Statistical solutions for error and bias in global citizen science datasets

By Tomas Bird, A. E. Bates, J. Lefcheck, N. Hill, Russell J. Thomson, G. Edgar, R. Stuart‐Smith, Simon Wotherspoon, M. Krkošek, Jf Stuart-Smith and 3 more. 2014

510 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/7bfded77ad21cb6037e2d2bfff25ee029333d742

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, background): “This can lead to a trade- off between the quality and quantity of data collected by CS projects (Bird et al., 2014; Pacifici et al., 2017).”
Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers (2016, background): “2012), although care needs to be taken to ensure that systematic bias is either precluded in study design or accounted for in the data analysis (Dickinson, Zuckerberg & Bonter 2010; Bird et al. 2014; Gonsamo & D’Odorico 2014).”
A global assessment of the direct and indirect benefits of marine protected areas for coral reef conservation (2018, methodology): “Such a hierarchical modelling framework is a common approach for analysis of large spatial- scale data sets, with advantages, disadvantages and alternative approaches previously discussed in relation to this dataset (Bird et al., 2014).”
Bias and information in biological records (2015, background): “However, statements about this value are often qualified with references to the biases and limitations inherent in opportunistic volunteer-collected data (Bird et al., 2014).”
Marine mammal conservation: over the horizon (2021, background): “One caveat, however, is that citizen science programmes are not a panacea and are most valuable when a scientifically robust design is implemented at the outset (Bird et al. 2014, Embling et al. 2015).”
Defining and observing stages of climate-mediated range shifts in marine systems (2014, background): “As all species cannot be monitored everywhere, creative solutions, such as engaging volunteers in citizen science to collect data, presents a cost-effective and increasingly recognized approach for global conservation efforts (Hochachka et al., 2012; Stuart-Smith et al., 2013; Bird et al., 2014).”
Assessing data quality in citizen science (preprint) (2016, background): “Additional research is also needed in the application of existing statistical and modeling tools to citizen science datasets, which sometimes present additional challenges (Bird et al. 2014).”