eBird: A citizen-based bird observation network in the biological sciences

By Brian L. Sullivan, Christopher Wood, Marshall J. Iliff, R. Bonney, Daniel Fink, Steve Kelling. 2009

1,985 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Ecology, Evolution, Behavior and Systematics

View Paper: https://www.semanticscholar.org/paper/7a42868a87a90c9227064b050ba339daab6192a0

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Citations in the corpus

Teaching Multiple Concepts to a Forgetful Learner (2018, background): “In citizen science projects such as eBird [34] and iNaturalist [38], the goal of a learner is to recognize multiple animal species from a given geographic region.”
Tackling Climate Change with Machine Learning (2019, background): “For example, by leveraging the general public’s enthusiasm for birdwatching, eBird has logged more than 140 million observations [499], which have been used for population and migration studies [503].”
Citizen Science as a Tool for Augmenting Museum Collection Data from Urban Areas (2017, background): “As funding for and the popularity of collecting specimens for museum collections has declined (Dalton, 2003; Gropp, 2003; Suarez and Tsutsui, 2004; Sullivan et al., 2009; Dickinson et al., 2010; LaDuc and Bell, 2010), citizen science can be an effective and inexpensive way to rapidly gain large…”
A review of citizen science and community-based environmental monitoring: issues and opportunities (2011, background): “The UK’ Breeding Bird Survey involves tens of thousands of participants annually (Sullivan et al. 2009).”
Creating individual accessible area hypotheses improves stacked species distribution model performance (2018, methodology): “…data were available from the Global Biodiversity Information Facility (GBIF; Global Biodiversity Informatics Facility, 2014) using the R 3.0.0 (R Core Team, 2015) package rgbif (Chamberlain, Ram, Barve, & Mcglinn, 2015) and directly from eBird in December 2013 (eBird, 2012; Sullivan et al., 2009).”
Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy (2009, background): “Immediately after these features were implemented, the number of individuals submitting data nearly tripled (Sullivan et al. 2009).”
Creating a Successful Citizen Science Model to Detect and Report Invasive Species (2011, background): “…6 www.biosciencemag.org Christmas bird counts, FeederWatch, Galaxy Zoo, REEF, and various other monitoring programs (Trumbull et al. 2000, Brewer 2002, Brossard et al. 2005, Lee et al. 2006, Cooper et al. 2007, Delaney et al. 2008, Bonney et al. 2009, Fitzpatrick et al. 2009, Sullivan et al. 2009).”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “…categorization in the wild we examined three real-world datasets: bird species, as photographed worldwide by birders who are members of eBird [47], tree species, as observed along the streets of Pasadena [55], and plants and animal species, as photographed by the iNaturalist…”
Repurposing Citizen Science Games as Software Tools for Professional Scientists (2018, background): “Early online projects primarily allowed the public to collect environmental data, such as the presence of species in specific ecosystems [42].”
Teaching Multiple Concepts to Forgetful Learners (2018, background): “In citizen science projects such as eBird [34] and iNaturalist [38], the goal of a learner is to recognize multiple animal species from a given geographic region.”
Realising the full potential of citizen science monitoring programs (2013, background): “Communication with volunteers could be through a newsletter informing them of recent activity and data gaps, or an online site that allows users to update their own lists, compare with others’ lists, and access information about an area of interest in a readily usable format (e.g. eBird; Marris, 201”
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data (2018, methodology): “We use the eBird basic dataset (EBD), which gathers large volumes of information on where and when birds occur in the world (Sullivan et al., 2009).”
What Is Citizen Science? – A Scientometric Meta-Analysis (2016, background): “The reasons for this has been described as managing problems of time [2], space [37] and large amounts of data that needs a human observer to be classified [3,38].”