New paper using IRT model with BRMS for online snake identification

New paper in Royal Society Open Access that uses a Bayesian Item Response Theory model built with Stan and BRMS in R. Item response was applied to score how well herpetologists could identify species of venomous and non-venomous snakes from images. Specifically we used a Graded Response Model with a 2-parameter logistic distribution. Much thanks for the extremely helpful guide to Bayesian IRT by @paul.buerkner! (see: )

Link to code repo in paper and here: (Release R code for IRT analysis in "Crowdsourcing snake identification with online communities of professionals and avocational enthusiasts" · akleinhesselink/snapp · GitHub )

	title = {Crowdsourcing snake identification with online communities of professional herpetologists and avocational snake enthusiasts},
	volume = {8},
	url = {},
	doi = {10.1098/rsos.201273},
	abstract = {Species identification can be challenging for biologists, healthcare practitioners and members of the general public. Snakes are no exception, and the potential medical consequences of venomous snake misidentification can be significant. Here, we collected data on identification of 100 snake species by building a week-long online citizen science challenge which attracted more than 1000 participants from around the world. We show that a large community including both professional herpetologists and skilled avocational snake enthusiasts with the potential to quickly (less than 2 min) and accurately (69–90\%; see text) identify snakes is active online around the clock, but that only a small fraction of community members are proficient at identifying snakes to the species level, even when provided with the snake's geographical origin. Nevertheless, participants showed great enthusiasm and engagement, and our study provides evidence that innovative citizen science/crowdsourcing approaches can play significant roles in training and building capacity. Although identification by an expert familiar with the local snake fauna will always be the gold standard, we suggest that healthcare workers, clinicians, epidemiologists and other parties interested in snakebite could become more connected to these communities, and that professional herpetologists and skilled avocational snake enthusiasts could organize ways to help connect medical professionals to crowdsourcing platforms. Involving skilled avocational snake enthusiasts in decision making could build the capacity of healthcare workers to identify snakes more quickly, specifically and accurately, and ultimately improve snakebite treatment data and outcomes.},
	number = {1},
	urldate = {2021-03-11},
	journal = {Royal Society Open Science},
	author = {Durso, A. M. and Bolon, I. and Kleinhesselink, A. R. and Mondardini, M. R. and Fernandez-Marquez, J. L. and Gutsche-Jones, F. and Gwilliams, C. and Tanner, M. and Smith, C. E. and Wüster, W. and Grey, F. and Ruiz de Castañeda, R.},
	note = {Publisher: Royal Society},
	pages = {201273}

EDIT: @maxbiostat edited this post to wrap the bib reference in code quotes and improve readability.


Thanks for fixing that @maxbiostat !

Always nice to see examples of published work, with a clear empirical foundation, using the tools the Stan community provides! One gets a view of how different areas/journals/etc. expect you to report a Bayesian analysis. I’ve already read parts of the replication package :) Thanks for sharing!

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