Improving data reliability in community science projects with post-validation criteria

Abstract

The use of community science is increasing rapidly but concerns about the credibility of community science and its ability to generate valid species observations limit its use within scientific research. Post-validation methods can be critical in filtering community science data to ensure it produces accurate results. We developed twenty-four validation criteria to conduct a scoping review assessing the use of community science in previous research to identify (1) the frequency that these criteria are applied, (2) methods to ensure community science data collection is accurate, and (3) post-validation techniques that filter inaccurate data. The application of validation techniques was observed only 15.8% of the time, revealing that further structured protocols are required to generate more credible data. We provide an accessible criteria checklist that will facilitate researchers’ validation of community science data, making it an effective primer in allowing community science to become a more reliable and prominent tool for species monitoring and conservation.