National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Data and Information Management Plan 

Arctic Network Inventory & Monitoring Program 

Natural Resource Report NPS/ARCN/NRR—2008/053 
ON THE COVER 

(Bull moose in the morning sun, Noatak National Preserve ) 

Photograph by: Lois Dalle-Molle, NPS 
Data and Information Management Plan 

Arctic Network Inventory & Monitoring Program 

Natural Resource Report NPS/ARCN/NRR—2008/053 

National Park Service 

4175 Geist Road 

Fairbanks, AK 99709 

August, 2008 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado 
The Natural Resource Publication series addresses natural resource topics that are of 

interest and applicability to a broad readership in the National Park Service and to others 

in the management of natural resources, including the scientific community, the public, 

and the NPS conservation and environmental constituencies. Manuscripts are peerreviewed to ensure that the information is scientifically credible, technically accurate, 

appropriately written for the intended audience, and is designed and published in a 

professional manner. 

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

current natural resource management information with managerial application. The series 

targets a general, diverse audience, and may contain NPS policy considerations or 

address sensitive issues of management applicability. Examples of the diverse array of 

reports published in this series include vital signs monitoring plans; monitoring protocols; 

"how to" resource management papers; proceedings of resource management workshops 

or conferences; annual reports of resource programs or divisions of the Natural Resource 

Program Center; resource action plans; fact sheets; and regularly-published newsletters. 

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

solely those of the author(s) and do not necessarily reflect views and policies of the U.S. 

Department of the Interior, NPS. Mention of trade names or commercial products does 

not constitute endorsement or recommendation for use by the National Park Service. 

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

are only available as long as the supply lasts. This report is also available from the 

Natural Resource Publications Management website 

(http://www.nature.nps.gov/publications/nrpm) and the Arctic Network Inventory and 

Monitoring website (http://www.nature.nps.gov/im/units/arcn), or by sending a request to 

the address on the back cover. 

Please cite this publication as: 

National Park Service. 2008. Data and information management plan: Arctic Network 

Inventory and Monitoring Program. Natural Resource Report NPS/ARCN/NRR—

2008/053. National Park Service, Fort Collins, Colorado. 

NPS D-169, August, 2008 

ii 
iii 

Change History 

Version numbers will be incremented by a whole number (e.g., Version 1.3 to Version 

2.0) when a change is made that significantly affects requirements or procedures. Version 

numbers will be incremented by decimals (e.g., Version 1.6 to Version 1.7) when there 

are minor modifications that do not affect requirements or procedures included in the 

plan. 

The following revisions have occurred to this plan since August 29, 2008 

Version # Date Revised by Changes Justification 
Contents 

Figures and Tables......................................................................................................................... ix 

Acknowledgements......................................................................................................................... x 

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

About the Arctic Network............................................................................................................... 1 

Purpose and Scope .......................................................................................................................... 2 

Data Management and the Inventory and Monitoring mission ...................................................... 3 

Goals of the Inventory and Monitoring Program............................................................................ 3 

Data Management Goals..................................................................................................... 4 

Data as a Valued Commodity ............................................................................................. 4 

Data Management Expectations ......................................................................................... 4 

Revisions to this Plan.......................................................................................................... 5 

Chapter 2. Data Management Infrastructure and System Architecture .......................................... 7 

Architecture..................................................................................................................................... 7 

NPS Infrastructure .......................................................................................................................... 9 

National Applications................................................................................................................... 10 

Alaska Region Infrastructure ........................................................................................................ 11 

Park and Network-Level Infrastructure ........................................................................................ 11 

Chapter 3. Data Management Process and Workflow.................................................................. 13 

Project Process.............................................................................................................................. 13 

Planning and Approval ..................................................................................................... 14 

Design and Testing ........................................................................................................... 15 

Implementation ................................................................................................................. 15 

Product Integration............................................................................................................ 15 

Evaluation and Closure..................................................................................................... 15 

iv 
Chapter 4. Data Management Roles and Responsibilities............................................................ 17 

Roles and Responsibilities............................................................................................................ 17 

Chapter 5. Data Acquisition and Processing................................................................................. 21 

Network Data................................................................................................................................ 21 

National Park Service Data........................................................................................................... 22 

External Data ................................................................................................................................ 24 

Museums and Herbaria ..................................................................................................... 24 

National and State Agencies............................................................................................. 24 

Non-Governmental and Other Organizations................................................................... 24 

GIS Data........................................................................................................................................ 24 

Data Management Process............................................................................................................ 24 

Acquisition of New Spatial Data .................................................................................................. 25 

Maintaining Digital Files.............................................................................................................. 25 

Data Discovery.............................................................................................................................. 26 

Chapter 6. Quality Assurance and Quality Control ...................................................................... 27 

National Park Service Mandate for Quality.................................................................................. 27 

Data Quality Goals and Objectives............................................................................................... 28 

Quality Assurance and Control Duties ......................................................................................... 28 

QA/QC Process Cycle................................................................................................................... 30 

Project Planning and Database Design ......................................................................................... 31 

Database Design - Record-level Tracking ........................................................................ 31 

Lookup Tables .................................................................................................................. 31 

Project SOPs ..................................................................................................................... 32 

Data Acquisition ........................................................................................................................... 32 

Data Entry ..................................................................................................................................... 33 

v 
Verification and Validation........................................................................................................... 33 

Methods for Data Verification.......................................................................................... 34 

Supplementary Methods................................................................................................... 34 

Validation...................................................................................................................................... 35 

Methods for Data Validation ............................................................................................ 35 

Data Quality Review and Communication ................................................................................... 36 

Conformance..................................................................................................................... 36 

Documenting and Communicating Quality ...................................................................... 37 

Data and File Management Operations......................................................................................... 37 

File Naming Standards...................................................................................................... 37 

Version Control................................................................................................................. 38 

Laboratory Data ................................................................................................................ 38 

Chapter 7. Data Documentation.................................................................................................... 39 

Metadata Profile............................................................................................................................ 39 

Documentation Roles and Responsibilities .................................................................................. 40 

Metadata Tools.............................................................................................................................. 40 

Metadata Process .......................................................................................................................... 40 

Metadata Maintenance .................................................................................................................. 42 

Protocol Versions.......................................................................................................................... 42 

Non-Program Data Documentation .............................................................................................. 42 

Derived Data Documentation ....................................................................................................... 42 

Legacy Data Documentation......................................................................................................... 42 

Chapter 8. Data Analysis and Reporting....................................................................................... 43 

Analysis......................................................................................................................................... 43 

Reporting....................................................................................................................................... 43 

vi 
Long-term Trends and Analyses................................................................................................... 44 

Funding and Timeliness................................................................................................................ 44 

Chapter 9. Data Dissemination .................................................................................................... 45 

Introduction................................................................................................................................... 45 

Data Distribution Mechanisms ..................................................................................................... 45 

Data Ownership ............................................................................................................................ 46 

National Park Service Policy on Data Ownership ............................................................ 46 

Establishing Data Ownership: Cooperative or Interagency Agreements ......................... 47 

Data Classification: protected vs. public ...................................................................................... 47 

Access Restrictions on Sensitive Data.......................................................................................... 49 

Dissemination Mechanisms.......................................................................................................... 50 

Arctic Network Primary Server ........................................................................................ 51 

Non-monitoring data......................................................................................................... 51 

Alaska Resources Library and Information Services (ARLIS) ........................................ 51 

NPS Focus......................................................................................................................... 52 

Water Quality Data ........................................................................................................... 52 

Data Availability........................................................................................................................... 52 

Data Release Policy ...................................................................................................................... 52 

Feedback Mechanisms.................................................................................................................. 53 

Data Error Feedback Response Procedures.................................................................................. 53 

Chapter 10. Data Maintenance, Storage and Archiving ............................................................... 55 

Data Maintenance ......................................................................................................................... 55 

Spatial Data....................................................................................................................... 55 

Files................................................................................................................................... 56 

File Backup Plan............................................................................................................... 56 

vii 
viii 

Hardcopy Data and Information - Maintenance ............................................................... 56 

Finalized Digital Datasets................................................................................................. 56 

Physical Material Archival ............................................................................................... 56 

Project Related Items........................................................................................................ 57 

Photographs....................................................................................................................... 57 

Chapter 11. Implementation.......................................................................................................... 59 

References..................................................................................................................................... 61 
Figures and Tables 

Figure 1. Arctic Network Parks..................................................................................................... 2 

Figure 2. Relationship of the national-level data management plan to network-level data 

management documents adopted from chapter 1.2 of the Data management guidelines for 

inventory and monitoring networks (National Park Service 2008). ............................................... 3 

Figure 3. An enterprise client-server database will be the heart of the Arctic Network data 

management system........................................................................................................................ 8 

Figure 4. Arctic Network generalized workflow showing major data repositories........................ 9 

Figure 5. Principle information technology components.............................................................. 10 

Figure 6. Information technology connectivity between Network, Park and National operations.

....................................................................................................................................................... 12 

Figure 7. Project life cycle............................................................................................................ 14 

Figure 8. Some common data management elements affecting degree of need for QA/QC. 

Planning and training for data collection (QA) and entry is always a premium........................... 28 

Figure 9. General course of data and associated QA/QC procedures. Quality control with regards 

to data analysis is specific to each project and addressed in appropriate standard operating 

procedures..................................................................................................................................... 30 

Figure 10. The metadata document contains all information pertaining to project data. ............. 39 

Figure 11. Generalized path of data within the Arctic Network. The primary data server is 

accessed via desktop and web-based applications. Analyzed or derived data products are stored 

on the primary server, archived and catalogued (integrated) along with certified data................ 41 

Figure 12. Water quality data flow. .............................................................................................. 52 

Table 1. The principal Inventory and Monitoring applications and data repositories.................. 10 

Table 2. Data stewardship responsibilities at each phase in the project life cycle. ...................... 17 

Table 3. Network personnel roles and responsibilities................................................................. 18 

Table 4. Examples of park-based natural resource information that is processed or documented 

by the network............................................................................................................................... 22 

Table 5. Data quality roles and responsibilities........................................................................... 29 

Table 6. Repositories for Arctic Network Program data and information.................................... 51 

ix 
x 

Acknowledgements 

We the willing, led by the unknowing, are doing the impossible for the ungrateful. We have done 

so much, with so little, for so long, we are now qualified to do anything, with nothing. 

--The Metro Para pledge 

Anyone who does much snowshoeing in Alaska knows that the person out in front breaking trail 

does all the work for those who follow. I derived, adapted, and at times outright copied much of 

the material in this plan from a group of visionary trailbreakers to whom I am greatly indebted. I 

would like to thank the following individuals who said what needed to be said much better than I 

ever could: Doug Wilder (Central Alaska Network), Dorothy Mortenson (Southwest Alaska 

Network), Rob Daly (Greater Yellowstone Network), John Boetch (North Coast Cascades 

Network), Brent Frakes (Rocky Mountain Network) and Margaret Beer, (Inventory and 

Monitoring Washington Support Office). I am deeply indebted to all the data managers who 

started ahead of me in our phased implementation. Your efforts have cleared an easy path for 

those of us who follow.
Chapter 1. Introduction 

Capt. Kirk: Spock, give me an update on the dark area ahead. 

Spock: No analysis due to insufficient information. 

Capt. Kirk: No speculation, no information, nothing? I've asked you three times for information on that thing and 

you've been unable to supply it. Insufficient information is not sufficient, Mr. Spock! You're the science officer. 

You're supposed to have sufficient data all the time. 

--"Star Trek" (1966) 

No resource management enterprise can make objective, rational decisions to meet its objectives 

without good data. In the absence of sensed and quantified information decisions can only be 

based on assumptions, emotion and intuition. The National Park Service’s Inventory and 

Monitoring program was developed to address the growing need among park managers for 

scientific information on which to make management decisions. The Inventory & Monitoring 

Program has committed to a building a program of effective ecosystem monitoring using state of 

the art data management practices to supply park managers with sound scientific information. 

