:

 All information added to the data sheet must be printed and clearly legible.

 If alterations to the information are necessary, the original information should be 

crossed out with a single line and the new information written next to the original 

entry. Information should never be erased, and old information should not be 

overwritten.

 After error-checks of field forms are complete, copies of all original data sheets 

should be made and checked for legibility and completeness (i.e., no data cut off at 

the edges). The copies of the data sheets will be stored as specified in the protocol 

SOP, and the original data sheets will be used for data entry. 

Use a handheld computer for data collection whenever possible. The use of handheld computers 

minimizes the need for manual data entry from field forms and associated transcription and data 

entry errors. Specially designed database or computer programs may be required for handheld 

computers, and the user interface should be customized to the project requirements. A 

customized data entry application has the advantage of incorporating on-the-spot QA/QC checks, 

so this data collection method probably provides the highest quality data. 

These portable units, however, are subject to environmental hazards such as heat, dust, and 

moisture. When handheld computers are used for data entry in the field, the data should be 

downloaded daily to avoid potential loss of information. Thus, if a handheld unit fails during 

data collection, only the current day‘s data are lost. Batteries should be checked prior to a data 

collection trip, and they should be charged at the end of every field day. The use of a memory 

card that will store the data in case of damage to the unit or battery failure can prevent accidental 

loss of data. Also, in case the unit becomes inoperable in the field, printed data sheets should 

always accompany field teams on data collection trips.

Use automated data loggers where appropriate. Instruments with their own data acquisition 

systems are useful for collecting some types of data, such as water and air quality data. These 

devices can be calibrated and programmed to automatically record data and store them for later 

download directly to a computer, thereby eliminating the possibility for manual data entry errors. 
56

Data loggers are an efficient means of recording continuous sensor data, but routine inspections 

are necessary. Moreover, environmental conditions, as well as power (e.g., sufficient battery

charge) and maintenance requirements, are potential pitfalls when using these instruments. 

Regular downloads should be required since physical memory is usually limited. 

Use a handheld tape recorder. Another alternative to paper field data forms is a handheld micro 

cassette tape recorder. 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, efficient 

method of collecting high quality data.

Consider calibration, maintenance, and minimum timing requirements of field equipment.

Accurate field measurements are possible only if field equipment is regularly calibrated and 

maintained. Where appropriate, consult reference manuals for recommended calibration and 

maintenance procedures. Once in the field, allow sufficient time for field equipment to adjust to 

its environment so it will record accurate measurements (for example, when using water quality 

probes and GPS units). Researchers should consider maintaining records of equipment 

calibration and failures that accompany their field data whenever possible.

Be organized and keep a log. Organization is the key to good data collection methods. 

Maintaining a log of important decisions and events will help clarify information and contribute 

to an accurate report.

Ensure that field crews receive proper training. Although protocols and SOPs are in place, they 

cannot guarantee that high quality data will be collected. Prior to routine data collection for a 

project, conduct training sessions to ensure that field personnel have a clear understanding of 

data collection procedures described in the SOPs. A training program may also include a process 

to certify that field staff understand and can perform the specified data collection procedures. 

The development of a training manual may be helpful for long-term monitoring data collection 

efforts and those that will involve a large number of field staff. Palmer and Landis (2002) 

provide an outline for a training manual and suggestions for planning training sessions.

Perform quantitative assessments of data quality. Repeating measurements is the primary tool 

for performing quantitative assessments of data. Project leaders should periodically review the 

work of field technicians to ensure that their work does not drift from standards during the course 

of the field season. Quantitative assessments may be considered if staff and funding are 

available, and Palmer and Landis (2002) describe several approaches that can be used.

6.6 – Data Entry

 ̳Data entry‘ is the initial set of operations where we transfer raw data from paper field forms into 

a computerized form linked to database tables. Spreadsheets should not be used for data entry 

(data can be exported to a spreadsheet for manipulations post entry). 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. 
57

Transferring data from field projects into the computer seems a fairly simple task. But the value 

of the data depends upon their accuracy, and we must feel confident about the overall quality of 

the data. Without proper preparation and some established guidelines, the quality and integrity of 

the data can be questionable. Ideally, data entry occurs as soon as possible–immediately after 

data collection is completed or as an on-going process during long projects–by a person who is 

familiar with the data. The primary goal of data entry is to transcribe the data from paper 

records into the computer with 100% accuracy. Yet, we know that a few transcription errors are 

unavoidable during data entry. Thus, all data should be checked and corrected during a data 

verification process. 

The data manager, along with the project leader, should provide training in the use of the 

database to all potential users – especially those responsible for data entry and quality assurance. 

The project leader makes certain that data entry technicians understand how to enter data and 

follow the protocols. Data entry technicians are responsible for becoming familiar with the field 

data forms and differences in handwriting. They must also become familiar with the database 

software, database structure, and any standard codes for data entry used by the Network. At 

minimum, data entry technicians should know how to open a data entry form, create a new 

record, and exit the database properly. They must learn how to commit both a  ̳field‘ entry and a 

―complete record‖ entry and to correct mistakes made while typing.

6.6.1 – Methods for Reducing Data Entry Errors

Enter or download data in a timely manner. All data should be entered or downloaded into the 

project database as soon as possible, preferably at least once a week. Try to avoid delaying data 

entry until all the project data have been collected. Downloaded data should be periodically 

stored on CD or some other semi-permanent media.

Design efficient data entry forms and methods. A full-screen data entry form that mimics the 

field data forms can effectively reduce manual data entry errors due to the 1:1 correspondence of 

the attributes. A strategy to distinguish between validated data and newly entered data should be 

adopted. Data can be entered into an empty, fresh database table to avoid contaminating existing 

data and the new data appended to the master data only after formal verification, validation, and 

documentation. Alternatively, we can include validation attributes that indicate which data have 

been checked and validated by the project leader in the database. Regardless of strategy, we must 

clearly document the process for validation in the protocol data management SOP.

Build automated error checking features into the database. The most robust QA/QC measures 

for data entry should be built into the database design to perform automatic validation checks of 

data. Data entry forms reduce transcription errors through auto-filled fields, range limits, pick 

lists, and spelling checks. They provide controlled access to the database (i.e., forms are set for 

data entry only, which prevents accidental deletion or alteration of existing data). They control 

the sequence of data entry (i.e., certain fields require an entry before more information can be 

entered). They warn the operator when errors are made and provide an opportunity for correction 

before the data are committed to a file.
58

 Auto-filled fields. Whenever possible, the data in a field should be auto-filled by the 

computer. For example, if a location ID is composed of a park code, project code, and 

a unique number, those elements are automatically inserted into the location ID field, 

ensuring that the record always contains a unique identifier.

 Range limits. Where the appropriate values for a particular field span a finite range, 

the data entry program can check the entered value against the specified minimum 

and maximum values for that parameter. When a value is outside the accepted range, 

a warning message appears and asks the user to reenter a valid value. For some fields, 

values outside a specified  ̳normal‘ range may be acceptable. In this case, the warning 

message asks the user to verify the entry before continuing.

 Pick lists. The data entry application may also use pop-up pick lists for standardized 

text items where spelling errors can occur. For example, rather than typing in a 

species code or name (where a misspelling generates a new species in the database), 

the code or name is selected from a list of valid species codes or scientific names and 

automatically entered into the species field. A pick list may also be used when only 

certain entries are acceptable. Lists are not appropriate for all written fields but should 

be used when appropriate.

 Unique constraints. Duplicate and incorrect data entry can often be caught with the 

application of unique constraints on data entry fields. These constraints are 

particularly useful when importing data from other applications.

Provide a clean, organized work environment. Desktop space near the computer should be free 

of clutter and distractions that could cause the technician to lose her place. There should be 

enough space for two stacks of paper documents, one from which data are being entered and one 

from which data have been entered. A pad or notebook and some fine colored markers should 

also be available for making notes. (The need for a clean workspace also applies to the 

verification and validation phases.)

If possible, use two data entry technicians for data entry. When one technician reads the data 

from the field data forms and another enters them into the computer, the work is often faster and 

results in a lower error rate. If only one person is available, he should work at a slower pace to 

avoid errors. Like many monotonous tasks, data entry can be done in a personal rhythm that 

reduces the tediousness of the work.

6.7 – Verification and Validation Procedures

We appraise data quality 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. Data verification checks that the digitized data match the source data, while 

data validation checks that the data make sense. It is essential that we validate all data as truthful 

and do not misrepresent the circumstances and limitations of their collection. Failure to follow 

SOPs for data entry, validation, and verification will render a data set suspect. Although data 

entry and data verification can be handled by personnel who are less familiar with the data, 

validation requires in-depth knowledge about the data. 
59

The data manager establishes SOPs for verification and validation and provides them to the 

project leader. Technicians will follow the SOPs for verification of data, make required changes, 

and document those changes. The project leader will ensure that the SOPs are followed. The 

project leader or designee will validate the data after verification is complete. The project leader

is also responsible for reviewing all data products and reports before they are released outside the 

Network. The data manager and project leader will evaluate the results of verification and 

validation and determine any procedural or data form revisions that may be indicated by the 

results.

6.7.1 – Methods for Data Verification

Data verification immediately follows data entry and involves checking the accuracy of the 

computerized records against the original source, usually hard copy field records, and identifying 

and correcting any errors. When we have verified the computerized data as accurately reflecting 

the original field data, we can archive the paper forms and manipulate and analyze most data on 

the computer.

Among the following methods there is 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. She 

compares the values recorded in the database with the original values from the hard copy 

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. All records are printed upon the completion of data entry. 

The values on the printout are compared with the original values from the hard copy. 

Errors are marked and corrected in a timely manner. This method‘s reliability increases if 

someone other than the person keying data performs the review. Alternatively, two 

technicians can perform this review. One technician reads the original data sheets (the 

reader), and the second reads the same data on the printout (the checker).

3) Duplicate data entry. The data entry technician completes all data entry, as normal. 

