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

Abstract

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.

A Tale of Two Crowds: Public Engagement in Plankton Classification

“…Previous discussions about working with "big" ecological data have focused largely on cyber-infrastructure capabilities, data management (Michener and Jones, 2012;Gilbert et al, 2014), and the need for datadriven approaches (Kelling et al, 2009). While novel analytical techniques such as machine learning and crowd-sourcing for processing large and complex ecological data sets are increasingly reported in the terrestrial literature (Kelling et al, 2013;Peters et al, 2014), marine examples are limited (Wiley et al, 2003;Dugan et al, 2013;Millie et al, 2013;Shamir et al, 2014). Given this paucity and the need to use "big" biological oceanography and marine ecology data for rapid assessment of ocean health and adaptive management of ecosystems, we present here an evolution of approaches applied to the problem of efficiently classifying tens of millions of images of individual plankters generated by ISIIS.…”

Understanding large‐scale, complex, human–environmental processes: a framework for social–ecological observatories

“…) and for integrated, iterative approaches to data modeling with learning (Peters et al . ) has more recently been discussed. In an international context, challenges to data sharing include unequal distribution among networks of information management expertise, user‐friendly tools, and resources (Vanderbilt et al .…”

Perspectives from a water research institute on Knowledge Management for Sustainable Water Management

“…The lack of accessibility of publications and technical content (e.g., located behind paywalls of publishers or professional societies) is a major impediment to text mining [Peters et al, 2014;Shemilt et al, 2014]. Text and data-mining (TDM) for non-commercial research purposes is explicitly allowed under UK copyright law if the researcher has lawful access (i.e., through a subscription or license) to the material (https://www.jisc.ac.uk/guides/text-and-data-mining-copyright-exception).…”