Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Peters, D. P. C., Havstad, K. M., Cushing, J., Tweedie, C., Fuentes, O., & Vilanueva-Rosales, N. Ecosphere, 5(6):67. http://dx.doi.org/10.1890/ES13–00359.1, 2014. ![Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@article{peters_harnessing_2014,
title = {Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology},
volume = {5},
url = {http://dx.doi.org/10.1890/ES13-00359.1},
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.},
number = {6},
journal = {Ecosphere},
author = {Peters, Debra P. C. and Havstad, Kris M. and Cushing, Judy and Tweedie, Craig and Fuentes, Olac and Vilanueva-Rosales, Natalia},
year = {2014},
keywords = {LTER, analytics, article, data deluge, journal, long-term data, machine learning, open data, paradigm shifts},
pages = {67. http://dx.doi.org/10.1890/ES13--00359.1}
}
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