The eBird enterprise: An integrated approach to development and application of citizen science

By Brian L. Sullivan, Jocelyn L Aycrigg, Jessie Barry, R. Bonney, Nicholas E. Bruns, C. Cooper, T. Damoulas, A. Dhondt, Thomas G. Dietterich, Andrew Farnsworth and 22 more. 2014

1,001 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/3d5049f3321802aefa9885c82e6de50c9f91b155

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Citations in the corpus

In crowdfunding we trust: A trust-building model in lending crowdfunding (2020, neutral): “crowd phenomena, such as citizen science (Aristeidou, Scanlon, & Sharples, 2017; Sullivan et al., 2014; Wildschut, 2017) and crowdsourcing (Cappa, Oriani, Pinelli, & De Massis, 2019; Ghezzi, Gabelloni, Martini, & Natalicchio, 2018; Howe, 2006; Maiolini & Naggi, 2011; Piazza, Mazzola, Acur, & Perrone”
Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, methodology): “We describe analytical approaches for using semi- structured CS data, using the example of estimating species distributions from data collected by the eBird CS project (Sullivan et al., 2014).”
Important Bird and Biodiversity Areas (IBAs): the development and characteristics of a global inventory of key sites for biodiversity (2018, methodology): “This might be helped by the use of increasing volumes of citizen science data, generated by schemes such as eBird (Sullivan et al. 2014), BirdTrack (http://app.bto.org/birdtrack/main/data-home.jsp) and BirdLasser (https://www.birdlasser.com/), and of apps for smartphones, which can be used to…”
Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling (2016, methodology): “We addressed these questions using dynamic SDMs based on occurrence data from eBird, a global online checklist program of bird observations collected across a range of spatial and temporal scales (Sullivan et al., 2014).”
The role of atmospheric conditions in the seasonal dynamics of North American migration flyways (2014, methodology): “…derived from spatio-temporal exploratory models (STEM; Fink et al., 2010) and occurrence information from the eBird citizen-science database (Sullivan et al., 2014) for the combined period 2004 to 2011 to test for the presence of geographically distinct migration flyways within the…”
Robust sound event detection in bioacoustic sensor networks (2019, background): “continental) scales are weather surveillance radar data [30] and crowdsourced observations of birders [31].”
AVONET: morphological, ecological and geographical data for all birds. (2022, methodology): “We did not generate new maps for the eBird dataset which can be integrated directly with citizen-science locality data and a suite of spatial research tools (Sullivan et al., 2014).”
Assessing data quality in citizen science (preprint) (2016, background): “Similarly, eBird assists its volunteers with dynamically generated dataentry forms that list the most common birds for a volunteer’s given location and time, increasing both volunteer awareness of the local species and data quality (Sullivan et al. 2014).”
Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark (2016, background): “Casual observations collected without following a structured protocol by members of the public may potentially contribute to research and conservation, and a growing number of unstructured ‘citizen science’ databases have become available in recent years (Devictor et al., 2010; Sullivan et al., 2014”
Contributions from citizen science to science education: an examination of a biodiversity citizen science project with schools in Central Europe (2018, background): “Citizen science (CS), i.e. involving citizens in scientific research projects, has become increasingly popular (Conrad & Hilchey, 2011; Sullivan et al., 2014).”
Lean Multiclass Crowdsourcing (2018, background): “GBIF [33]) that enable global biodiversity studies [29].”
Synthesizing multiple data types for biological conservation using integrated population models (2018, background): “…in an IPM framework (i.e., with local-scale capture-recapture data) has the potential to reveal large-scale population trends, shifts in species niches, and/or the varying importance of demographic processes across spatial and temporal scales (Sauer and Link, 2011; Sullivan et al., 2014).”
The Biological Records Centre: a pioneer of citizen science (2015, methodology): “The NBN Record Cleaner has been developed since 2001, but other citizen science projects have subsequently independently developed a similar framework of automated and expert verification (Bonter & Cooper, 2012; Sullivan et al., 2014).”