gomoses2018-goss (002).pptx [read-only]marti goss, ben shorr, amy merten noaa | damage assessment,...
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2/13/2018
1
Gulf of Mexico Oil Spill and Ecosystem Science ConferenceMarti Goss, Ben Shorr, Amy Merten
NOAA | Damage Assessment, Remediation, and Restoration ProgramFebruary 7, 2018
• Natural Resource Damage Assessment (NRDA) and Response data collected under different authorities, different formats, different destinations and management
• 15 million records publicly available
• 100,000 environmental samples
• Over 500,000 photos
Marsh Assessment
Oyster Collections
Telemetry Data
Shoreline Data
Water Column
Marine Mammal & Turtle Assessment
Toxicity Data
Seafood Safety
2/13/2018
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NRDA data collection through restoration planning, implementation, and monitoring efforts• Inform understanding of restoration actions• 15+ years data collection across restoration
life cycle• Wide variety of project types• Multiple trustees
DIVER: https://www.diver.orr.noaa.gov
Many other restoration and research efforts underway
What does that collaboration look like?
How do we advance our capacity to leverage data collection in the Gulf of Mexico?
How do we improve our understanding of LTDM activities and approaches?
Collaboration is needed to address challenges of data quality, documentation, storage, product integration, discovery, accessibility, and archiving
https://www.neighborhoodindicators.org
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Determine the characteristics of a successful common operating picture Understand the existing DWH data management systems Identify ways to make the DWH data interoperable
Coastal Response Research Center
Foster collaboration among GOM partners with respect to data management and integration for restoration planning, implementation, and monitoring
Identify standards, protocols, and guidance for LTDM being used by these partners
Work towards best practices on public distribution and access of these data
http://www.blastam.com
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Pre-workshop survey• Participants’ LTDM roles, goals, and challenges
DWH data collection lessons learned• Multidisciplinary questions, clear objectives relative to decisions
Uses of long term data• Status and trends, restoration actions, future spill response
Existing LTDM systems• DIVER, ERMA, NCEI systems, GRIIDC, DWH Project Tracker, CIMS, GCOOS
Environmental Disasters Data Management (EDDM) working groups • Field Protocols, Common Data Models, and “Gold” Standards
http://blog.strategyzer.com
Goal: Provide information and data services to improve quality and speed of decision-making
Field Protocols• Inventory existing resources for field data collection
• Inventory existing equipment, devices, and monitors
• Describe sampling protocols to allow for getting data included in existing systems
Common Data Models• Document data models, portals, and web services being used
• Crosswalk existing data models
• Identify ways to be interoperable (field collection, analysis, etc.)
“Gold” Standards • Identify functionality needed for disaster response decision support tools
• Identify “gold standard” criteria to evaluate data and procedures (i.e., QA/QC, security, etc.)
• Identify critical data types for baseline data
• Define terms (data dictionaries)
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Identify high priority challenges of data stakeholder groups: • Users, Generators, Managers
Answer key questions organized by topic:• Interoperability
• Discovery / Searchability
• Data access
• Data synthesis
• Data usability
• Metadata / Data documentation
https://commons.wikimedia.org
Data Managers Data Generators Data Users
Interoperability • Scale of interoperability down to the
metadata record
• Understanding/funding to meet
minimum standards
• Making interoperability part of
collection efforts
• Standardization of
data
Ease of
Discovery &
Searchability
• Lack of common vocabulary
• Multiple portals using a common
internet search engine
• Search by “keywords”
• Design for user experience is difficult
• Staff resources to monitor the
input and sharing of data
• Understanding user data
needs/search preferences
• Achieving
characteristics of a
good repository
Data Access • Funding for infrastructure
• Data volume
• Creating common interfaces for
standards
• Number of people accessing data
(infrastructure behind access)
• Restrictions & sensitivity & patents &
security
• N/A • Funding
• IT security
• Confidentiality of data
Data Usability • Accuracy, resolution, level of
confidence, fitness of uses
• Sufficient record level data
• Versioning
• Data of known quality (sufficient
information to describe quality)
• Sufficient data
documentation to
compare data
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Data Management Standards:• Identify categories of standards needed
• Data management standards gap analysis
• Provide feedback to funding entities
Interoperability:• Determine interoperability efficiencies between systems
• Compile key features for data warehouses and repositories
• Describe current and future uses of LTDM systems
Discovery / Searchability:• Develop and share search technology (keyword, semantic,
geospatial, and temporal searches)
• Incorporate keywords into data and metadata
• Leverage architecture of existing systems
Participate on the Working Groups• Data Management Standards
• Interoperability
• Discovery / Searchability
Hear more about how you can participate:
• Tomorrow morning @ 7:30 am in Celestin B
Workshop presentations and final report: • https://crrc.unh.edu/DWH_DataManagement
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Jonathan Blythe, BOEM Julie Bosch, NOAA NCEI Laura Bowie, GOMA Libby Featherston, FIO Jim Gibeaut, GoMRI Jessica Henkel, RESTORE Council Steve Jones, Geological Survey of AL Barb Kirkpatrick, IOOS Kirsten Larsen, NOAA NCEI
Matt Love, Ocean Conservancy Laurie McGilvary, Dept. of Treasury Amy Merten, NOAA ORR Tamay Ozgokmen, University of Miami Mike Peccini, NOAA NMFS Jon Porthouse, NFWF Jamey Redding, NOAA RC Dave Reed, FL FWRI Lauren Showalter, NAS Greg Steyer, USGS
Thank you to the committee members:
And thanks to the workshop facilitators, presenters, and participants!
Key Questions:• To whom is it important? Why?
Data synthesizers answering complex, interdisciplinary questions
• How does it happen? Establish standards for the entire life of the data stream
Achieving Success: • clear plans that follow standards• proper resources and training • catalog of existing frameworks to help establish a
common vision across organizations
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Key Questions:• What are characteristics of a good repository?
Abundant keywords and a common vocabulary
• How is metadata quality ensured? Early focus and training
• How are user needs met? Know the user; identify at start of data collection
Achieving Success: • early involvement by the data management team• definition of user needs• ability to edit metadata once it has been collected
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