1. ISEMP Data Management System

2. Support entire workflow Based on required functions Based on understanding of the data ISEMP Data Management System

3. ISEMP work flow - Programmatic coordination - Develop and test sampling designs - Develop and test field protocols - Develop and test metrics or indicators - Design and implement experiments - Develop and test evaluation tools * DM system should support development and testing of sampling designs, protocols, and metrics

4. Functional Requirements Programmatic Needs (regional) –Enforce data standards –Long-term storage and retrieval –Data integration –Web-based data distribution –Regional analyses Data Generator Needs (local) –Validation at data entry –Summarize raw data –Ad-hoc analysis –Submittal to central warehouse * DM system must support both regional and local needs

5. ISEMP Data Management System

6. The ISEMP Data Any information that may help us understand status & trends of listed salmonids, to understand effectiveness of restoration projects, and to understand the processes that drive status, trend, and effectiveness. Geographic scope includes three ISEMP pilot basins * Broad contextual scope * Focus on small geographic area (pilot study)

7. Generic Structure for Empirical Data Time Stamp Location Protocol Object Attribute Value Units Observation Latitude Longitude Site Name Sub Basin Watershed Stream Distance Location Author Reference Date Version Narrative Protocol Class Sub Class Taxa Object * Time, location, and protocol are characteristics of an observation

9. How did we get started? By compiling Excel spreadsheets –(a process called data normalization)

10. Non-normalized

11. 1st Normal Form

12. 2 nd Normal Form * Normalizing data facilitates data analysis

13. Generalized Data Structure

14. ISEMP Data Management System

15. ATM’s (distributed data templates) Enforce data format Validate data at data entry Summarize data Support ad-hoc analysis Submittal to central warehouse

16. Smolt Trap ATM

17. Fish Distribution Over Time

18. Fulton Condition

19. Species Codes

20. Habitat ATM

22. Conclusions Support development and testing of sampling designs, protocols, and metrics Support both regional and local needs Broad contextual scope Focus on small geographic area Time, location, and protocol are characteristics of an observation Normalizing data facilitates data analysis

23. Questions?

29. Site Protocol Statistical Design Data Collection Event Stream Habitat Fish Water Quality Aquatic Resources Schema MetadataObservation Class