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Considerations for Regional Data Collection, Sharing and Exchange

White Paper:. Considerations for Regional Data Collection, Sharing and Exchange. Bruce Schmidt StreamNet Program Manager Pacific States Marine Fisheries Commission Presentation to CBFWA Members Advisory Group May 18, 2009. www.streamnet.org.

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Considerations for Regional Data Collection, Sharing and Exchange

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  1. White Paper: Considerations for Regional Data Collection, Sharing and Exchange Bruce Schmidt StreamNet Program Manager Pacific States Marine Fisheries Commission Presentation to CBFWA Members Advisory Group May 18, 2009 www.streamnet.org

  2. If everyone wants a regional data delivery system… 2002 Council Contract with SAIC 2000 BiOp RPA 198 Why isn’t there one? 2008 BiOp RPA 72 CBCIS / NED 2006 Col. Basin Data Center Fish and Wildlife Program

  3. Increasing Interest in Data Sharing Coordinated Monitoring ESA Recovery Planning Population Assessments (VSP) Need for Shared Data Listing- Delisting Decisions Inter-jurisdictional Management Co- management BiOp Compliance Legal: Remand, US v OR, PST, etc.

  4. Regional Data SystemsFunctional and Proposed RMIS StreamNet PTAGIS DART IBIS Fish Passage Center PNW Water Quality Data Exchange RECFIN PACFIN Columbia Basin Center of Knowledge NED & PNAMP Columbia Basin Data Center CBCIS 2002 BiOp RPA 198 SAIC

  5. Current Situation SOTR Population models Population Assessments Subbasin planning Planning HLIs Management Public, etc. Comprehensive Data Delivery System Database Projects ? ? db db db Data sources: Agencies / Tribes / Projects

  6. Database Project Agency Database Data sources: field offices, research projects

  7. What would a comprehensive data delivery system look like? Output Interface Data Management System Pacific Northwest Water Quality Data Exchange dd db db … Water Habitat Habitat Fish db db db db db db db Multiple types of data from multiple sources, standardized by data type

  8. Comprehensive data delivery Output Interface Data Management System dd db db … Water Habitat Habitat Fish db db db db db db db db db db db db Multiple types of data from multiple sources, standardized by data type

  9. Comprehensive data delivery Output Interface Data Management System Functionally Equivalent! Data flow can be automated dd db db … Water Habitat Habitat Fish db db db db db db db db db db db db Multiple types of data from multiple sources, standardized by data type

  10. Advantages of automation • Efficiency, Speed • Automatic data updates • Accuracy • Canned products • Translation to regional format • Any database technology

  11. Comprehensive data delivery Output Interface Data Management System standards Standards Standards … Water Habitat Fish db db db db db db db db db db db db Multiple types of data from multiple sources, standardized by data type

  12. Comprehensive data delivery Output Interface … Water Habitat Fish db db db db db db db db db db db db Multiple types of data from multiple sources, standardized by data type

  13. How does the Data Sharing Guide help? • Organize discussions • Consider ALL components • Provide a blueprint • Identify needed support Assure that when a system is built, the data are there to deliver!

  14. Approach: • Non-prescriptive • Recognizes multiple approaches

  15. Approach: • Content neutral • Data management, any data type • About the container, not the contents

  16. Approach: • Agency neutral • Fish management, land management, environmental quality, etc.

  17. Approach: • A basic primer • Provides a checklist of considerations • Elementary • Some already met

  18. The Data Sharing Guide is intended to help development of coordinated data management, from field to highest reporting need Primary focus is on infrastructure to assure data availability

  19. Previous Proposals • Pitched the output tool • Ignored getting data to the tool The Data Sharing Guide is intended to encourage consideration of all aspects

  20. Any data system will need: Uninterrupted data flow, source to output

  21. Data Flow Steps in data flow Regional data application GOAL Data interoperability Post data on the Web Post metadata on the Web Describe full data set(metadata) Regional standards Agency data QA / QC Agency database system Local use of data Describe the data (metadata) Quality Assurance / Quality Control Input to electronic form Field data creation (local office/project)

  22. Data Flow Steps in data flow Regional data application GOAL Data interoperability Post data on the Web Post metadata on the Web Describe full data set(metadata) Regional standards Agency data QA / QC Agency database system Local use of data Describe the data (metadata) Quality Assurance / Quality Control Input to electronic form Field data creation (local office/project)

  23. Data Flow Steps in data flow Regional data application GOAL Data interoperability Post data on the Web Post metadata on the Web Describe full data set(metadata) Regional standards Agency data QA / QC Agency database system Local use of data Describe the data (metadata) Quality Assurance / Quality Control Input to electronic form Field data creation (local office/project)

  24. Data Flow Steps in data flow Regional data application GOAL Any missed step can prevent data flow! Data interoperability Post data on the Web Post metadata on the Web Significant effort to bridge the gaps! Describe full data set(metadata) Regional standards Agency data QA / QC Agency database system Local use of data Describe the data (metadata) Quality Assurance / Quality Control Input to electronic form Field data creation (local office/project)

  25. Any data system will need: Data validated by agency

  26. Any data system will need: Data and metadata on the Internet

  27. To be useful, shared data on the Internet must be machine readable! Database: Access SQL Server My SQL Oracle GIS Spread-sheet: Excel Etc. Delimited Text (ASCII) Portable Document Format (.pdf) Extensible Markup Language (XML) Word Processing: Word Perfect Word

  28. Any data system will need: Data roll together • Common definitions & formats, or • convertible to standard

  29. All steps must be in place for seamless data delivery We can implement these steps before deciding on a data sharing tool

  30. Sampling Crews Sampling Agencies Roles Database Projects Policy Makers Funding Entities Regional Data Users

  31. Data Collectors (field crews) • Sample • Enter the data • Quality control • Data maintenance • Answer local questions

  32. Data Collecting Agencies/Projects • Establish procedures, standards • Agency data systems • Post data • Select the posting approach • Biological and IT responsibilities

  33. Funding / recommending entities • Negotiate • methods • data disposition • Contract language • Support data infrastructure

  34. Regional policy makers • Priorities • Policies to encourage data sharing • Agency commitments

  35. Database projects • Technical guidance • Systems development • Perform data tasks for agencies • Support regional data needs • Manage data channels

  36. Data Sharing Guide will: • Inform development of regional data system • Assure all steps are in place (infrastructure) • Help us get started

  37. Questions? www.streamnet.org Funded by: Through: Fish and Wildlife Program Administered by:

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