Citizen science is when members of the public directly work with scientists on a particular question or issue. Participation can range from a large number of single interactions to repeated and complex sampling that requires substantial training. A new paper now explains how to train participants, validate the collected data, and produce rigorous scientific papers from the outcomes. Key highlights include the need to increase the quality of data when designing a project, and to apply quality control afterwards to check for issues with the collected data. Studies with large numbers of participants will benefit from simple data collection methods combined with advanced statistical methods that check for agreement among scores submitted by multiple participants; studies with fewer participants require a different suite of tactics including advanced training, outstanding explanatory materials, and independent verification of results. These two approaches (large and simplified vs. small, careful, and complex) can both yield valuable scientific information. The study was conducted by SAFS professor Julia Parrish and research coordinator Hillary Burgess, and their coauthors, and is published in the journal Integrative and Comparative Biology.