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

Beach monitoring network

Manual data

MERIS

3D Simulation

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

StreamsStreams

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

PIsPaul Hanson, UWMiron Livny, UW CSAnHai Doan, UW CSChin Wu, UW CEEKen Chiu, SUNY-B CSMatt Hipsey, UWAFang-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

1

2

3

4

CDI

Each site has a POP that saves sensor data to text file.

Each site has a POP that saves sensor data to text file.

Web services for data accessWeb services

for data access Vega data model on mySQL

Vega data model on mySQL

20+ observatories

50+ sensing platforms

>100 million records

Projected > 1 billion by 2012

1 2 3 4 5 6 7log 10

8 9

GLEON Numbers(September 2009)

169 members from 25 countries

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

Lake Observatory

+ I T Development

GLEONObservational Data

Repositories

Query and display observational data

dbBadgerSoftware suite

Streaming data

Web, e.g.,dbBadger

Mendota buoyLSPA

New to this proposal

Model suite

Existing

22

XY

Z

10-4

10-3

10-2

10-1

100

100

105

1010

Frequency (Hz)

Power Spectrum

chlorophyll

phycocyanin

dissolved oxygen

hourdayweek

Po

we

r

11

33

Multi-dimensional virtual data

Total Chl

Mendota group:CFL, CEE, SSEC and others

CDI Team:Wisconsin, NY, UWA,

NCHC, GLEON

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

Get Involved!(Thur, 10:15 break)

Frequency

Sp

atia

l ext

en

t

Minute Hour Day Month Season

2

1

Me

ters

Eco

syst

em

3

4

Model input

Model input

Circle size data quality

Lake Mendota, Wisconsin

Target scale of model

Target scale of model

Sensor network data

Sensor network data

Unstructured data on singular events from watershed

Unstructured data on singular events from watershed

Historical data

Historical data

Unstructured data from life-guards and city of Madison

Unstructured data from life-guards and city of Madison

Unstructured data

Raw sensor data level 1

Other data 1 Other data 2Sensor data level 2,3

Model(filters, transforms, etc.)

Standard data-model interface file (e.g., NetCDF)

Standard data-model interface file (e.g., NetCDF)

Model(process)

Model(QA)

Standard data-model interface file (e.g., NetCDF)

Environment: Condor on clusterWorkflow: DagMan

Model(QA)

Transfer protocols?

Transfer protocols?

Data structure?

Data structure?

Data structure?

Data structure?

PCB model coupling?

PCB model coupling?

Algorithms?Algorithms?

Visualization?Visualization?

Virtual private server

END

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

GLEON: an international grassroots network of people, data, and lake

observatories

ActivitiesShare experience, expertise, and dataCatalyze joint projects Develop toolsConduct multi-site trainingCreate opportunities for studentsMeet 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 fileXML file XML file

Ziggy

Vega, global db,mySQL POP

XML file

(http pull)

Any db

Site-specific

Local db Buoy system

Ope

n so

urce

Pro

prie

tary

e.g.,Logger-

Net

Buoy system

e.g.,Logger-

Net

Webservice

1

2

3 1