a data management plan template for ecological restoration ... · integrating data into a centrally...

A Data Management Plan Template for Ecological Restoration and Monitoring

Great Lakes Restoration Initiative

Brick M. FevoldResearch Scientist Advisor, GDIT

Judy Schofield1, Rob Sutter1, Craig Palmer1, Elizabeth Benjamin1, Molly M. Amos1, Louis Blume2

1 GDIT, Alexandria, Virginia2 U.S. Environmental Protection Agency, Chicago, IL

Disclaimer: The views expressed in this presentation are thoseof the author(s) and do not necessarily represent the views orpolicies of the U.S. Environmental Protection Agency.

• Initiated in 2010

• 16 federal agencies

• $2.56 billion FY 2010-2017

• Five focus areas

1. Toxic Substances and Areas of Concern

2. Invasive Species

3. Nonpoint Source Pollution Impacts on Nearshore Health

4. Habitats and Species

5. Foundations for Future Restoration Actions

Great Lakes Restoration Initiative

Interagency Ecological Restoration Quality Committee

Data Management Best Practices for Ecological Restoration Projects

Appendix A:

Guidance Document

1. Provide justification for the need to develop a data management plan

2. Identify helpful resources to guide data management planning

3. Introduce a data management plan template for ecological restoration projects

Presentation Objectives

project-open-data.cio.gov

Federal institutions

are REQUIRING 1-2

page synopsis of data

management

planning to be

submitted as part of

grant requests.

Data Management – A Federal Mandate

Data Management – in Ecological Restoration

Levels of Organization

Data Compilation

Integrating data into a centrally managed DMS by multiple crews, institutions

or research laboratories

Centralized

www.itrelease.com

SOP Implementation

Data collection and QA/QC oversight by collaborating

institutions across multiple regions

Decentralized

www.itrelease.com

Roles and Responsibilities

Data Management – in Ecological Restoration

Developing and Implementing a Data Management Plan

dataone.org

usgs.gov/datamanagement

lib.umn.edu/datamanagement

EPA Quality System Requirements

epa.gov/quality

Federal Government ‘Open Data Policy’

• project-open-data.cio.gov

National Science Foundation

nsf.gov

National Information Standards Institute

niso.org

Helpful On-Line Resources:

Web-based Interactive Instruction

Data Management Plan Template

1

3

4

21

Cover Page - Introduction DMP Template - Detailed

21

DMP Template - Simplified

Data Management Plan (DMP) Implementation

DMP Template Elements:

▫ Description and Administration

▫ Acquisition and Collection

▫ Organization and Storage

▫ Processing and Analysis

▫ Preservation and Archiving

▫ Sharing and Re-Use

Co-occurring elements

▫ Quality Assurance

▫ Metadata Documentation

▫ Data Backup & Security

Quality Assurance

Metadata Documentation

Data Backup & Security

Sharing & Reuse

Project Description,

Administration & Requirements

Preservation & Archiving

Processing & Analysis

Organization & Storage

Acquisition & Collection

Key Elements to Include in Your

Data Management Plan

*Copies of this DMP Template are available

near the entrance to the session room


The who, what, where, when and

why of project management.

Managing organization,

including roles and

responsibilities of key staff

involved in data management

Summary of project goals and

objectives, geographic scale and

timeline, DM budget needs

Funding institutions and key

policy stipulations

Established ‘data-use

agreements’ and proprietary

interests

Template Elements:











Elements:











What are the data, how are they

acquired or collected?

Type and volume of data

expected to be acquired and

generated

Secondary data sources, and

logical rules guiding acquisition

Methods (SOPs) of collection of

primary data sources

Data management training

Analytical laboratories

Specialized instrumentation


susitna-watanahydro.org

invensis.net

Begins at Office

invensis.net

Ends at Office

Conducted in Field

Acquisition and Collection

Conducted in Lab

Scientific Instruments

Electronic Data Conveyance

solinst.com S. Stevens, NPS

GPS Instruments Mobile Devices

Develop standard protocols to guide users in the digital transfer of electronic data between devices and the data management system (DMS)

Acquisition and Collection

How, where, and with what are

data stored, managed, and

secured?

