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.

Citizen science (CS) is promoted as a useful practice for the achievement of the Sustainable Development Goals (SDGs). In this contribution we explore how CS aligns to the SDGs overarching pledge to ‘Leave no one behind’. We propose a framework to evaluate exclusionary processes in CS. We interlink three dimensions of CS inspired by existing CS typologies with five factors underpinning exclusionary processes. With this, we are able to situate existing literature on various exclusionary effects in CS within a structured framework. We hope this contribution sparks a discussion and inspires practitioners’ reflections on a more inclusive practice in CS.

The relationship between settlement form and the historical persistence of concentrations of diverse socio-economic activity in Greater London’s suburban centres through successive phases of rapid urban transformation is examined. Particular consideration is given to the development of three suburbs in Greater London: Barnet, South Norwood and Surbiton. Conzenian and space syntax approaches are combined within an integrated GIS environment. Both these approaches identify the historical grain of settlement forms as the key to understanding how socio-economic activity becomes organized in the built environment. Using Surbiton as a case study the analysis demonstrates firstly, how the configuration of Greater London’s historical road network relates to the persistence of socio-economic activity in the built environment over time, and secondly, how diverse, localized patterns of such activity are accessible at a range of morphological scales. It is concluded that the relationship between suburban built form and socioeconomic activity is both configurational and historical in nature.

Volunteered geographic information is information that originates outside the realm of professional data collection by scientists, surveyors, and geographers. Quality assurance of such information is important in defining if it is fit for purpose. There are several approaches that can be used to provide quality assurance. These include the “crowdsourcing” approach, which relies on the number of people that edited the information, the “social” approach, based on gatekeepers and moderators, the “geographic” approach, which uses broader geographic knowledge, the “domain” approach, which relates to the understanding of the knowledge domain of the information, the “instrumental observation” approach, which relies on technology, and the “process‐oriented” approach, which follows a procedure to ensure that the data is of adequate quality. These approaches need to be used with an understanding of the fitness for purpose of the information for a specific context and task.