English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

So-called 'citizen science' data elicited from crowds has become increasinglypopular in many fields including ecology. However, the quality of thisinformation is being frequently debated by many within the scientificcommunity. Therefore, modern citizen science implementations require measuresof the users' proficiency that account for the difficulty of the tasks. Weintroduce a new methodological framework of item response and linear logistictest models with application to citizen science data used in ecology research.This approach accommodates spatial autocorrelation within the item difficultiesand produces relevant ecological measures of species and site-relateddifficulties, discriminatory power and guessing behavior. These, along withestimates of the subject abilities allow better management of these programsand provide deeper insights. This paper also highlights the fit of itemresponse models to big data via divide-and-conquer. We found that the suggestedmethods outperform the traditional item response models in terms of RMSE,accuracy, and WAIC based on leave-one-out cross-validation on simulated andempirical data. We present a comprehensive implementation using a case study ofspecies identification in the Serengeti, Tanzania. The R and Stan codes areprovided for full reproducibility. Multiple statistical illustrations andvisualizations are given which allow practitioners the extrapolation to a widerange of citizen science ecological problems.

Bayesian item response models for citizen science ecological data