Project 2490: M. A. O'Leary, K. Alphonse, M. Arce Hernandez, D. Cavaliere, A. Cirranello, T. Dietterich, M. Julius, S. Kaufman, E. Law, M. Passarotti, A. Reft, J. Robalino, N. B. Simmons, S. Smith, D. Stevenson, E. Theriot, P. M. Velazco, R. Walls, M. Yu, M. Daly. 2018. Crowds replicate performance of scientific experts scoring phylogenetic matrices of phenotypes. Systematic Biology. 67 (1):49-60.
Specimen: Cyclotella meneghiniana (unvouchered)
View: Composite - mixed images

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: https://doi.org/10.1093/sysbio/syx052

Project DOI: 10.7934/P2490, http://dx.doi.org/10.7934/P2490
This project containsMatrices
  • 259 Media
  • 1 Matrix
  • 1 Document
  • 14 Taxa
  • 22 Specimens
  • 8 Characters
Total size of project's media files: 350.56M

Download Project SDD File
Total scored cells: 103
Total media associated with cells: 106
Total labels associated with cell media: 1
Characters
Total characters: 8
Total characters with associated media: 3
Total characters with media with labels: 0
Total character states: 23
Total character states with associated media: 23
Total character states with media with labels:0
Total unordered/ordered characters:8/0
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MorphoBank Project 2490

    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
    Edward Theriot
    Project Administrator
    0000000
    (0, 0, 0)
    000
    Matthew Julius
    Full membership
    0000000
    (0, 0, 0)
    000
    Maureen O'Leary
    Full membership
    0000000
    (0, 0, 0)
    000
    Anna Mengjie Yu
    Full membership
    0000000
    (0, 0, 0)
    000


    Taxonomic Overview for Matrix 'M24106' (13 Taxa)

    taxon unscored
    cells
    scored
    cells
    no cell
    support
    NPA
    cells
    "-" cellscell images labels on
    cell images
    member
    access
    [1] Cyclotella atomus atomus
    Last Modified in 07/05/16
    0800081none
    [2] Cyclotella distinguenda
    Last Modified in 07/05/16
    0800080none
    [3] Cyclotella atomus marina
    Last Modified in 07/05/16
    0800080none
    [4] Cyclotella gamma
    Last Modified in 07/05/16
    0800080none
    [5] Cyclotella choctawhatcheeana
    Last Modified in 07/05/16
    0800090none
    [6] Cyclotella cryptica
    Last Modified in 07/05/16
    1700090none
    [7] Cyclotella litoralis
    Last Modified in 07/05/16
    0800080none
    [8] Cyclotella cf. meneghiniana (l1263)
    Last Modified in 07/05/16
    0800080none
    [9] Cyclotella nana
    Last Modified in 07/05/16
    0800080none
    [10] Cyclotella quillensis
    Last Modified in 07/05/16
    0800080none
    [11] † Cyclotella undescribed miocene ect 765
    Last Modified in 07/05/16
    0800080none
    [12] Cyclotella striata
    Last Modified in 07/05/16
    0800080none
    [13] Cyclotella stylorum
    Last Modified in 07/05/16
    0800080none


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    Taxon list922
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    Views for media list1333
    Bibliography427
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    Project downloads

    type number of downloads Individual items downloaded (where applicable)
    Total downloads from project135
    Project downloads131
    Matrix downloads4Cyclotella qualitative characters (4 downloads);