Citizen science as seen by scientists: Methodological,epistemological and ethical dimensions

Abstract

Citizen science as a way of communicating science and doing public engagement has over the past decade become the focus of considerable hopes and expectations. It can be seen as a win–win situation, where scientists get help from the public and the participants get a public engagement experience that involves them in real and meaningful scientific research. In this paper we present the results of a series of qualitative interviews with scientists who participated in the ‘OPAL’ portfolio of citizen science projects that has been running in England since 2007: What were their experiences of participating in citizen science? We highlight two particular sets of issues that our participants have voiced, methodological/epistemological and ethical issues. While we share the general enthusiasm over citizen science, we hope that the research in this paper opens up more debate over the potential pitfalls of citizen science as seen by the scientists themselves.

Keywords

citizen science environment education public engagement public participation in scientific research

1. Introduction

Citizen Science (CS hereafter) has over the past decade become a very influential and widely discussed concept with many scientists and commentators seeing it as the future of genuine interactive and inclusive science engagement (Cohn, 2008; Cooper et al., 2007). CS is a contested term with multiple origins, having been coined independently in the mid-1990s by Rick Bonney in the US (see Bonney et al., 2009) to refer to public-participation engagement and science communication projects, and in the UK by Alan Irwin (1995) to refer to his developing concepts of scientific citizenship which foregrounds the necessity of opening up science and science policy processes to the public. These two origins of the term contribute to the multitude of meanings, interpretations and hoped-for goals that researchers associate with CS. This paper will be mostly about the former (US) use of the term, nevertheless, Irwin’s idea is not irrelevant here as it has influenced the uptake of CS in science and environmental policy circles (for example Forestry Commission, 2011) and the terms are conflated often enough to have had enormous influences on each other.

Mainly referring to the US understanding of the term, CS is often seen by scientists as an idea that successfully combines public engagement and outreach objectives with the scientific objectives of scientists themselves (Silvertown, 2009). CS can easily appear to be a ‘win–win’ scenario and as such help address thorny issues such as how to attract more scientists into public engagement work (Bauer and Jensen, 2011; Poliakoff and Webb, 2007) as well as how to get the public to participate directly in the process of science and thus learn more about the processes of scientific enquiry. In this mode of CS, public engagement is an integral part of the enterprise, where, however, the public engagement is often taken to be a more traditional informal science learning activity that works as part of a drive by scientists to educate, raise awareness and increase enthusiasm around the science in question, rather than the more dialogic or ‘upstream’ science and science policy engagement events that some public understanding of science (PUS) scholars talk about when they try to define the term (for example Rowe and Frewer’s 2004 classification understands the value of public engagement generally as one that informs policy). CS should, however, not be seen as a type of public engagement, as there are also CS projects where the public engagement is seen more as a fortunate side-product of asking the public for help with the science (Citizen Science Alliance, no date). Public engagement is usually, but not necessarily, part of a CS project’s set of goals.

Accompanying the scientists’ general enthusiasm towards CS, there has been an increasing interest within social science in analysing the CS concept more thoroughly, through empirical case studies of the public–expert relationship (Cornwell and Campbell, 2012), the experience and motivations of participants (Mankowski et al., 2011; Raddick et al., 2010), or the learning outcome of public participants either in terms of traditional science knowledge or in terms of knowledge of the scientific method (Crall et al., 2012; Cronje et al., 2011; Jordan et al., 2011; Trumbull et al., 2000). These studies are looking at CS in a more analytically critical light than the often very enthusiastic and optimistic assessments from scientists, for example Trumbull et al. (2000) demonstrate that despite early hopes the CS projects they looked at were not particularly successful in increasing knowledge of scientific method.

The focus of these studies, however, has mostly been on what learning outcomes CS can deliver, or less frequently on other issues surrounding the public participation in CS. Social science analysis of other issues surrounding CS, such as whether it can fulfil its promises on the science side has been largely absent. In particular the voice of the scientists themselves has been relatively silent (Bonney et al., 2009). Because of this, reflections, lessons learned and worries that scientists may have encountered while working on CS have rarely been voiced. Scientists themselves have of course published prolifically on their experiences of CS (such as Cooper et al., 2007; Delaney et al., 2008; Evans et al., 2005; Gallo and Waitt, 2011; Lee et al., 2006), reflecting among other things on what worked and what did not work so well during the projects. While this literature is certainly insightful about scientists’ experiences, there is still a need for independent social scientists to have a look behind the curtains and find out what scientists working on CS think about it, and where they see the real challenges that CS needs to address if we want it to be successful. One particular reason for this need is that we can expect ‘publication bias’ from scientists’ writing about their own experiences. Projects that for whatever reason did not work very well, will provide little motivation for participating scientists to publish their experience.