This plan provides the vision and the roadmap for successfully accomplishing the Arctic 

Network’s data management goals. 

About the Arctic Network 

The Arctic Network is one of thirty-two networks in the National Park Service’s Inventory and 

Monitoring program and one of four networks in Alaska. The network consists of five public 

parklands, all of which are located in northwestern Alaska (Figure 1): 

• Bering Land Bridge National Preserve 

• Cape Krusenstern National Monument 

• Noatak National Preserve 

• Kobuk Valley National Park 

• Gates of the Arctic National Park and Preserve 

These parks are among the largest and most remote of all the national park lands and cover 

myriad arctic and subarctic habitats from the western sea coast to the eastern peaks of the Brooks 

Range. Administratively, the Arctic Network is integrated with and operates out of the Fairbanks 

Administrative Center in Fairbanks, Alaska and the Western Arctic Parklands offices in Nome 

and Kotzebue, Alaska. More information about the Arctic Network can be found on the web at 

http://science.nature.nps.gov/im/units/arcn/index.cfm. 

1 
Figure 1. Arctic Network Parks 

Purpose and Scope 

This document is not intended to address national I&M Program guidelines, Directors Orders, or 

legislative requirements. These items are addressed in Data management guidelines for inventory 

and monitoring networks, also referred to as the National Data Management Plan (National Park 

Service 2008), which provides broad guidance in terms of the National I&M Program, NPS 

Directives, and other legislative requirements. 

This network-level data management plan is intended to act as a supplement to the National Data 

Management Plan, and function as a guide for establishing and maintaining a system that serves 

the specific data management needs of the Arctic Network. This plan establishes the general 

concepts and procedures the Arctic Network, its cooperators, partners, and potentially individual 

park units will use to ensure the quality, interpretability, security, longevity, and availability of 

program data and related information. Projects initiated by this program and the personnel 

involved with those projects must follow the guidance provided in this plan. 

This plan will reference stand-alone standard operating procedures and task instructions that 

provide details on specific network operational procedures (i.e., back-up procedures, document 

and data documentation, document and data tracking, etc.). Figure 2 shows the relationship of the 

national-level data management plan to network-level data management documents. Standard 

operating procedures are available on the Arctic Network website and file server. 

2 
General 

Specific 

National Data 

Management Plan 

Network Data 

Management Plan 

Network Standard 

Operating Procedures 

Network Task 

Instructions 

Figure 2. Relationship of the national-level data management plan to network-level data 

management documents adopted from chapter 1.2 of the Data management guidelines for 

inventory and monitoring networks (National Park Service 2008). 

Data Management and the Inventory and Monitoring mission 

Providing sound, quantifiable information to park managers is a laudable goal for the Inventory 

and Monitoring program. Program directors have acknowledged that while such a goal is 

worthwhile, it is not easy without technical support. Someone has to keep the hardware spinning 

and the numbers crunching in order to reliably funnel synthesized information to individuals 

needing it. Data management fulfills this role by providing expertise in the technical aspects of 

biological science including client-server databases, application development, metadata 

generation, web development and geographic information systems. By expressing up-front 

support for data management the Inventory and Monitoring program is not only publicly 

committing to a culture of good data management practices but also expressing pride in the 

program's resulting technical products. 

Goals of the Inventory and Monitoring Program 

The Inventory and Monitoring program has five broad goals: 

1. Inventory the natural resources under National Park Service stewardship to determine 

their nature and status. 

2. Monitor park ecosystems to better understand their dynamic nature and condition and to 

provide reference points for comparisons with other, altered environments. 

3. Establish natural resource inventory and monitoring as a standard practice throughout the 

National Park system that transcends traditional program, activity, and funding 

boundaries. 

4. Integrate natural resource inventory and monitoring information into National Park 

Service planning, management, and decision making. 

3 
5. Share National Park Service accomplishments and information with other natural 

resource organizations and form partnerships for attaining common goals and objectives. 

Data Management Goals 

The Arctic Network's main data management goals are listed below: 

• Acquire, store, manage and archive Arctic Network data 

• Ensure high data quality 

• Document and disseminate data and information 

• Ensure the long-term access to and utility of data. 

Data as a Valued Commodity 

Data is an expensive asset. Data acquisition is time-consuming, arduous, and typically very 

costly. Despite the recognition of data's value, data management has, historically taken a 

backseat to higher priority activities such as publishing results and obtaining funding for new 

research. The causes for this lack of dedication toward good data management are varied but 

generally include a traditional lack of: 

1. User-friendly software 

2. Metadata standards 

3. Internet data warehouses and data servers 

4. Internet in any consistent and reliable form 

5. Expertise in database design and data normalization 

6. Requirements from funding agencies to publish data along with results 

7. Culture of data stewardship 

8. Mandates for data management funding 

This situation is changing, however. Good data management practices are becoming more 

commonplace with time. Organizations are realizing the secondary value of data to large-scale 

synthetic research works as well as landscape modeling efforts that rely on data from many 

small, on the ground projects. Well documented and preserved data is also valuable for 

opportunistic research projects, or background information on which to base estimates of trend or 

for time-series comparisons. Indeed major funding agencies are now requiring that data be 

published with results. Government agencies are following suit and the Inventory & Monitoring 

program is leading the charge. The NPS Inventory and Monitoring program has mandated 

visionary provisions for data management staffing and funding. 

Data Management Expectations 

• Inventory and Monitoring data will be held to a high standard of quality 

• Data management will be a collaborative effort benefitting everyone in the network. Each 

stakeholder must commit the time and resources required 

• Data management will be a beforethought, rather than an afterthought 

• Data management requires support from our collaborators in establishing and reinforcing 

a culture of good data management practices 

4 
Revisions to this Plan 

Success seldom emerges fully fledged on the first try. Success is rather the very public 

culmination of a hidden, cyclical path of trial, failure, learning and revision. As our program 

progresses we fully expect to make and learn from mistakes. We also will keep aware of new 

technologies and potential partnerships and attempt to exploit them for the benefit of the 

program. Most of all, we will not hesitate to modify this document if it will enhance the 

probability of achieving our goals. 

5 

Chapter 2. Data Management Infrastructure and System 

Architecture 

Sattinger's Law: It works better if you plug it in. 

Just as no one would expect a carpenter to build a quality home without a good hammer, one 

cannot expect to build and maintain a data management system without high quality computing 

resources. As a network we are fortunate that we have a broad array of hardware, software and 

support services already in place and available to us through the Fairbanks Administrative 

Center, the Alaska Regional Office and the Washington Support Office. These resources are 

discussed in chapter two of the National Data Management Plan (National Park Service 2008). 

Managing data for an enterprise as large as the Arctic Network requires that information be very 

centralized and accessible to the level of the least common denominator. Centralized data 

systems are systems where an enterprise database stores a master copy of all required data. 

Clients (or data users) use a variety of software applications to log in to a master database. The 

central database is designed to handle multiple users through user permissions management, 

record-locking and concurrency management. Such a system ensures that all users have the 

correct permission to access or edit data, and all users have the latest snapshot of the data. In 

addition, data dissemination is instantaneous as the data store can update public and private 

websites, geographic information systems and internet map servers 'on the fly'. Data access 

issues can be provided via multiple portals to the system Figure 3. In the discussion that follows, 

systems architecture signifies the applications, database systems, repositories, and software tools 

that make up the digital framework of our data management enterprise. This chapter will focus 

on the details of the Arctic Network data management system architecture. 

Architecture 

The Arctic Network will employ a highly centralized model of data management. At the heart of 

the system is an enterprise database that will contain not only monitoring and project data, but 

also project tracking information and a suite of commonly used lookup tables and related 

information. Our workflow consists of loading high quality information into the database and 

subsequently extracting data as needed for administration, analysis and reporting. A centralized 

scheme such as this has numerous advantages over more distributed systems including simplified 

backups, granular security schemas, minimized dataset versioning problems and efficient 

common access to data resources to stakeholders. Metadata, and in some cases, data files, will be 

uploaded to the NPS Data Store as well as the Permanent Data Stack (X:\ Drive) at the Alaska 

Regional Office in Anchorage. 

7 
Figure 3. An enterprise client-server database will be the heart of the Arctic Network data 

management system. 

In addition to the primary data server we will employ a common file server with structured 

directories for each project (NPS 2007c). This shared network file server will store the myriad 

files generated by projects including everything from administrative details, through draft reports 

and analyses to working data files. Investigators will be required to use these directories and 

keep them up to date. 

Figure 4 presents a generalized schematic of the Arctic Network data management infrastructure 

as well as a life cycle for Arctic Network data. While variations to the cycle will occur based on 

project specifics, the majority of data handled by the Arctic Network will follow this general 

course. 

8 
Figure 4. Arctic Network generalized workflow showing major data repositories 

NPS Infrastructure 

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

and servers (Figure 5). 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 5 illustrates these components. 

9 
Figure 5. Principle information technology components.

National Applications 

The basic tenet of I&M information management is to provide integrated natural resource 

databases and information systems that provide park staff with relevant, high-quality data for 

management decisions, resource protection, and interpretation. The NPS Natural Resource 

Program Center (NRPC) and the I&M Program actively develop and maintain a national-level 

information management framework and suite of applications that use a combination of desktop 

applications and Internet-based systems. 

The principal applications and data repositories developed for the I&M Program and servicewide programs, and referenced throughout this document, are summarized below (Table 1, NPS 

2007i,o). See the National Data Management Plan (National Park Service 2008) for more detail. 

Table 1. The principal Inventory and Monitoring applications and data repositories. 

Application Description 

Naturebib Bibliographic Database 

Dataset Catalog Metadata Tool 

NPStoret Water Quality Database 

Natural Resource Database Template (NRDT) Monitoring Application Framework 

10 
NPS Data Store Web-Based Dataset Search Tool 

Vital Signs Internet Map Services (VSIMS) Web Based Spatial Data Dissemination Tool 

Natural Resource Monitoring Protocol 

Clearinghouse 

Web-Based Clearinghouse Of Monitoring 

Protocols 

Natural Resources Monitoring Partnerships 

(NRMP) 

Web-Based Clearinghouse Of Monitoring 

Projects And Protocols 

NPS Automated National Catalog System 

(ANCS+) 

Curatorial Cataloging System 

Research Permit And Reporting System 

(RPRS) 

Web-Based Research Permit Manager 

Alaska Region Infrastructure 

The Alaska Regional Office offers a central repository for many types of information and acts as 

the backbone for base cartographic data in the Alaska region. The regional GIS Team provides 

support of various spatial data operations via a request process and a liaison assigned to each 

park. The Alaska Regional Office GIS Team also maintains a server that will act as offsite 

backup for all Arctic Network data. Implementation of this plan will include arrangement for 

transfer of Arctic Network data to the Alaska Regional Office GIS server in addition to the NPS 

Data Store. 

To summarize, the Alaska Regional Office provides the following that the Arctic Network will 

utilize to meet its goals: 

• Wide Area Network file server for general file exchange and storage 

• GIS and related tabular data accessible via custom applications distributed to the parks as 

well as the Alaska GIS Data Clearing House (www.nps.gov/akso/gis). 

• File server to provide offsite storage for all Arctic Network data 

Park and Network-Level Infrastructure 

The Arctic Network infrastructure is composed of elements from the national Inventory and 

Monitoring Program, the NPS Alaska region and Arctic Network-member parks. The Arctic 

Network relies primarily on the system infrastructure of the Fairbanks Administrative Center and 

the park headquarters of the Western Arctic Parklands in Nome and Kotzebue. The Arctic 

Network will manage a primary repository located in the Fairbanks Administrative Center for 

data and information generated by the Network. These data will be accessible to park personnel 

via custom applications as well as the network website and shared network drive (O:\ drive, NPS 

2007c). The primary Arctic Network repository will be backed up to an offsite server. Applicable 

portions of the Arctic Network data will be stored in national Inventory and Monitoring Program 

databases such as NPSpecies, NatureBib and NPS Data Store (NPS 2007i,o). Certain datasets 

will be maintained by outside organizations, but metadata will be maintained in the primary 

11 
Arctic Network repository or the NPS Data Store. Arctic Network operations, where possible, 

will be designed to function within and augment Park operations (Figure 6). 