Random records are then selected (every nth record) and entered into an empty replica of 

the permanent database, preferably by someone other than the person keying the 

permanent data. Then a query is used to automatically compare the duplicate records 

from the two data sets and report any data mismatches. The data technician manually 

reviews any disparities and makes corrections if necessary. This method adds the 

overhead of retyping the selected records, as well as the creation of a comparison query, 

but it becomes increasingly successful as the value of n decreases. Professional data entry 

services frequently use this method.

We can also use the entered data to calculate simple summary statistics with statistical software. 

These summary statistics can help us catch a duplicate or omitted entry. For example, we can 

view the number of known constant elements, such as the number of sampling sites, plots per 

site, or dates per sample. We can pose the same question in different ways; differences in the 
60

answer provide clues to errors. The more checks we devise to test the completeness of the data, 

the greater our confidence that we have completely verified the data.

To minimize transcription errors, our policy is to verify 100% of records to their original source 

by permanent staff. In addition, 10% of records are reviewed a second time by the project leader, 

and we report the results of that comparison with the data. If the project leader finds errors in her 

review, then we verify the entire data set again.

Although we may have correctly transcribed the data from original field notes or forms, they still 

might be inaccurate or illogical. For example, entries of stream pH of 25.0 or a temperature of 

95°C in data files raise doubt about their accuracy; and such entries almost certainly are 

incorrect, whether or not they were properly transcribed from field forms. This process of 

reviewing computerized data for range and logic errors we call validation, and it can accompany 

data verification only if the operator has comprehensive knowledge about the data. 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. Corrections or deletions of logical or 

range errors in a data set require notations in the original paper field records about how and why 

the data were changed. Modifications of the field data should be clear and concise while 

preserving the original data entries or notes (i.e., no erasing!). Validation efforts should also 

include a check for the completeness of a data set since field sheets or other sources of data could 

easily be overlooked.

General step-by-step instructions are not possible for data validation because each data set has 

unique measurement ranges, sampling precision, and accuracy. Nevertheless, validation is a 

critically important step in the certification of the data. Invalid data commonly consist of slightly 

misspelled species names or site codes, wrong dates, or out-of-range errors in parameters with 

well defined limits (e.g., elevation). But more interesting and often puzzling errors can be

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). We call these types of erroneous data logic 

errors because using them produces illogical (and incorrect) results. The discovery and 

correction 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.

6.7.2 – Methods for Data Validation

The following general methods can be used to validate data. Specific procedures for data 

validation depend upon the vital sign being monitored and will be included in the monitoring 

protocols.

Data entry application programming. Certain components of data validation are built into data 

entry forms. The simplest validation during data entry is range checking, such as ensuring that a 

user attempting to enter a pH of 20.0 gets a warning and the opportunity to enter a correct value 

between 1.0 and 14.0 (or better yet, within a narrow range appropriate to the study area). Not all 

fields, however, have appropriate ranges that are known in advance, so knowledge of what are 

reasonable data and a separate, interactive validation stage are important.
61

Edwards (2000) suggests the use of  ̳illegal data‘ filters, which check a specified list of variable 

value constraints on the master data set (or on an update to be added to the master) and create an 

output data set. This output data set includes an entry for each violation, along with identifying 

information and an explanation of the violation. He illustrates the structure of such a program, 

written in the SAS® programming language.

A caveat should be interjected regarding the operative word  ̳illegal‘. Even though a value above 

or below a given threshold has never before been observed and the possibility that it could occur 

seems impossible, such an observation is not always an illegal data point. Edwards (2000) points 

out that one of the most famous data QA/QC blunders to date occurred when NASA‘s computer 

programs deleted satellite observations of ozone concentrations that were below a specified 

level, seriously delaying the discovery of the ozone hole over the South Pole.

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 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 a better way to explain this quality assurance goal. If contamination is not 

detected during data collection, it is usually only detected later if an outlying data value results. 

When we detect an outlier, we should try to determine if some contamination is responsible.

We can use database, graphic, and statistical tools for ad-hoc queries and displays of the data to 

detect outliers. Some of these outlying values may appear unusual but prove to be quite valid 

after confirmation. Noting correct but unusual values in documentation of the data set saves 

other users from checking the same unusual values.

Other exploratory data analyses. Palmer and Landis (2002) suggest that in some cases, 

calculations for assessments of precision, bias, representativeness, completeness, and 

comparability may be applicable and that for certain types of measurements, evaluation of a 

detection limit may also be warranted (the authors provide examples of procedures that may be 

applicable). Normal probability plots, Grubb‘s test, and simple and multiple linear regression 

techniques may also be used (Edwards, 2000; the author provides SAS and S-Plus code for 

constructing normal probability plots and examples of output showing normal and non-normal 

distributions).

6.8 – Version Control

Our network manages files from a multitude of sources, comprising many formats with many 

iterations of a particular product. Some of the files are complete, some are works-in-progress, 

and for others the status cannot be determined. In addition to files it collects, NCCN also 
62

generates many files, some of which fall into the complete, works-in-progress, and undetermined 

status categories. Determining the status of a single file can be difficult, but determining the 

current file within a series of similarly named files can be almost impossible.

Version control is the process of documenting the temporal integrity of files as they are being 

changed or updated. Change includes any alteration in the structure or content of the files, and 

such changes should not be made without the ability to undo mistakes caused by incorrect 

manipulation of the data. Whenever we complete a set of data changes, we should save the file 

with a unique name, a simple act that should become routine for all data handlers.

Prior to any major changes to a file, we should store a copy of the file with the appropriate 

version number. This allows the tracking of changes over time. With proper controls and 

communication, versioning ensures that only the most current version is used in any analysis.

The data manager determines the version control method that will be used, and other network 

personnel are responsible for accurately designating versions for any files upon which they have 

worked. Software tools that assist in file management can be helpful–for example, to creating

databases that include fields to record the file‘s revision history. Backup routines can be built 

into the databases that allow for automatic file renaming and archiving. Important program files 

can be catalogued in a simple index or more formally tracked and archived using professionally 

developed version control software. 

6.8.1 – Version Control Options

Dates. Using a date provides logical version control. The date is usually formatted as 

YYYYMMMDD, where DD is optional depending on the frequency of changes. Indicating the 

month as a three-character text string (e.g., 2005Sep12) increases readability and avoids

confusion over which digits signify year, month and date.

Sequential numbers. We can designate versioning of archived data sets by adding a number to 

the file name, e.g., 001 or V1.0 for the first version. We assign each additional version a 

sequentially higher number. We should also document the date that a file becomes a new 

version, perhaps through assignment of database folder (or directory) names.

Version control software. We can eliminate the work of differentiating multiple versions of 

documents by using version control software to append modifying characters to the file name. 

Such software applications track changes made to a document, add comments related to the 

different document iterations, and retrieve the document at any recorded stage of development. 

These applications are available in either desktop or online formats.

Version control software should:

Preserve previous versions of documents for possible recovery

Track documents as they change during the course of the developmental and editorial 

phases of document/report creation
63

Prevent conflicts between multiple collaborators by prohibiting multiple edits to the same 

file at the same time

Evaluate the document creation process by tracking who changes a file, when they make 

the change, and what changes they make

Reduce storage requirements by eliminating multiple copies of complete documents

6.9 – Data Quality Review and Communication

The National Park Service requires QA/QC review prior to communicating/disseminating data 

and information. Only data and information that adhere to NPS quality standards can be released.

Director‘s Order #11B states that all information (e.g., brochures, research and statistical reports, 

policy and regulatory information, and general reference information) distributed by the NPS 

(including information obtained from sources outside of the NPS) must be accurate, reliable and 

timely in nature. Therefore, we must evaluate and identify the types of information that will be 

subject to the guidelines. Information disseminated to the public must be approved by the 

appropriate reviewing officials and programs. The QA/QC standards used in producing the 

information and that substantiate the quality of the information must be formally documented. 

Also, mechanisms must be in place for receiving and addressing comments/complaints 

pertaining to the quality of data.

Data are distributed to the public through the NCCN I&M web page, national web sites such as 

the Biodiversity Data Store and the Natural Resource/GIS Data Store, and public access 

databases such as NPSpecies and NatureBib. Any information distributed through any of these 

mechanisms must undergo internal QA/QC procedures and be approved for release.

6.9.1 – Data Quality Review Methods

The NCCN Inventory and Monitoring Program will establish guidelines and protocols to ensure 

compliance with DO #11B. These protocols will document both internal and external review 

procedures for data and information disseminated outside the network, as well as a system for 

processing complaints about data quality.

Edwards (2000) suggests the initiation of quality circles, regular meetings of project leaders, the 

data manager, and data management personnel for discussing data quality problems and issues. 

These meetings promote teamwork attitudes while focusing brainpower on data quality issues. 

Participants become more aware of quality issues and learn to anticipate problems. Moreover, all 

participants develop a greater appreciation of the importance of their role in data quality and the 

entire monitoring effort.

6.9.2 – Value of Feedback from QA/QC Procedures

Quality assurance procedures may need revision to improve data quality if random checks reveal 

unacceptable levels. Quality checks should not be performed with the sole objective of 

eliminating errors, as the results may also prove useful in improving the overall quality assurance 

process. For example, if the month and day are repeatedly reversed in a date field, the data entry 
64

technicians may require retraining about the month/day entry order. If retraining is unsuccessful 

in reducing the error‘s occurrence, the computer program may need to be rewritten so that month 

and day are entered separately, field length limits are enforced, or a pick list is created. In this 

manner, the validation process will serve as a means of improving quality as well as controlling 

the lack of quality.

We can modify field data forms to avoid common mistakes when necessary. With knowledge of 

validation errors and exploratory data results in hand, the field data forms as the source of the 

logic errors can be more easily re-evaluated. Often minor changes, small annotations, or adding 

check boxes to a field form can remove ambiguity about what to enter on the form. Perhaps 

surprisingly, when we find the same type of validation error occurring repeatedly in different 

data sets, the field form–not the field crew–is usually at fault. Repeated errors found during 

validation can also mean that protocols or field training are at fault, which can then be corrected.