What precisely is meant by the term CS is under contention; the website ‘Citizen Science Central’, run by the Cornell Lab of Ornithology, provides a working definition of ‘projects in which volunteers partner with scientists to answer real-world questions’ (Citizen Science Central, 2013) which they see as sufficiently broad to include a wide variety of different types of CS. This variety can be classified along different ways. While Bonney et al. (2009; see also Shirk et al., 2012) classify CS by the amount and quality of the public contribution (i.e. how engaged the role of public participants is), Wiggins and Crowston (2011) differentiate more finely between several categories such as who leads on the projects (e.g. scientists vs. local communities), how the projects are set up in practice (e.g. internet based vs. real-life interaction) or what the main purpose of the project is (e.g. science or public engagement). Even these otherwise comprehensive classifications, however, leave out some other activities that have sometimes been labelled CS, such as interested amateurs in the history of science who have advanced scientific knowledge, like Benjamin Franklin (noted as a forerunner of CS by Silvertown, 2009) or other amateur-led science projects such as the AIDS activists famously analysed by Epstein (1996), where the term ‘volunteer’ would be rather inappropriate.

The variability in interpretations of what we mean by CS, especially the difference between the US (Bonney) and UK (Irwin) meanings of the term, can arguably be seen as furthering the general enthusiasm of social scientists as well as natural scientists towards CS. As CS is a polysemous concept, social scientists in the Irwin tradition (such as Hemment et al., 2011) tend to attach to CS all their hopes and aspirations of a genuinely two-way dialogic public engagement exercise that gives an equal voice to the lay-experts and demonstrates that the divide between science and the public(s) is not as rigid as often supposed; this literature talks about ‘democracy’ and CS in a way that is almost completely absent in the scientists’ reflections on CS. Contrast Hemment et al.’s working definition of CS (‘an expanding field experimenting with alternative models of “public” knowledge production and democracy’, 2011: 62) with that of the Citizen Science Central website quoted above.

Some social science PUS researchers (for example in Bell et al.’s (2009: 189–192) review of ‘informal science learning’ activities) recognise that CS interpreted in Bonney’s sense can be analysed as a more or less traditional public engagement exercise with the added bonus of teaching hands-on science skills but otherwise unrelated to Irwin’s democratic aspirations of scientific citizenship. Others, writing within Irwin’s tradition of CS, likewise realise that Bonney’s CS does not seem to be relevant to their concerns either (Stilgoe, 2009: 22). Nevertheless, the concept of CS is nebulous enough for endorsements of it often to confuse the aspirations of one interpretation to be applicable to the other. Take up of CS by the statutory sector, for example a recent Defra/Forestry Commission initiative in the UK (Forestry Commission, 2011), is often enthusiastically justified because of its potential to both act as a public awareness raising campaign in the CS sense of Bonney as well as provide the lay-person with a policy voice in the CS sense of Irwin. While we do not mean to say that these hybrid CS interpretations are unworkable, we, however, find that the hybridity of these conceptualisations of CS has rarely been noted, and they are therefore rather undertheorised.

This paper will present a qualitative study of 30 interviews with scientists working on the ‘OPAL’ (Open Air Laboratories) project in England. We will try to unpack the concept of CS by looking specifically at issues the scientists have encountered while working on the project. While none of the issues we will examine here were deemed to be a major stumbling block for the participating scientists, they nonetheless represented recurring worries, suggestions for enhancing the experience and lessons learned which seem to have been cut out of the otherwise public-focussed social science literature on CS and which we therefore feel are in need of highlighting. If we as PUS scholars are serious about promoting and enhancing CS as a future model for public engagement, we feel it is necessary to raise some otherwise possibly uncomfortable questions that arose from our discussions about how public participation affects the scientific research that is being engaged with, and how it affects the public, the participating scientists and the public–expert relationship on a wider scale.