Figure 6. Information technology connectivity between Network, Park and National operations. 

12 
Chapter 3. Data Management Process and Workflow 

43rd Law of Computing: Anything that can go wr--

root@localhost:>Segmentation violation -- Core dumped 

The organizational umbrella under which questions are asked and related data is collected is 

typically called a study or a project. Inventory and Monitoring projects are of two general forms: 

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

work done in preparation for long-term monitoring and 2) Long-term projects, which are mainly 

monitoring projects central to the Inventory and Monitoring Program, but may also include 

multi-year research projects and monitoring performed by other agencies and cooperators. Longterm projects typically require a higher level of documentation, peer review, and adherence to 

standards to ensure consistency over time. Regardless of the expected duration of a project, each 

has a life cycle that can be broken into phases. These phases are discussed below. 

Project Process 

Both short and long-term project types can be divided into five primary stages (Figure 7): 

1. Planning and approval; 

2. Design and testing; 

3. Implementation; 

4. Product integration; 

5. Evaluation and closure. 

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

Primary responsibility for these activities rests with various individuals according to the different 

phases of a project. Major tasks associated with each phase of the project life cycle are described 

in Figure 7. 

13 
Figure 7. Project life cycle 

Planning and Approval 

This initial phase is when many of the preliminary decisions are made regarding project scope 

and objectives. Funding sources, permits, and compliance are also addressed in this phase. 

Primary responsibility rests with project leaders and program administrators. Data management 

responsibilities include reviewing contracts, agreements, or permits for language that describes 

the formats, specifications, and timelines for project deliverables (NPS 2007a,b,d,k,m ). 

14 
Design and Testing 

During this phase, 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 essential that the project leader and the data manager work together throughout this phase in 

order to build the basis for good data management throughout the project. An important part of 

this collaboration is the development of the data design and data dictionary, where the specifics 

of data to 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. Once the project methods, data design, and data dictionary have been developed 

and documented, a database can be built to meet project requirements (NPS 2007d,f, 

Southwould, 2002a,b). 

Implementation 

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

Products such as reports, maps, geographic information system (GIS) themes, and other products 

are developed and delivered during this phase. 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: they provide training and support for global positioning 

systems (GPS), database, and GIS applications, ensure data verification and validation, 

summarize or format data as needed for analyses, and assist with data documentation and 

product development (NPS 2007n). The specific roles of data management staff during this 

phase will vary depending on the project. As much as possible, these roles should be worked out 

in advance of project 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 

(e.g., protocol, study plan, agreement). Raw and derived data products, metadata, reports and 

other documentation should be delivered to the project leader, who then works with the data 

manager to move products to their final repositories (NPS 2007k). 

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. Depending on the project, 

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, 

integrating species-related data into NPSpecies, creating or updating metadata files, and posting 

the resulting products to national clearinghouses such as the NPS Data Store (NPS 2007e,h,i,o). 

Evaluation and Closure 

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 short-term projects, this phase 

15 
represents the completion of the project. After products are catalogued and made available, the 

program administrator, project leader, and data manager 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 (NPS 2007a,d). 

16 
Chapter 4. Data Management Roles and Responsibilities 

Roles and Responsibilities 

Data stewardship is the responsibility of all network personnel. All network personnel have a 

stake in collecting, managing, and analyzing data. It is imperative that everyone's responsibilities 

be clearly described and that each individual be aware of their duties (NPS 2007a,k). Table 2, 

below, shows the roles and responsibilities of each person with respect to the project work flow 

as described in Figure 4. 

Table 2. Data stewardship responsibilities at each phase in the project life cycle. 

Project Stage General Description Principal Responsibility 

Design and testing ● Develop methodology or adapt 

existing methods 

● Establish data collection protocols 

● Establish project schedule 

● Design and test database 

● Design data sheets 

Project Leader 

Quantitative Ecologist 

Data Manager 

GIS Specialist 

Implementation ● Record locations, measurements and 

observations in the field 

● Digitize source maps 

● Key in data from a hardcopy source, 

convert existing data sources 

● Process images 

● Prepare summary tables, maps, reports 

Project Crew Member 

Project Crew Leader 

GIS Specialist 

Data Manager 

Analysis and 

reporting 

● Use data to address monitoring 

objectives, specifically, status and 

trends of vital signs 

● Use data to describe and interpret 

relationships among vital signs 

● Synthesize data for different types of 

end users 

● Prepare periodic and final reports 

Project Leader 

Quantitative Ecologist 

Integration ● Apply procedures that keep data 

organized and usable; make data 

available 

● Document project and data 

● Archive data and products 

● Catalog data and products 

● Publish results 

● Post on Internet 

Project Leader 

Data Manager 

GIS Specialist 

Museum Curator 

Quantitative Ecologist 

IT Specialist 

17 
Project Stage General Description Principal Responsibility 

Evaluation ● Evaluate if project meets objectives 

● Determine changes to methods, 

procedures, or data design 

Project Leader 

Data Manager 

GIS Specialist 

Quantitative Ecologist 

 

Table 3. Network personnel roles and responsibilities 

Role Primary responsibilities related to data management 

Project Crew Member Record and verify measurements and observations based on project 

objectives and protocols. 

Document methods, procedures and anomalies. 

Project Crew Leader Supervise crew members to ensure their data collection and 

management obligations are met, including data verification and 

documentation. 

Data/GIS Specialist or 

Technician 

Perform assigned level of technical data management and/or GIS 

activities, including data entry, data conversion, and documentation. 

Work on overall data quality and stewardship with project leaders, 

resource specialists, and the Network data manager. 

Information 

Technology/Systems 

Specialist (park IT 

support staff) 

Maintain local area network, establish and maintain system security, 

and keep software and hardware systems up to date. Additionally, IT 

staff are responsible for maintaining connections between the LAN and 

the internet. They work with the Network data manager and GIS 

liaisons to establish a directory structure (including drive partitions) 

that provides local access and security for natural resource data. They 

manage the infrastructure for digital data backups for the local area 

network. 

Network Coordinator Interface with project leaders to ensure that timelines for data entry, 

validation, verification, summarization/analysis and reporting are met. 

Additionally, the Network Coordinator must review and approve 

proposed changes to project protocols prior to implementation. 

Project Leader Oversee and direct operations, including data management 

requirements, for one or more Network projects. 

 ,Network Data Manager ,Maintain communication with project staff

. and resource specialist regarding data management

Resource Specialist Understand the objectives of the project, the resulting data, and their 

scientific and management relevance. 

Make decisions about data with regard to validity, utility, sensitivity, 

and availability. Describe, publish, release, and discuss the data and 

associated information products. 

GIS Manager Support park management objectives. Coordinate and integrate local 

GIS and resource information management with Network, Regional, 

and National standards and guidelines. 

18 
Role Primary responsibilities related to data management 

Network Data Manager and operational support for,training ,Provide overall Network planning

integration of data and information and ,coordination ,the awareness

 ,data ,information needs ,including people ,management activities

software, and hardware National Serve as Point of Contact for . Park 

Service applications (NPSpecies). Coordinate internal and external data 

management activities. 

Database Manager Apply particular knowledge and abilities related to database software 

and associated application(s)

Network Ecologist Ensure useful data are collected and managed by integrating natural 

resource science in network activities and products, including objective 

setting, sample design, data analysis, synthesis, and reporting

Data Manager (National 

Level)

Provide service-wide database design, support, and services, including 

receiving and processing to convert, store, and archive data in servicewide databases

Other End Users These 'information consumers' include park managers and 

superintendents, researchers, staff from other agencies, and the public. 

End users are responsible for the appropriate use and application of data 

and derived products and for providing feedback for improvements.

19 

Chapter 5. Data Acquisition and Processing 

Williams and Holland's Law: If enough data is collected, anything may be proven by statistical methods. 

Data is dynamic and flows through an organization following a distinct path from acquisition 

through processing, analysis, reporting and archival. While project process and workflow was 

discussed earlier the details of data acquisition and processing require more discussion. This 

chapter describes the general procedures the Arctic Network follows for acquiring and 

processing natural resource-related data. Procedures will vary depending on the data source, but 

can be broadly placed into three general categories: 

Network Data: Data resulting from projects that are initiated, sponsored, or funded by the Arctic 

Network. 

Other National Park Service (NPS) Data: Data resulting from projects that are initiated, 

sponsored, or funded by park units, or by regional or national NPS programs. 

External data: Data produced or managed by agencies, organizations, or individuals other than 

the National Park Service. 

These categories are addressed in the sections below. In addition we discuss digital file 

management, software recommendations and the handling of spatial data, 

Network Data 

Acquisition and processing of biological inventory data is a straightforward process due to the 

high level of network control over the projects. Most projects are completed by cooperators 

working under a contract or agreement that has been written specifically by the network for the 

project. In these agreements Arctic Network itemizes data deliverables and formats that are 

required from the cooperator. The data manager works closely with the principal investigator and 

the project leader to develop these data criteria, and to develop a database modeled on the 

Natural Resource Database Template, where appropriate, that is tailored to the specific data 

needs of the project. Additional data specifications usually included in agreements are 

procedures for acquiring and documenting project photographs, collecting specimens, using 

global positioning systems (GPS), and delivering geospatial data. Data processing typically 

consists of reviewing the project deliverables, integrating the project into the various Arctic 

Network cataloging systems, and distributing project results. Principal investigators are 

responsible for verifying and validating the data they submit; however, Arctic Network performs 

additional quality checks on all datasets. Data management staff are usually in contact with the 

principal investigator after the submission of final products in order to resolve discrepancies or 

answer questions that inevitably arise as staff work with the data. 

Data acquisition for network monitoring projects is guided by established protocols that include 

step-by-step procedures for all aspects of data collection, data entry, quality assessment (QA), 

quality control (QC), reporting, analysis, distribution, and archiving. Depending on the protocol, 

data can be acquired by park staff, network staff, cooperators, or other agencies or organizations. 

Monitoring data need to go through processing and integration steps that are similar to inventory 

21 
data; however, many of the steps are repeated annually or on a schedule corresponding to 

sampling frequency. Each monitoring project will have a processing and integration checklist 

that is specific to the project to ensure that all steps are completed. 

National Park Service Data 

A fundamental step in developing and maintaining the information base for Arctic Network is 

locating, evaluating, and documenting, on an ongoing basis, park-related natural resource 

information. The term “data mining” refers to this process, which requires regular visits to 

network parks and establishing data mining procedures specific to each park. The range of 

materials that require documenting is broad, ranging from historical inventories, to complex 

databases, to remote sensing data. 

As a general rule, the goal of network data mining is not to acquire and store, at the network, 

copies of all data residing at parks. Instead, cataloging tools are used to describe and document 

data sources so that potential users can find them. Data that are an exception to this rule are 

documents or datasets that are of specific need or interest to network staff, or that complement 

network projects. The highest priority items are past or currently existing monitoring efforts that 

coincide with our network vital signs. 

Parks within the network vary widely in how natural resource information is organized and 

stored. While some parks have developed efficient and standardized methods of cataloging new 

information as it arrives, other parks have no formalized approach for organizing their natural 

resource materials. 

Arctic Network works on-site with park staff to locate and catalog their natural resource 

information. Table 4 summarizes the range of park-based data that the network is cataloging, and 

the associated processing steps. 

Table 4. Examples of park-based natural resource information that is processed or documented 

by the network. 

