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Addressing Common Student Technical Errors in Field Data Collection: An Analysis of a Citizen-Science Monitoring Project

    Authors: Joanna Philippoff1,*, Erin Baumgartner2
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    Affiliations: 1: Curriculum Research & Development Group, College of Education, University of Hawai’i at Mānoa, Honolulu, HI 96822; 2: Department of Biology, Western Oregon University, Monmouth, OR 97361
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Published 01 March 2016
    • ©2016 Author(s). Published by the American Society for Microbiology.
    • [open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.

    • *Corresponding author. Mailing address: Curriculum Research & Development Group, College of Education, University of Hawai’i at Mānoa, 1776 University Ave., Honolulu, HI 96822. Phone: 808-956-4951. E-mail: [email protected].
    Source: J. Microbiol. Biol. Educ. March 2016 vol. 17 no. 1 51-55. doi:10.1128/jmbe.v17i1.999
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    Abstract:

    The scientific value of citizen-science programs is limited when the data gathered are inconsistent, erroneous, or otherwise unusable. Long-term monitoring studies, such as Our Project In Hawai’i’s Intertidal (OPIHI), have clear and consistent procedures and are thus a good model for evaluating the quality of participant data. The purpose of this study was to examine the kinds of errors made by student researchers during OPIHI data collection and factors that increase or decrease the likelihood of these errors. Twenty-four different types of errors were grouped into four broad error categories: missing data, sloppiness, methodological errors, and misidentification errors. “Sloppiness” was the most prevalent error type. Error rates decreased with field trip experience and student age. We suggest strategies to reduce data collection errors applicable to many types of citizen-science projects including emphasizing neat data collection, explicitly addressing and discussing the problems of falsifying data, emphasizing the importance of using standard scientific vocabulary, and giving participants multiple opportunities to practice to build their data collection techniques and skills.

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References & Citations

1. Baumgartner E, Zabin C 2008 A case study of project-based instruction in the ninth grade: a semester-long study of intertidal biodiversity Environ Educ Res 14 97 114 10.1080/13504620801951640 http://dx.doi.org/10.1080/13504620801951640
2. Baumgartner E, Zabin C, Philippoff J, Cox TE, Knope M 2009 Ecological monitoring provides a thematic foundation for student inquiry 191 209 Yagar R Inquiry: the key to exemplary science National Science Teachers Association Press Arlington, VA
3. Bird TJ, et al 2014 Statistical solutions for error and bias in global citizen science datasets Biol Conserv 173 144 154 10.1016/j.biocon.2013.07.037 http://dx.doi.org/10.1016/j.biocon.2013.07.037
4. Brownell SE, Kloser MJ 2015 Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biology Stud Higher Educ 40 3 525 544 10.1080/03075079.2015.1004234 http://dx.doi.org/10.1080/03075079.2015.1004234
5. Cox TE, Philippoff J, Baumgartner E, Smith CM 2012 Expert variability provides perspective on the strengths and weaknesses of citizen-driven intertidal monitoring program Ecol Appl 22 4 1201 1212 10.1890/11-1614.1 22827128 http://dx.doi.org/10.1890/11-1614.1
6. Cox TE, Philippoff J, Baumgartner E, Zabin C, Smith CM 2013 Spatial and temporal patterns of intertidal communities along the main Hawaiian islands Pacific Sci 67 1 23 45 10.2984/67.1.3 http://dx.doi.org/10.2984/67.1.3
7. Foster-Smith J, Evans SM 2003 The value of marine ecological data collected by volunteers Biol Conserv 113 199 213 10.1016/S0006-3207(02)00373-7 http://dx.doi.org/10.1016/S0006-3207(02)00373-7
8. Freitag A, Pfeffer MJ 2013 Process, not product: investigating recommendations for improving citizen science “success” PLOS one 8 5 1 5 10.1371/journal.pone.0064079 http://dx.doi.org/10.1371/journal.pone.0064079
9. Fuccillo K, Crimmins T, de Rivera C, Elder T 2015 Assessing accuracy in citizen science-based plant phenology monitoring 59 7 917 926
10. Lovell S, Hamer M, Slotow R, Herbert D 2009 An assessment of the use of volunteers for terrestrial invertebrate biodiversity surveys Biodivers Conserv 18 3295 3307 10.1007/s10531-009-9642-2 http://dx.doi.org/10.1007/s10531-009-9642-2
11. Orr T, Baumgartner E 2012 Participatory science improves scientific literacy in pre-service elementary teachers Proceedings of the National Association of Biology Teachers Four-Year College and University Section 2012 Research Symposium [Online.] http://www.nabt.org/websites/institution/File/docs/Four%20Year%20Section/2012%20Proceedings/Orr%20and%20Baumgartner%20NABT%20symposium.pdf
12. Silvertown J 2009 A new dawn for citizen science Trends Ecol Evol 24 467 472 10.1016/j.tree.2009.03.017 19586682 http://dx.doi.org/10.1016/j.tree.2009.03.017
13. van Strien AJ, van Swaay CAM, Termaat T 2013 Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analyzed with occupancy models J Appl Ecol 50 1450 1458 10.1111/1365-2664.12158 http://dx.doi.org/10.1111/1365-2664.12158

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/content/journal/jmbe/10.1128/jmbe.v17i1.999
2016-03-01
2019-10-19

Abstract:

The scientific value of citizen-science programs is limited when the data gathered are inconsistent, erroneous, or otherwise unusable. Long-term monitoring studies, such as Our Project In Hawai’i’s Intertidal (OPIHI), have clear and consistent procedures and are thus a good model for evaluating the quality of participant data. The purpose of this study was to examine the kinds of errors made by student researchers during OPIHI data collection and factors that increase or decrease the likelihood of these errors. Twenty-four different types of errors were grouped into four broad error categories: missing data, sloppiness, methodological errors, and misidentification errors. “Sloppiness” was the most prevalent error type. Error rates decreased with field trip experience and student age. We suggest strategies to reduce data collection errors applicable to many types of citizen-science projects including emphasizing neat data collection, explicitly addressing and discussing the problems of falsifying data, emphasizing the importance of using standard scientific vocabulary, and giving participants multiple opportunities to practice to build their data collection techniques and skills.

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