Abstract
Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a “leaderboard” framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally “valuable?” Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.
Highlights
In October 2018, Corey traveled to Malaita, Solomon Islands, with the Australian Museum to conduct a biodiversity assessment with the local Kwaio people
What is the difference between submitting an eBird checklist from a remote part of the world and submitting an eBird checklist while walking the dog near his home in Sydney, as Corey does most days? Is one inherently more “valuable” to the database than the other? In this paper, we examine this question, highlighting that not all citizen science observations are created equal
We focus on citizen science projects in which the main intent is to collect broadscale biodiversity data, but the arguments apply to any citizen science projects that sample in space and time
Summary
Citation: Callaghan CT, Rowley JJL, Cornwell WK, Poore AGB, Major RE (2019) Improving big citizen science data: Moving beyond haphazard sampling. PLoS Biol 17(6): e3000357. https://doi.org/ 10.1371/journal.pbio.3000357 Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Provenance: Peer reviewed; not commissioned.
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