6.9.3 – Monitoring Conformance to Plans and Standards

Data managers can use periodic data audits and quality control inspections to maintain and 

improve their data quality program. They must verify that staff is operating in conformance with 

the data quality procedures specified in this plan and the protocol specific data management 

plans. They should track and facilitate the correction of any deficiencies. These quality checks 

promote a cyclic process of continuous feedback and improvement of the both the data and 

quality assurance process. 

Periodic checks by the data manager to see if network staff are adhering to the data quality 

procedures established in the Data Management Plan and protocol SOPs 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 results of quality assessments should be documented and reported to the research staff and 

the network coordinator. The project leader and coordinator are responsible for ensuring that 

non-conformities in data management practices are corrected.

6.9.4 – Communicating Data Quality

The Network will use data documentation and metadata to notify end users, project leaders, and 

network management of data quality. A descriptive document for each data set/database will 

provide information on the specific QA/QC procedures applied and the results of the review. 
65

Descriptive documents or formal FGDC-compliant metadata will document quality for spatial 

and non-spatial data files posted on the Internet.

Credits

This chapter was adapted from material developed by Deborah Angell (Sonoran Desert 

Network). Special thanks to Doug Wilder (Central Alaska Network) and Sara Stevens 

(Northeast Coastal and Barrier Network) for contributions and ideas.

67

Chapter 7 – Data Set Documentation

Note: This chapter is currently in revision as of September 2005.

7.1 – Purpose of Metadata

Data documentation is a critical step toward ensuring that data sets are useable for their intended 

purposes well into the future. This involves the development of metadata, which can be defined 

as information about the content, quality, condition and other characteristics of data. 

Additionally, metadata provide the means to catalog data sets within intranet and internet 

systems, thus making the data available to a broad range of potential users. 

Data sets seem to sometimes take on lives of their own. Some seem to have the ability to 

reproduce and evolve on multiple hard drives, servers and other storage media. Others are 

masters at remaining hidden in digital formats or in forgotten file drawers. In addition, once data 

are discovered, a potential data user is often left with little or no information regarding the 

quality, completeness, or manipulations performed on a particular ―copy‖ of a data set. Such 

ambiguity results in lost productivity as the user must invest time tracking information down or, 

in the worst case, the data set is rendered useless because answers to these and other critical 

questions cannot be found. As such, data documentation must include an upfront investment in 

planning and organization. 

While the importance of metadata is universally accepted within the data management 

community, the approaches for collection and levels of detail are varied (sometimes referred to 

as the ―101 ways‖). However, following are some important considerations for the development 

of data documentation strategies. 

Executive Order 12906, signed by President Clinton in 1994, mandates federal agencies 

to ―...document all new geospatial data it collects or produces, either directly or 

indirectly...‖ using the Federal Geographic Data Committee (FGDC) Content Standard 

for Digital Geospatial Metadata (CSDGM). In addition, EO 12906 directs agencies to 

plan for legacy data documentation and provide metadata and data to the public. 

The FGDC Biological Data Profile contains all the elements of the CSDGM and includes 

additional elements for describing biological data sets. Metadata created in compliance 

with the Biological Data Profile can be added to the National Biological Information 

Infrastructure (NBII) Clearinghouse. Although not a requirement, completion of the 

Biological Data Profile for appropriate data sets is recommended.

All GIS data layers must be documented with applicable FGDC and NPS metadata 

standards (NPS Metadata Profile).

While there are numerous tools available for developing metadata, the NPS Integrated 

Metadata System Plan is limited to three recommended desktop applications: Dataset 

Catalog, ArcCatalog, and Spatial Metadata Management System (SMMS). 

7.2 – NPS Integrated Metadata System Plan and Tools

As noted above, the NPS Integrated Metadata System Plan is limited to three recommended 

desktop applications for collecting metadata. These include Dataset Catalog (developed by the 
68

I&M Program), and two commercial off the shelf metadata tools, ArcCatalog and SMMS. The 

following is a brief description of each of these tools, including their potential utility in metadata 

creation. A fourth tool, the Metadata Parser (mp, developed by USGS) and its utility in metadata 

creation is also briefly discussed.

Dataset Catalog: Dataset Catalog is a tool for cataloging abbreviated metadata on geospatial and 

biological data sets pertaining to park(s) and/or a network. It provides parks and/or networks a 

means whereby they can inventory, organize, and maintain information about data set holdings 

locally. While Dataset Catalog is not intended to be an exhaustive metadata listing, it does assist 

parks and networks in beginning to meet the mandates of EO 12906. With the current version of 

Dataset Catalog (version 2), records can be exported as an FGDC text file, which can than be 

imported into other metadata tools. Version 2.1 (in development) will include the ability to 

export records in Extensible Markup Language (XML). The I&M Program recommends that all 

relevant data sets at I&M parks and networks be cataloged in at least simple Dataset Catalog 

format. Our plan is plan to follow this recommendation. 

Spatial Metadata Management System: SMMS is a tool with the capability to create, edit, 

view, and publish metadata that is compliant with FGDC requirements. SMMS uses an MS 

Access database structure combined with an advanced FGDC-compliant metadata editor. The 

software allows selection of views depending on whether the user wants the full standard, 

biological, or the minimal compliant view of Sections 1 and 7. There is online Help to describe 

the purpose, usage or mandatory status of metadata elements. The context-sensitive help file 

provides the FGDC definition for each field on the screen. In addition to Help files, there are 

sample metadata records for most sections that provide "real world" examples. The NPS 

Integrated Metadata System Plan recommends SMMS for FGDC Biological Profile and other 

geospatial metadata creation. We are currently using SMMS, although this platform may be 

dropped in the near future as customization of ArcCatalog metadata tools makes it the optimal 

tool.

 

ArcCatalog: ArcCatalog is a management tool for GIS files contained within the ArcGIS 

Desktop suite of applications. With ArcCatalog, users can browse, manage, create, and organize 

tabular and GIS data. In addition, ArcCatalog comes with support for several popular metadata 

standards that allow one to create, edit, and view information about the data. There are editors 

to enter metadata, a storage schema, and property sheets to view the data. With ArcCatalog 

users can view GIS data holdings, preview geographic information, view and edit metadata, 

work with tables, and define the schema structure for GIS data layers. Metadata within 

ArcCatalog is stored exclusively as Extensible Markup Language (XML) files. The NPS 

Integrated Metadata System Plan recommends ArcCatalog for gathering GIS-integrated 

geospatial metadata. An optional but highly recommended extension for ArcCatalog is the NPS 

Metadata ArcCatalog Extension developed by NPS Midwest Region GIS Technical Support 

Center. The extension fixes several ArcGIS 8 metadata errors and provides added functionality 

for NPS users. Development is also underway to provide Biological Profile editing capability 

and NPS Profile support.

 

Metadata Parser: The MetaParser (mp) program is used to validate metadata records by 

checking the syntax against the CSDGM and to generate compliant output files for posting to 
69

clearinghouses. It 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.

Until recently many NPS data stewards collected, parsed and stored metadata (and GIS data sets) 

in the NPS GIS Clearinghouse managed by North Carolina State University (NCSU). However, 

efforts are currently underway to unify and streamline metadata development. This new 

approach utilizes existing desktop metadata creation applications, as well as an online integrated 

metadata database (NR-GIS Metadata) and a web based data server (NR-GIS Data Server). NRGIS Metadata and Data Store will comprise a web based system to integrate both data 

dissemination and metadata maintenance. It will be possible to update Dataset Catalog records 

in the NR-GIS Metadata database or in the source desktop application (i.e., ArcCatalog, Dataset 

Catalog, SMMS). Non-sensitive NR-GIS Metadata records are automatically posted to NPS 

Focus. 

7.3 – Metadata Process and Workflow 

Note: SOPs and guidance documents are under development for utilizing NPS Metadata 

Editor and ArcCatalog.

7.3.1 – Identify Relevant Data Sets and Compile Pertinent Metadata

NCCN data can be grouped, at least initially, in three broad categories based on origin. These 

categories include legacy data sets (primarily data collected prior to the inception of the NCCN 

I&M program), non-programmatic data sets (ongoing data collection efforts conducted by 

entities outside the scope of the I&M program), and I&M data sets.

Legacy Data

In many cases, legacy data are initially identified as part of data mining efforts. Unfortunately, 

many of the legacy data sets will be missing pertinent information, and the originator may no 

longer be in contact. Thus, an ―adequate‖ level of documentation may not be possible. 

However, the data and all supporting documentation related to it should be assembled and 

reviewed (see chapter 5 for further information on legacy data acquisition and processing). 

Many legacy data sets will need to be converted to a standard database format for incorporation 

and future analyses. Data entry, validation and verification procedures will follow those 

contained within this DMP. A processing and revision log will be maintained with the data set

for capture of pertinent metadata.

Non-programmatic Data

The I&M program is not the only entity gathering relevant inventory and/or monitoring data 

pertinent to park management. We will make every effort to capture and assimilate all relevant 

data. The outside entity will be contacted, and a request will be made for available metadata 

and/or a metadata interview will be conducted. As with legacy data, data files may need to be 

converted to a standard database format for analysis. Data entry, validation and verification 

procedures will follow those contained within this DMP. A processing and revision log will be 

maintained with the data set for capture of additional metadata. 
70

I&M Data

For new projects, metadata development will begin up front, with an interview with the principal 

investigator to explain what will be needed to properly document the data. In most instances, 

this will include completion of a basic metadata survey for inclusion in the data manager‘s 

project file, as well as submission of supporting documentation (proposal, SOPs, etc.). In 

addition, a database structure will be developed by, or in close consultation with, the data 

manager, to ensure compliance with the principles and procedures contained within this DMP. 

Updates and revisions to the metadata will be conducted in tandem with data submissions. 