Data Management System

(DMS)

Hard-copy documentation – data

forms, log books, custody forms

Work flows to guide storage of

‘raw’ and processed data

Filename conventions, version

control, backup and restore plans

Policies guiding data read/write

access and censorship


Elements:











Data Management System

Project documentation provide important details informing DMS development

Type and volume of data to be collected

Hierarchy of sampling units

Spatial and temporal scale/resolution

Domain ranges and valid values

Policies

• Institutional Requirements

• Funder Requirements

• Collaborator Proprietary Interests

Project Proposals

• Project Extent

• Monitoring Timeline

Quality Documents

• Sampling Objective Statements

• QC Sampling

SOPs & Data Forms

• Variables

• Format & Units

• Value Range & Validation

Instrument Manuals

Organization and Storage

• File-Based ‘Folder’ Data Management

Data Management Systems (DMS)

MS Window®

10

WiFi Connectivity (e.g., ODBC and MySQL)

Source: Modified from Kolb et al., 2013.

Consideration or System Feature

File-Folder StructureIntegrated Systems

Desktop Relational Database

Enterprise Relational Database

Data Management System None Centralized Decentralized

Example ApplicationsOS1, Excel, Lotus 123,

Quattro Pro

Access, Microsoft SQL,

Express, SQLLite

SQL Server, Oracle,

PostgreSQL, MySQL

Technical Capacity Basic Intermediate Advanced

Desktop or Server-based Both Both Server-based

Spatially Enabled No Optional Optional

Security Options Low Moderate High

Multiuser Data Entry No No Yes

Size of Data Set Unlimited Limited Unlimited

Web-based Optional No Yes

Cloud-storage Use Optional Optional No

Cost of Development Low Intermediate High

Level of Programming Basic Intermediate Expert

• Relational DBMS - Data Management


Workflow Diagrams

Visual aids to guide handling of data from ‘raw’ to processed (i.e., manipulated) formats

Data Format Standards


Policies and procedures involved in

data manipulation.

Electronic data-entry and digital

file transfer protocols

Logical workflows to guide data

reduction and metric calculation

Design of tables and

spreadsheets to be interoperable

Computer/software utilities and

code used in processing and

analysis

Statistical models and tests used

to validate assumptions

Elements:












Logical Workflows

To guide pre- and post-processing of data into usable format(s)• GPS location data• digital images, video and audio• physical samples and voucher specimens• data reduction and metric (or index) calculation• statistical tests (to validate model assumptions)• statistical tests (to test hypotheses)

Processing and Analysis

Enhancing data value by making

data available for future and

secondary uses.

Policies that guide data sharing

for intended primary

applications and secondary re-

use

Control of access to ‘sensitive

information’ (censorship)

Identify ‘Community of Interest’

Data exchanges and repositories

o Digital Object Identifiers

(DOI)

Elements:












Digital Data Archives and Repositories

• DRYAD Digital Repository – http://datadryad.org

• EPA’s Central Data Exchange (CDX) – https://cdx.epa.gov

• EPA’s Water Quality Exchange (WQX) and Water Quality Portal (WQP) data

systems – https://www.epa.gov/waterdata/water-quality-data-wqx

• Knowledge Network for Biocomplexity – https://knb.ecoinformatics.org

• NOAA’s Data Integration Visualization Exploration and Reporting (DIVER)

Explorer – https://www.diver.orr.noaa.gov/

• USDA Forest Service Geodata Clearinghouse – https://data.fs.usda.gov/geodata/

• USFWS Geospatial Services – https://www.fws.gov/gis/data/regional/index.html

• U.S. Government Open Data, Data.Gov – https://www.data.gov/

• VegBank – http://vegbank.org

Preservation and Archiving

On-going activities to maintain

data quality, ease of use, and

protection.

Ensuring data reliability and

logical consistency.

o QA strategies that maintain

data integrity across all DM

activities

Creating a ‘fingerprint’ to identify

and describe your data.

Securing and protecting data for

intended use and secondary

application (re-use).


Elements:











Summary

• Data management planning should be considered as:

o equally important as other best practices conducted in ecological restoration

o a management action to be implemented throughout the project or data life-cycle

o a management action that includes preservation and archival to facilitate data sharing and reuse

• Data management planning involves 3 components:

o data management policy

o data management system

o a data management plan

The effective management and preservation of project data for primary and secondary uses are, by definition, quality assurance strategies.

Data that are preserved are data that can be shared.

Questions?

Brick Fevold, GDIT; Email: [email protected]