Project 2502: M. A. O’Leary, K. Alphonse, A. H. Mariangeles, D. Cavaliere, A. Cirranello, T. G. Dietterich, M. Julius, S. Kaufman, E. Law, M. Passarotti, A. Reft, J. Robalino, N. B. Simmons, S. Y. Smith, D. W. Stevenson, E. Theriot, P. M. Velazco, R. L. Walls, M. Yu, M. Daly. 2018. Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes. Systematic Biology. 67 (1):49-60.
Specimen: Sicyonia brevirostris (USNM:254665)
View: body, lateral

Abstract

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 the non-expert students, or “citizen scientists.” Crowdsourcing, or data collection by non-experts, 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 non-expert crowds is, however, often questioned and little data has been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 non-experts, and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared to 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 non-expert 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 non-experts. We show that there are important instances in which crowd consensus is not a good proxy for correctness.


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Article DOI: 10.1093/sysbio/syx052

Project DOI: 10.7934/P2502, http://dx.doi.org/10.7934/P2502
This project containsMatrices
  • 548 Media
  • 1 Matrix
  • 9 Documents
  • 15 Taxa
  • 160 Specimens
  • 7 Characters
Total size of project's media files: 2G

Download Project SDD File
Total scored cells: 105
Total media associated with cells: 105
Total labels associated with cell media: 67
Characters
Total characters: 7
Total characters with associated media: 7
Total characters with media with labels: 0
Total character states: 14
Total character states with associated media: 14
Total character states with media with labels:0
Total unordered/ordered characters:7/0
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MorphoBank Project 2502
  • Creation Date:
    21 July 2016
  • Publication Date:
    30 May 2017
  • Project views: 66629

    This research
    supported by

    Authors' Institutions

    • Ohio State University

    • Oregon State University

    • University of Arizona

    • University of Michigan

    • University of Texas at Austin

    • University of Waterloo

    • American Museum of Natural History

    • Drexel University

    • Kenx Technology Inc

    • New York Botanical Garden

    • St. Cloud State University

    • Stony Brook University

    • Whirl-i-gig

    • College of William and Mary

    • Howard Hughes Medical Institute



    Members

    member name taxa specimens media chars character
    media
    labels
    cell scorings
    (scored, NPA, "-")
    cell
    medialabels
    rules
    Javier Robalino
    Project Administrator
    Last logged in 12/20/16
    151605487210105
    (102, 0, 3)
    105671
    Maureen O'Leary
    Full membership
    Last logged in Never
    0000000
    (0, 0, 0)
    000


    Taxonomic Overview for Matrix 'M24143' (15 Taxa)

    taxon unscored
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    [1] Aristaeopsis edwardsiana
    Last Modified in 07/22/16
    0700077none
    [2] Aristeus virilis
    Last Modified in 07/22/16
    0700072none
    [3] Austropenaeus nitidus
    Last Modified in 07/22/16
    0700070none
    [4] Benthesicymus investigatoris
    Last Modified in 07/22/16
    0600171none
    [5] Benthonectes filipes
    Last Modified in 07/22/16
    0600177none
    [6] Farfantepenaeus aztecus
    Last Modified in 07/22/16
    0700077none
    [7] Haliporoides sibogae
    Last Modified in 07/22/16
    0700077none
    [8] Melicertus latisulcatus
    Last Modified in 07/22/16
    0700077none
    [9] Metapenaeus ensis
    Last Modified in 07/22/16
    0700072none
    [10] Penaeopsis eduardoi
    Last Modified in 07/22/16
    0700077none
    [11] Penaeus monodon
    Last Modified in 07/22/16
    0700070none
    [12] Plesiopenaeus armatus
    Last Modified in 07/22/16
    0700077none
    [13] Sicyonia brevirostris
    Last Modified in 07/22/16
    0700070none
    [14] Sicyonia lancifer
    Last Modified in 07/22/16
    0600176none
    [15] Solenocera melantho
    Last Modified in 07/22/16
    0700077none


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    Document downloads1Syst Biol Supplementary Material (1 download);