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Data Sharing Across Agencies: A guide to getting on the same page Bruce Schmidt Pacific States Marine Fisheries Commission Presentation to Oregon Chapter,

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Presentation on theme: "Data Sharing Across Agencies: A guide to getting on the same page Bruce Schmidt Pacific States Marine Fisheries Commission Presentation to Oregon Chapter,"— Presentation transcript:

1 Data Sharing Across Agencies: A guide to getting on the same page Bruce Schmidt Pacific States Marine Fisheries Commission Presentation to Oregon Chapter, AFS February, 2010

2 Cross Jurisdictions Legal: Remand, US v. OR, PST, etc. ESA Recovery Planning Coordinated Monitoring Population Assessments (VSP) Inter-jurisdictional Management Co- management High Level Indicators, SOTR Increasing Wide-scale Programs BiOp Compliance Need Shared Data

3 Benefits of Sharing Data Fosters collaboration Expand scope of analyses (ESU, DPS) Contributes to multi-agency processes (e.g., ESA analyses, recovery planning and list/delist decisions; co-management; etc.) Could distribute cost of data collection

4 2002 Council Contract with SAIC Talk of a Comprehensive Regional Data Delivery System… 2000 BiOp RPA 198 CBCIS / NED 2008 BiOp RPA Col. Basin Data Center Fish and Wildlife Program So why isn’t there one? NOAA Guidance

5 Impediments to Sharing Data Sampling is focused Do things differently Data often not consolidated internally Data not on Web Hard for others to understand, use the data Concerns over misuse, first use, etc. Cost

6 Current Data Sharing Status: Individual agencies (States, Tribes, Federal) Independent Different mandates Sample different environments Longstanding approaches No mandate to share data Little support, capability to share data

7 Current Data Sharing Status: Wide scale database projects Focused – specific data types Not comprehensive

8 Current Data Sharing Status: Proposed regional data delivery applications Comprehensive Never built, or not completed Mechanisms to supply data not in place

9 Ideal Distributed Network … Multiple types of data from multiple sources, standardized by data type FishHabitatWater db Output Tool Regional Data Access Standards Others

10 Use Regional Data Projects db … Multiple types of data from multiple sources, standardized by data type WaterHabitatFish db Output Tool Regional Data Access dd db Others Pacific Northwest Water Quality Data Exchange StreamNet But, Current Reality is more like this: db

11 Data sources: field offices, research projects, etc. Database Project Agency Database Consolidated v. Individual Offices Approach

12 Most Expedient db … Multiple types of data from multiple sources, standardized by data type WaterHabitatFish db Output Tool Regional Data Access dd db Others

13 The Essential Driver of a Comprehensive Data Delivery System db … Multiple types of data from multiple sources, standardized by data type WaterHabitatFish db Output Tool Regional Data Access dd db Data flow can be automated Others

14 Automation is the key! Efficiency, Speed Accuracy Automatic data updates Canned products Translation to regional format Essential for any data portal technology

15 The ultimate endpoint?? … Multiple types of data from multiple sources, standardized by data type WaterHabitatFish db Others

16 Considerations for Regional Data Collection, Sharing and Exchange ‘The Data Sharing Guide’ A White Paper to: Outline steps Avoid overlooking critical steps Describe roles entities play Break it down: it’s not really that difficult ftp://ftp.streamnet.org/pub/streamnet/projman_files/Data_Sharing_Guide_ pdf

17 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!

18 The Data Sharing Guide addresses data flow from field to highest reporting need Non prescriptive Any data type Any agency Non-technical Primary focus is on infrastructure and processes to assure data accessibility

19 What’s Needed? 1. Uninterrupted data flow, source to output

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

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

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

23 What’s Needed? 1. Uninterrupted data flow, source to output 2. Data validated by agency

24 What’s Needed? 1. Uninterrupted data flow, source to output 2. Data validated by agency 3. Descriptive information (metadata) X. Y

25 What’s Needed? 1. Uninterrupted data flow, source to output 2. Data validated by agency 3. Descriptive information (metadata) 4. Data and metadata on the Internet

26 What’s Needed? 1. Uninterrupted data flow, source to output 2. Data validated by agency 3. Descriptive information (metadata) 4. Data and metadata on the Internet XML SQL Database Spreadsheet ASCII Word Processing PDF

27 What’s Needed? 1. Uninterrupted data flow, source to output 2. Data validated by agency 3. Descriptive information (metadata) 4. Data and metadata on the Internet 5. Data able to roll together Ideal: standard data collection, definition, codes Minimum: Data translate to a standard

28 All of this is needed for seamless data delivery to ANY regional tool

29 Sampling Crews Sampling Agencies Funding Entities Regional Data Users Database Projects Policy Makers Roles Create the data Data Entry QA Describe the data Maintain the data Establish procedures Set standards, codes Agency data systems Post data / metadata Biological & data mgt. responsibilities Negotiate key issues Metrics Methods Data disposition Contract language Support data automation Set priorities Policies to remove obstacles to data sharing Agency support Technical assistance System development Data tasks for agencies Post data & metadata Feed regional data tools

30 The Data Sharing Guide Will Help To: Inform development of agency data systems Inform development of a regional data system Avoid skipping essential components Get us started

31 Questions? Funded by:Through: Fish and Wildlife Program Administered by: Photographs courtesy of CalFish


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