Improving data quality in citizen science

Abstract

Context​: Citizen Science is a growing field in today’s technology driven world, where participants collect information or observations of particular phenomena in multitudes of domains. As citizen science deals with a lot of people submitting data, it is prone to data quality issues due to multiple factors such as inaccurate, incomplete or invalid data which might lead to unintended results. ​Goal​: To identify the attributes that define data quality and also provide a set of mechanisms that needs to be followed in order to achieve better data quality standard. Methods​: This thesis studies different kinds of issues that occur in citizen science projects, the attributes of data quality that are affected by the issues, mechanisms which can help in solving them. ​Result​: At the end, framework ​Illu was proposed which suggests a set of mechanisms which if followed can improve the data quality in citizen science projects. ​Conclusion​: In conclusion, it can be said that citizen science projects are prone to unreliable data if the researchers and scientist conducting the studies do not take into account the data quality aspects and incorporate the solutions to tackle the issue.