Abstract
Citizen scientists can help professional scientists amass much larger datasets than would be possible without their input, but the quality of these data may impact their utility. Therefore, it is imperative to develop standard practices that maximize the accuracy of data produced by citizen scientists. One method increasingly used to improve data accuracy in citizen science-based projects is just-in-time training (JITT), in which volunteers are given on-demand resources to train them on the spot or in conjunction with the research they are performing. In this article, we examine whether JITT improves citizen scientist accuracy of subject identification, specifically wildlife identification from camera trap photos. Ninety-four participants with varying degrees of experience in biology were asked to identify photos from camera traps in Los Angeles, California set to capture photos of wildlife in an urban habitat. Without access to JITT, citizen scientists with no background in biology had lower accuracy than professional biologists (no background: mean = 51.8%, standard error [SE] = 6.0%; professional biologist: mean = 77.6%, SE = 2.1%). However, when participants with no background in biology received JITT, they were able to identify wildlife with a similar level of accuracy as professional biologists (no background: mean = 81.9%, SE = 3.6%; professional biologist: mean = 85.1%, SE = 2.5%). There was a significant interaction between biology background and training treatment (F-ratio = 7.61, <em>p</em> = 0.0009). The increase in accuracy of novice citizen scientists who received JITT was due primarily to fewer misidentifications of species overall but also to increased confidence in classification of species (participants selected the “Don’t Know” option less frequently). From these results, we conclude that the use of JITT can significantly improve subject identification accuracy for citizen scientists with no background in biology.
Highlights
Citizen science is a powerful tool for garnering interest in science from the nonscientific community, as well as for allowing researchers to collect data at greater volumes and at a larger scale than would be feasible with a more limited number of professional scientists (Bhattacharjee 2005; Bonney et al 2009; Silvertown 2009)
Three participants were excluded from the analysis because they did not meet the minimum requirement of five image identifications, resulting in 91 participants
When the participants did not receive any training, the volunteers with biology backgrounds identified with higher accuracy than the volunteers with no background in biology
Summary
Citizen science is a powerful tool for garnering interest in science from the nonscientific community, as well as for allowing researchers to collect data at greater volumes and at a larger scale than would be feasible with a more limited number of professional scientists (Bhattacharjee 2005; Bonney et al 2009; Silvertown 2009). Even without any formal scientific background, citizen scientists have contributed to ecological research by successfully identifying millions of camera trap images (Swanson et al 2016), by quantifying species diversity (Casanovas, Lynch and Fagan 2014), and by contributing to global biodiversity datasets such as eBird (Sullivan et al 2014) and iNaturalist (White et al 2015). Large-scale citizen science projects and incorporation of these datasets into research are not as common as they could be because many researchers are skeptical of the accuracy of data produced by non-experts (Bonney et al 2014; Kosmala et al 2016; Swanson et al 2016).
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