Designing citizen science initiatives through a top-down framework

Abstract

The term citizen science, also referred to as public participation in scientific research and knowledge production, is gaining increasing recognition as a well-developed and valued approach. This approach has a wide global reach and is utilized across a broad range of scientific fields and disciplines. However, the current literature highlights several challenges associated with citizen science data and processes, which often result in subpar quality and, as a result, lead to undesirable outcomes in citizen science projects. These issues have a direct impact on the sustainability of such projects. To mitigate these negative effects, we propose that standardizing best practices for citizen science projects could enhance process performance and, in turn, improve their outcomes. One of the most important concerns in any kind of process is the quality of the data used at the various stages of the data life cycle, from its generation by participants (researchers and citizens) to the usage and exploitation of the data. The main contribution of this work is twofold: first, it identifies the best practices associated with citizen science projects, and second, it explores how these practices can be enhanced by incorporating principles of data quality management and data governance. As a result, we produced CI.SCI.FORM, a framework that can be used to support institutions to better propose and execute their citizen science projects. This framework consists of two main components: a process reference model (PRM) and a process assessment model. In this paper, we are going to first introduce the CI.SCI.FORM PRM, which includes 16 processes, which are organized into four distinct blocks.