Improving Data Quality and Reliability of Citizen Science Observations Through Model Based Reasoning Techniques Using the GLOBE Observer App

Data collected by citizen scientists have proven valuable in atmospheric science research; however data quality remains a concern when working with these data. Thus scientists have applied statistical methods on these data to correct for errors. This demonstrates the need to address the data quality issue for future applications in scientific investigations. Therefore the focus of this research is to develop a method that improves data quality and reliability while enhancing citizen science awareness and attention toward atmospheric science. The exploration of data quality based on submitted observations through NASAs GLOBE Observer app by citizen science participants will be conducted. This analysis will isolate the top three factors concerning data quality. These results will then be used to construct a training regimen based on model based reasoning (MBR), a heuristic approach to understanding physical processes, that addresses those data quality concerns. To determine the efficacy of the MBR training, a comparison will be conducted with traditional training regimens already in use through the GLOBE Observer app to a group of citizen scientists enrolled in the service learning program at El Paso Community College. Results from this study will be presented here.