Introduction

As citizen science matures, it finds itself part of a growing plethora of approaches democratising the processes of scientific enquiry and related modes of knowledge creation. Digital social innovation (DSI) and do-it-yourself (DIY) science are two examples that share citizen science's ideals and challenges of enabling citizen engagement (see also Mazumdar et al. in this volume). Considering typical challenges and types of citizen engagement models in DSI and DIY science, and how such platforms relate to approaches in participatory citizen science, may help the fields to learn from each other and inform new projects and approaches.

In citizen science (Bonney 1996; Cohn 2008), citizens are commonly involved in different types of activities in scientific projects, which are mostly led by professional scientists in institutional settings (Bonney et al., ‘Public Participation’, 2009; Shirk et al. 2012). The underlying assumption of science as the primary legitimate source of knowledge requires citizen participation to conform to the scientific process (Wyler & Haklay in this volume). More flexible forms of engagement relax this requirement by giving citizen participants more influence on the project design (e.g., in the choice of problems or outcome types) and empowering them to collaborate with different actors, among which scientists are but one kind (see also Ballard, Phillips & Robinson in this volume). This broadens the scope of projects, their goals and outcomes, and the types of activities performed by citizens. In particular, participatory citizen science and ‘extreme citizen science’ (Haklay 2013; Stevens et al. 2014) emphasise citizen involvement in core activities of the scientific process, such as problem definition, data analysis and interpretation (see also Gold & Ochu in this volume). These projects design tools for empowering participation from different societal groups (e.g., marginalised communities) in activities that would normally require scientific skills and knowledge. In doing so, they bring scientific enquiry to ‘non-scientific’ problems (e.g., problems important to the volunteers’ communities) and ‘non-scientific’ knowledge (e.g., indigenous knowledge, local needs) (see Danielsen et al. in this volume).

Do-it-yourself science extends this to more informal, experimental methods and a broader range of outcomes: DIY scientists are people who create, build or modify objects and systems in creative ways, often with open source tools, and who share the results and knowledge (Nascimiento et al. 2014, 30). This includes non-specialists, hobbyists and amateurs, but also professional scientists doing science outside their traditional institutional settings. Many DIY science projects are private or community-based initiatives that use scientific methods combined with other forms of enquiry to explore techno-scientific issues and societal challenges (Nascimento et al. 2014; see also Mazumdar et al. in this volume).

This openness to different types of knowledge, outcomes and social settings is also part of the field of social innovation, which emphasises the societal impact of both scientific and practical knowledge creation. The concept of social innovation commonly describes novel solutions to social problems that are more appropriate than existing ones (e.g., more effective, efficient or sustainable) and that create value for society as a whole (Phills et al. 2008, 36). Many social innovations are increasingly based on the use of digital technologies, such as social networks, open data, open source hardware and software. Such digital social innovations are often defined as new solutions to societal needs developed through collaboration between innovators and target users, supported by digital technologies (Bria et al. 2015, 9). This resonates with an early view of citizen science as a science that addresses the needs of citizens and involves them in the scientific development process (Irvin 1995, xi). Such views of (participatory) citizen science and social innovation thus converge in the goal of producing knowledge that addresses societal needs.

A key commonality of participatory citizen science, DIY science and DSI is the focus on citizen engagement with different professional actors in a process of collaborative development and knowledge co-creation, in other words, a process of collective learning. Ideally, they all aim at engaging individual citizens and local communities in the entire process of scientific, exploratory or creative inquiry: from the problem definition and data collection, to analysis and interpretation, solution implementation and take-up. While exhibiting important differences in scope and focus, forms of participation and intended impact, all three approaches face similar challenges of motivating, enabling and structuring citizen engagement. They therefore explore various forms of collective intelligence that often require a lot of groundwork to be implemented (e.g., mobilising large numbers of participants) and can be overwhelming for a single project. A growing number of platforms aim at supporting citizen engagement in DSI by facilitating various forms of collective intelligence (for an overview see Bria et al. 2015).