As OPAL is a CS project that has public engagement as one of its primary aims some of the issues raised in the interviews are not unique to CS and reflect wider perceptions of public engagement and its aims and functions. Previous research on scientists’ and communicators’ perceptions of public engagement has shown that it is an appreciated activity which, however, can be disruptive to scientists if it is seen as an add-on to their already heavy workload, and can therefore carry risks as well as opportunities for scientists (Burchell et al., 2009; Porter et al., 2012).

Itself a contested term, public engagement also faces differences in interpretation between science learning activities that follow a fairly traditional ‘deficit’ idea of science communication and a more democratically involved science policy exercise that foregrounds dialogue and participation, and research on scientists and science communicators has found shifts in interpretative repertoires and conflicting discourses (Burchell, 2007; Davies, 2008), demonstrating that the shift from deficit to dialogue has not occurred wholesale within those who professionally communicate science (Jensen and Holliman, 2009).

OPAL represents a particularly interesting programme to focus a study on because it includes a portfolio of different smaller and larger scale projects that represent different ways and epistemologies within the wider CS concept. The following section will provide a brief overview of OPAL and its structure. We will then present our analysis of the interviews structured around the two sets of issues about CS that the interviews have raised and which we think require further theoretical conceptualisation and research. While we need to stress that we do not necessarily agree with all the points raised during the interviews, we feel that they are certainly important enough questions that need to be asked and analysed in further detail. The final section will attempt to pull the different strands back together and ask where this leaves the future development of CS.

2. The Open Air Laboratories

OPAL is a large-scale public engagement project in England which has been running since 2007, funded by a Big Lottery grant. A substantive part of OPAL is run along the CS model, where members of the public are gathering data on a variety of environmental issues which is then used by OPAL scientists for research as well as wildlife monitoring. OPAL is self-consciously a public engagement project, with a philosophy of bringing scientists into the community, but which also seeks to make valuable and important contributions to environmental science. The OPAL portfolio includes two types of project, national and regional.

On the national scale, there are six different national surveys run by research groups in different universities and public research institutions, focussing on soil, air and water pollution, climate change and two on biodiversity (a seventh survey on tree health had not been launched yet at the time the interviews were carried out). For each of these surveys, members of the public can download easy to follow instructions from the OPAL website, and/or request a ‘survey pack’ to be mailed to them. The surveys can then be carried out by members of the public and the results submitted to the OPAL website. In addition, OPAL scientists also organise field-days and direct engagement events with local community groups where they direct the public in carrying out surveys together with delivering more traditional engagement activities.

A second category of projects run by OPAL involves regional centres, based on a university team at one of nine regional universities (based on the UK government office regions in England). The task of the regional projects is twofold. One is to help deliver the national surveys through direct engagement with local communities, targeted especially at deprived and ‘hard-to-reach’ communities, as well as engaging in their own environmental science research programmes. Secondly, most of the regional research projects also follow a CS model, albeit usually in a more engaged and intensive manner which relies less on the quantity of public response but on a more intensive one-on-one engagement between individual members of the public and scientists. For example the Birmingham group is running bird-ringing training sessions leading to accredited status for participants in their study. The difference between the national scale OPAL CS projects and the regional ones is important here, because this influences the strategies the scientists have developed in order to deal with issues such as data quality and validation as well as potential problems they encounter in recruiting and relying on individual participants; both these themes will be further examined in the following section.

Next to these two categories of projects and partners, OPAL also includes partners and associate partners such as the Environment Agency and the National Biodiversity Network; the latter has also developed specialist recording software (Indicia, 2013) for use by amateur natural history societies. Other initiatives include an online photographic identification service (iSpot, no date) and also a variety of other activities, focussed either on other public engagement activities such as a ‘weather roadshow’, open days and developing and supporting links with amateur natural history societies through a grant scheme, or on funding PhD projects attached to the general research projects but which do not directly involve public input or interaction with the public. For a fuller description of OPAL, its aims and philosophy, see Davies et al. (2011) and Davies et al. (2013), as well as the project website (OPAL, no date).

