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Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes
Journal article   Open access   Peer reviewed

Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes

Maureen A O'Leary, Kenzley Alphonse, Arce H Mariangeles, Dario Cavaliere, Andrea Cirranello, Thomas G Dietterich, Matthew Julius, Seth Kaufman, Edith Law, Maria Passarotti, …
Systematic biology, v 67(1), pp 49-60
01 Jan 2018
DOI: https://doi.org/10.1093/sysbio/syx052
PMID: 29253296
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1093/sysbio/syx052View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open

Abstract

Animals Classification - methods Crowdsourcing - standards Phenotype Phylogeny Professional Competence Reproducibility of Results
Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of nonexpert students, or "citizen scientists." Crowdsourcing, or data collection by nonexperts, frequently via the internet, has enabled scientists to tackle some large-scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by nonexpert crowds is, however, often questioned and little data have been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 nonexperts and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared with scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a nonexpert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large-scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and nonexperts. We show that there are important instances in which crowd consensus is not a good proxy for correctness.

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8 citations in Web of Science
8 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Evolutionary Biology
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