Data Description Processing 

Gray literature, unpublished reports, research 

Summaries

Review and document in NatureBib. Evaluate for 

species data and link to NPSpecies. Obtain copies for 

network files (preferably electronic) 

as appropriate; upload full-text document to NatureBib 

(if not subject to copyright restrictions)

Published reports, journal articles Review and document in NatureBib. Evaluate for 

species data and link to NPSpecies. Obtain copies for 

network files (preferably electronic) 

as appropriate; upload full-text document to NatureBib 

(if not subject to copyright restrictions) 

 Investigator Annual Reports If IAR is the only reference to a project, document in 

NatureBib. If a more comprehensive report has 

resulted from the project, create a NatureBib record for 

the report and reference the IAR in the NatureBib 

record. 

22 
Data Description Processing 

 Photographs If photos are part of an assemblage accompanied by 

documentation, enter into NPS Data Store. Evaluate 

for NPSpecies data (photo vouchers) 

Databases, spreadsheets, other electronic 

data assemblages — undocumented

Review for associated documentation. Locate project 

leader, if possible, for further explanations. Create 

NPS Data Store record and document to the extent 

possible in the Arctic Network data catalog. 

Databases, spreadsheets, other electronic 

data assemblages — documented

Create NPS Data Store record. Evaluate for 

NPSpecies data. Obtain copies for network files as 

appropriate 

GIS data — undocumented Retain copy at Arctic Network office as applicable. 

Document in Arctic Network GIS Inventory database. 

Create metadata. 

GIS data — documented Retain copy at Arctic Network office as applicable. 

Document in Arctic Network GIS Inventory database; 

maintain metadata. 

Species lists Create NatureBib record if author and date are 

included with list. Evaluate for NPSpecies. 

Data collected by NPS regional or national 

programs (e.g., air quality, sound, night sky) 

Create NPS Data Store record 

Data collected and managed by other 

agencies (e.g., stream gauging stations,

climate, breeding bird data, other monitoring

programs)

Create NPS Data Store record; evaluate for 

NPSpecies 

ANCS+ natural history specimen data Evaluate for inclusion in voucher table of NPSpecies. 

Park library records (ProCite) Evaluate for inclusion in NatureBib 

Park archives Evaluate for inclusion in NatureBib, NPS Data Store, 

NPSpecies 

Arctic Network data management staff complete most of these cataloging tasks, and have been 

the primary users of NatureBib, NPSpecies, and NPS Data Store (NPS 2007i,o). Most parks have 

had no extra staff available to perform this work or to devote to using the databases. Arctic 

Network places a high priority on maintaining and building these databases, and will continue to 

perform the work as needed. Data management staff will provide training to park staff interested 

in learning to use the databases, and will provide reports from the databases in formats that are 

useful and easily accessible to park staff (e.g., hard-copy printouts, web-based species lists). 

Most datasets developed at parks are not maintained in fully-normalized database formats, nor do 

they need to be. However, datasets designated as being of high priority to the Arctic Network 

will be converted to current database standards. To avert database problems for new projects, 

network data management staff are available to help parks as possible or needed with database 

design, using the Natural Resource Database Template as a model. 

23 
External Data 

Organizations and agencies outside the National Park Service have been an ongoing source of 

natural resource data for the network and are included in the network’s data mining activities. 

Processing external data follows the same steps and procedures as processing park data: 

information is evaluated, entered into one or more cataloging databases, and is then posted or 

made available as appropriate. 

Museums and Herbaria 

One of the more challenging network tasks has been acquiring information on specimens that 

were collected within park units, but that are housed in a variety of repositories across the 

country (DOI 1993). The Inventory and Monitoring Program at the Washington Office has 

obtained specimen data from major museums and herbaria that have their holdings in electronic 

format. Smaller regional and state repositories will be checked as time and budget allows. 

Frequently the associated and required Automated National Cataloging System (ANCS+) records 

for these specimens do not exist at the parks. Arctic Network works with park museum curators 

when receiving and managing specimen data from external sources, and provides them with 

formatted data for uploading into ANCS+, if appropriate. 

National and State Agencies 

Occasionally national and state agencies conduct research in national parks. Resulting data may 

be specific to a park unit, or may encompass a much broader geographic area. The Arctic 

Network focuses primarily on creating NatureBib records for reports, publications, or other 

documents resulting from agency projects. 

Non-Governmental and Other Organizations 

Arctic Network also acquires data from private and non-profit organizations. References to 

theses and dissertations related to park research are located through on-line database searches, as 

are journal articles and other publications that reference park-based research. 

GIS Data 

The Arctic Network will leverage the GIS data store housed by the Alaska Regional Office in 

Anchorage. Spatial information for the Alaska parks are searchable via NPS Theme Manager 

software. Spatial data is replicated to the Fairbanks Administrative Center nightly and available 

to all network personnel via a mapped network drive. Spatial data collected by the Arctic 

Network will be archived with associated metadata with the Alaska Regional Office and the NPS 

Data Store. 

Data Management Process 

The major elements in the process for executing data management within the framework may be 

summarized as: 

• Data acquisition (field work, satellite download, etc.) 

• Raw data archival (physical and possibly digital material) 

• Data entry/import 

• Quality assurance and control) 

24 
• Documentation (metadata, etc.) 

• Analysis & reporting 

• Data cataloging, documentation, and archival (digital) 

The elements listed above roughly comprise a data life cycle within the Arctic Network. Figure 4

presents the relative timing of these elements within a project (NPS 2007a). For the life cycle to 

have actionable meaning, we must also specify how and where these core data management 

elements will be done. For the most part, these are documented in the standard operating 

procedures for each vital sign. There are, however, some commonalities to these procedures 

dictated in this plan. 

Figure 7 presents a generalized schematic of the data life cycle for Arctic Network data. While 

variations to the cycle occur based on project specifics (e.g. in the case of climate or air quality 

data which will be managed by outside entities), the majority of data handled by the Arctic 

Network will follow this general course. 

Acquisition of New Spatial Data 

Spatial data acquired by the network must meet established standards and product specifications 

(NPS 2007a, NPS 1994). Additional standards or requirements may be identified on a projectby-project basis. Spatial data gathered using global positioning system (GPS) units must also 

meet established standards (NPS 2007n). The Alaska Regional office maintains a GPS guidance 

website (http://www.nps.gov/gis/gps/) that the Arctic Network will use. This website addresses 

GPS instruments, instrument settings, field operation, data collection, data processing, and a 

standardized method for acquiring and managing location data. 

Maintaining Digital Files 

The Arctic Network maintains hierarchical directory structures for storing project-related digital 

files (NPS 2007c). The key aspects of this file management strategy are as follows: 

• Provides a consistent and logically organized structure that complements the 

Network’s day-to-day operations. 

• Minimizes confusion about where information is located. 

• Allow for efficient, comprehensive, regular and secure means of backing up 

information. 

• Accommodates various security settings for different types of users; 

• System is hierarchical and minimizes the ambiguity of where information should go. 

Ideally, the organization should be logical enough that one could navigate through the 

entire structure without requiring a map; 

• Separate in-progress/draft/unofficial/in-progress files from final/official information; 

• Be broad enough to clearly accommodate all types of information handled by the 

Network staff; 

• Be simple without sacrificing functionality; 

• Minimize the number of sub-folder levels and also refrain from having more than 8-

10 folders at any given level; 

25 
• Information should never be arbitrarily divided in a way that increases the possibility 

of corruption or loss (e.g., metadata should never be separated from the data) 

• When possible and appropriate, the structure should conform to other NPS and 

national directory structure standards 

• 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. 

Our system can be summarized as follows: 

• The Arctic Network will manage a primary repository located in the Fairbanks 

Administrative Center for data and information generated by the Network. These data 

will be accessible via national databases, custom desktop and web applications as 

well as the Arctic Network website and will be open to authorized personnel. 

• The primary Arctic Network repository will be backed up to an off-site server in the 

Alaska Region. 

• Required portions of the Arctic Network data will be stored in national Inventory and 

Monitoring Program databases such as NPSpecies, NatureBib and NPS Data Store. 

• Certain Arctic Network datasets will be maintained by outside organizations however 

metadata for these will be maintained in the primary Arctic Network repository or the 

NPS Data Store. 

Data Discovery 

It is impossible to predict wholly the demands which will be made on a data interface for the 

complex body of data and information the Arctic Network intends to manage. We assume, 

however, the following basic needs: 

• Robust data browsing: 

o View summary results from any one vital sign measure 

o Results comparison between vital signs 

o View monitoring results by time and location 

• Robust data access (download): 

o Easy data subsetting (data searches that allow data to be selected by data, 

location, subject, etc.) 

o Useful format choices 

o Dissemination tracking 

o Accompanying metadata 

• Robust data upload for Project Leaders and authorized personnel 

• Robust data discovery so that our data and information may be easily found by outside 

stake holders 

Our data management applications, both desktop and web based must reflect the above 

requirements. 

26 
Chapter 6. Quality Assurance and Quality Control 

Sturgeon's Law: 90% of everything is crud. 

Weinberg's Second Law: If builders built buildings the way programmers wrote programs, then the first woodpecker 

that came along would destroy civilization. 

Scientific analyses and conclusions are only as good as the data from which they are derived. 

Our ability to detect trends or patterns in ecosystem processes is enhanced by data of 

documented quality that minimize error and bias. Data of inconsistent or poor quality can result 

in loss of sensitivity and incorrect interpretations and conclusions. The Arctic Network 

monitoring program will bring together analyses from an array of widely-varying projects aimed 

at detecting changes in the environment over time. The inherent complexity in this endeavor 

demands stringent and consistent quality assurance (QA) and quality control (QC) measures be 

applied throughout the program. 

Quality assurance involves planning quality into the data while quality control consists of 

monitoring the system or appraising the product after the product is developed. Data 

management must ensure that our projects produce and maintain data of the highest possible 

quality. The Network will establish and document protocols for the identification and reduction 

of error at all stages in the data lifecycle (NPS 2007b,c,d,e,f, h). These stages include project 

planning and database design, data collection and entry, verification and validation 

(certification), documentation (including data quality and sensitivity review), processing, and 

archiving (Figure 7). The final stage in the data life course is dissemination/integration (NPS 

2007l). Detailed QA/QC procedures for these stages are included in the protocols for each 

project initiated by the Arctic Network (Oakley et al. 2003). This chapter of the data 

management plan presents more broadly based procedures and policy that govern specific 

operations within a Arctic Network project. Figure 8 illustrates selected QA/QC procedures in 

context of the amount of control necessary and data confidence. 

National Park Service Mandate for Quality 

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. 

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 soundness of the data or the confidence one has in the data. Integrity is 

related to objectivity; however it is possible to have subjective data of high integrity. The 

27 
integrity of data is also related to data security, 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 Inventory and Monitoring program. 

Data Quality Goals and Objectives 

The amount of quality assurance and control effort needed is dependent on the scope of a project. In 

an ideal world, no expense would be spared to generate data of immaculate quality. In reality, the best 

strategy is to maximize the returns of data quality with respect to available resources and priority of 

results. These trade-offs are summarized in Figure 8. 

More planning/control needed Less planning/control needed 

<------------------------------------------------------------------------------> 

● Complex data model 

● Multi-stage data acquisition 

● No use of field sheets and/or data ranges 

● Data entry long after collection 

● Data entry by someone not familiar with data 

collection 

● Simple data model 

● Direct from field to database (handhelds, data 

logger, etc.) 

● Immediate data entry after collection 

● Data entry by personnel familiar with collection 

methods 

● Database control (ranges, common lookup 

tables, etc.) 

Figure 8. Some common data management elements affecting degree of need for QA/QC. 

Planning and training for data collection (QA) and entry is always a premium 

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. As part of data management operations, the Arctic Network will 

develop a comprehensive set of SOPs and tools for quality assurance and control in field 

procedures, data entry/validation/verification and data use (Oakley et al., 2003). This will likely 

include development of new field-computer-based methods for acquiring data as well as custom 

programming of database interfaces to both protect the data and control how the data are viewed 

or summarized. 

