2. I have worked for the Wisconsin DNR for about 10 years. I manage data on Wisconsins 15,000 lakes, program databases and develop web pages. My background is in Environmental Geography and computer programming

8. Volunteers participating statewide

9. Tables with Rows and Columns Tables are Connected

10. Station ID Station NameLatitudeLongitude 133320Lake Wingra - Center 43.055-89.415 133083Wingra Creek – Olin Avenue 43.052-89.383 133081Wingra Creek – Beld Street 43.049-89.394

11. Station IDStation NameLatitudeLongitude 133320Lake Wingra - Center43.055-89.415 133083Wingra Creek – Olin Avenue43.052-89.383 133081Wingra Creek – Beld Street43.049-89.394 Station IDDate Monitored Data Collectors 1333206/15/2005Joe Smith 1333207/1/2005Judy Jones 1333207/20/2005Joe Smith

12. Station ID Station NameDate Monitored Data Collectors 133320Lake Wingra - Center 6/15/2005Joe Smith 133320Lake Wingra- Middle 7/1/2005Judy Jones 133320Wingra Lake - Center 7/20/2005Joe Smith

13. 1997-1999 Paradox and Microsoft Access. Reports not yet online. Repetition of information in tables.

15. 2000 Oracle Database. Reports and volunteer data entry online. Non-generic tables and columns.

16. Secchi_feetDid Disk Hit Bottom? Water Color 9NoGreen 12.25NoGreen 14NoBlue

17. 2007 Oracle Database. Reports and volunteer data entry online. Tables and Columns Reusable

18. ParameterResultUnits Secchi Depth14Feet Hit Bottom?No Water ColorBlue Stream Flow144Cfs # of Zebra Mussels on Plate100

25. Who: People Where: Monitoring Stations Why: Projects When: Date/Time How: Methods and Equipment

29. Who: People Where: Monitoring Stations Why: Projects When: Date/Time How: Methods and Equipment One More?

60. Who: People Where: Monitoring Stations Why: Projects When: Date/Time How: Methods and Equipment How Good: Quality Assurance Steps Taken

61. Flowages Seepage Lakes Drainage Lakes Spring Lakes Drained Lakes

87. Databases should be well organized, but generic enough to accommodate a variety of information Metadata should always be recorded along with the data, to assure repeatability 20, 50, 100 years from now. Metadata should include How Good? Quality assurance information to help prove that the data is good.

88. Our lakes are dynamic, diverse and ever- changing. Our database must be up to the task of adapting to changes (new aquatic invasive species) in data we collect. Reports and map products should be automated as much as possible to enable people to get up-to-date data fast, when needed.