The concept of citizen science – also known as public involvement in scientific research and knowledge generation – is increasingly acknowledged as a robust and respected methodology. It is widely applied across numerous scientific domains on a global scale. Nevertheless, recent literature points to various challenges related to the data and methodologies employed in citizen science, which frequently compromise data quality and lead to less-than-ideal project outcomes. These challenges pose significant risks to the long-term viability of such initiatives. To address these issues, we suggest that implementing standardized best practices could enhance the efficiency of citizen science processes and lead to better results. A central concern in any process is the quality of data throughout its life cycle, from its initial collection by both researchers and citizen participants to its eventual application and analysis. This proposal offers a dual contribution: it first identifies key best practices relevant to citizen science initiatives and then examines how these practices can be strengthened through the integration of data quality management and data governance principles. Based on this approach, we developed CI.SCI.FORM, a framework designed to assist institutions in more effectively designing and implementing their citizen science projects. The framework is composed of two core elements: a Process Reference Model (PRM) and a Process Assessment Model (PAM). In this paper, we begin by presenting the PRM, which includes 16 processes grouped into four distinct blocks.
Guerra-García et al. (2025) studied this question.