3. The scientists’ view

Forty-one semi-structured interviews were conducted between October 2011 and March 2012 with scientists and science communicators working at both the regional and the national projects; because this paper will concentrate specifically on the scientists’ experiences of the CS component of OPAL, we will look at the subset of 30 interviewees who either were working scientists (including postgraduate research students) or have had a science education to at least a PhD level. Questions asked included their perceptions of public engagement in general, how public engagement in their view complements or interacts with their research and how it sits within the institutional context at their universities or public research institutions. Interviews were, with the participants’ permission, recorded and fully transcribed and later analysed qualitatively for emerging themes. Quoted extracts below will be identified by a unique number as well as indicating whether the scientist works at a national or regional project.

It is important to note straight away that participating in OPAL has been a very positive experience for almost everyone concerned, and participants have published peer-reviewed papers, defended successful PhD theses and occasionally received promotions as a result of their CS work. Most of the highlighted issues in this paper were felt to have been solved or navigated successfully. Nevertheless, the processes and experiences they gathered by navigating them we feel need to be recorded and analysed further as part of the social science conversation about CS, in the hope that the positive experience from OPAL can point to even more successful projects in the future.

This section will present two particular issues that were raised by the interviewees; these are the methodological and epistemological dimensions (arising from concerns over the interpretation and gathering of data) and ethical dimensions (arising from concerns about how the public is potentially being treated as well as ethical implications for scientists).

Data quality and how to deal with it

Data quality was almost universally recognised as one of the problems that scientists working in CS need to address. While isolated interviewees were ‘not so worried about [the accuracy]’ (31 national), this was a clear area of worry for the majority of interviewees. This worry would be expressed in one of two ways, one was a personal worry about data quality, the other was a worry about the reaction of the wider scientific community (such as journal peer-reviewers) even if they themselves were more relaxed.

Needless to say, the issues about data quality were in the end solved to the satisfaction of the participating scientists and therefore represented no stumbling block for the enterprise as a whole. What is interesting, however, is the range of methods and approaches used between and even within the different OPAL sub-projects. We can roughly divide these into

providing training/close supervision: By giving participants extensive training and/or supervising them in the gathering and interpreting of the data they collect, scientists have in a sense mimicked the way their own expertise has developed. This type of reassurance over the quality of the data was of course only available in those sub-projects of OPAL that had the time and resources available to provide individual training; this simultaneously also restricted the number of public participants that the CS project could reach as well.

cross-checking for consistency with existing literature: Used more in some of the national projects and in conjunction with other methods, this involved looking at the data that came back from the public and comparing it with what would be expected based on previous research in the area.

cross-checking for consistency with their own observations: Similar to the above, this involved the scientists going out themselves to observe the public, do the survey themselves and look at how the public data compares.

quiz-style questionnaire at the end of surveys: One survey that asked members of the public to identify a series of species included a ‘quiz’ at the end which the scientists used to gauge the reliability of the public data.

simplifying the tasks asked of the public and/or adapting the research questions: Most frequently the way the scientists tried to deal with data quality was by trying to make sure the questions are simple and easy enough that there is little that can go wrong. This was used in tandem with the other methods. Because OPAL was conceived as a public education and engagement exercise that should be accessible to anyone, regardless of age and ability, simplicity of the task was part of every project from the start. It did, however, also act as a way of reassuring the scientists that the data collected was reliable:

we did [quality control] mainly by … in anticipation, by devising the simplest possible protocol for … so for example, we asked people to count the number of spiders, count the number of ants, the number of flies, the number of bees, and these are things that you can easily say ok, we know what a spider looks like, it’s got eight legs … you know what an ant looks like, you know what a ladybird looks like. We didn’t have any way of checking that people really did know what a ladybird looked like but we did give them printed guides. (23 national)

A related measure was to formulate the research questions specifically to address things that can be answered through relatively simple and accessible surveys. One respondent even argued that ‘there is no such thing as quality of data, it’s what you use the data for. Different uses will require different quality’ (11 national).

These diverse ways of dealing with data quality are already discussed at length in the CS literature as written by scientists (see for example Delaney et al., 2008; Galloway et al., 2006; and especially Bone et al., 2012 on the OPAL soil survey), but have, however, been relegated as a back-issue in the social scientific literature on CS. In presenting these issues as worries or obstacles encountered by the participating scientists, we do not intend to pass judgement on the conceptual validity of the public contribution to science; on the contrary, the success of OPAL and other CS programmes shows demonstratively the value that public participation can bring to scientific research.