7.3.2 – Create Dataset Catalog Record

Because metadata documentation can be accomplished in a variety of formats and levels of 

detail, it can become a consuming, some might say exhaustive task. As a starting point, we will 

develop a simple Dataset Catalog record for relevant spatial and non-spatial data. This approach 

provides brief metadata for all NCCN data holdings in a searchable, centralized location. In 

addition, managers can identify and prioritize data sets for which formal metadata will be 

developed and identify the status of metadata documentation for a particular data set (i.e., 

planned, in work, complete). These records can be imported into the online NR-GIS Metadata 

System or continue through additional processing steps based on data type, source, and 

importance. 

Prioritization of data sets for further documentation will be based upon current or anticipated 

future use. In other words, data sets that will be used repeatedly in analysis or with high 

probability for data sharing will be addressed first. All GIS layers will be documented with 

applicable FGDC and NPS metadata standards.

7.3.3 – Select Metadata Tool and Complete Record

Historically, parks in the NCCN and Pacific West Region have used the Fire and Mountain 

Ecology Bioinformatics Lab at the University of Washington through CESU agreements for 

completion of FGDC compliant metadata and posted parsed records to the clearinghouse at the 

Metadata Clearinghouse on the Fire and Mountain Ecology website 

(http://www.cfr.washington.edu/research.FME/research/metadata.htm). However, with recent 

improvements to centralize metadata efforts and customized metadata tools, we plan to shift over 

to in house metadata creation using ArcCatalog as the primary metadata tool with extensions to 

incorporate biological profile information. The review of these new tools will be underway 

during FY05.

7.3.4 – Make Information Available

At a minimum, metadata and associated data will be submitted to NR-GIS Metadata and Data 

Store. This will be accomplished utilizing the recommended desktop applications. Additionally, 

information on data holdings should be conveyed in a meaningful manner for park resource 

managers, researchers, and others with a potential interest/stake in park management and/or 

research endeavors. Similar to metadata creation the mechanisms and formats for accomplishing 

this are varied. In addition to FGDC Text files, Dataset Catalog can output a list of all records, 

single record reports, and/or a data dictionary report. Depending on the target audience, these 
71

standardized outputs can be useful in conveying information on program data holdings and 

summaries of database structures. Obviously, customized queries and reports can also be 

generated. Other standardized outputs include ArcCatalog stylesheets. The NPS Metadata 

ArcCatalog Extension contains custom stylesheets, which can be invoked from the metadata 

toolbar. These can be utilized to depict pertinent details in a more coherent format than standard 

metadata outputs. 

Credits

This chapter was adapted from material developed by Teresa Leibfreid (Cumberland Piedmont 

Network) and Bill Moore (Mammoth Cave National Park). Special thanks to Doug Wilder 

(Central Alaska Network) for significant contributions and ideas.

73

Chapter 8 – Data Analysis and Reporting

Sound data management practices are the key to having a credible and useful monitoring 

program. However, a successful monitoring program requires that data are analyzed, interpreted, 

and provided to managers and others at regular intervals and in a format appropriate for each 

audience.

8.1 – Types of Reports

There are several types of documents that result from reporting and analysis:

Annual reports

Analysis and synthesis reports

Scientific journal articles and book chapters

Interpretation and outreach brochures and displays

It is important to indicate at project inception the types of reports that will be generated and who 

will be responsible for generating each. These considerations clarify expectations for these 

reports and ensure that there is sufficient program accountability, documentation, and evaluation.

The NCCN Monitoring Plan presents an overview for monitoring projects whose implementation 

will be funded in the near-to mid-term. The Implementation Plan also includes timetables and 

schedules for report generation related to each project, the types of analyses included, and peer 

review requirements. More detailed description of data analysis will also be contained in the 

protocols associated with each monitoring project. 

8.1.1 – Annual Reports

The major purposes of annual reports are to:

 summarize annual data and document monitoring activities for the year

 describe current condition of the resource being monitored

 document changes in monitoring protocols

 increase communication within the park and network.

Many of our monitoring programs will be active each year, and those programs will generate 

annual reports each year. However, some sampling regimes do not require annual activity. For 

example, some protocols collect data every third year, every fourth year, or even every tenth 

year. Those programs will produce ―annual‖ reports only when there are significant monitoring 

activities to document. These reports will be generated from automated data analyses developed 

for each monitoring project.

Audience: Network staff, park staff including administration, scientists working in parks

Review: internal network review
74

8.1.2 Analysis and Synthesis Reports

The role of analysis and synthesis reports is to:

determine patterns/trends in condition of resources being monitored

discover new characteristics of resources and correlations among resources being 

monitored

analyze data to determine amount of change that can be detected by this type and level of 

sampling

provide context, i.e. interpret data for the park within a multi-park, regional or national 

context

recommend changes to resource management (feedback for adaptive management).

These reports can provide critical insights into resource status and trends, which can then be used 

to inform resource management efforts and regional resource analyses. This type of analysis, 

more in depth than that of the annual report, requires several seasons of sampling data. 

Therefore, these reports are not written more frequently than every three to five years for 

resources sampled annually. For resources sampled less frequently, or which have a particularly 

low rate of change, intervals between reports may be longer.

It is important that results from all monitoring projects within and across all NCCN parks be 

integrated across disciplines in order to interpret changes to park resources. This will be 

accomplished with a network synthesis report produced at no more than 10-year intervals.

Audience: superintendents, park resource managers, network staff, and external 

scientists

Review: external, blind peer-review with at least 3 subject-matter experts including 

one statistician

8.1.3 – Scientific Journal Articles and Book Chapters

This aspect of the program will be directed by the program managers, and is more at their 

discretion than previous reports. Publishing scientific journal articles and book chapters is 

primarily conducted to communicate advances in knowledge and is a very important, widely 

acknowledged means of quality assurance and quality control, via the academic peer-review 

process. Putting a program‘s methods, analyses, and conclusions under the scrutiny of a 

scientific journal‘s peer-review process is one of the best ways to ensure scientific rigor. This 

may be an important role for USGS.

Scientific journal articles and book chapters produced by NCCN efforts are tracked by the 

NCCN monitoring program; new publications are part of the Annual Administrative Report and 

Work Plan, which is sent to the regional and national offices each year. Additionally, all 

scientific journal articles and book chapters will be entered into the NatureBib database. 

Principle Investigators of recently published work in the NCCN frequently make presentations at 

professional workshops and conferences and will be invited to present their findings at Technical 

Committee and Board of Directors meetings.
75

Audience: scientific community

Review: peer-review conducted by journal or book editor

8.1.4 – Interpretation and Outreach

Scientific information gained from monitoring programs usually requires a concerted effort to be 

translated for the general public. Oral presentations include Science Days, which is a program 

led by the North Cascades Science Advisor who invites Network researchers to present their 

work and results and to share this information with the interested public. Network staff members 

also speak at trainings for seasonal employees and to special interest groups (e.g., Washington 

Native Plant Society, elder hostels, Olympic Park Institute, etc.). Network staff share discoveries 

with the public in written form by contributing articles to natural history newsletters and other 

media in the parks which produce them. In the future, the Network plans to produce brochures 

and fact sheets regarding monitoring. 

Interpretation and outreach is a perfect place for the NCCN Vital Signs Monitoring program to 

team up with the NCCN Research Learning Center (RLC). The RLC promotes research in parks, 

as well as acting as a bridge between scientists and the public. The NCCN Network Coordinator 

and NCCN program leads are working with the RLC program to form connections with college 

students, partners, and the interested public to provide Vital Signs Monitoring program 

information to the community in a digestible form.

Audience: the interested public

Review: reviewed by project leads for accuracy

8.2 – Identifying Project Objectives and Data Products

The development of data products will be guided by project objectives and when available, by 

the protocols and data management and data analysis SOPs (guidance documents in 

development).

Project scopes-of-work, and whenever possible, annual reports from previous years will be used 

to identify the tables and fields necessary for data summary. Assuming a database already exists, 

one can begin by identifying the core or critical tables with the desired data addressing the 

project goals. Then one may review the total database design including Entity-Relationship 

Diagrams (ERDs), priority fields, and the tables that contain them, to work towards building 

queries for data summary and analysis. These are also the preliminary steps that will be used for 

report automation (below). 

Individual fields will be identified which provide the source data for the desired analyses. Very 

often, the fields are to come from two or more tables. So it will become necessary to join tables 

using queries. ERDs will be used to determine the most effective way to write queries. Usually 

analysis queries do not yet exist, and these will be built by the Data Manager in coordination 

with the Project Leader. The Project Leader will start by identifying one or two variables to be 

used in the analysis, and then add independent variables that will be used for grouping purposes
76

such as site, habitat, or time interval. These will form the basis for the analysis queries. The 

analysis will be developed through a series of queries until either the data are exported to outside 

applications (see below) or the desired result is calculated. 

8.3 – Automated Reporting and Data Summaries 

The automation of data summaries and annual reports facilitates our network‘s ability to manage 

multiple projects. The Network uses Microsoft Access to automate its data summaries and 

reports although the process would be basically the same in other application environments.

8.3.1 – Annual Report as Template

The development of automated reporting would be greatly facilitated by existing annual reports 

when these are available. If not, a detailed annual report template will be worked out between the 

Project Leader and the Data Manager. The job of the Data Manager will be to take the extracted 

tables and fields and to process them to create output that is similar, if not identical, to the 

existing annual report template. This task requires that the Project Leader have a very clear idea 

of what will be needed for the annual reports. Usually, the Data Manager will need only to 

develop the necessary queries, macros, modules and reports. The process of automation is then to 

link the various database objects to a simple user-interface.

8.3.2 – Project Leader Interviews

Often, the Project Leader will need to modify the existing annual reports to better meet the 

requirements of Vital Signs projects. The Data Manager will plan on expanding and improving 

the analysis and reporting products during the automation phase, if possible, because the 

underlying database objects will have to be thoroughly reviewed anyway. Developing the 

automated reporting system beyond the existing annual reports will require frequent and repeated 

interviews by the Data Manager with the Project Leader. Once again, the project objectives will 

facilitate communication between the Data Manager and the Project Leader. The Network will 

often sketch out hardcopies of the data summaries, annual reports and user-interfaces in pencil 

prior to starting the implementation phase.