Quality Assurance and Control Duties 

Everyone involved with the handling of project data plays a part in producing and maintaining 

high quality data. Selected QA/QC roles and responsibilities are shown in 

Table 5, below. 

28 
Table 5. Data quality roles and responsibilities 

Actor Role 

Project Manager ● Be aware of quality protocols and convey 

their importance to technicians and field 

crews 

● Ensure compliance with the protocols 

● Plan for and ensure proper execution of 

data verification and validation 

● Review all final reports and information 

products 

Technicians ● Follow established protocols for data 

collection, data entry, and verification 

established in the project SOPs 

Data Manager ● Develop Network-wide protocols and 

SOPs to ensure data quality 

● Make project managers, 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 data quality control operations 

29 
QA/QC Process Cycle 

Figure 9. General course of data and associated QA/QC procedures. Quality control with regards 

to data analysis is specific to each project and addressed in appropriate standard operating 

procedures. 

Data quality assurance and control is a process that expands on the project life cycle described 

earlier in this document. Figure 9 shows the general course of Arctic Network data and 

associated QA/QC procedures. Quality control with regard to data analysis is specific to each 

30 
project and addressed in appropriate standard operating procedures. Each step in the cycle 

shepherds the data through collection, verification, documentation and archival. 

Project Planning and Database Design 

Project planning is of the utmost importance for ensuring success and this is especially true for 

data management and database design (Southwould 2002a,b, NPS 2004, NPS 2006b,f). We hope 

to get beyond traditional methods of data management whereby thought is only given to database 

design long after the field season is over. Experience has taught us that the amount of planning 

that went into a data product is clearly communicated through the consistency, logical integrity 

and accuracy of the raw data. Poor data management should not be disguised by misleading data 

summaries, maps, graphs and statistics. We hold that raw data invariably confesses data 

management habits, good or poor, during quality assurance and control efforts. Good habits 

produce good data. To avoid problems at the outset we have instituted a policy of only dealing 

with structured data. Our rule of thumb is that if the chosen data management software does not 

require you to format the data it will contain before data entry (i.e. databases, not spreadsheets) it 

is not acceptable. To even the load of data management tasks throughout the year, we will 

develop and provide each investigator with an annual calendar of tasks, milestones and deadlines 

that should be accomplished or met during the project life cycle (NPS 2007a). In addition we 

will institute good practices that will maximize data quality including: 

• Adherence to principles of data normalization, business rules and programmatic quality 

control mechanisms 

• Use of appropriate software (generally relational databases) 

• Pre-planning for data management early in the project development cycle. 

• Common lookup tables for values of parameters recorded in the field by more than one 

project (such as common weather metrics) 

• Each project has SOPs for core data management (collection, entry, verification, etc.) 

• Each project uses, at a minimum, standard, pre-printed field sheets for data collection. 

• Data entry computer applications resemble field sheets 

• Automated error checking features be included in database applications. 

• Database application design will maximize the use of auto-fill, auto-correct, value range 

limits, pick lists, and other constraints specific to projects 

• Database applications will include a means to track errors reported on the data after 

dissemination. 

• Database maintenance logs will be maintained for each Arctic Network database and 

housed in association with database files. 

Database Design - Record-level Tracking 

As a standard part of database design, the Arctic Network will build into database tables fields 

that track at the record-level who entered the data, precise entry time and the protocol version 

under which the data were collected. The benefits to overall data integrity outweigh any 

inconveniences this overhead data may cause. 

Lookup Tables 

The Arctic Network will utilize to the fullest extent possible common lookup tables for variables 

recorded by multiple projects. Examples include weather variables (such as precipitation 

31 
intensity, wind speed, etc.), standard equipment and settings (e.g., GPS models and datums) and 

field personnel. 

Project SOPs 

Each vital sign protocol will include SOPs that address core data management practices with 

quality control in mind. These may include: 

• Field crew training (addressing both data collection and entry) 

• Standardized data sheets 

• Use of handheld computers 

• Equipment maintenance and calibration 

• Procedures for handling data (including specimen) in the field 

• Data backup, entry, verification and validation 

Data Acquisition 

Attention to detail during data collection is crucial to overall data quality. The Arctic Network 

adopts the following precepts regarding data collection that affect data quality. 

• At a minimum, data will be collected on formatted, project-specific data sheets that 

reflect the overall data design for the project and maximize limitations on values that may 

be recorded for different parameters. Sheets will be designed to minimize the amount of 

writing necessary to effectively record observations. Data sheets will be printed on a 

stock appropriate to field conditions. 

• Any project using field equipment will include an SOP for calibration (including the 

timing of calibration) and maintenance. Such SOPs will specify establishment of an 

equipment maintenance log. 

• Edits on field sheets while in the field will be done by drawing a single line through the 

information to be changed and adding any replacement information in clear writing next 

to the original entry. 

• Completed field forms will be proofed for errors each day in the field. 

• Wherever possible and appropriate, data loggers or field-based computers will be used to 

collect data. The use of handheld computers requires separate SOPs to describe their use 

and will include direction for daily review and back up of digital data. 

Standardized data sheets that identify the pieces of 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. 

• Upon return from the field, 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 

32 
sheets will be stored as specified in the protocol SOP, and the original data sheets will be 

used for data entry. 

Data Entry 

‘Data entry’ is the initial set of operations where we transfer raw data into a computerized form 

linked to database tables. When data are gathered or stored digitally in the field (e.g., on a data 

logger), data entry consists of the transfer of data (downloading) to a file in an office computer 

where they can be further manipulated. The goal of data entry is to transcribe field observations 

into a computer database with 100% accuracy. That is, exactly what was recorded in the field 

should be entered into the database. Subsequent data verification is conducted to ensure that raw 

data matches entered data. Following verification, data validation may result in changes to the 

entered data. Data entry is a separate operation from data validation and care must be taken to 

not impose validation (beyond that automatically imposed by programming rules in a database) 

during data entry. 

The Arctic Network adopts the following precepts affecting data quality with regards to data 

entry: 

• Data will be entered as soon as possible after collection. 

• Data entry will be done by someone familiar with data collection. The project leader 

(with assistance from the data manager if needed) must ensure that data entry staff is 

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. 

• If possible, data will be entered by two qualified persons; one to read the observations 

and one to enter. 

• Data will be entered into pre-designed database applications that resemble field sheets 

and maximize error control. Data will not be entered into spread sheets. 

• To the extent possible, data entry will be automated. This may simply entail downloading 

of data from field-based computers but may include the application of new technology to 

allow for machine-driven data entry. 

Verification and Validation 

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 (planning). 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. 

The Arctic Network adopts the following precepts regarding data verification: 

33 
• Options for verification methods are given below. Project leaders are responsible for 

specifying in the project protocol one or more of these methods. At the discretion of the 

project leader, additional verification methods may be applied. 

• The project leader is responsible for proper execution of data verification. 

• Data verification is carried out by staff sanctioned by the project leader who are ideally 

familiar with data collection and entry. 

• 100% of records will be verified against original source data. 

• 10% of records will be reviewed after initial verification by the project manager. If errors 

are found, the entire data set verified again. 

• A record of each dataset’s verification process including number of verification iterations 

and results will be prepared by the project leader as part of formal metadata generation. 

• Certain crucial datasets may require duplicate entry, in which two people enter the same 

data and then the results are compared for disparity and corrected. 

Methods for Data Verification 

Each of the following methods has a direct correlation between effectiveness and effort. The 

methods that eliminate the most errors can be very time consuming while the simplest and 

cheapest methods will not be as efficient at detecting errors. 

1) Visual review at data entry. The data entry technician verifies each record after input. 

Recorded values in the database are compared with the original values from the hard copy. 

Identified errors are immediately corrected. 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 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. When one technician performs this review, the 

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 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. A database 

query is then used to automatically compare the duplicate records from the two datasets and 

report any mismatches. Disparities are manually reviewed and correction applied if necessary. 

This method adds the overhead of retyping the selected records, as well as the creation of a 

comparison query. However, it becomes increasingly successful as the value of n decreases. 

Professional data entry services frequently use this method. 

Supplementary Methods 

Calculate simple summary statistics and range checks with statistical software to identify 

duplicate or omitted records. 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 answer provide clues to errors. The more checks we 

34 
devise to test the completeness of the data, the greater our confidence that we have completely 

verified the data. 

Validation 

Validation is the process of reviewing computerized data for range and logic errors and may 

accompany data verification only if the operator has comprehensive knowledge of the data and 

subject. More often, validation is a separate operation carried out after verification by a project 

specialist who can identify generic and specific errors in particular data types. 

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 and a required component of any Arctic 

Network project protocol. Invalid data commonly consist of slightly misspelled species names or 

site codes, the wrong date, or out-of-range errors in parameters with well defined limits (e.g., 

pH). But more interesting and often puzzling errors are detected as unreasonable metrics (e.g., 

stream temperature of 70°C) or impossible associations (e.g., a tree 2 feet in diameter and only 3 

feet high). We call these types of erroneous data logic errors because using them produces 

illogical (and incorrect) results. The discovery of logic errors has direct, positive consequences 

for data quality and provides important feedback to the methods and data forms used in the field. 

Histograms, line plots, and basic statistics can reveal possible logic and range errors. 

The Arctic Network adopts the following precepts regarding data validation: 

• Each Arctic Network project protocol will address a process for data validation that includes at 

least one of the methods outlined in the Network data management plan. That process will 

adhere to the precepts given in the plan. 

• Corrections or deletions as a result of data validation require notations in the original paper 

field records about how and why the data were changed. 

• 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. 

• The Arctic Network will maximize the use of automated routines and/or data 

summary/visualization such as histograms, line plots, and basic statistics to reveal possible logic 

and range errors. 

• The Arctic Network will maximize the use of database programming to control data entry. In 

general, this will be achieved via the use of lookup tables but may be accomplished by field-type 

design in a data base (such as “yes/no” field-types). 

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 (project subject) and will be included in 

the project protocols. 

1) Data entry application programming. Certain components of data validation are built into data 

entry forms. This method is essentially part of database design. Not all fields, however, have 

35 
appropriate ranges that are known in advance, so knowledge of what are reasonable data and a 

separate, interactive validation stage are important. 

Caution must be exercised when using lookup tables to constrain variable values. Values 

occurring outside the range set by a lookup table (established during database design) may not 

always be invalid. As part of data validation procedures, the project leader is responsible for 

correct use of lookup tables or other automated value range control. 

2) Outlier Detection. According to Edwards (2000), “the term outlier is not (and should not be) 

formally defined. An outlier is simply an unusually extreme value for a variable, given the 

statistical model in use.” Any data set will undoubtedly contain some extreme values, so the 

meaning of ‘unusually extreme’ is subjective. The challenge in detecting outliers is in deciding 

how unusual a value must be before it can (with confidence) be considered ‘unusually’ unusual. 

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. 

3) Other exploratory data analyses. 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). 

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 may be 

released (NPS 2007l). Data and Information disseminated to the public must be approved by the 

appropriate reviewing officials and programs. Documentation of the QA/QC standards used in 

producing the information and that substantiate the quality of the information must be formally 

certified and distributed with related data and information. Also, mechanisms must be in place 

for receiving and addressing comments/complaints pertaining to the quality of data. 

Conformance 

As part of the close out and evaluation stage of each Arctic Network project, QA/QC procedures 

will be reviewed by the project leader and any recommendations for change made in the project 

annual report. Additionally, the Arctic Network data manager will review the QA/QC procedures 

included in the data management plan and revise as needed. While this review will be part of the 

regularly scheduled plan review, QA/QC procedures are subject to revision as needed by the 

Network data manager. 

36 
The data manager will conduct periodic “spot checks” of random Arctic Network monitoring 

projects to ensure compliance with data management plan and project protocol QA/QC 

procedures. The data manager will track and facilitate the correction of any deficiencies found 

during this process. The data manager will submit a report of findings to the project leader and 

the network coordinator within a month of completing any review of QA/QC procedures. The 

project manager and Network data manager are responsible for ensuring that non-conformities in 

data management practices are corrected. 

