A Data Management Plan Template for Ecological Restoration ... · Integrating data into a centrally...
Transcript of 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
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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
Data Management Plan Template
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:
▫ 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
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
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
Data Management Plan Template
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
Data Management Plan 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
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
Organization and Storage
Workflow Diagrams
Visual aids to guide handling of data from ‘raw’ to processed (i.e., manipulated) formats
Data Format Standards
Organization and Storage
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:
▫ 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
Data Management Plan Template
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:
▫ 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
Data Management Plan Template
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).
Data Management Plan 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
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]