8.3.3 – Automated Report Generation

Following discussions on data summary and reporting requirements, the next step is to 

implement the automated report. Except in the simplest of situations, we will develop each 

analysis independently through a series of Access queries. The resultant query will be loaded as 

one of several controls (textboxes) into the summary report. All queries that feed into a single 

report may be triggered from a form button, a macro, or a combination of both. The Network 

will use modules and VBA for automation only to the extent that more complex automation 

applications are required. 
77

8.4 – Exporting Data for Analysis and Exploration

At times, data will need to be exported out of the database to other software applications. The 

Network is planning to export data from Access databases for most statistical analysis beyond 

means, standard deviations, and other descriptive statistics. NCCN will use third party statistical 

software for frequency distribution plots, tests for normality and analysis of variance such as 

SAS, SPSS and NCSS. Other external software requiring data exports will most likely include 

special application software such as GS+ for geostatistical analysis and Distance 4.0 for 

estimating animal population abundance using distance sampling. 

Analysis objectives will be reviewed by the Data Manager and Project Leader together prior to

attempting data exports. The field order [order of variables in the resulting  ̳flat‘ file] is most 

easily controlled while the data are still in the relational database. Therefore, the Data Manager 

will assist in setting up the necessary queries to provide the field order. Field types are typically 

determined early on, when the database is being designed. To minimize risk of data loss as a 

result of data type conversion, changes in data types will be kept to a minimum once monitoring 

projects are underway.

8.5 – Making Data Available for Third-Party Analysis

In order to create a data flow for subsequent analysis by third parties, the Network will establish 

a timeline and data processing steps including error-checking, summarizing, analyzing, and 

distributing data. A data flow model including error-checking, analyzing, reporting, and 

distribution is described in detail in Chapter 4. What follows are the steps and timeline involved 

in having each project leader or the data manager complete these tasks, and make their particular 

data set available to others each year. (In development)

Credits

This chapter was adapted from concepts and material developed by Gareth Rowell and Mike 

Williams (Heartland Network and Prairie Cluster Prototype), and Dorothy Mortenson 

(Southwest Alaska Network). 

79

Chapter 9 – Data Distribution and Dissemination

9.1 – Data Ownership

9.1.1 – National Park Service Policy on Data Ownership

The National Park Service defines conditions for the ownership and sharing of collections, data, 

and results based on research funded by the United States government. All cooperative and 

interagency agreements, as well as contracts, should include clear provisions for data ownership 

and sharing as defined by the NPS: 

 All data and materials collected or generated using NPS personnel and funds become 

the property of the NPS. 

 Any important findings from research and educational activities should be promptly 

submitted for publication. Authorship must accurately reflect the contributions of 

those involved. 

 Investigators must share collections, data, results, and supporting materials with other 

researchers whenever possible. In exceptional cases, where collections or data are 

sensitive or fragile, access may be limited.

The Office of Management and Budget (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. Specifically, OMB Circular A-110 establishes property standards within 

cooperative agreements with higher institutions and non-profit organizations. Section 36 of 

Circular A-110, ―Intangible Property‖ describes the following administrative requirements 

pertinent to data and ownership:

(a) The recipient (i.e., organization receiving federal monies for natural resource 

inventory and/or monitoring) may copyright any work that is subject to copyright and 

was developed, or for which ownership was purchased, under an award. The Federal 

awarding agency(ies) (i.e., the NPS) reserve a royalty-free, nonexclusive and irrevocable 

right to reproduce, publish, or otherwise use the work for Federal purposes, and to 

authorize others to do so. 

Section 36 also states:

(c) The Federal Government has the right to: 

(1) obtain, reproduce, publish or otherwise use the data first produced under an 

award 
80

(2) authorize others to receive, reproduce, publish, or otherwise use such data for 

Federal purposes 

(d) (1) In addition, in response to a Freedom of Information Act (FOIA) request for 

research data relating to published research findings produced under an award that were 

used by the Federal Government in developing an agency action that has the force and 

effect of law, the Federal awarding agency shall request, and the recipient shall provide, 

within a reasonable time, the research data so that they can be made available to the 

public through the procedures established under the FOIA (5 U.S.C. 552(a)(4)(A)). 

(2) The following definitions apply for purposes of paragraph (d) of this section: 

(i) Research data is defined as the recorded factual material commonly accepted 

in the scientific community as necessary to validate research findings, but not any 

of the following: preliminary analyses, drafts of scientific papers, plans for future 

research, peer reviews, or communications with colleagues. This "recorded" 

material excludes physical objects (e.g., laboratory samples)...

(ii) Published is defined as either when: 

(A) Research findings are published in a peer-reviewed scientific or 

technical journal; or 

(B) A Federal agency publicly and officially cites the research findings in 

support of an agency action that has the force and effect of law.

(iii) Used by the Federal Government in developing an agency action that has the 

force and effect of law is defined as when an agency publicly and officially cites 

the research findings in support of an agency action that has the force and effect 

of law.

9.1.2 – Establishing Data Ownership Guidelines

To ensure that proper ownership, format, and development of Network products is maintained, 

all cooperative or interagency work should be conducted as part of a signed collaborative 

agreement. Every cooperative or interagency agreement or contract involving NCCN should

include OMB Circular A-110 cited under the Reports and Deliverables Section of all agreements 

and contracts. 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 should include a list of expected 

deliverables and products clearly defined within each agreement or contract. Details on 

formatting and media types that will be required for final submission should also be included. 

Expected products 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 NCCN

agreements and contracts:
81

―All reports and deliverables must follow the most recent version of the North Coast and 

Cascades Network Product Specifications.‖

Researchers should also provide a schedule of deliverables that includes sufficient time for NPS 

review of draft deliverables before scheduled final submissions.

9.2 – Data Distribution 

One of the most important goals of the I&M Program is to integrate natural resource inventory 

and monitoring information into NPS planning, management, and decision making. To 

accomplish this goal, procedures should be developed to ensure that relevant natural resource 

data are made available for management decision-making, research, and education. Providing 

well-documented data in a timely manner to park managers is especially important to the success 

of this program. We will work to make certain that:

Data are easily discoverable and obtainable

Data that have not yet been subjected to full quality control will not be released

unless required in response to a FOIA request

Distributed data are accompanied by complete metadata that clearly establishes the 

data as a product of the NPS I&M Program (where appropriate)

Sensitive data are identified and protected from unauthorized access and 

inappropriate use

A complete record of data distribution/dissemination is maintained

To accomplish this, we will use a number of distribution methods that will allow information 

collected and developed as part of the program to become widely available to park staff and the 

public.

9.2.1 – Data Distribution Mechanisms

The primary mechanism for distributing data products will be the internet. This will allow the 

data and information to reach a broad community of users. In support of the NPS I&M Program, 

web-based applications and repositories have been developed to store a variety of park natural 

resource information. We will use the following applications and repositories to distribute our 

data products:

 NatureBib – a master web-based database housing natural resource bibliographic 

data for parks (NatureBib Home Page)

 NPSpecies – a master web-based database to store, manage and disseminate scientific 

information on the biodiversity of all organisms in all NPS units (NPSpecies Home 

Page)

 Biodiversity Data Store – a digital archive of documents, GIS and non-GIS data files 

that document the presence/absence, distribution and/or abundance of any taxa in 

NPS units (Biodiversity Service Center Home Page)
82

 Natural Resource and GIS Metadata and Data Store - online repository for 

metadata and associated data products. (NPS NR-GIS Metadata and Data Store Home 

Page)

 North Coast and Cascades Network Website – provides detailed information about 

the network and its I&M Program. Metadata on all inventory and monitoring products 

developed as part of the Network‘s I&M plan will be posted to this site. Data and 

products will either be available through the site or users will be directed to where the 

data are stored (North Coast and Cascades Network Home Page).

Table 9.1 below provides a list of types of information that will be uploaded to these sites. 

Web Application Name Data types available at site

NPSpecies Data on park biodiversity (species information)

NatureBib Bibliographic citations related to park resources

Biodiversity Data Store The raw or manipulated data and products associated 

with Inventory and Monitoring data that have been 

entered into NPSpecies

NR-GIS Metadata and Data Store Metadata and spatial/non-spatial data products

NCCN Website Reports and metadata for all I&M projects 

administered by NCCN

Currently, the NR-GIS Metadata and Data Store and the Biodiversity Data Store are under 

development. Until procedures and further guidance become available for the use of these two 

repositories, we will disseminate all I&M data via the NCCN website. When both repositories 

are completely operational, the Network will upload all applicable data and information to each 

of those sites as needed.

Because network data will reside in the repositories listed above, this data will automatically be 

searchable via the integrated metadata and image management system and search gateway called 

NPS Focus. This system is being built with Blue Angel Enterprise software for metadata 

management and the LizardTech Express Server for image management. Currently ten NPS and 

two non-NPS databases have been integrated into the NPS Focus prototype in either full or 

testbed form for one-stop searching. NPS Focus has been released as an Intranet version only 

(http://npsfocus.nps.gov/), though a public version is projected in the near future. 

Of the ten databases uploaded to date to NPS Focus, NatureBib and the NR-GIS Metadata and 

Data Store are most applicable to existing network bibliographic and spatial metadata. The 

network will continue to upload data and information to these two sites, which will coincide with 

the ability to search for these records through the NPS Focus portal. As NPS Focus reaches 

further development, other databases and repositories utilized by the network are expected to be 

searchable through this portal as well. 
83

9.2.2 – Data Classification: Protected vs. Public

All data and associated information from I&M activities should be assessed to determine 

sensitivity. This includes, but is not limited to, reports, metadata, raw and manipulated spatial 

and non-spatial data, maps, etc. Network staff should carefully identify and manage any 

information that is considered sensitive. The Network should clearly identify and define those 

data needing access restrictions and those to make public.