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 protocols 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 

Documenting and Communicating Quality 

The final step in the QA/QC for a given dataset is the preparation of summary documentation 

that assesses the overall data quality. A statement of data quality will be composed by the project 

leader and incorporated into formal metadata as well as the Arctic Network primary data 

repository. Metadata for each dataset/database will also provide information on the specific 

QA/QC procedures applied and the results of the review. Typically, data quality information will 

be conveyed as part of FGDC-compliant metadata. Metadata and data will be available via both 

the Arctic Network website and the NPS Data Store. 

Data and File Management Operations 

File Naming Standards 

Because of the scope and quantity of files being consolidated into one place, it is critical that 

computer files be given specific and unique names that will uniquely identify them indefinitely. 

The following conventions apply to all Arctic Network file names: 

• No spaces or special characters within the name 

• Use date for version control, as described below 

• Use underscore as delimiters 

File names for final products will begin with 'ARCN' followed by a the project identification (if 

project-related), a brief title and a version date in YYYYMMDD format. Each of these file name 

elements (excepting the extension) is preceded by an underbar (“_”). Additional underbars may 

be added for clarity. As an example “ARCN_draftData_Management_Plan_20060822.odt” 

indicates a draft version of the data management plan dated August 22, 2006. 

37 
“ARCN00022_StudySiteLocations_20051112.csv” indicates a file related to project 22 that 

contains study site locations in comma separated values format. 

Version Control 

Prior to any major changes to a file, a copy of the file with the appropriate version control (in this 

case the file name is the version control) is made. As indicated above, files are stored with the 

appropriate eight digit date which serves as version control. This allows the tracking of changes 

over time and facilitates collaboration between multiple personnel working on common files. 

With proper controls and communication, versioning ensures that only the most current version 

is used in any analysis. Using the date, formatted as YYYYMMDD, provides a logical version 

control and also allows files to easily be sorted by date within a file manager. Working 

directories should have an '/old' directory. Major revisions should be accomplished by copying 

the latest version, amending the date portion of the file name and archiving the old version in the 

/old directory. When the final version is complete the contents of the /old directory should be 

deleted or compressed and archived (i.e. 'zipped') off the network. 

Laboratory Data 

All data for a given vital sign, including lab results, will be housed in a distinct relational 

database accessed via custom applications. Laboratories that will be entering analysis results for 

a given vital sign will be supplied with a copy of the application so that data may be entered in 

the manner and format matching that of the rest of the data for a given monitoring parameter. 

While most professional laboratories exercise their own QA/QC procedures, results received by 

a project leader are subject to the same QA/QC measures exacted on other project data. 

38 
Chapter 7. Data Documentation 

Arnold's Laws of Documentation:

1. If it should exist, it doesn't. 

2. If it does exist, it's out of date.

3. Only documentation for useless programs transcends the first two laws.

Formal, standard metadata serves as a unifying document for all sources of information about 

project data (Figure 10). While metadata are addressed in context throughout this plan, this 

chapter outlines the Arctic Network strategy for generating and handling metadata. 

The information needed to understand and use a project's data is embedded in project documents 

such as study plans, reports, and protocols. Data design documents such as database descriptions 

and field sampling protocols are often critical to effective analysis of project data. Project 

tracking applications used by managers may also contain indispensable information about project 

data. Further, standard data handling such as backup, entry, verification and validation may 

produce information that affects the way in which the data may be used. 

Figure 10. The metadata document contains all information pertaining to project data. 

The metadata document will be critical to project organization. The FGDC standard is broadly 

used and will allow for easy and rapid transfer and sharing of project details, both within the 

NPS and without. 

Metadata Profile 

Executive Order 12906, mandates that federal agencies “...document all new geospatial data it 

collects or produces, either directly or indirectly...” using the Federal Geographic Data 

39 
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 datasets. Metadata created in compliance with the 

Biological Data Profile can be added to the National Biological Information Infrastructure 

Clearinghouse. Although not a requirement, completion of the Biological Data Profile for 

appropriate datasets is recommended. 

Documentation Roles and Responsibilities 

Documentation duties may be summarized as follows: 

Project Leader Data Manager

• Document project planning, 

execution and progress in reports, 

correspondence, project-tracking 

database, etc. 

• Facilitate metadata generation by 

providing tools and assistance in 

their use 

• Generate formal dataset metadata • Establish metadata dissemination 

protocols (cataloguing of 

metadata on accessible servers) 

• Format metadata for 

dissemination 

The duties for the project leader represent a broad scope of tasks, including direction of project 

personnel and general project management; all aimed at producing a comprehensive metadata 

document. 

Metadata Tools 

All GIS data layers must be documented with applicable FGDC and NPS metadata standards. 

The NPS GIS Committee requires all GIS data layers be described with FGDC standards and the 

NPS Metadata Profile. Numerous tools exist for developing metadata, but the most appropriate 

tool is the NPS Metadata Tool and Editor since this tool integrates with the NPS Data Store and 

provides a robust metadata editing environment. The end goal, however, is a formal, 

comprehensive metadata document that meets federal (FGDC) and NPS standards. The tools and 

means of achieving this end product are numerous and changing. Project leaders may use any 

tool deemed suitable for the project that meets program documentation requirements. 

Metadata Process 

Figure 7 and Figure 9 indicate the points within the standard project workflow for metadata 

development. Metadata development begins with project design and planning (Figure 4). 

Personnel generating metadata should follow the excellent standard operating procedures 

provided by the NPS Data Store (NPS 2005a-f). 

40 
Figure 11. Generalized path of data within the Arctic Network. The primary data server is 

accessed via desktop and web-based applications. Analyzed or derived data products are stored 

on the primary server, archived and catalogued (integrated) along with certified data. 

41 
In general, a metadata document will be initiated in the design and planning stage of a project. 

As the project progresses, this document will be augmented by the project leader to include 

relevant project details. Full development of metadata for project data will be completed after the 

dataset is certified by the project leader. Metadata are saved on the primary Arctic Network 

server along with data and copied to the Inventory and Monitoring Program NPS Data Store 

(Figure 10). Hypertext links within the metadata document will point to accompanying datasets 

served on the primary Arctic Network server. In general, a single metadata document will apply 

to both raw and certified versions of the data. Derived data (typically data resulting from the 

analysis of certified data) may require a separate metadata document. Metadata records will be 

stored with both hard copy and digital archive data. 

Metadata Maintenance 

The Arctic Network SOP for metadata generation and maintenance will provide details on the 

mechanics of handling metadata. Generally, metadata will be created by the project leader, with 

assistance from the data manager. The Network data manager ensures that updated metadata are 

copied to the NPS Data Store. 

Protocol Versions 

Arctic Network data will include a means for recording the protocol version under which each 

piece of data was collected. The metadata document will contain information about protocol 

versions used to collect the data. 

Non-Program Data Documentation 

Data which are generated and/or managed outside the Inventory and Monitoring Program but 

used in analysis with Arctic Network data or distributed in any manner by the NPS require the 

same level of documentation produced for Arctic Network-generated data. This includes data 

produced under contract with the NPS. Metadata for non-program data will be requested from 

the originating entity by the project leader. Any contracts entered into by the Arctic Network 

with data producers will stipulate the submission of FGDC-compliant metadata. The Network 

data manager will assist the project leader in metadata acquisition by providing tools, format 

protocols and file transfer services. 

Derived Data Documentation 

Like regular project data, derived data products may not be released without proper 

documentation. Metadata for any such derived data will include a data lineage pointing to parent 

datasets. 

Legacy Data Documentation 

Data acquired via data mining or other means does not enjoy a level of stewardship afforded 

project data. When older data that have no current steward (typically a project leader) are found 

to be useful, metadata for those data must be established. No data may be released without 

accompanying metadata. If ancillary data are used in the creation of derived data products (used 

in analytical comparison to project data), metadata for those data are necessary to fully document 

the derived data product. 

42 
Chapter 8. Data Analysis and Reporting 

Velilind's Laws of Experimentation: 

1. If reproducibility may be a problem, conduct the test only once. 

2. If a straight line fit is required, obtain only two data points. 

Our network's mission is to provide sound science for park management decision-making. 

Generating meaningful results is a cornerstone of the Inventory and Monitoring Program. The 

associated data management role is to provide valid data in formats that support the statistical 

design of our monitoring program as well as ad hoc querying, analysis and reporting of data. 

Data managers have an integral role in providing quality data to scientists, but the analysis itself 

is the purview of project leaders and the network biometrician. In depth information regarding 

data analysis can be found in the Arctic Network monitoring plan as well as the monitoring 

protocols and standard operating procedures for each vital sign (NPS 2007a,d). 

Analysis 

Each Arctic Network vital sign is required to have a series of standard operating procedures 

(Oakley et al., 2003). A subset of these procedures defines data handling, analysis and reporting 

including sections on generating data summaries and statistical analyses, recommended reporting 

schedules, report formats with examples of summary tables and figures and methods for longterm trend analysis. The data manager will work closely with the network biometrician in the 

development of these standard operating procedures. Data management staff will also aid in 

adapting database objects, fields, and values to support the formats and functions necessary for 

analysis using statistical software applications.

Reporting 

Reporting is critical to the long-term success of the Arctic Network. Results must be credible and 

delivered in a timely fashion to the appropriate audiences in a manner that is understandable to 

them (NPS 2006a, NPS 2007a). There are multiple audiences for monitoring data produced by 

the Arctic Network program, and each requires information formatted and presented in specific 

ways. The main audience for monitoring data is the resource managers of each network park and 

other managers in the National Park Service system, who will use the information to assist with 

their management decisions. 

Some basic summary and reporting functions required by a vital sign monitoring protocol can be 

developed within database applications where data are stored. Examples include descriptive 

statistics (mean, standard deviation, sample size). The biometrician and others will use existing 

and custom data conversion and export functions to prepare datasets for import into other 

software applications. Spatial analysis and maps will be produced by network and/or park 

affiliate staff. Ad hoc queries and reports will be handled on a case by case basis due to their 

dynamic nature. Arctic Network will enhance its web site over time to deliver reports and 

provide supplemental background data and information. 

43 
Long-term Trends and Analyses 

Most long-term data analysis will involve statistical software applications. Data formats required 

by statistical software often involve arrays of binary or discrete values that represent one or more 

parameters. Data analysts and the network data manager will identify and develop the data 

conversion routines necessary to generate these formats for analysis. 

Geographic Information System functions can also contribute to understanding long term status 

and trends of vital signs and ecosystems. Methods may be developed to visualize time-series 

data, perform geostatistical functions, and do spatial network analysis with hydrography features. 

Tabular and spatial results can be shared in reports and made available on the Network’s web site 

and via internet map services. 

Funding and Timeliness 

The Arctic Network strategy towards data analysis and reporting rests upon providing sufficient 

funding for these activities so that they occur promptly—that is, to report on the previous 

phenological year (Oct-Sept) by the following March. The Arctic Network will also focus on 

producing an annual integrated “State of the Parks” report that effectively communicates the 

changes and trends observed in each Vital Sign to our primary audience—the natural resource 

managers of each park. 

44 
Chapter 9. Data Dissemination 

Too often we forget that genius. . . depends upon the data within its reach, that Archimedes could not have devised 

Edison's inventions.

