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GLEON Information Technology. How it’s done. Need a team Need a mission to guide/prioritize Move the data Sense data Stream data Store data Get data Model data Need funding (GBMF, NSF). GLEON Sites September 2008. SUNY Binghamtom: Ken Chiu and students. San Diego:
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How it’s done • Need a team • Need a mission to guide/prioritize • Move the data • Sense data • Stream data • Store data • Get data • Model data • Need funding (GBMF, NSF)
GLEON Sites September 2008 SUNY Binghamtom: Ken Chiu and students San Diego: Tony Fountain Sameer Tilak Peter Shin And you! Lake Observatory + Taiwan (NCHC): Fang-Pang Lin Hsui-Mei Chou IT Development Wisconsin: Luke Winslow Barbara Benson David Balsiger
(unofficial) Missions • Help many sites get online • (Help) Curate and manage the data • Provide access to heterogeneous systems • Support data analysis
Lake Erken Lake Sunapee Documented on GLEON Web site 2 days, Oct. ‘07 5 days, June ‘07 2007-08 IM Installations Brazil, remotely, Apr. ‘08 Mendota, June ’08 Plug-n-play Lake Annie 1+ days, Feb ‘08
The buoys of GLEON: sensor platforms from around the world Lake Sunapee, New Hampshire (USA) Yang Yuan Lake, Taiwan Lake Rotorua, NZ Lake Paajarvi, Finland Trout Lake, Wisconsin (USA) Lake Taihu, China Lake Mendota, (WI, USA) Lake Erken, Sweden
Field Station Sense Standardized configuration Text file with arbitrary configuration Lake Stream Other Location, e.g., GLEON Central Model MySQL Database (Vega data model Data Turbine e.g., NowCasting Store MySQL Database (Vega data model dbBadger Get Web
GLEON Central L. Erken L. Sunapee Trout L. Nowcasting New Zealand; Taihu
To Date… • Nearly 35 sampling sites from seven research programs • Approx. 50 million data points • Ability to access, plot and model data What could possibly be next?
Plenty • More visualization • Crossing boundaries between systems • Data QA/QC tools • More modeling
2. Crossing Boundaries GLEON Central database New Zealand database Others, such as Ireland, Kinneret, New Zealand, etc.
2. Crossing Boundaries • Controlled vocabulary • Agreement on method • Agreement on availability • Provenance (data “versions”)
3. QA/QC • Controlled vocabulary • Algorithms for detection • Guidelines on action • Agreement on annotation • Provenance (data “versions”)
4. More Models • Controlled vocabulary • Types • Physical characteristics (Kroiss) • Data filtering and smoothing • Common transformations • Predictive • Implementation • Storage and visualization of output
Priority, process, timing • Controlled vocabulary • Committee • GLEON 8, Feb ’09 • ? • ? • ? • ?