The Freedom of Information Act, 5 U.S.C. § 552, referred to as FOIA, stipulates that the United 

States Government, including the National Park Service, must provide access to data and 

information of interest to the public. FOIA, as amended in 1996 to provide guidance for 

electronic information distribution, applies to records that are owned or controlled by a federal 

agency, regardless of whether or not the federal government created the records. FOIA is 

intended to establish a right for any person to access federal agency records that are not protected 

from disclosure by exemptions. Under the terms of FOIA, agencies must make non-protected 

records available for inspection and copying in public reading rooms and/or the Internet. Other 

records are provided in response to specific requests through a specified process. The 

Department of the Interior‘s revised FOIA regulations and the Department‘s Freedom of 

Information Act Handbook can be accessed at http://www.doi.gov/foia/ for further information.

In some cases, public access to data can be restricted according to stipulations in one Executive 

Order, Director‘s Order #66 (draft), and four resource confidentiality laws: the National Parks 

Omnibus Management Act (16 U.S.C. 5937), the National Historic Preservation Act (16 U.S.C. 

470w-3), the Federal Cave Resources Protection Act (16 U.S.C. 4304), and the Archaeological 

Resources Protection Act (16 U.S.C. 470hh). Through these laws and policies, NPS is directed 

to protect information about the nature and location of sensitive park resources. Any information 

that could result in harm to natural resources can be classified as  ̳protected‘ or  ̳sensitive‘ and 

withheld from public release (National Parks Omnibus Management Act). 

The following guidance for determining whether information should be protected is suggested in 

the draft Director‘s Order #66 (the final guidance may be contained in the Reference Manual 

66):

Has harm, theft, or destruction occurred to a similar resource on federal, state, or private 

lands?

Has harm, theft, or destruction occurred to other types of resources of similar commercial 

value, cultural importance, rarity, or threatened or endangered status on federal, state, or 

private lands?

Is information about locations of the park resource in the park specific enough so that the 

park resource is likely to be found at these locations at predictable times now or in the 

future?

Would information about the nature of the park resource that is otherwise not of concern 

permit determining locations of the resource if the information were available in 

conjunction with other specific types or classes of information?
84

Even where relatively out-dated, is there information that would reveal locations or 

characteristics of the park resource such that the information could be used to find the 

park resource as it exists now or is likely to exist in the future?

Does NPS have the capacity to protect the park resource if the public knows its specific 

location?

Natural Resource information that is sensitive or protected requires the:

Identification of potentially sensitive resources 

Compilation of all records relating to those resources 

Determination of what data must not be released to the public

Management and archival of those records to avoid their unintentional release 

Classification of sensitive data will be the responsibility of NCCN staff, the park 

superintendents, and investigators working on individual projects. NCCN staff will classify 

sensitive data on a case by case, project by project, basis. They will work closely with 

investigators for each project to ensure that potentially sensitive park resources are identified, 

and that information about these resources is tracked throughout the project. 

NCCN staff is also responsible for identifying all potentially sensitive resources to the principal 

investigator(s) working on each project. The investigators, whether NCCN staff or partners, will 

develop procedures to flag all potentially sensitive resources in any products that come from the 

project, including documents, maps, databases, and metadata. When submitting any products or 

results, investigators should specifically identify all records and other references to potentially 

sensitive resources. Note that partners should not release any information in a public forum 

before consulting with Network staff to ensure that the information is not classified as sensitive 

or protected. 

For example, information may be withheld regarding the nature and/or specific locations of the 

following resources recognized as  ̳sensitive‘ by NPS. According to National Parks Omnibus 

Management Act, if NPS determines that disclosure of information would be harmful, 

information may be withheld concerning the nature and specific location of:

 Endangered, threatened, rare or commercially valuable National Park System 

resources (species and habitats)

 Mineral or paleontological objects 

 Objects of cultural patrimony

 Significant caves

Note that information already in the public domain can, in general, be released to the public 

domain. For example, the media has reported in detail the return of condors to the Grand 

Canyon. If an individual requests site-specific information about where the condors have been 

seen, this information, in general, can be released. However, the locations of specific nest sites 

cannot be released.
85

9.2.3 – Access Restrictions on Sensitive Data

NCCN staff is responsible for managing access to sensitive data handled by the Program. All 

potentially sensitive park resources will be identified and investigators working on NCCN

projects will be informed that:

 All data and associated information must be made available for review by NCCN

staff prior to release in any format 

 Any information classified as protected should not be released in any format except 

as approved in advance by the National Park Service

The Network Coordinator, NPS project liaison, or Data Manager identifies 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. 

For each project, NCCN staff provides a complete list of all references to potentially sensitive 

park resources in each park to the park superintendent for review. Each superintendent then 

determines which information should be protected.

When preparing or uploading information into any NCCN database, our staff will ensure that all 

protected information is properly identified and marked. NCCN staff will work together to 

ensure that all references to protected information are removed or obscured in any reports, 

publications, maps, or other public media.

NCCN staff will remove any sensitive information from public versions of documents or other 

media. They will isolate sensitive from non-sensitive data and determine the appropriate 

measures for withholding sensitive data. The main distribution applications and repositories 

developed by the I&M Program, (see section 9.2.1) are maintained on both secure and public 

servers, and all records that are marked  ̳sensitive‘ during uploading will only become available 

on the secure servers. Procedures for assigning a sensitivity level to specific records when 

uploading to both the NPSpecies and NatureBib databases are discussed in the guidance 

documents for maintaining NPSpecies and NatureBib (in development), as well as at the 

following websites: 

 http://science.nature.nps.gov/im/apps/npspp/index.cfm

 http://science.nature.nps.gov/im/apps/nrbib/

Thus, access to data on sensitive park resources can be limited to Network staff or research 

partners. However, limits to how these data are subsequently released must also be clearly 

defined. It is crucial that the Network staff institute quality control and quality assurance 

measures to ensure that the person doing the uploading of records into the online applications is 

familiar with the procedures for identifying and entering protected information.
86

9.2.4 – Public Access to Network Inventory and Monitoring Data

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. Network project data will 

be available to the public at one or more internet locations: 

 The NCCN web site 

 Public servers for the NPSpecies and NatureBib databases

 Public server for the Biodiversity Data Store

 Public server for the NR/GIS Data Store

We will regularly provide updated information about inventories and monitoring projects, 

including annual reports and detailed project reports through the NCCN web site. Information on 

species in the national parks, including all records generated through the I&M Program, will be 

maintained and assessable through the NPSpecies database. Bibliographic references that refer to 

NPS natural resources will be accessible through the NatureBib database. Documents, maps, and 

data sets containing resource information from all sources, and their associated metadata, will be 

accessible through the Biodiversity Data Store and/or NR-GIS Data Store. Each of these 

databases/repositories will be available via both a secure server and a public server, and the 

public can access all information in these databases except those records marked as  ̳sensitive.‘

9.2.5 – Data Availability 

Both raw and manipulated data resulting from the Network‘s inventory and monitoring projects 

will be fully documented with FGDC compliant metadata and made available to the public via 

the Network‘s website. The metadata for all data sets will be made accessible to the public as 

soon as they are provided and verified by the Investigator(s) or project leaders. 

Data sets for short-term projects will be provided to the public on the NCCN website, two years 

following the year the data were collected or following publication of the investigator‘s results 

(whichever comes first). Long-term (monitoring) studies will be provided to the public in four or 

five year intervals, or when trend analyses have been completed and reported on by the network. 

This will be specific to each network monitoring protocol (refer to the NCCN Monitoring Plan

for further information). Before data are posted, the Investigator or project leader will be asked 

to verify the final data set and metadata if necessary. Once NCCN staff and the investigator 

verify the data set, the data will be made accessible to the public, provided no sensitive 

information is identified.

NCCN staff will notify investigators prior to making data sets available to the public. This will 

allow each investigator the opportunity to request in writing to further restrict access to the data 

set by the public. NCCN staff will review the Investigator‘s request and determine whether the 

request will be granted and for how long the data set will remain restricted.
87

9.2.6 – Data Acquisition Policy

The NCCN will develop a data set acquisition policy that will be made available to all NCCN

website users who wish to acquire program data and information. This policy will include such 

things as: 

 A mandatory questionnaire that must be completed and mailed to the project data 

manager before data can be acquired. This questionnaire will allow NCCN staff the 

ability to maintain a distribution log specifying recipient name and contact 

information, intended use of data, export file format, delivery date and method, and 

data content description noting range by date and geography of data delivered.

 A statement about use and appropriate citation of data in resulting publications.

 Request that acknowledgement be given to the National Park Service Inventory and 

Monitoring Program within all resulting reports and publications.

All data sets with public access available on the NCCN website will be accompanied by the 

acquisition policy. 

9.3 – Data Feedback Mechanisms

The NCCN website will provide an opportunity for NPS staff, cooperators and the public to 

provide feedback on data and information gathered as part of the NCCN I&M Program. A 

―comments and questions‖ link will be provided on the main page of the site for general 

questions and comments about our program and projects. A more specific ―data error feedback‖ 

link will direct comments to the NCCN staff pertaining to errors found in website accessible 

data. Annual reporting of progress will be presented to the Board of Directors and to the 

Technical Committee on a yearly basis, and feedback will be expected during and following 

these presentations. 

9.3.1 – Data Error Feedback Procedures

The following feedback procedures describe the process which NCCN will use to receive and 

verify data errors identified by public and private data users:

 Web users send in a notification about an alleged error through the NCCN website. 

NCCN staff then sends an acknowledgment to the notifier.

 NCCN staff then inputs the information into a data error log table incorporated in either 

each of the network monitoring databases or a specific error tracking database developed 

for the network. 

 NCCN then determines if the data questioned by the notifier are correct or incorrect. If 

the data are correct, then NCCN staff informs the notifier that no corrections are to be 

made and the information stands. If the data are incorrect, the Network staff makes the 
88

appropriate corrections and notifies the original data collectors (cooperator, other agency, 

park staff, etc...). 

 Once the data are corrected, the NCCN website will be refreshed with the corrected 

information.

 Throughout this process, NCCN staff will continually inform the notifier via e-mail of the 

status. 

