@conference {JRN54935,
	title = {A machine learning guided system for automatically processing data from an array of wireless sensors},
	booktitle = {International Conference on Complex Systems},
	year = {2018},
	month = {07/2018},
	address = {Cambridge, Massachusettes},
	abstract = {<p>We present a system that automatically processes data from an array of remote sensors in a wireless network and makes these data available online. Our system automatically performs QA/QC using the GCE LTER Data toolbox for Matlab. Additionally, the GCE toolbox help us to manage metadata and data in multiple formats. Users can explore data with tools to graph, select stations, variables, and time. We have connected this QA/QC process with our Knowledge, Learning, and Analysis System (KLAS) to allow users to perform experiments with the data collected. KLAS can learn from users{\textquoteright} behavior using machine learning to serve as a guide during the process of approving provisional data and during the experimentation process by making recommendations. KLAS can help the scientific community to reuse data, methods, and models.</p>
},
	author = {Geovany Ramirez and Debra C. Peters and John P. Anderson}
}
@conference {JRN54420,
	title = {Knowledge, Learning, Analysis System (KLAS)},
	booktitle = {2015 LTER All Scientist Meeting},
	year = {2015},
	month = {09/2015},
	abstract = {<p>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.</p>
},
	author = {Geovany Ramirez and Debra C. Peters and Kris Havstad and Cushing, Judy and Craig Tweedie and Fuentes, Olac and Villanueva-Rosales, Natalia}
}
