Data-Intensive Science: A New Paradigm for Biodiversity Studies

By Steve Kelling, Wesley M. Hochachka, Daniel Fink, Mirek Riedewald, R. Caruana, G. Ballard, Giles Hooker. 2009

334 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecological Modeling, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/df9bf28634d6499d3f9cf876f02c0d0cb5c3322d

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Citations in the corpus

Computational Sustainability (2011, background): “…to obtain the right model parameters and determine current species distribution, machine learning and statistical techniques are required to analyze large amounts of raw species data (Dudik 2007; Dietterich 2009; Elith et al. 2006; Kelling et al. 2009; Munson et al. 2009; and Phillips et al. 2004).”
The future of citizen science: emerging technologies and shifting paradigms (2012, background): “Geographic coverage will remain a challenge for continental-scale observations, particularly in sparsely populated areas, but these shortcomings may be addressed with advanced analytical methods (eg Kelling et al. 2009).”
Biodiversity inventory and informatics in Southeast Asia (2010, background): “…amateur bird watchers have contributed greatly to bird distribution databases in the US and Europe (e.g., eBird10 and the Avian Knowledge Network, Kelling et al. 2009), and elsewhere (Greenwood 2007), and numerous other projects are beginning to utilize this vast human resource for serious…”
Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository (2015, background): “similar path, and has turned into a ‘data-intensive science’ (Kelling et al., 2009; Michener and Jones, 2012).”
Big Data, new epistemologies and paradigm shifts (2014, background): “…in new and exciting ways interdisciplinary research that conjoins domain expertise (as it is less limited by the starting theoretical frame), and that it will lead to more holistic and extensive models and theories of entire complex systems rather than elements of them (Kelling et al., 2009).”
The ethics of smart cities and urban science (2016, background): “more holistic and extensive models and theories of entire complex systems rather than elements of them [16,33].”
Ecoinformatics: supporting ecology as a data-intensive science. (2012, background): “Data integration for crosscutting studies is generally a manual process and can consume the majority of time involved in conducting collaborative research [1,11].”
How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study (2015, neutral): “(Hochachka et al., 2009), Education 2 (4”
The environment ontology: contextualising biological and biomedical entities (2013, background): “Biologically motivated research is generating [1-3] and archiving [4,5] ever-larger quantities of computerised data from environmental samples.”
Can we trust Big Data? Applying philosophy of science to software (2016, background): “The third camp, the data-driven one, is a hybrid of sorts in that ‘‘it seeks to generate hypotheses and insights ‘born from the data’ rather than ‘born from theory’’’ (Kelling et al., 2009 as cited by Kitchin, 2014).”