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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #321203

Title: Knowledge, Learning, Analysis System (KLAS)

item RAMIREZ, GEOVANY - New Mexico State University
item Peters, Debra
item Havstad, Kris
item CUSHING, JUDY - Evergreen State College
item TWEEDIE, CRAIG - University Of Texas - El Paso
item FUENTES, OLAC - University Of Texas - El Paso
item VILLANUEVA-ROSALES, NATALIA - University Of Texas - El Paso

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 7/31/2015
Publication Date: 9/1/2015
Citation: Ramirez, G.A., Peters, D.C., Havstad, K.M., Cushing, J., Tweedie, C., Fuentes, O., Villanueva-Rosales, N. 2015. Knowledge, Learning, Analysis System (KLAS). [abstract]. 2015 LTER All Scientists Meeting, August 30-September 2, 2015. Estes Park, CO. Paper No. 242.

Interpretive Summary:

Technical Abstract: The goal of KLAS is to develop a new scientific approach that takes advantage of the data deluge, defined here as both legacy data and new data acquired from environmental and biotic sensors, complex simulation models, and improved technologies for probing biophysical samples. This approach can be implemented as a knowledge-driven, open access system that "learns" and becomes more efficient and easier to use as data streams increase in variety and size. We are in an early stage of KLAS, but we expect that it will adapts the scientific method to accommodate vast amounts of data, and make them accessible to a broad range of user via an open access with an iterative learning process. Also, we expect KLASS to transform ecology and environmental sciences by shortening the time lag between individual discoveries and leaps in knowledge by scientific community. New experiments can be strategically designed based on feedback from the hypothesis-data-analysis loop. The scope of KLAS is not limited to ecology and environmental sciences, it can be used to manage and process data for different fields. KLAS will be able to interconnect and reuse hypothesis, methodologies, data, process, and knowledge gained from previous experiments from one domain to another domain smoothly and transparently to the user.