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Relational databases as a tool to manage environmental data at the research plot scale

Relational databases as a tool to manage environmental data at the research plot scale. Tyler Erickson Institute of Arctic and Alpine Research University of Colorado Deborah Donahue Data Manager, Snow Hydrology Research Group Donald Bren School of Environmental Science and Management

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Relational databases as a tool to manage environmental data at the research plot scale

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  1. Relational databases as a tool to manage environmental data at the research plot scale Tyler Erickson Institute of Arctic and Alpine Research University of Colorado Deborah Donahue Data Manager, Snow Hydrology Research Group Donald Bren School of Environmental Science and Management University of California

  2. Q: Why Relational Databases? A: To Manage Large Data Sets

  3. Storage Choices • Field notes • Good for data collection • Difficult to share

  4. Storage Choices • Field notes • The bookshelf

  5. Storage Choices • Field notes • The bookshelf • The file cabinet

  6. Storage Choices • Field notes • The bookshelf • The file cabinet • Etc.

  7. Distributable Storage Choices • Text data files • Most flexible; easy to set up & distribute • Difficult for analysis

  8. Distributable Storage Choices • Text data files • Spreadsheets • Good for analysis • Inflexible distribution

  9. Distributable Storage Choices • Text data files • Spreadsheets • Database • Best for large high-dimensional datasets; flexible distribution • Work intensive to set up & maintain

  10. Distributable Storage Choices • Text data files • Spreadsheets • Database • GIS • Best for spatial data • Uses a relational database

  11. It’s 4pm and you are somewhere near Granby, Colorado… Do you know where your data are?

  12. 1 2 3 4 5 6 7 8

  13. Data in Need of a Database • Data Characteristics • Large volume • Constant format • Need for sharing

  14. Designing a Database Data Modeling • Identifying related entities • Sample locations, spatial extent, sampling methods, recorded data, data quality, topography… • Determining relationships between entities

  15. Relationships CARDINALITY: Defines the numeric relationships between occurrences of the entities • One-to-One • (one member to one home address) • One-to-Many • (one region/state/province to many members) • Many-to-Many • (many members to many papers)

  16. Example: Keeping Track of the WSC

  17. Example Table Data

  18. (MMSA Example)

  19. Database Information System

  20. Database Information System

  21. Database Information System

  22. Database Information System

  23. Database Information System

  24. Other Examples • EPA • http://www.epa.gov/storet/

  25. Other Examples • EPA • USGS Surface Water • http://waterdata.usgs.gov/nwis/sw

  26. Other Examples • EPA • USGS Surface Water • Snotel • http://www.wrcc.dri.edu/snotel.html

  27. Summary • Relational databases can be overkill for small data sets. • Relational databases are a good choice for large data sets with a consistent format, that need to be distributed to many users.

  28. Acknowledgments • NASA-EOS • Mammoth MountainEnergy Balance Monitoring Sitehttp://neige.bren.ucsb.edu/mmsa/

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