1887

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

    Authors: Joanna Philippoff1,*, Erin Baumgartner2
    VIEW AFFILIATIONS HIDE AFFILIATIONS
    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: philippo@hawaii.edu.
    Source: J. Microbiol. Biol. Educ. March 2016 vol. 17 no. 1 51-55. doi:10.1128/jmbe.v17i1.999
MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
  • PDF
    182.71 Kb
  • XML
  • HTML
    32.86 Kb

    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.

Key Concept Ranking

Lead
0.90625
Elements
0.773976
Filtration
0.6462406
Water
0.6462406
Leaves
0.40390038
0.90625

References & Citations

1. Baumgartner E, Zabin C2008A case study of project-based instruction in the ninth grade: a semester-long study of intertidal biodiversityEnviron Educ Res149711410.1080/13504620801951640 http://dx.doi.org/10.1080/13504620801951640
2. Baumgartner E, Zabin C, Philippoff J, Cox TE, Knope M2009Ecological monitoring provides a thematic foundation for student inquiry191209 Yagar RInquiry: the key to exemplary scienceNational Science Teachers Association PressArlington, VA
3. Bird TJ, et al2014Statistical solutions for error and bias in global citizen science datasetsBiol Conserv17314415410.1016/j.biocon.2013.07.037 http://dx.doi.org/10.1016/j.biocon.2013.07.037
4. Brownell SE, Kloser MJ2015Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biologyStud Higher Educ40352554410.1080/03075079.2015.1004234 http://dx.doi.org/10.1080/03075079.2015.1004234
5. Cox TE, Philippoff J, Baumgartner E, Smith CM2012Expert variability provides perspective on the strengths and weaknesses of citizen-driven intertidal monitoring programEcol Appl2241201121210.1890/11-1614.122827128 http://dx.doi.org/10.1890/11-1614.1
6. Cox TE, Philippoff J, Baumgartner E, Zabin C, Smith CM2013Spatial and temporal patterns of intertidal communities along the main Hawaiian islandsPacific Sci671234510.2984/67.1.3 http://dx.doi.org/10.2984/67.1.3
7. Foster-Smith J, Evans SM2003The value of marine ecological data collected by volunteersBiol Conserv11319921310.1016/S0006-3207(02)00373-7 http://dx.doi.org/10.1016/S0006-3207(02)00373-7
8. Freitag A, Pfeffer MJ2013Process, not product: investigating recommendations for improving citizen science “success”PLOS one851510.1371/journal.pone.0064079 http://dx.doi.org/10.1371/journal.pone.0064079
9. Fuccillo K, Crimmins T, de Rivera C, Elder T2015Assessing accuracy in citizen science-based plant phenology monitoring597917926
10. Lovell S, Hamer M, Slotow R, Herbert D2009An assessment of the use of volunteers for terrestrial invertebrate biodiversity surveysBiodivers Conserv183295330710.1007/s10531-009-9642-2 http://dx.doi.org/10.1007/s10531-009-9642-2
11. Orr T, Baumgartner E2012Participatory science improves scientific literacy in pre-service elementary teachersProceedings 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 J2009A new dawn for citizen scienceTrends Ecol Evol2446747210.1016/j.tree.2009.03.01719586682 http://dx.doi.org/10.1016/j.tree.2009.03.017
13. van Strien AJ, van Swaay CAM, Termaat T2013Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analyzed with occupancy modelsJ Appl Ecol501450145810.1111/1365-2664.12158 http://dx.doi.org/10.1111/1365-2664.12158
jmbe.v17i1.999.citations
jmbe/17/1
content/journal/jmbe/10.1128/jmbe.v17i1.999
Loading

Citations loading...

Supplemental Material

No supplementary material available for this content.

Loading

Article metrics loading...

/content/journal/jmbe/10.1128/jmbe.v17i1.999
2016-03-01
2017-09-26

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.

Highlighted Text: Show | Hide
Loading full text...

Full text loading...

/deliver/fulltext/jmbe/17/1/jmbe-17-51.xml.a.html?itemId=/content/journal/jmbe/10.1128/jmbe.v17i1.999&mimeType=html&fmt=ahah

This is a required field
Please enter a valid email address
Please check the format of the address you have entered.
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error