ISEMP Data Management System

• Support entire workflow• Based on required functions• Based on understanding of the data

ISEMP Data Management System

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

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

ISEMP Data Management System

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)

Generic Structure for Empirical Data

Time StampLocationProtocolObjectAttributeValueUnits

ObservationLatitudeLongitudeSite NameSub BasinWatershedStreamDistance

Location

AuthorReferenceDateVersionNarrative

Protocol ClassSub ClassTaxa

Object

* Time, location, and protocol are characteristics of an observation

How did we get started?

• By compiling Excel spreadsheets – (a process called data normalization)

Non-normalized

1st Normal Form

2nd Normal Form

* Normalizing data facilitates data analysis

Generalized Data Structure

ISEMP Data Management System

ATM’s (distributed data templates)

• Enforce data format• Validate data at data entry• Summarize data• Support ad-hoc analysis• Submittal to central warehouse

Smolt Trap ATM

Fish Distribution Over Time

Fulton Condition

Species Codes

Habitat ATM

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

Questions?

Site

Protocol

StatisticalDesign

DataCollection

Event

StreamHabitat

Fish

WaterQuality

Aquatic Resources Schema

Metadata Observation Class