- iterative development of task and tool design 
- volunteer training and testing 
- use of standardized and calibrated equipment 
- expert validation 
- replication and calibration across volunteers 
- skill-based statistical weighting of volunteer classifications 
- accounting for random error and systematic bias 

Techniques for Producing High Quality Citizen-Science Data 

Kosmala, M., Wiggins, A., Swanson, A., & Simmons, B. (2016). Assessing data quality in citizen science. Frontiers in Ecology and the Environment, 14(10), 551–560. http://www.jstor.org/stable/24891216

Georgetown University

Although the methods for data collection may seem too sophisticated for volunteers to accurately carry out, volunteers are actually able to mostly master them once they are guided through the process. Designing protocols that can be carried out by ordinary people is also important for the replication process, where people outside of the study must be able to gather data in the exact same way in order to gauge the reliability of the study’s data.

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