Citizen science is a collective term for projects that engage both professional scientists and non-specialists in the process of gathering, evaluating or computing scientific data. It has been around for well over a century, and its development can be linked to the professionalisation of science that began in the late nineteenth century, when science moved from the domain of the gentleman scientist to emerge as a distinct occupation (Miller-Rushing et al. 2012). There are two major developmental strands that have contributed to the pattern of citizen science projects we see today and which have greatly impacted on how projects are organised. In 1995 Alan Irwin used the term citizen science to refer to the action of citizens in addressing local environmental issues that relied on the collection and analysis of scientific data (Irwin 1995). This type of citizen science has a bottom-up approach in which citizens ask research questions that are of direct relevance to themselves and their communities. The outcomes may be used to influence public policy or regulatory decisions. This type of citizen science research has also been known as community action research , or participatory action research (Eitzel et al. 2017).
At about the same time, the term citizen science was also used by Rick Bonney and his colleagues at the Cornell Laboratory of Ornithology to describe a growing number of projects that offered opportunities for non-specialists to become involved in authentic scientific research (Bonney et al. 2009). These types of projects are often top-down researcher-led initiatives, where professional scientists enlist the help of volunteers to either collect or evaluate data usually after a brief period of training. These efforts can result in scientific publications and the production of new knowledge. While this type of citizen science had its roots in ornithology it now encompasses a wide range of scientific disciplines at numerous institutions and organisations. These two distinct approaches to citizen science have been described as citizen-led co-created projects with local community groups on the one hand, and scientist-led participation initiatives that are open to all sectors of society on the other (Roy et al. 2012). This book focuses on the latter.
Scientist-led citizen science projects can have multiple aims and be applied in a variety of settings, both small and large-scale. They have benefits for both the scientists who set up projects, and for the individuals who take part. A significant proportion of citizen science projects have enlisted volunteers to collect ecological, biological or environmental data. Data can be collected from a variety of geographical locations and over time in order to track phenological changes in wildlife, bird migration patterns, or more recently, biological or environmental markers of climate change (Devictor et al. 2010). Given this geographic and temporal scale, such projects would be difficult, if not impossible, without the contributions of citizen scientists. While some have questioned the robustness of this data, the numbers of citizen-produced datasets are increasingly finding their way into credible scientific publications and are increasingly valued (Cooper et al. 2014).
As well as fulfilling specific research objectives, citizen science can play a role in informal science learning and have the potential to increase scientific literacy among participants. Some scientists who set up projects have used them as public engagement tools in order to connect non-specialists with the process, as well as the outcomes, of science. Participants get an insight into the âblack boxâ of research and develop a greater understanding of research protocols, the (sometimes repetitive) nature of data analysis, and the way in which new knowledge is disseminated though peer-reviewed journals. Perhaps one of the most important features of citizen science however, is its potential to produce lasting partnerships between scientists and non-specialists.
The number and scope of citizen science projects has increased dramatically over the past two decades â much of this is a direct result of developments in information and communication technology (ICT) and the Internet. These developments have made it easier to manage projects, recruit and communicate with volunteers, collate data, and disseminate research findings much more widely. It is now possible for prospective participants to get involved on a scale never seen before.
In a response to the growing diversity of citizen science projects, some researchers have produced typologies to classify them. Several have focused on the degree of participation of the citizen scientist, for example Shirk et al. (2012) classify projects into 5 different types: contractual (where communities ask researchers to carry out a specific piece of research and report back with the results); contributory (designed by scientists giving âthe publicâ the opportunity to contribute data); collaborative (generally designed by scientists but where non-specialists help to refine and input into project design, analysis of data, or the dissemination of findings); co-created (the project is designed by scientists and non-specialists working together); and collegial (non-credentialed individuals design, implement and communicate their findings independently without involvement of professional researchers). Wiggins and Crowston (2015) have developed a typology that takes into consideration the highly variable approaches taken to different citizen science projects. They use several aspects of participation and project design such as funding, goals, participation activities, data quality processes, rewards , and social opportunities to group projects.
While these typologies can be helpful in understanding different approaches and styles of project, some have criticised them for focussing on just one aspect of participation, and perhaps conflating the tasks completed by non-specialist participants with âempowerment â (Kimura and Kinchy 2016). For example, even if a project is instigated by a community group in response to water pollution, it does not necessarily confer any power on them during confrontations with industries that may be the cause of local pollution. Conversely, non-specialist participants may not have had a hand in the development of a project, but can after time, form specialised project communities which may be empowered to collaborate more closely with scientists, or begin asking their own research questions.
The Impact of Digital and Communication Technologies on Citizen Science
During the past two decades, developments in ICT have changed the way scientists work in a number of ways â most notably in the creation and integration of new knowledge. Digital technologies have influenced how scientists communicate with one another, and how they communicate with those outside the scientific community. For example, online sharing of data has facilitated scientific collaboration ; and the rise of open notebooks , online repositories, and open-access journals has aided the dissemination of scientific results (Nielsen 2012; Scanlon 2013).
Scientists are able to communicate more widely with interested non-specialists through websites, blogs , podcasts, and through social media . Some maintain that the development of Web 2.0 technologies which facilitate data sharing and the production of user-generated content, has begun to blur the boundary between professionals and an increasingly informed online public, and that this may have important consequences for the way scientific knowledge is generated (Stodden 2010; Blank and Reisdorf 2012). This rise in digital science combined with the expansion of new avenues of communication has been referred to as Open Science or Science 2.0 (Könneker and Lugger 2013; Burgelman et al. 2010). This phenomenon generally describes the trend towards an increased connectivity between scientists, and an increased capability for non-scientists to access science and the scientific community.
The growth of open science has been accompanied by an increase in the accuracy and productivity of scientific instrumentation and data storage technologies. This new era of big data has led to what has become known as the data deluge , as scientists in some disciplines now acquire, store and mine huge volumes of digital data (Clavin 2013; Creighton 2010). For example, the Large Hadron Collider generates approxi...