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

TitleHarnessing the power of big data: infusing the scientific method with machine learning to transform ecology
Publication TypeJournal Article
Year of Publication2014
AuthorsPeters DC, Havstad K, Cushing J, Tweedie C, Fuentes O, Villanueva-Rosales N
JournalEcosphere
Volume5
Issue6
Start PageArticle 67
Date Published06/2014
Accession NumberJRN00625
ARIS Log Number300471
Keywordsanalytics, data deluge, long-term data, machine learning, open data, paradigm shifts
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 dataintensive,
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.

URL/files/bibliography/14-013.pdf
DOI10.1890/ES13-00359.1