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Training design, data type, and data reliability in citizen science

Authors: Margaret Gaddis*, University of Colorado - Colorado Springs
Topics: Human-Environment Geography, Field Methods, Sustainability Science
Keywords: citizen science, data reliability, training design, andragogy
Session Type: Paper
Presentation File: No File Uploaded

The work of citizen scientists expands the data collection possibilities in natural resource management and increases science literacy. The problem is that some scientists and land managers view the data collected by citizen scientists as unreliable. This dissertation investigated training parameters and perceptions of data reliability across 22 citizen science programs around the world. A sequenced methodology including document analysis, survey, and semi-structured interviews indicated strong alignment between citizen science training, andragogy, and social learning theory. A bimodal distribution of citizen science programs related type of data collected and training design. Little training existed when data collection involved photography only. Citizen scientists brought prior skills to the task but no new procedure learning was required for the data collection task. When citizen scientists collected more complex measurements, classroom and field mentoring facilitated learning.
Computer technologies validated photo and water quality data. Therefore, quantitative data analysis supported the training leaders’ perception of data reliability. Terrestrial data had a range of reliability qualifications including video and paper quizzing, field observation of methods implemented, periodic data checks, and follow-up mentoring when data quality was poor. Managers of terrestrial citizen science programs were confident in the reliability of the data for the land management, policy, and research applications required, but these perceptions were not supported by quantitative data. The implementation of training measurement to validate data reliability is a tool for future investigations and improvements to promote data reliability in citizen science.

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