Nevertheless, the fact that this is an issue that the scientists involved felt was important shows that it is crucial for the success of CS. CS not only needs to be beyond reproach in the science that it produces, but it also needs to be visibly seen to be so. Many of the interviewees worried about the reception of their work from the wider scientific community rather than about the data quality per se:

I think … when I’ve been trying to get articles published, people are a bit wary about public data and the quality control side of things obviously … and especially with [our research topic] I think because there’s been nothing done before using members of the public really to collect data, people are very sceptical about it. (2 national)

I think the general perception would be that citizen science has too many shortcomings and is not well enough validated, generally that would be the perception of colleagues that I interact with on a regular basis. (30 national)

Interestingly, however, negative reactions from the wider scientific community were more often expected or anticipated than actual: when results from the various CS projects have been out to peer review, the reception has more often been positive, despite the expectations in the above quotes:

yea, so the first feedback that was from two or three papers we published. So the last one which was mainly presenting this as a tool for participation, the feedback was fantastic I mean the reviewers were positive and the paper was published straight away. (11 national)

It therefore seems that CS still suffers from an image problem in some ways, i.e. that it not only needs to persuade potential reviewers and the wider community of scientists that it can produce worthwhile results, but it also needs to persuade potential participating scientists that it is worth working on. Here hopefully the continued success of OPAL and other CS projects will be instrumental in paving the way for future public participation projects, and indeed, some interviewees saw particular value in OPAL as a ‘proof of concept’ (16 national) that this type of science can work.

Issues over data quality, whether real or anticipated, also led to secondary considerations over how the perception of public data affects the science being produced. This worry was not expressed particularly frequently but expresses an issue that might need to be addressed. Because of the worries about the quality of public data, how should we deal with anomalous results? As some projects dealt with data quality by comparing the public data with what would be expected from the published literature, anomalous data might end up being discarded, and even if that was not one of the validation strategies used, then it might still end up facing a higher amount of scepticism from peer reviewers than comparable data collected by trained scientists: ‘the results tend to go against all previous, all previous studies you know in the [field?] which could be a new discovery, or it could be rubbish, there’s no way of knowing’ (28 national). Although they rarely expressed the issue as forcefully as in the above quote, even some of the very positive and enthusiastic OPAL scientists were doubtful whether this type of science could ever lead to revolutionary results. ‘Whether or not they contribute to a better science? You know I can see how that can, but … it hasn’t … I don’t think. … you get some ideas, but you don’t get, you don’t get eureka moments’ (32 national).

While this does not represent our view, or that of the majority of the interviewees, we believe that it nevertheless is an important issue that needs addressing. Among the general enthusiasm about CS and its potential contributions to science it is often forgotten that there are still plenty of potentially conservative scientists who may not have signed up to the enthusiasm. While it is to be hoped that continued scientific successes of CS projects like OPAL will pave the way towards CS becoming more a part of the scientific mainstream, we might not be able to expect too much scientific success because it is not yet part of the mainstream. In any case, the issues surrounding scientists’ acceptance of CS will need further reflection in the literature on CS because they are clearly something that scientists working on these projects are worried about.

Ethical issues: Use of public data and using the public

Because the public is actively helping collect the data that is then being used in scientific publications and stored in databases such as that of the National Biodiversity Network, the public arguably has a valid stake in the ownership of that data. The very least that a CS project should therefore do is be transparent with the data it collects and what it is being used for, and keep people updated on the process:

I really would like to see regional feedback on the project. Not just the end product which will be the national OPAL report. […] Our people have collected the data, and I think somehow it would be good to get them to receive the feedback. (8 regional)

In this area, the issues surrounding CS morph into those of the open access movement – one interviewee even complained that the two are often conflated (23 national). Many of the arguments that arise from the open access movement are applicable to CS; however, it is worth noting where CS throws up extra ethical issues that are unique to CS. These relate to the fact that members of the public have an active stake in the production of data, not merely as taxpayers as in the open access debates, but as co-producers of the data.

