Data resulting from citizen science investigations are often questioned as most participants do not (yet) have a thorough scientific education. This is especially true for projects taking place in schools, and conducting citizen science in this context is further complicated by different motivations of participants and a busy school curriculum. Herein we present strategies to ensure quality of data generated by the citizen science project Plastic Pirates in which schoolchildren investigated litter pollution at and in rivers. We show how formulating concise research questions, offering accompanying educational material, employing data quality mechanisms in the field (photographs, standardized sampling methods and self-evaluation) as well as transparently detailing which datasets were excluded from analysis was vital to accomplish the acceptance of resulting citizen science data by the scientific community.