Credits

This chapter was adapted from material developed by Sara Stevens (Northeast Coastal and 

Barrier Network) and Doug Wilder (Central Alaska Network).
89

Chapter 10 – Data Maintenance, Storage and Archiving

Effective long-term data maintenance depends on thoughtful and appropriate data 

documentation. An essential part of any archive is its accompanying explanatory materials 

(Olson and McCord 1998). This chapter will refer to, and in some cases elaborate on, metadata 

standards and data set documentation procedures that are more fully explained in Chapter 7 

(Data Documentation). 

Data, documents, and anything that results from projects and activities that use NCCN data are 

all crucial pieces of information. To ensure high-quality long-term management and maintenance 

of this information, we will implement procedures to protect information over time. These 

procedures will permit a broad range of users to easily obtain, share, and properly interpret both 

current and archived information.

10.1 – Digital Data Maintenance

In general, digital data maintained for a long time will be one of two types: short-term data sets, 

for which data collection and modification have been completed (i.e., inventory projects); and 

long-term monitoring data sets, for which data acquisition and entry will continue indefinitely.

Following the lead of the National Park Service and the National Inventory & Monitoring 

program, NCCN has adopted MS-Access as its database standard and ArcGIS as its spatial data 

management standard. NCCN will remain current and compatible with National Park Service 

and National Inventory and Monitoring version standards for these software programs.

Technological obsolescence is a significant cause of information loss, and data can quickly 

become inaccessible to users if they are stored in out-of-date software programs or on outmoded 

media. Effective maintenance of digital files depends on the 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 software, or they must be saved in formats that are independent of 

specific platforms or software (e.g., ASCII delimited files). 

Any data set for which data entry or updates is still occurring will be stored under the "Active 

Data Projects" directory on the NCCN server. We reserve the "Project Archive" directory for 

data sets that will no longer change. 

10.1.1 – Short-term Data Sets

Upon project finalization, project leaders are responsible for creating a set of ASCII commadelimited text files for all short-term data sets created or managed by NCCN. Each database table 

will have a corresponding ASCII file. These files will be accompanied by a README.TXT file 

that explains the contents of each file, file relationships, and field definitions. These ASCII files 

are in addition to the native version of the data set (typically in database or spreadsheet format). 

Creating these text files will help ensure that the data are usable in a wide range of applications 
90

or platforms. All finalized files will be stored on the NCCN server's archive section in the 

appropriate project folder. 

In addition to creating ASCII files, NCCN will also update completed and archived data sets that 

may have been created in older versions of MS-Access, with the goal of having no data set more 

than two versions behind the current version used by NCCN. There is a risk of reducing 

performance in the process of conversion; for example, complex data entry forms or reports may 

not function properly in an upgraded version. To the extent possible, we will maintain proper 

functionality of data entry forms and reports. However, the priority will be to ensure basic table 

and relationship integrity. All previous versions of the data set will be saved.

10.1.2 – Long-term Monitoring Data Sets

Long-term monitoring data sets require regular updates and conversion to current database 

formats. All active or long-term databases will conform to the current NPS and I&M software 

version standards. 

Monitoring projects will also have variable long-term data archiving requirements. Raw data sets 

that are later manipulated or synthesized may need to be stored in perpetuity. Modifications to 

protocols will typically require complete data sets to be archived before modifications are 

implemented. Depending on the monitoring project, it may be necessary to preserve interim data 

sets (data "milestones") over the long term. We will save archived data sets or subsets destined 

for long-term archiving, whenever possible, in their native formats in addition to ASCII text 

files. Data archiving requirements for ongoing projects will be spelled out in the data 

management SOPs for each monitoring project.

10.1.3 – Quality Control of Converted Data

All ASCII files created from databases will undergo Quality Control (QC) to ensure that 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 a program scientist) will evaluate the 

ASCII files and documentation to verify that tables, fields, and relationships are fully explained 

and presented in a way that is useful to secondary users. 

Databases that are converted from one version of MS-Access to an upgraded version will require 

additional QC. This is especially important if the databases are being actively used for data entry 

or analysis. Forms, queries, reports, and data entry all will be thoroughly tested. 

10.1.4 – Version Control

Previous versions of databases will be saved in their native format and archived in addition to the 

current version. 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. File names of the archived revisions should clearly indicate 

the revision number or date.
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10.1.5 – Spatial Data

Spatial data sets that are essential to NCCN will be maintained in a format that remains fullyaccessible by the current ArcGIS version. ArcGIS has maintained compatibility with previous 

data formats, and while shapefiles have retained all functionality in ArcGIS, coverages may 

require conversion to ArcGIS format 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. 

10.2 – Storage and Archiving Procedures – Digital Data

Digital data will be stored in a repository that ensures security and ready access to the data in 

perpetuity. 

10.2.1 – Directory Structure for Digital Archives

Figure 10.1 illustrates an example of a directory structure for both active and archived data files, 

including GIS data. This directory structure was devised, in part, to accommodate two different 

backup schedules: one for rapidly-changing files; the other for relatively static files.

Figure 10.1. Schematic of proposed data components of the NCCN servers

NCCN Server

GIS

NCCN

Databases

Active Data Projects

Completed Projects

Libraries

Project Archive

NCCN Photos

Directories that are 

shaded (GIS and 

Archive) are on 

backup schedule B 

(see Table 10.1).

Ongoing Projects (e.g., monitoring)
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10.2.2 – Directory Structure for Individual Projects

The organizing and naming of folders and files should be intuitive enough that users unfamiliar 

with a specific project can still easily navigate it. Since each project has its own variations and 

idiosyncrasies, a standardized structure isn't realistic. However, all project archives will include

several to most of the following elements:

administrative documents such as agreements, correspondence, and research permits 

programmatic documents including protocols, procedures, and supporting documents

interim data sets or milestones

data sets submitted by contractors 

data sets reformatted or manipulated by NCCN (e.g., data converted to NRDT format, 

data sets migrated to current software formats)

data sets in ASCII format

conceptual or statistical models used for data interpretation

final reports

README files, including an explanation of directory contents, project metadata, and 

version documentation

Once final data and reports have been submitted, draft products do not need to be maintained.

10.2.3 – Backup Procedures for Digital Data

The risk of data loss can come from a variety of sources, including catastrophic events (e.g., fire, 

flood), user error, hardware failure, software failure or corruption, and intentional acts of 

vandalism. Performing regular backups of data and arranging for off-site storage of backup data 

sets are the most important safeguards against data loss. 

NCCN is integrated into the NPS Pacific West Region (PWR) wide area network and benefits 

from the interconnectivity of the PWR system. Data residing on one of the NCCN servers are

backed up to another NCCN server using an automated backup system (see Figure 3.5). This 

integrated system is in development and final backup schedules will be included as appendices to 

this DMP and will be developed in cooperation with IT system administrators. Currently each 

NPS unit functions independently with backups done locally and stored off site. 
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Table 10.1. Proposed backup schedule for NCCN servers

Schedule A – frequently-changing files; estimate total of 50-75 GB by 2006. Used for all files located 

on NCCN server

Tape Set 1 week 1: full backup and daily incrementals 

week 2: daily incrementals

week 3: daily incrementals

week 4: daily incrementals – rotate off-site

Tape Set 2 week 5: full backup and daily incrementals 

week 6: daily incrementals

week 7: daily incrementals

week 8: daily incrementals – rotate off-site

Tape Set 3 week 9: full backup and daily incrementals 

week 10: daily incrementals

week 11: daily incrementals

week 12: daily incrementals – rotate off-site

Schedule B – large files, relatively static; estimate total of 350GB by 2006. Used for all files located 

on NCCN server\GIS and NCCN server\Archive

Tape Set 1 week 1: full backup

weeks 2-26: weekly differential backup. Rotate off-site at week 26.

Tape Set 2 week 27: full backup

weeks 28-52: weekly differential backup. Rotate off-site at week 52.

Tape Set 3 week 1 (year 2): full backup

weeks 2-26 (year 2): weekly differential backup. Rotate off-site at week 27.

Backups of data stored on the personal computers of staff are the responsibility of each staff 

member. NCCN recommends that staff regularly copy important files onto a personal directory 

on the local-area network, where daily backups will be performed. Staff may also place data in 

the archive section of the NCCN server with the approval of the data manager. 

The quantity of NCCN data is increasing rapidly. Thus, we will need to reevaluate the backup 

schedule often, and at least annually. Backup routines represent a significant investment in 

hardware, media, and staff time; however, they are just a small percentage of the overall 

investment that we make in program data. 

10.2.4 – Testing Backup Files

All backups should be run with "verify" toggled on to compare source files against the backup 

files and detect any errors and discrepancies. However, to protect against hardware failures, a 

RAID-5 array with multiple hard drives for data storage and ArcServe for backing up to these 

hard drives will provide redundancy that prevents data loss in the event of hard drive failure.

Periodic test restoration of a few files will assure that the system works and keep administrators 

refreshed in technique.

10.2.5 – Data and Network Security

NCCN local and wide area networks currently conform to Department of Interior security 

guidelines. Only NCCN park and network staff and system administrators have permission to 
94

access files on the NCCN servers, and restrictions have been established on archived data files. 

Directories containing completed project data or interim versions of ongoing projects are 

designated as read-only for all staff with the exception of the data managers. Therefore, any 

changes must be routed through the data manager, who is responsible for ensuring that 

documentation and README files associated with the data set are also updated.

10.3 – Storage and Archiving Procedures – Documents and Objects

The guidelines in this section apply to documents such as final reports prepared by staff or 

contractors, program administrative documents, contracts and agreements, memoranda of 

agreement, and other documents related to NCCN administration, activities and projects. These 

guidelines also apply to physical items such as natural history specimens, photographs, or audio 

tapes. In most instances, these documents and objects are essential companions to the digital data 

archives described earlier. 