The question of ownership also arises over how to apportion credit for any resulting scientific publication. On the more involved end of the spectrum (represented by the OPAL regional programmes), public involvement is very time and effort intensive, and therefore the contribution individual members of the public make to the eventual science can be quite substantial. In one regional sub-project this was addressed by crediting those that ‘did a lot of work’ (3 regional) as co-authors; however, in another sub-project directed by the same scientist, the number of participants provoked some unease over this approach:

obviously when the papers are written … they’ll receive it as ‘this is what you’ve done’. Authorship’s puzzling me on this, because we can’t put twenty people on authorship. So I’m struggling with that one. I might try it and see what happens. (3 regional)

Offering co-authorship to everybody involved was therefore not really seen as an automatic solution.

Because a CS project is asking public participants for their free time and efforts, it was argued almost unanimously that CS has to give something of value back in return. This added value for the public was often seen to be the public engagement. Though many interviewees saw the main benefits of doing (any kind of) public engagement in environmental sciences as furthering public awareness of environmental change, interviewees also realised that engagement needs to have reasons for the individuals participating:

For them, the public, I don’t think you couldn’t market it in that way to them, ‘come and do my surveys and increase your awareness’ that’s a little bit patronising as far as I’m concerned. […] I would say that when you’re pitching it to them it should be more about, most people, the fun and the enjoyment and the discovery and the … that kind of more ‘do this it will be entertaining and you’ll enjoy it and you’ll learn something new’ but not to be patronising and go ‘you need to be educated don’t you?’ That’s patronising. (19 regional)

For OPAL this meant that the public engagement aspect of the project was a very important part of the concept of CS as a whole. From its inception, OPAL management have consciously sought to avoid the term ‘volunteer’ to describe public participants in order to make clear that the project is aiming to give something back to the community rather than just take their free labour.

The use of public participation for science research, however, brought further issues in terms of what can be expected of the public, and for some interviewees this put sometimes quite severe limitations on how the science was conducted. This was an issue brought up by interviewees working in some of the regional projects that required more intense and close interaction with members of the public, often over the course of several weeks or months. Because people are participating on a voluntary basis, scientists could not expect them to turn up regularly for work or training or stay with the project over long periods of time. This has brought additional stress to the scientists overseeing the work in terms of time-management and resources.

Especially with young people, if volunteers are busy, and if they wake up and they feel a bit tired in the morning they’re just going to text you and say I’m not coming in today, so you’re left standing there having waited for half an hour or whatever and you realise they’re not coming, and that can be a real pain as well. (25 regional)

The experience was not of course all negative, and the above interviewee has also reported finding amazingly helpful and competent participants, and some aspects of the work would not have been possible without their help. Nevertheless this points to a wider structural and ethical issue within CS that puts restraints on what it is possible to do with CS research.

Because although you do get people to volunteer for you, to get them to volunteer on a weekly basis … either they’ve got responsibilities that […] prevent them from going out as much as you’d like them to, or maybe they just don’t prioritise it as highly as you’d like them to, but then again the volunteers are doing this out of the kindness of their heart and so, you know, you’ve got to accept that. (41 regional)

Because CS is about involving members of the public in scientific research in their own free time, interviewees have also found that often recruitment can be a problem. On the national survey projects, the pressures were less acutely felt. Because most national survey scientists had never done any CS before, there was little previous experience to go on and therefore most reported that their initial expectations of the level of returned surveys were optimistic. This was, however, less felt as a problem because even the much lower level of returns they received was still enormously valuable for the purposes of their science.

For the more intense regional projects, however, this was often seen as a serious issue, and has led one group to a radical redesign of the projects from a survey-type one to a more extensive training programme (3 regional). Other projects felt they ran out of time because of their problems with recruitment:

And … people have done it, are very happy, but we’ve not had enough groups wanting to do it … or not wanting to do it, we haven’t maintained the contact with those groups quite firmly enough to have it a, it’s just starting now really at the end of the project. (8 regional)

As the above quote shows, this was not necessarily seen as a problem with the concepts of the projects as such, but more that they had not quite realised how much effort would need to go into initiating and maintaining contact with the relevant community groups; this was therefore felt to be one of the learning outcomes of their project. One group, however, had to abandon one programme of CS entirely because of (among other things) a lack of enthusiasm from the community groups they had planned to engage with (14 regional). Difficulties CS projects can have with recruiting participants were also noted by Evans et al. (2005) in their assessment of their own projects, noting especially that well-educated and middle class people tended to dominate therefore limiting the potential for outreach to deprived groups that is central to projects like OPAL.

