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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 

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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