Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills
This paper is the culmination of several facilitated exercises and meetings between external researchers and five citizen science (CS) project teams who analyzed existing data records to understand CS volunteers’ accuracy and skills. CS teams identified a wide range of skill variables that were “hid...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ubiquity Press
2022-11-01
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| Series: | Citizen Science: Theory and Practice |
| Subjects: | |
| Online Access: | https://theoryandpractice.citizenscienceassociation.org/articles/476 |
| _version_ | 1811178216023392256 |
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| author | Karen Peterman Veronica Del Bianco Andrea Grover Cathlyn Davis Holly Rosser |
| author_facet | Karen Peterman Veronica Del Bianco Andrea Grover Cathlyn Davis Holly Rosser |
| author_sort | Karen Peterman |
| collection | DOAJ |
| description | This paper is the culmination of several facilitated exercises and meetings between external researchers and five citizen science (CS) project teams who analyzed existing data records to understand CS volunteers’ accuracy and skills. CS teams identified a wide range of skill variables that were “hiding in plain sight” in their data records, and that could be explored as part of a secondary analysis, which we define here as analyses based on data already possessed by the project. Each team identified a small number of evaluation questions to explore with their existing data. Analyses focused on accurate data collection and all teams chose to add complementary records that documented volunteers’ project engagement or the data collection context to their analysis. Most analyses were conducted as planned, and included a range of approaches from correlation analyses to general additive models. Importantly, the results from these analyses were then used to inform the design of both existing and new CS projects, and to inform the field more broadly through a range of dissemination strategies. We conclude by sharing ways that others might consider pursuing their own secondary analysis to help fill gaps in our current understanding related to volunteer skills. |
| first_indexed | 2024-04-11T06:14:43Z |
| format | Article |
| id | doaj.art-f1e17e49b3c64b0c994c0f8920b92c43 |
| institution | Directory Open Access Journal |
| issn | 2057-4991 |
| language | English |
| last_indexed | 2024-04-11T06:14:43Z |
| publishDate | 2022-11-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Citizen Science: Theory and Practice |
| spelling | doaj.art-f1e17e49b3c64b0c994c0f8920b92c432022-12-22T04:41:06ZengUbiquity PressCitizen Science: Theory and Practice2057-49912022-11-017110.5334/cstp.476177Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ SkillsKaren Peterman0Veronica Del Bianco1Andrea Grover2Cathlyn Davis3Holly Rosser4Catalyst Consulting GroupUniversity of Maryland Center for Environmental ScienceUniversity of NebraskaUniversity of Maryland Center for Environmental ScienceUniversity of NebraskaThis paper is the culmination of several facilitated exercises and meetings between external researchers and five citizen science (CS) project teams who analyzed existing data records to understand CS volunteers’ accuracy and skills. CS teams identified a wide range of skill variables that were “hiding in plain sight” in their data records, and that could be explored as part of a secondary analysis, which we define here as analyses based on data already possessed by the project. Each team identified a small number of evaluation questions to explore with their existing data. Analyses focused on accurate data collection and all teams chose to add complementary records that documented volunteers’ project engagement or the data collection context to their analysis. Most analyses were conducted as planned, and included a range of approaches from correlation analyses to general additive models. Importantly, the results from these analyses were then used to inform the design of both existing and new CS projects, and to inform the field more broadly through a range of dissemination strategies. We conclude by sharing ways that others might consider pursuing their own secondary analysis to help fill gaps in our current understanding related to volunteer skills.https://theoryandpractice.citizenscienceassociation.org/articles/476citizen sciencedata validationsecondary analysisvolunteersskill |
| spellingShingle | Karen Peterman Veronica Del Bianco Andrea Grover Cathlyn Davis Holly Rosser Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills Citizen Science: Theory and Practice citizen science data validation secondary analysis volunteers skill |
| title | Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills |
| title_full | Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills |
| title_fullStr | Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills |
| title_full_unstemmed | Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills |
| title_short | Hiding in Plain Sight: Secondary Analysis of Data Records as a Method for Learning about Citizen Science Projects and Volunteers’ Skills |
| title_sort | hiding in plain sight secondary analysis of data records as a method for learning about citizen science projects and volunteers skills |
| topic | citizen science data validation secondary analysis volunteers skill |
| url | https://theoryandpractice.citizenscienceassociation.org/articles/476 |
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