Discussion: In defence of modest goals

Amid a literature that can sometimes be seen as portraying CS as an unequivocally ‘good thing’, potential problems that scientists may encounter when working on CS can easily end up being overlooked or downplayed. In this paper we have identified several points that scientists were worried were affecting their work, but which have to date received no wider discussion in the literature.

Although the first point regarding the quality of publicly gathered data has been addressed at length in the growing literature produced by scientists working on CS detailing the methods they have used on how to overcome quality issues, scientists’ underlying worries about the data being accepted by other scientists have been sidelined. It would be unwise to brush aside these issues, because these worries can become quite distressing for those involved, particularly to research students who are more reliant on external opinions of the worth of public data and therefore their science. However, as our interviews have suggested it was in fact these quite substantial worries about data quality that drove them to be methodologically innovative in their approach to interpreting, validating and manipulating their data and making sure that the science being produced was indeed new, important and worth everyone’s time. Conversely, it could even be possible that an overly enthusiastic assessment of CS’s methodological and epistemological issues can actually have a negative effect of producing complacency.

In these respects it seems that overly grand aspirations for CS can ultimately set CS up to fail when it does not deliver. Instead we propose that there are virtues in defining more modest goals: an acknowledgement that methodological questions regarding data quality are still in need of addressing and addressing convincingly, as well as an acknowledgement about the limits of what we can expect from public expertise and contributions. This would much better drive methodological innovations as well as leave us in a better position to address potentially serious objections that are likely to be raised by the wider scientific community towards the use of publicly gathered data.

The ethical questions also indirectly affect the science that is being conducted. The discussion below will go beyond OPAL as we (and our interviewees) feel that these issues have been dealt with but also because OPAL’s structure is such that some potential ethical problems will not be present. However, since CS is a very ambiguous term and individual projects sit along a spectrum from very broad and occasional public involvement to narrow, very time and effort intense and committed contributions, these issues need to be formulated and thought about more systematically than at present. The explicit acknowledgement within OPAL management mentioned above that the role of public participants cannot be seen as that of volunteers, points towards a very thoughtful attitude which needs highlighting much more widely than OPAL currently does.

The first ethical issue identified by the interviewees, public access to (their) data and science, is probably more easily dealt with. It, however, leads on to the related issue of explicit acknowledgement of the public contribution on the same terms as that of the scientists, through for example co-authorship in scientific publications. While there is of course a practical issue here that can and usually is being solved on a case-by-case basis by individual scientists, we also believe that this issue might need addressing through a more abstracted debate. This is particularly relevant if we see the value of CS as a collaborative enterprise, where lay–expert boundaries are being broken down and the public become fully equal contributors to scientific knowledge. Conceiving CS in this way, attributing authorship is only one of a series of ethical problems that need addressing, and probably the least urgent. The real remuneration for professional scientists’ work is not just authorship but of course their pay. If public participation and expertise is to be thought of as equal in status to the professional contribution then it is legitimate to ask how we can justify that one part of a project is being done by people who are getting paid for it, and another is being done by people doing it for free.

In CS projects like OPAL that double as public engagement, the public engagement aspect can be seen as a remuneration of sorts for the public. In return for a little bit of effort, public participants get public engagement in the form of free learning materials and the opportunity to try their hand at real scientific data gathering and research (as well as a fun day out and all the other benefits of participating in a public engagement event). As we noted above, OPAL management as well as the interviewees have been very aware of this, and consistently stressed that the public need to get something tangible for themselves in return for participating, and this is something we feel needs to be made explicit in the design of future CS projects, especially those that may not be particularly focussed on public engagement and therefore would need to find value for participants through other ways.

There is, however, another aspect to the use of free public labour in that it affects ethical considerations not only towards the public, but also towards other professional scientists. This issue can best be illustrated through drawing parallels with the increasingly heated public debates around the use of unpaid internships (Curiale, 2010). Not only do unpaid interns work for free (or, rather, in exchange for ‘work experience’), internships also crowd out jobseekers who find they have to compete against people who are doing the same jobs for free. The parallels of course only go so far, and we are not suggesting here that CS projects are open only to those with independent means, as is the main accusation towards internship – CS does not (in general) demand strict attendance and long hours (though as noted above, this brings its own problems to scientists because it is harder to plan the research), although we would suggest that CS projects that require substantial amounts of time and effort from their participants would do well to think about these issues. For OPAL, this is not an issue; on the contrary, through actively targeting deprived communities, OPAL and similar CS projects actively try to be a force for social mobility.

