Wouldnt 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 Beach monitoring network

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

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

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!

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

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

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

20+ observatories 50+ sensing platforms >100 million records Projected > 1 billion by log GLEON Numbers (September 2009) 169 members from 25 countries

CDI Uses existing GLEON infrastructure Open to all interested scientists Its a way of doing science Starts at the data repository Implement existing technologies Develop some new technologies

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

Some CDI Activities – 1 st 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

Get Involved! (Thur, 10:15 break)

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

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

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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 arent funded to create technology –Simplicity, autonomy, compatibility –Technology transfer? Who wants to adapt anothers 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

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

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

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

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