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Wouldn’t it be cool if…

Wouldn’t it be cool if…. …at the press of a button, we could calculate Wedderburn number and other physical lake characteristics smooth buoy data to specific scales isolate patterns by scale. …we could mine non-traditional data sources to help understand our lakes. Lake Mendota, Wisconsin.

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Wouldn’t it be cool if…

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  1. Wouldn’t it be cool if…

  2. …at the press of a button, we could • calculate Wedderburn number and other physical lake characteristics • smooth buoy data to specific scales • isolate patterns by scale

  3. …we could mine non-traditional data sources to help understand our lakes Lake Mendota, Wisconsin Beach monitoring network

  4. …combine multiple data sources to simulate lakes 3D Simulation MERIS Manual data Buoy data

  5. …we could work in teams to produce science that transcends site boundaries

  6. CDI-Type II: Collaborative Research: New knowledge from the GLEON PIs Paul Hanson, UW Miron Livny, UW CS AnHai Doan, UW CS Chin Wu, UW CEE Ken Chiu, SUNY-B CS Matt Hipsey, UWA Fang-Pang Lin, NCHC Many GLEON collaborators!

  7. Lauri Arvola University of Helsinki, Lammi Biological Station, Finland Thorsten Blenckner, Institute of Ecology & Evolution, Sweden Evelyn Gaiser Florida International University David Hamilton University of Waikato, New Zealand Zhengwen Liu Nanjing Institute of Geography and Limnology, China Diane McKnight University of Colorado, Boulder David da Motta Marques, Universidad Federal do Rio Grande do Sul, Brazil Kirsti Sorsa Public Health Madison, Wisconsin Peter Staehr University of Copenhagen, Denmark

  8. CDI: Cyber-enabled Discovery and Innovation transform ecological sensor networks from data collectors to knowledge generators through integration of the people, data, and cyberinfrastructure of lake sensor networks.

  9. 4 Modeling CDI 3 Web services for data access Human interface Vega data model on mySQL 2 Virtual private server Each site has a POP that saves sensor data to text file. 1

  10. GLEON Numbers (September 2009) Projected > 1 billion by 2012 >100 million records 50+ sensing platforms 20+ observatories 169 members from 25 countries 1 2 3 4 5 6 7 8 9 log 10

  11. CDI • Uses existing GLEON infrastructure • Open to all interested scientists • It’s a way of doing science • Starts at the data repository • Implement existing technologies • Develop some new technologies

  12. Web, e.g., dbBadger Mendota buoy LSPA CDI Team: Wisconsin, NY, UWA, NCHC, GLEON dbBadger Software suite Streaming data Query and display observational data GLEON Observational Data Repositories Existing New to this proposal 1 2 Condor Model suite Total Chl Multi-dimensional virtual data Z 3 Y X Mendota group: CFL, CEE, SSEC and others

  13. Some CDI Activities – 1st year • QA/QC Sensor network data • Implement basic signal processing • Incorporate manually sampled data • Workshops to calibrate nD models • Run nD models • Web display of lake data

  14. Get Involved!(Thur, 10:15 break)

  15. Unstructured data Unstructured data from life-guards and city of Madison Unstructured data on singular events from watershed 4 Target scale of model Ecosystem 3 Historical data 1 Model input Spatial extent 2 Model input Sensor network data Meters Circle size  data quality Minute Hour Day Month Season Frequency Lake Mendota, Wisconsin

  16. Visualization? PCB model coupling? Data structure? Transfer protocols? Data structure? Algorithms? Environment: Condor on cluster Workflow: DagMan Standard data-model interface file (e.g., NetCDF) Standard data-model interface file (e.g., NetCDF) Model (process) Model (filters, transforms, etc.) Standard data-model interface file (e.g., NetCDF) Sensor data level 2,3 Other data 1 Other data 2 Model (QA) Model (QA) Virtual private server Raw sensor data level 1

  17. END

  18. Opinions About Technology Solutions • Best long-term solution is unknowable • Tools to move data rapidly to shareable state • Are short-term needs at odds with long-term solutions? • Solutions for all ecologists • Most ecologists aren’t funded to create technology • Simplicity, autonomy, compatibility • Technology transfer? • Who wants to adapt another’s system? • Outsource, partner, federate • Culture of experimentation and change • Must try solutions from outside science • Social networks as science networks? • Look to current graduate students

  19. GLEON: an international grassroots network of people, data, and lake observatories Activities Share experience, expertise, and data Catalyze joint projects Develop tools Conduct multi-site training Create opportunities for students Meet and communicate regularly

  20. Briefly… • GLEON as an organization • Current technology – from sensors to ecologists • CDI – data to knowledge • Points not covered: grassroots approach, decision making and timing; controlled vocabulary; metadata; the science of GLEON

  21. Vega Data Model Value oriented structure Store data from any number of sites Highly optimized ‘Values’ table Query Times < 1 sec GLEON central ~30 million values Streams

  22. Buoy system Virtual Private Server, Ubuntu Linux Proprietary Open source Web service Vega, global db, mySQL 3 1 POP e.g., Logger- Net Ziggy Any db (http pull) Site- specific XML file XML file XML file XML file 2 FTP (push) 1 POP Ziggy, state, metadata e.g., Logger- Net Buoy system Local db Text file

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