Does replacing the term “citizen science” do more harm than good?
In this chapter, we address the perennial question of what is citizen science? by asking the related question, why is it challenging to define citizen science? Over the past decade and a half, we have seen the emergence of typologies, definitions, and criteria for qualifying citizen science. Yet, citizen science as a field seems somewhat resistant to obeying a limited set of definitions and instead attracts discussions about what type of activities and practices should be included in it. We explore how citizen science has been defined differently, depending on the context. We do that from a particularly European perspective, where the variety of national and subnational structures has also led to a diversity of practices. Based on this background, we track trade-offs linked to the prioritisation of these different objectives and aims of citizen science. Understanding these differences and their origin is important for practitioners and policymakers. We pay attention to the need for definitions and criteria for specific contexts and how people in different roles can approach the issue of what is included in a specific interpretation of citizen science.
This article offers an assessment of current data practices in the citizen science, community science, and crowdsourcing communities. We begin by reviewing current trends in scientific data relevant to citizen science before presenting the results of our qualitative research. Following a purposive sampling scheme designed to capture data management practices from a wide range of initiatives through a landscape sampling methodology (Bos et al. 2007), we sampled 36 projects from English-speaking countries. The authors used a semi-structured protocol to interview project proponents (either scientific leads or data managers) to better understand how projects are addressing key aspects of the data lifecycle, reporting results through descriptive statistics and other analyses. Findings suggest that citizen science projects are doing well in terms of data quality assessment and governance, but are sometimes lacking in providing open access to data outputs, documenting data, ensuring interoperability through data standards, or building robust and sustainable infrastructure. Based on this assessment, the paper presents a number of recommendations for the citizen science community related to data quality, data infrastructure, data governance, data documentation, and data access.
Soon after publication the authors were made aware of an error within Table 3 of the original publication. The example given as the 'Scientist' term 'Citizen scientist, Scientist-citizen, public scientist, community scientist' previously read: "Citizen scientists investigated anecdotal evidence to construct hypotheses regarding developmental disorders that members of the public claimed were triggered by a MMR vaccine." This should have read: "Citizen scientists investigated anecdotal evidence to construct hypotheses regarding developmental disorders that members of the public claimed were triggered by chemical pollution." The corrected Table 3 is presented here.
Positioning citizen science within the broader historical public engagement framework demonstrates how it has the potential to effectively tackle research and innovation issues. Citizen science approaches have their own challenges, which need to be considered in order to achieve this aim and contribute to wider and deeper public engagement. However, programme evaluations, which discuss lessons learned in engaging the public and other stakeholders with science are rare. To address this gap, we present the H2020-funded DITOs project and discuss the use of logic models in citizen science. We share the project’s assumptions, design considerations for deeper engagement and its impact pathways demonstrating how logic models can be utilised in citizen science to monitor programme effectiveness and for their successful implementation. We hope that this work will inspire citizen science practitioners to use similar tools and by doing so, share their experiences and potential barriers. This knowledge is essential for improving the way citizen science is currently practiced and its impacts to both science and society.
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