Citizen science decisions: A Bayesian approach optimises effort

Ecological Informatics ◽

10.1016/j.ecoinf.2021.101313 ◽

2021 ◽

pp. 101313

Author(s):

Julie Mugford ◽

Elena Moltchanova ◽

Michael Plank ◽

Jon Sullivan ◽

Andrea Byrom ◽

...

Keyword(s):

Citizen Science ◽

Bayesian Approach

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Using demographics toward efficient data classification in citizen science: a Bayesian approach

PeerJ Computer Science ◽

10.7717/peerj-cs.239 ◽

2019 ◽

Vol 5 ◽

pp. e239

Author(s):

Pietro De Lellis ◽

Shinnosuke Nakayama ◽

Maurizio Porfiri

Keyword(s):

Bayesian Inference ◽

Citizen Science ◽

Bayesian Approach ◽

Prior Information ◽

Data Accuracy ◽

Majority Voting ◽

Accurate Information ◽

Training Set ◽

Level Of Education ◽

Efficient Data

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

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