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Title: Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology

Author
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: Ecosphere
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/5/2014
Publication Date: 6/1/2014
Publication URL: https://handle.nal.usda.gov/10113/58891
Citation: Peters, D.C., Havstad, K.M., Cushing, J., Tweedie, C., Fuentes, O., Villanueva-Rosales, N. 2014. Harnessing the power of big data: Infusing the scientific method with machine learning to transform ecology. Ecosphere. 5(6) Article 67.

Interpretive Summary: Although big data is often discussed as being useful for ecology and envionmental scientists, most of the challenges with this data deluge are associated with open data and metadata, standardization, and accuracy. However, many scientists have not accepted the data deluge as an integral part of their research because the current scientific method is not scalable to large, complex datasets. Here, we provide a way to integrate a data-intensive, machine learning approach with a hypothesis-driven, mechanistic approach to allow scientists to access and use big data. This novel knowledge, learning, analysis system (KLAS) will improve discovery and problem solving. Machine learning leads to more efficient, user-friendly analytics as the streams of data increase while hypothesis-driven decisions lead to the strategic design of experiments to fill knowledge gaps and to elucidate mechanisms. KLAS will transform ecology and environmental sciences by shortening the time lag between individual discoveries and leaps in knowledge by the scientific community, and will lead to paradigm shifts predicated on open access data and analytics in a machine learning environment.

Technical Abstract: Most efforts to harness the power of big data for ecology and environmental sciences focus on data and metadata sharing, standardization, and accuracy. However, many scientists have not accepted the data deluge as an integral part of their research because the current scientific method is not scalable to large, complex datasets. Here, we explain how integrating a data-intensive, machine learning approach with a hypothesis-driven, mechanistic approach can lead to a novel knowledge, learning, analysis system (KLAS) for discovery and problem solving. Machine learning leads to more efficient, user-friendly analytics as the streams of data increase while hypothesis-driven decisions lead to the strategic design of experiments to fill knowledge gaps and to elucidate mechanisms. KLAS will transform ecology and environmental sciences by shortening the time lag between individual discoveries and leaps in knowledge by the scientific community, and will lead to paradigm shifts predicated on open access data and analytics in a machine learning environment.