Ecoinformatics Conference Service, Environmental Information Management 2008

Recent Information Management System Enhancements at the North Temperate Lakes LTER

David Balsiger, Barbara Benson, Jeff Maxted, Luke Winslow

Last modified: 2008-08-21


The information management team at the North Temperate Lakes (NTL) LTER has been developing enhanced functionality in several areas: data acquisition, data access including expanded access to spatial data, and the management of sensor network data. Two in-house programs for data acquisition have been substantially modified. MobileFish is used by the crew collecting annual fish data and provides data capture on a PDA. Key features of this application include the capability to set up sampling events prior to going into the field, the efficient entry of data and metadata during the actual sampling, prompting by the software that promotes adherence to sampling protocols, and a built-in data screening algorithm for fish lengths and weights. Z3, a program originally designed for zooplankton counting and measuring, is being reworked to include extra features and to be customizable for other counting and measuring uses such as those with benthic invertebrates or fish scales. The management of sensor data has challenged us to investigate new data models and collaborate to develop new tools. The Vega data model is a flexible database architecture designed to accommodate additions and changes in sensor deployments without database structure changes. GLEONDN has been developed to handle simple quality assurance/quality control, exceptions in streaming data, and insertion of data into a repository. dbBadger is a web-based application and allow users to quickly and easily discover and retrieve stored sensor data. dbBadger can also align, interpolate, and aggregate time series data based on the needs of the user. To enhance data discovery we have designed and are beta-testing a search interface to the NTL Data Catalog on the NTL website. This interface allows the user to select a Project type, Theme, Location, and Period of Interest as well as to add search criteria for text strings in various metadata fields such as dataset title, investigator or species name. Migrating to a server-based GIS architecture is enabling us to provide spatial data through web mapping services. This enhances our capacity to serve updated vector and raster data to users from our extensive spatial data catalog. Server-based GIS enables us to serve geoprocessing services, including ecological models that users can apply to spatial data via an internet browser.