--Ernest Dimnet

Introduction 

Data management within the Arctic Network Inventory and Monitoring Program aims to ensure 

that: 

• Data are easily discoverable and obtainable 

• Only data that have been subjected to full quality control are released 

• Distributed data are accompanied by complete metadata which clearly establishes the 

data as a product of the NPS Inventory and Monitoring Program 

• Sensitive data are identified and protected from unauthorized access and inappropriate 

use 

• A complete record of data distribution/dissemination is maintained 

Data Distribution Mechanisms 

The primary distribution of the network’s inventory and monitoring data will occur on the 

internet. This approach makes data and information available to a broad community of users and 

does not require a local system to receive and process multiple data requests. The following webbased applications and repositories have been developed to store park natural resource 

information (NPS 2007i,o): 

• NatureBib–a master web database housing natural resource bibliographic citations for 

Inventory and Monitoring Program parks 

• NPSpecies–a master web-based database to store, manage and disseminate scientific 

information on the biodiversity of all organisms in all National Park units 

• Biodiversity Data Store–a digital archive of document, GIS data set and non-GIS data 

set files that document the presence/absence, distribution and/or abundance of any taxa in 

National Park Service units 

• NPS Data Store -online repository for metadata and associated data products. 

• Arctic Network Website–provides detailed information about the Inventory and 

Monitoring network and its activities. Metadata, datasets, and related products such as 

summary reports for all network-funded inventory and monitoring products will be 

available via the web site. Park-funded and non-NPS-sourced materials related to the 

vital signs monitoring program will be linked via the network website where possible. 

Storing network metadata and datasets in the data stores listed above ensures dissemination to a 

wide audience. 

45 
Data Ownership 

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 National Park Service: All data and materials collected or 

generated using National Park Service personnel and funds become the property of the National 

Park Service. 

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 (higher institution or non-profit 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) (in this case the National Park Service) 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 

(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: 

46 
(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. 

Establishing Data Ownership: Cooperative or Interagency Agreements 

To ensure that proper ownership, format, and development of network products is maintained, all 

cooperative or interagency work must be conducted as part of a signed collaborative agreement. 

Every cooperative or interagency agreement or contract involving the Arctic Network must 

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.”

Cooperative or interagency agreements or contracts must include a clearly defined list of 

deliverables and products. Details on formatting and media types that will be required for final 

submission must be included. Typical products include, but are not limited to, field notebooks, 

photographs (hardcopy and digital), specimens, raw data, and reports. 

All reports generated by or for the Arctic Network must follow current format guidance set by 

the Alaska Region Inventory and Monitoring Program. Other products resulting from 

cooperative work must follow Alaska Region Inventory and Monitoring formatting 

specifications. 

The following statement must be included in the Reports and Deliverables section of all Arctic 

Network agreements and contracts: 

“All reports and deliverables must follow the most recent versions of the Inventory and 

Monitoring product specifications.”

Cooperative agreements and contracts should also provide a schedule of deliverables that 

includes sufficient time for NPS review of draft deliverables before scheduled final submissions. 

Data Classification: protected vs. public 

All data and associated information from Inventory and Monitoring activities must be assessed to 

determine their sensitivity. This includes, but is not limited to, reports, metadata, raw and 

manipulated spatial and non-spatial data, maps, etc. Network staff must carefully identify and 

47 
manage any information that is considered sensitive. The Network must 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 however, 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 www.doi.gov/foia for further information. 

In some cases, public access to data can be restricted. Under one Executive Order, Director’s 

Order #66 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), the National Park Service is directed to protect information about the nature and 

location of sensitive park resources. Through these regulations, 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? 

• 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: 

48 
• 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 Inventory and Monitoring data will be the responsibility of the Arctic 

Network staff, the park superintendents, and investigators working on individual projects. 

Network 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. 

The Network staff is also responsible for identifying all potentially sensitive resources to project 

leaders working on each project. The project leaders, whether network 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 the National Park Service. According to 

National Parks Omnibus Management Act, if the 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. 

Access Restrictions on Sensitive Data 

Arctic Network staff are responsible for managing access to sensitive data handled by the 

Program. All potentially sensitive park resources will be identified and investigators working on 

network projects will be informed that: 

• All data and associated information must be made available for review by network staff 

prior to release in any format 

49 
• 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 project leader for each project. Reciprocally, the project 

leader must identify any known references to potentially sensitive park resources. 

• When preparing information into any Arctic Network repository, Arctic Network staff 

ensures that all protected information is properly identified and marked. All references to 

protected information are removed or obscured in any reports, publications, maps, or 

other public forum. 

Network 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 Inventory and Monitoring Program, are maintained on both secure and public 

servers and all records marked ‘sensitive’ during uploading will only become available on the 

secure servers. 

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 person uploading records to the online applications (repositories) is 

familiar with the procedures for identifying and entering protected information. 

Dissemination Mechanisms 

Data and information will be made available to two primary audiences: public and NPS employees. Table 

7 lists the data and product repositories the Arctic Network will use. Data and related products will also 

be available by request via contacts on the Arctic Network website. 

Item Repository

Reports (public) 

• Digital 

NPS Focus, Arctic Network data server 

(Fairbanks) 

• Hard copy Alaska Resources Library and 

Information Services (ARLIS), park 

libraries 

• Bibliography NatureBib 

Arctic Network-generated digital 

datasets and data products (public, nonsensitive) 

• Certified data and data products 

(including photographs) 

• Metadata 

NPS Data Store, Arctic Network data 

server, NPSpecies, EPA STORET. 

50 
Arctic Network-generated digital project 

data and info (NPS staff, sensitive) 

• Raw, validated and analyzed data 

• Metadata 

• Submitted reports 

• Digital photographs 

• Digital presentations 

Arctic Network data server, backup 

server (Anchorage). 

Project product materials 

• Vouchers 

• Specimen 

University of Alaska Museum, Arctic 

Network or park office (according to 

project protocol) 

Non-product project items (hard copy) Arctic Network office 

Table 6. Repositories for Arctic Network Program data and information. 

Arctic Network Primary Server 

The Arctic Network will maintain and use file, database and web servers housing electronic data 

and information managed by the program. These tools will allow staff to browse, evaluate, 

export, analyze and integrate vital sign monitoring data and information for research, 

management and reporting purposes. Databases for each vital sign will be designed in MS 

Access and with back-end tables stored in an enterprise database at the national office in Fort 

Collins, Colorado. Spatial data will be stored in a shapefile or geodatabase format. The data 

dissemination system will be developed to include a web-interface for data browsing, query and 

download. 

Non-monitoring data 

The distinction between “monitoring” and “non-monitoring” data may be construed as the 

difference between “active” and “finalized” data. The brunt of Arctic Network data management 

planning focuses on monitoring data, however, in general the Arctic Network will make every 

effort to upgrade non-monitoring datasets, such as legacy and inventory data, such that they may 

be served via the client/server database on the Arctic Network primary data server. Data 

formatted in this manner will be more easily browsed, manipulated and discovered by end-users. 

Alaska Resources Library and Information Services (ARLIS) 

Alaska Resources Library and Information Services (ARLIS) provides universal access to 

natural and cultural resources information and serves as a central library for local, state and 

federal agencies in Alaska. The park service in Alaska has historically used ARLIS for a variety 

of information needs and the Arctic Network will build on this relationship by ensuring that all 

appropriate program output are catalogued with ARLIS for broad distribution. 

51 
NPS Focus 

Arctic Network data and products residing in NatureBib and NPS Data Store will automatically 

be searchable via NPS Focus (http://npsfocus.nps.gov); a digital library. As NPS Focus develops, 

additional databases and repositories utilized by the network are expected to be searchable 

through this portal. 

Water Quality Data 

Water quality data collected to meet regulatory requirements is managed according to guidelines from the 

NPS Water Resources Division. This includes using the NPSTORET desktop database application at 

the parks to help manage data entry, documentation, and transfer. The Network oversees the use 

of NPSTORET according to the Network’s integrated water quality monitoring protocol and 

ensures the content is transferred at least annually to NPS Water Resource Division for upload to the 

STORET database (Figure 12). 

Figure 12. Water quality data flow. 

Data Availability 

In general, data will be available upon completion of analysis and reporting. Data for which 

analysis and reporting has not been completed but are otherwise certified (verified and validated) 

will be released no later than one year after certification. 

Data release or integration into dissemination mechanisms depends on both the project protocol 

and project leader. No data may be released without proper certification. 

Arctic Network staff will notify project leaders prior to making datasets available to the public. 

This will allow each project leader the opportunity to specify in writing any access restrictions 

that should apply. Any such specifications must indicate how long the data will remain restricted. 

Data Release Policy 

The Arctic Network will maintain the following policy regarding the release of data and related 

products: 

52 
1. Only fully documented, certified, non-sensitive data and data products may be uploaded 

to public distribution repositories or otherwise released to the public or other non-NPS 

recipient. 

2. Any released data or data products must be accompanied by full metadata and any 

additional supporting documentation as determined by the project leader. 

3. Arctic Network monitoring program data will be made available according to regular 

reporting and data integration schedules. 

4. Arctic Network non-monitoring data will be released upon completion of data 

certification (verification and validation) and on condition of project leader approval. 

However, data for which analysis and reporting has not been completed but are otherwise 

certified (verified and validated) will be released no later than one year after certification. 

5. Distribution instructions for each dataset will be included in respective metadata. 

6. Distribution logs specifying recipient name and contact information, intended use of data, 

export file format, delivery date and method, data content description noting range (by 

date and geography) of data delivered and description of distributed items (noting that 

metadata and possibly other supporting documentation were distributed with data), will 

be kept for each distinct dataset or product managed by the Arctic Network. 

7. At the discretion of the project leader, any reports utilizing the data may also accompany 

distributed data . 

Feedback Mechanisms 

Comments and questions concerning Inventory and Monitoring project data are welcome at any 

time and may be submitted via e-mail or telephone to the primary contact of a project or to the 

Network Coordinator. The Arctic Network website will also provide an opportunity for NPS 

staff, cooperators and the public to provide feedback on data and information distributed as part 

of Arctic Network operations. A “comments and questions” link will be provided on the main 

page of the site for general questions and comments about the Network’s program and projects. 

A more specific “data error feedback” link will direct comments to Arctic Network staff 

pertaining to errors found in website accessible data. Data feedback and response activity will be 

reported to the Board of Directors and to the Technical Committee on a yearly basis. 

Data Error Feedback Response Procedures 

The Arctic Network will use the following procedure to respond to data feedback: 

• Arctic Network staff (or automated process) will immediately acknowledge receipt of 

any feedback to the sender. 

• Data error reports will be recorded in an error log associated with each database . 

• Data errors will be investigated within one week of submission. If appropriate and 

feasible, confirmed errors are immediately corrected in all data repositories. If immediate 

correction is not possible, the data in question are immediately removed from all 

dissemination mechanisms. 

• A report detailing the reported error and response will be prepared by the network data 

manager and submitted to the project leader, network coordinator and the error reporter. 

If a data error is not immediately correctable, the error response report will include 

recommendations for correcting the error. 

53 
• An appropriate level of communication to stake holders will be maintained during all 

data error investigations. 

54 
Chapter 10. Data Maintenance, Storage and Archiving 

I have not lost my mind - it's backed up on disk somewhere.

--Unknown 

Data is a valuable commodity for a research organization. Data is also highly complex, taking a 

number of different forms, both dynamic and static, that must be dealt with in unique ways. The 

maintenance, storage and archiving of data must be carefully thought out and executed if it's 

value is to be preserved. Arctic Network data maintenance, storage and archiving procedures 

aim to ensure that data and related documents and materials (digital and physical) are: 

• Kept up-to-date with regards to content and format such that the data are easily accessed 

and their heritage and quality easily learned 

• Physically secure against environmental hazards, catastrophe, and human malice 

• Archived in a manner that expedites recovery if needed 

Data Maintenance 

Data maintenance procedures in the Arctic Network apply primarily to network-generated data. 

We anticipate most maintenance activity will involve active monitoring datasets. Finalized 

Arctic Network project data will also be maintained along with active data in a common 

relational database system. Related metadata as well as database application interface tags will 

discern between active and finalized data. 