Direction for managing these materials (as well as digital materials) is provided in NPS 

Director's Order 19: Records Management (2001) and its appendix, NPS Records Disposition 

Schedule (NPS-19 Appendix B, revised 5-2003). NPS-19 states that all records of natural and 

cultural resources and their management are considered mission-critical records. NPS-19 further 

states: 

―Mission critical records are permanent records that will eventually become archival 

records. They should receive the highest priority in records management activities and 

resources and should receive archival care as soon as practical in the life of the record.‖

Section N of Appendix B, which provides guidelines on natural resource-related records 

(including, specifically, the results of Inventory and Monitoring Programs), indicates that all 

natural resource records are considered "permanent," that is, are to be transferred to the National 

Archives when 30 years old. It also indicates that non-archival copies of natural resource-related 

materials are potentially important for the ongoing management of NPS resources" and should 

not, in any instance, be destroyed. 

10.3.1 – Documents

All paper documents managed or produced by the NCCN will be housed in one of the secure 

repositories located in parks within the Network. Digital files are archived on the NCCN 

servers. These may include project files, administrative documents and non-record copies of 

documents that are archived at an off-site facility. Examples include: meeting minutes, 

correspondence, memoranda of understanding, contracts and agreements, research permits, and 

interim and selected final reports produced by the program or under its auspices. NCCN will use 

acid-free paper and folders for all permanent records in the central files. In addition to 

maintaining these paper records, NCCN will maintain electronic versions on the NCCN servers 

whenever possible. The central files are maintained by an NCCN administrative clerk, under the 

guidance of the data managers and network coordinator.
95

An archival repository provides temperature and humidity-controlled facilities, a professional 

archival staff, and meets all museum standards set by NPS. These repositories will be used for 

original documents and associated materials produced by the network (e.g., photographs, field 

notes, specimens, permits) that are a high priority to maintain under archival conditions. 

Examples include: original inventory reports and accompanying slides and maps; original 

vegetation mapping reports; NCCN Implementation Plan (Phase 1, 2 and 3 reports). Copies of 

these reports will be maintained in the NCCN I&M central files, and all will have an electronic

equivalent (e.g., PDF) for distribution or reproduction.

For all materials submitted to an archive, the submitter will provide essential cataloging 

information such as the scope of content, project purpose, and non-record copies of documents 

that are presented using archival-quality materials (acid-free paper and folders, polypropylene or 

polyethylene slide pages). 

Many NCCN reports and documents encompass data from multiple parks, which has made it 

difficult to accession archival copies into a specific network park museum. In these instances 

NPS PWR Seattle Library will catalog records that reference parks included in a report or 

document and will prepare finding aids to help potential users locate the materials.

NCCN will provide high-quality copies of park-related documents resulting from NCCN

projects, along with electronic versions, to park resource management staff. Parks may choose to 

accession these materials into their museums, incorporate them into their central files, or house 

them in their resource management library as they deem appropriate. NCCN will not manage 

documents at the Park level.

10.3.2 – Specimens

The Network will provide specimens collected under the auspices of the NCCN to the Network 

Park in which they were collected, or to a repository approved by a Park (where the specimens 

are considered on loan). NCCN will provide Park curators with associated data required for 

cataloging each specimen. These data will be in comma-delimited format (.CSV) format for 

automated uploading into ANCS+. Data will be provided to non-NPS curators in Microsoft 

Excel format.

10.3.3 – Photographs

Archivists have been reluctant to fully embrace digital photography, and some have expressed 

concern that, with the acceleration of technological change, documentary heritage is in danger of 

being lost in the information age (Cox 2000). 

NCCN has chosen to take a conservative approach and requires staff and contractors to provide 

photos as 35mm slides (preferably Kodachrome or Ektachrome), which have a proven long-term 

stability (Wilhelm and Brower 1993). If contractors cannot provide slides, NCCN requests 4x6 

color prints. Original images are a high priority for placing in archival storage conditions. 
96

Slides should be labeled using indelible pigment ink or using laser-printed, archival-quality slide 

labels. Slide labels should include: a unique ID, project name, photographer, photo date, a brief 

identification of contents (e.g., species name, plot ID), and geographic location (UTMs or 

description). All slides will be stored in polypropylene slide sleeves at the park office until 

transferred to the appropriate long-term curation facility. In addition, all slides will be scanned 

and saved as TIFF files, and these electronic copies will be used as the primary means of 

distributing or reproducing the images.

If photographs are provided, they will be stored in individual polypropylene sleeves and within 

archival boxes. Each photo will be labeled on the back, using archival-quality labels that are 

either laser-printed or hand-labeled, with the same information elements required for slides. If a 

contractor is submitting photographs, corresponding digital images in TIFF format should also 

be submitted

Every image, regardless of format, will have an entry in the NCCN Photo Database (guidance 

documents in development), where attributes such as electronic file name, keywords, project, 

photo description, photographer, date, and location will be catalogued. All photo files and the 

associated photo database are housed on the archive portion of the NCCN server (see Figure 

10.1).

Role of Curators in Storage and Archiving Procedures 

Curators for parks within NCCN are an ongoing source of expertise, advice, and guidance on 

archiving and curatorial issues, and they have a role in almost every project undertaken by the 

Network. Project leaders should involve Park curators when projects are in the planning stage, to 

ensure that all aspects of specimen preservation or document archiving will be considered and 

that any associated expenses are included in project budgets.

Credits

This chapter was adapted from material developed by Margaret Beer (Northern Colorado Plateau 

Network).
97

Glossary

Base cartographic data – standard map layers such as boundaries, roads, trails, hydrography; 

typically not project-specific

EBLA – Ebey‘s Landing National Historical Reserve, www.nps.gov/ebla

FGDC – Federal Geographic Data Committee determines standards for geospatial data

FOVA – Fort Vancouver National Historic Site, www.nps.gov/fova

GIS – Geographic Information Systems, software used to analyze spatial data and create maps

Guidelines – general descriptions of data management practices and procedures 

I&M – the NPS Inventory and Monitoring Program: http://science.nature.nps.gov/im/index.cfm

IAR – Investigator‘s Annual Report, turned in each year by principal investigator; describes 

work accomplished during preceding year on a specific project

IT – information technology, composed of hardware, software, digital media, computer and 

networking services, plus the personnel and knowledge base needed to maintain them

Inventory – ―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 to either a particular point in space (synoptic) or time (temporal).‖ 

(Source: http://science.nature.nps.gov/im/monitor/ProgramGoals.cfm#Definitions ) 

Layer – a GIS digital equivalent of a mylar map overlay made up of points, lines or polygons 

LEWI – Lewis and Clark National Historical Park, www.nps.gov/lewi

Local Area Network (LAN) – an interconnected system of computers connected to one or more 

network servers by cabling and administered as a collective unit

Long-term monitoring project – a monitoring study designed to assess the status of a natural 

resource vital sign over time

Metadata – information about a data set that tells the who, why, when, where and how the data 

was collected

MORA – Mount Rainier National Park, www.nps.gov/mora
98

NBII – National Biological Information Infrastructure, a system of standards for describing 

biological data

Network – in the context of the NPS Inventory and Monitoring program, monitoring networks 

are groups of parks linked by geography and shared natural resource characteristics. 

The network arrangement is intended to provide a minimum infrastructure for 

initiating natural resource monitoring in the approximately 270 parks with significant 

natural resources.

NOCA – North Cascades National Park Service Complex, www.nps.gov/noca

OLYM – Olympic National Park, www.nps.gov/olym

Pick list – a drop-down list of potential values to choose from to enter data into a table or form

Primary key – a unique identifier field in a database table that can link records in different 

tables to each other 

Protocol – a set of sampling procedures and/or experimental processes followed in inventory 

studies or pilot monitoring projects. In the context of the Inventory and Monitoring 

Program, this often indicates a monitoring protocol, which is the set of formal 

documents and sampling processes describing how a vital sign will be monitored. 

Monitoring protocols are composed of a narrative section, standard operating 

procedures and supplementary information (databases, reports, tools, hardcopy 

materials)

Prototype – in the context of the NPS Inventory and Monitoring program, prototype programs 

play a special role as centers of excellence and as sources of technical guidance and 

expertise for other monitoring programs across NPS. 

Query – an object created in a relational database that extracts selected data from table(s)

Referential integrity – where cascade updates to related fields and cascade deletes to related 

records are maintained

Relational database – a database with data stored in multiple tables where each table contains 

data on one subject and tables are linked by key fields to link records in different 

tables

Relationship – in a relational database it is the way that records are linked between tables; can 

be one to one, one to many or many to many

RPRS – Research Permit Reporting System for tracking research conducted in NPS units

SAJH – San Juan Island National Historical Park, www.nps.gov/sajh
99

Standard Operating Procedures (SOPs) - detailed step-by-step instructions for carrying out 

sampling procedures in monitoring Protocols; dynamic in nature and frequently 

updated; can reference overall data management plan, standards and DMOPs 

Standard - a data management practice or policy implemented Service-wide or network-wide 

(e.g., recommended naming conventions, FGDC metadata format); referenced by 

overall data management plan, monitoring SOPs and DMOPs

SQL – Structured Query Language used to write statements used by software programs to 

manipulate data; also the name of a software program that runs on a server that makes 

data available to clients

Theme – outdated name for GIS layer made up of points, lines or polygons for mapping features

WASO – Washington Support Office

Wide Area Network (WAN) – an interconnected system of local area networks servers 

connected to each other by phone lines or dedicated server lines; administration 

decentralized 

101

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and Susan G. Stafford (eds), Environmental Information Management and Analysis: Ecosystem 

to Global Scales. London: Taylor & Francis Ltd.

Cox, R. J. 2000. Searching for authority: archivists and electronic records in the New World at the fin-desiècle. First Monday 5:1. http://firstmonday.org/issues/issue5_1/cox/index.html

Edwards, Don. 2000. Data quality assurance. Pp. 70-91 in Michener, William K., and James W. Brunt 

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Vogt-O‘Connor, D. 1997. Caring for photographs: general guidelines. National Park Service Conserve O 

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Vogt-O‘Connor, D. 2000. Planning digital projects for preservation and access. National Park Service 

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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, 

Grinnel, IA. 744 p.

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-48, February 2009
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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