1. Ecoinformatics Workshop Summary SEEK, LTER Network Main Office University of New Mexico Aluquerque, NM

2. Topics covered 1.Grid networks – Ecogrid 2.Workflow systems – Kepler / Ptolemy II 3.Metadata compilers – Morpho 4.Databases – MySQL, MetaCAT, DBDesigner 5.QA/QC – SAS, S-Plus, Access, Excel 6.Interactive & Dynamic web sites – DreamWeaver

3. SEEK Overview

4. Grid Networks

5. SEEK EcoGrid Goal: standardize interfaces (using web and grid services) –We have standardized data via EML –Integrate diverse data networks from ecology, biodiversity, and environmental sciences Grid-standardized interfaces –Uniform interface to: Metacat, SRB, DiGIR, Xanthoria, etc. Anyone can implement these interfaces Hides complexity of underlying systems Metadata-mediated data access –Supports multiple metadata standards –EML, Darwin Core as foci Computational services –Pre-defined analytical services –On-the-fly analytical services

6. EcoGrid Node

7. EcoGrid client interactions Modes of interaction –Client-server –Fully distributed –Peer-to-peer EcoGrid Registry –Node discovery –Service discovery Aggregation services –Centralized access –Reliability –Data preservation

8. Kepler: scientific workflows EML provides semi-automated data binding Scientific workflows represent knowledge about the process; Kepler captures this knowledge

9. Kepler: ecological modeling

10. Lotka-Volterra Predator Prey Model

11. Running the model

12. Elk/Wolf Predator Prey Model

13. Running the model

14. Metadata what are they? and why should they be created?

15. Metadata Example In front of you are two tuna cans. How do you decide which one to buy?

16. Metadata helps you decide which one to get ! Metadata Example

17. Ecological Metadata Language Adopted by the LTER Information Management Metadata specification developed by the ecology discipline for the ecology discipline Based on prior work of Ecological Society of America and others (Michener et. al., 1997) Seven years in development – 14 versions –EML 2.0.1 Implemented as an XML Schema Supports four separate modules –Dataset –Citation –Software –Protocol

18. Associated Metadata Data Set Data Table Xml files

19. Morpho provides a way for ecologists to share data by defining a common structure to document their data uses an XML format to create the common structure.

20. Morpho - tree editor

21. Morpho – entering metadata Again, chose from the earlier entries, another, data package or enter new information

22. Morpho - metadata Once data is up loaded to Morpho you can edit data or metadata This is the window that press finish in the morpho wizard.

23. Databases Small scale & on local computer – Access Bigger & on server - MySQL

24. Example - why use a database? Coordinate field data collection and data entry forms DATE SITE WEB PLOT QD SPECIES OBS COVER HEIGHT COUNT PHEN COMMENTS 2/3/1999 FPC 1 E 1 ERPU8 1 0.5 4 13 V NA 2/3/1999 FPC 1 E 1 ERPU8 2 0.1 2 16 V NA 2/3/1999 FPC 1 E 1 GUSA2 1 0.01 4 2 V NA 2/3/1999 FPC 1 E 1 GUSA2 2 0.1 5 1 V NA 2/3/1999 FPC 1 E 1 GUSA2 3 0.5 12 1 V NA

25. Database example Divide to 4 tables: –Location table –Species table –Visit table –Observation table DATE SITE WEB PLOT QD SPECIES OBS COVER HEIGHT COUNT PHEN COMMENTS 2/3/1999 FPC 1 E 1 ERPU8 1 0.5 4 13 V NA 2/3/1999 FPC 1 E 1 ERPU8 2 0.1 2 16 V NA 2/3/1999 FPC 1 E 1 GUSA2 1 0.01 4 2 V NA 2/3/1999 FPC 1 E 1 GUSA2 2 0.1 5 1 V NA 2/3/1999 FPC 1 E 1 GUSA2 3 0.5 12 1 V NA

26. Database example Location Visit Observation Species

27. Database example

32. QA/QC QC –Designing data sheets –Data entry using Validation rules Filters Lookup tables –Validate entered data Double entry Prior data Filters

33. QA/QC QA –Graphics Box plots Scatterplots Normal probability plots –Formal statistical methods Grubbs’test Edwards 2000

34. QA/QC The goal of QA is NOT to eliminate outliers! Rather, we wish to detect unusual & extreme values.

43. µ µ - 3σ µ + 3σ

49. What did I learn? Know your subject. Have a plan. Some planning (little time) in advance will save a lot of head-ache (and time and money and missed opportunities) later. Unorganized data might become a quick way to wall yourself off the increasingly collaborative and computerized research world.