Most biases in citizen collected data are the same or similar to those that are apparent in professionally collected data. For this reason, statistical controls should be put into place to address these commonly shared biases. variability in volunteers' ages, previous experiences, formal education, attitudes, and training types may influence data reliability. 

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- 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

"Assessing data quality in citizen science"

Refining tasks to make them more accessible and clear can help produce higher quality data. 

Iterative development of task and tool design 

This may include training volunteers or requiring pre-qualification of volunteers via a skills test. Field analyses of volunteer data appear to suggest that trained volunteers produce data at or above the level of hired field assistants. 

Volunteer training and testing 

If possible, loan out this equipment to volunteers so that data is being collected by reliable and standardized sources. 

Use. of standardized and calibrated equipment 

Volunteers can collect "vouchers", or evidence, to be submitted to experts for validation and analysis. However, expert validation of every data point is impractical and inefficient depending on the project size 

Expert validation 

When applicable, consensus can be drawn across data points to improve data quality by ensuring the data is replicable. When replication across all data points isn't practical, calibration of volunteer data can be done so as to draw connections between volunteer data and control groups. 

Replication and calibration across volunteers

Volunteer skill can be evaluated through cross examination with other volunteers. Skill levels can then be incorporated into the design model so as to have the best volunteers for certain areas in those areas. 

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