Spatial Data 

Most spatial data (monitoring data in particular) will reside in a management system comprised 

of file and web servers and an enterprise database. The benefits to this arrangement are numerous 

and include: 

• Secure, high volume storage space, 

• Concurrency 

• Multiple user environment, 

• Availability of centralized lookup tables and communal information 

• Universal access for NPS personnel. 

Ongoing spatial datasets will reside in our enterprise database with snapshots archived with 

metadata on the NPS Data Store and in the Permanent Data Stack at the Alaska Regional Office. 

Static datasets will be processed and archived with the data store. Dataset metadata and error 

logs will record data maintenance activities. Database maintenance logs will record any changes 

to project databases. 

A data catalog will be maintained on the Arctic Network website and reflect changes or updates 

to datasets. These changes or update records will typically reflect data collection, entry, and 

certification dates but may also record actions to correct errors in the dataset or to migrate data to 

new digital formats. National and regional repositories for Arctic Network data and information 

will be updated to reflect current stores on the Arctic Network server. 

55 
Files 

Files will be housed on the primary Arctic Network shared directory (O:/ drive) in Fairbanks. We 

will use a standard directory structure (NPS 2007c). Non-sensitive files will be replicated as 

appropriate to the Arctic Network website to provide access to outside collaborators and to 

publish the details of our projects. 

File Backup Plan 

The primary Arctic Network server will be housed in the Fairbanks Administrative Center as part 

of the local area network. These data will be backed up off-site to either or both the regional 

office in Anchorage and/or the Fairbanks Alaska Public Lands Information Center at least 

weekly. Both of these backup sources will be read-only and accessible for data restoration 

purposes only. Project leaders should additionally employ their own data backups as project 

work progresses. 

Hardcopy Data and Information - Maintenance 

Hardcopy materials, including paper data sheets, photo negatives, field notebooks and other 

items will be scanned where appropriate and stored in on the Arctic Network shared directory. 

Originals will be tagged with the project ID and filed at the Fairbanks Administrative Center. 

Copies will be made for off-site storage as these materials are received. Catalogues of archived 

material will be searchable on the Arctic Network website and maintained to reflect basic 

accessioning information for each item. Collections will be maintained according to NPS 

archiving protocols (NPS 2000). 

Finalized Digital Datasets 

Non-spatial datasets that are considered complete and inactive will be archived with the NPS 

Data Store in ASCII (text) format unless a proprietary format must be used. “Native format” is 

defined as the format in which data for a given project are generated. The “native” format may 

be different for raw, checked and analyzed versions of the data. Note that “specimen” or 

“sample” data, i.e. physical objects taken from the field do not fall under this definition of 

“native” format. Data are considered complete and inactive when accompanying metadata, as 

generated by the project leader or other authorized personnel, list the project status as 

“complete”. Associated digital content such as submitted project reports, photographs, 

presentations, etc., will be archived along with project data. 

Physical Material Archival 

Project Products: Table 6 specifies locations for physical items considered project products 

such as reports. Additional hard copy project product items such as maps, posters, slides, 

photographs (including aerial), tape recordings, etc., will be stored in the Arctic Network main 

office. These items (including project reports) will be archived according to NPS standards and 

follow the procedures outlined below under “Non-Product Items”. 

Specimen and Samples: Field samples and specimens will be archived according to NPS 

standards following procedures provided in project-specific protocols. Unless otherwise 

specified in the project protocol, specimen and samples will be provided to the network park in 

56 
which they were collected for curation according to park policy. The Arctic Network will 

provide park curators with associated data and material required for curation. 

Project Related Items 

Items such as field notebooks and other materials that may be considered raw data or important 

project information such as correspondence, permits, agreements, etc., will be catalogued and 

filed in the Network’s main office. Such items will be archived along with related project 

material noted above under “Project Products”. 

Project materials will be consolidated and “packaged” for archival on an annual basis. The 

project leader should package the annual project information as follows: 

• A coversheet or memo listing all materials included in the package (see Appendix I for 

project checklist form). 

• All materials are clearly labeled with: 

• Park code 

• Date, or range of dates 

• Field notes on acid free paper and stored in a 3-ring binder or book box 

• Other paper materials such as reports and data printouts also on acid free paper and 

placed individually in a acid free, labeled folder 

• Specimens, such as plants or fish, are properly labeled 

• For specimens not residing in archives, loan paper work is complete and a copy of the 

form is stored with the project package. 

• CD or DVD of all electronic materials. Case should be labeled with: 

o Park Code 

o Inventory and Monitoring Network Code 

o Date created 

o Range of dates for information 

o Accession Number 

o List contents 

The archive specialist will catalog this information into NPS Rediscovery and arrange for proper 

storage of the materials. 

Photographs 

The Arctic Network is developing a comprehensive photographic database designed to track and 

manage images of park resources. Photographs from each Arctic Network project will be entered 

into this database where attributes such as electronic file name, keywords, project, description, 

photographer, date and location will be catalogued. Digital photo management will generally 

follow guidelines established in a draft management strategy prepared by the Southwest Alaska 

Inventory and Monitoring Network. 

57 

Chapter 11. Implementation 

The Arctic Network will follow the guidelines in Chapter 13 of the National Data Management 

Plan (National Park Service, 2008) for implementing this data management plan. 

59 

References 

DOI. 1993. Departmental manual museum property handbook (411 DM, Volume 1). U.S. 

Department of Interior, Unpubl. Manual. Washington D.C. . 

Edwards, D. 2000. Data quality assurance. Pages 70-91 in Michener, W.K., and J.W. Brunt, 

editors, 2000, Ecological data: design, management and processing. Blackwell Science Ltd. 

Methods in Ecology Series, Malden, MA. 

NPS. 1994. "Executive Order 12906: Coordinating geographic data acquisition and access: The 

National Spatial Data Infrastructure. ." Retrieved 12-04, 2007, from 

http://govinfo.library.unt.edu/npr/library/direct/orders/20fa.html. 

NPS. 1999. "OMB Circular A-110: Uniform administrative requirements for grants and 

agreements with institutions of higher education, hospitals, and other non-profit 

organizations." Retrieved 12/5/2007, 2007, from 

http://www.whitehouse.gov/omb/circulars/index.html. 

NPS. 2000. DIRECTOR'S ORDER #24:NPS museum collections management, National Park 

Service. 

NPS. 2002. DIRECTOR'S ORDER #11B: Ensuring quality of information disseminated by the 

national park service. National Park Service. 

NPS. 2002. DIRECTOR'S ORDER #66: FOIA and protected resource information, National 

Park Service. 

NPS. 2004. Recommended naming standards for inventory and monitoring databases, National 

Park Service Washington Support Office, Ft. Collins, Colorado. 

NPS. 2005a. NR-GIS Data Store: Metadata parsing guidance. National Park Service. Ft. Collins, 

Colorado. 

NPS. 2005b. NR-GIS Data Store: Creating metadata. National Park Service. Ft. Collins, 

Colorado. 

NPS. 2005c. NR-GIS Data Store: Creating non-spatial metadata. National Park Service. Ft. 

Collins, Colorado. 

NPS. 2005d. NR-GIS Data Store: Creating simple geospatial metadata. National Park Service. 

Ft. Collins, Colorado. 

NPS. 2005e. NR-GIS Data Store: Metadata and data store instructions. National Park Service. Ft. 

Collins, Colorado. 

61 
NPS. 2005f. NR-GIS Data Store: Metadata authoring guidance. National Park Service. Ft. 

Collins, Colorado. 

NPS. 2006a. Instruction to authors—Natural Resource Report and Natural Resource Technical 

Report. Natural Resource Report NPS/NRPC/NRR—2006/001, National Park Service Ft. 

Collins, Colorado. 

NPS. 2006b. Natural Resources Database Template specifications, National Park Service Ft. 

Collins, Colorado. 

NPS. 2007a. Checklist for initiating a new arctic network project: Guidelines and tasks checklist 

for integrating new projects with the arctic network data catalog. Arctic Network Data 

Management Standard Operating Procedure NPS/ARCN/DMSOP-2007-01. National Park 

Service, Fairbanks, Alaska 

NPS. 2007b. Specifications for study plans submitted to the inventory and monitoring program, 

alaska region: Guidelines and template for writing effective inventory and monitoring 

program study plans. Arctic Network Data Management Standard Operating Procedure 

NPS/ARCN/DMSOP-2007-02. National Park Service, Fairbanks, Alaska 

NPS. 2007c. Project file management: Guidelines for keeping arctic network project files 

organized, secure and accessible. Arctic Network Data Management Standard Operating 

Procedure NPS/ARCN/DMSOP-2007-03. National Park Service, Fairbanks, Alaska 

NPS. 2007d. Project deliverables specifications: Guidelines for delivering high quality data 

products. Arctic Network Data Management Standard Operating Procedure 

NPS/ARCN/DMSOP-2007-04. National Park Service, Fairbanks, Alaska 

NPS. 2007e. Data quality assurance and control: Quality assurance/quality control (qa/qc) 

guidance. Arctic Network Data Management Standard Operating Procedure 

NPS/ARCN/DMSOP-2007-05. National Park Service, Fairbanks, Alaska 

NPS. 2007f. Database planning toolkit: Task checklist and guidance for inventory and 

monitoring data application development. Arctic Network Data Management Standard 

Operating Procedure NPS/ARCN/DMSOP-2007-07. National Park Service, Fairbanks, 

Alaska 

NPS. 2007h. Certifying data products: Dataset certification benchmarks and certification form. 

Arctic Network Data Management Standard Operating Procedure NPS/ARCN/DMSOP2007-11. National Park Service, Fairbanks, Alaska 

NPS. 2007i. Overview of national inventory and monitoring program databases: A quick 

introduction to national level databases and data management resources. Arctic Network 

Data Management Standard Operating Procedure NPS/ARCN/DMSOP-2007-14. National 

Park Service, Fairbanks, Alaska 

62 
63 

NPS. 2007k. Monitoring project roles and responsibilities: Coordinating personnel and tasks for 

efficient project collaboration. Arctic Network Data Management Standard Operating 

Procedure NPS/ARCN/DMSOP-2007-16. National Park Service, Fairbanks, Alaska 

NPS. 2007l. Approving information for distribution: Process for determining sensitivity and 

classifying information for distribution. Arctic Network Data Management Standard 

Operating Procedure NPS/ARCN/DMSOP-2007-17. National Park Service, Fairbanks, 

Alaska 

NPS. 2007m. Compliance review process: Shepherding Inventory and Monitoring projects 

through the compliance process in Gates of the Arctic National Park and Preserve. Arctic 

Network Data Management Standard Operating Procedure NPS/ARCN/DMSOP-2007-18. 

National Park Service, Fairbanks, Alaska 

NPS. 2007o. Data stores for Inventory and Monitoring program informational products: 

overview of nps information warehouses. Arctic Network Data Management Standard 

Operating Procedure NPS/ARCN/DMSOP-2007-20. National Park Service, Fairbanks, 

Alaska 

NPS. 2007a. Specifications for geospatial and other data deliverables of gis and resource 

mapping, inventories, and studies., National Park Service. Fort Collins, CO. . 

National Park Service. 2008. Data management guidelines for inventory and monitoring 

networks. Natural Resource Report NPS/NRPC/NRR—2008/035. National Park Service, 

Fort Collins, Colorado. 

Oakley, K. L., Lisa P. Thomas and S. G. Fancy 2003. "Guidelines for long-term monitoring 

protocols." Wildlife Society Bulletin 31(4): 1000-1003. 

Selleck, J., K. KellerLynn 2006. Editorial style guide for park science and natural resource year 

in review, National Park Service Ft. Collins, Colorado. 

Southwould, A. 2002a. Database specifications for inventory and monitoring studies. , National 

Park Service, Anchorage, Alaska. 

Southwould, A. 2002b. Recommended database strategies including I&M database template, 

National Park Service, Anchorage, Alaska. 
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-169, August, 2008
National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

1201 Oakridge Drive, Suite 150 

Fort Collins, CO 80525 

www.nature.nps.gov 

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