learning about wisconsin lakes through a new integrated database system
DESCRIPTION
Learning about Wisconsin Lakes through a New Integrated Database System. Annual Reports and Awards. Volunteers participating statewide. What is a Database?. Tables with Rows and Columns Tables are Connected. Tables with Rows and Columns. Tables are Connected. - PowerPoint PPT PresentationTRANSCRIPT
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Volunteers participating statewide
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•Tables with Rows and Columns•Tables are Connected
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Station ID
Station Name Latitude Longitude
133320 Lake Wingra - Center
43.055 -89.415
133083 Wingra Creek – Olin Avenue
43.052 -89.383
133081 Wingra Creek – Beld Street
43.049 -89.394
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Station ID Station Name Latitude Longitude
133320 Lake Wingra - Center 43.055 -89.415
133083 Wingra Creek – Olin Avenue 43.052 -89.383
133081 Wingra Creek – Beld Street 43.049 -89.394
Station ID Date Monitored
Data Collectors
133320 6/15/2005 Joe Smith
133320 7/1/2005 Judy Jones
133320 7/20/2005 Joe Smith
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Station ID
Station Name Date Monitored
Data Collectors
133320 Lake Wingra - Center
6/15/2005 Joe Smith
133320 Lake Wingra- Middle
7/1/2005 Judy Jones
133320 Wingra Lake - Center
7/20/2005 Joe Smith
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1997-1999
•Paradox and Microsoft Access. •Reports not yet online. •Repetition of information in tables.
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2000
•Oracle Database. •Reports and volunteer data entry online. •Non-generic tables and columns.
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Secchi_feet Did Disk Hit Bottom?
Water Color
9 No Green
12.25 No Green
14 No Blue
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2007
•Oracle Database. •Reports and volunteer data entry online. •Tables and Columns Reusable
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Parameter Result Units
Secchi Depth 14 Feet
Hit Bottom? No
Water Color Blue
Stream Flow 144 Cfs
# of Zebra Mussels on Plate 100
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Who: PeopleWhere: Monitoring StationsWhy: ProjectsWhen: Date/TimeHow: Methods and Equipment
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Who: PeopleWhere: Monitoring StationsWhy: ProjectsWhen: Date/TimeHow: Methods and Equipment
One More?
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Who: PeopleWhere: Monitoring StationsWhy: ProjectsWhen: Date/TimeHow: Methods and EquipmentHow Good: Quality Assurance Steps Taken
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•Flowages•Seepage Lakes•Drainage Lakes•Spring Lakes•Drained Lakes
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• 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.
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• 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.