This, however, leaves the issue of CS potentially outsourcing jobs that otherwise paid scientists would do. In the UK university setting there have recently been several scandals with the University of Birmingham and University College London advertising for unpaid research assistant positions, where the benefit for the applicant was an enhanced CV, but not actual pay. This caused enough consternation within the academic community, because this practice is seen as unfair towards both the applicants as well as the junior postdoctoral academics who might feel potential jobs are being outsourced. Responding to considerable outrage among the academic community in the pages of the Times Higher Education and elsewhere, both universities eventually dropped these job adverts (Perlin, 2012). A central question then over whether CS can be seen through the same ethical lens is whether these perceptions are also present when postdocs think about CS. Although we did not ask this question explicitly, there were tantalising hints that this question was indeed being thought about by at least some of the more junior OPAL scientists.

I suppose another way of looking at it […] as about to be unemployed scientists, there’s people who are doing it for free [laughs]. They’re taking my job away from me [laughs]. I’ve got all this training [laughs]. But … I don’t know, I don’t know what their role is, I don’t know how well they want it done … depends on what the study is I think there is a lot you need to know and a lot of information about the individual project before you can cast any … citizen science is brilliant or it’s not very good … (29 regional)

This remark though needs to be seen within the context of an interview in which the respondent made it clear they were generally very disillusioned with their scientific career and postdoctoral life generally for unrelated reasons, and the laughter indicates that the interviewee might not have been completely serious. Outsourcing is also not likely to be an issue if the CS project makes clear that the research would otherwise not have been doable by paid scientists, in OPAL’s case for example it was frequently mentioned that the sheer breadth and geographical variation of at least the national survey projects was something that only a CS project could possibly deliver; as respondent 29 remarked above, this issue depends on what the study is. Nevertheless, for the design of future CS projects and if we want to roll out CS projects on a much larger scale, we feel this is an issue that needs further thought and conceptualisation.

4. Conclusion

The considerable amount of enthusiasm we have seen in the scholarly literature has been mainly echoed within our interviews, as well as with participating members of the public (Davies et al., 2013), suggesting in many ways a bright future for CS. However, we feel that it is not being helped by a literature that is on the whole too enthusiastic and optimistic about what CS can deliver while at the same time being ambiguous about what it actually is. If the hopes attached to CS are too high and ambitious we set up any new CS project to disappoint or worse when it fails to deliver. If we overlook the problems faced by scientists with persuading the wider scientific community of the validity of CS work then we fail the scientists who are potentially putting their careers on the line when they sign up to participate in CS. If we overlook the ethical problems that CS raises we may end up unintentionally strengthening the lay–expert boundaries that CS was thought to overcome, through potentially fostering a sense of resentment by junior scientists who might feel their jobs are being outsourced, or by leaving us open to accusations of unintentionally exploiting free labour.

To guard against these potential unintended consequences of CS, we suggest that next to all the enthusiastic endorsements of the many undoubted positive aspects of CS we also keep in mind the limits of what CS can realistically achieve, and keep up a conversation about how to address the limitations of CS. Modest goals are sometimes more desirable than unrealistic expectations.

Footnotes

Acknowledgements

We would like to thank the OPAL scientists who participated in the study, Linda Davies, Roger Fradera and Laura Gosling.

Funding

The study was funded by the Big Lottery Fund through the Changing Spaces programme and the Open Air Laboratories (OPAL) project.

Author biographies

Hauke Riesch is a lecturer at the Department of Sociology and Communications at Brunel University. His research interests are the public understanding of science, especially risk and the environment, science blogging, popular science and the sociology and philosophy of science.

Clive Potter is Reader in Environmental Policy at Imperial College London. His research interests include issues around stakeholder and public engagement in public policy decisions relating to rural land use and biosecurity.

References

Bauer

Jensen

(2011) The mobilization of scientists for public engagement. Public Understanding of Science 20(1): 3–11.

Bell

Lewenstein

Shouse

Feder

(eds) (2009) Learning Science in Informal Environments: People, Places, and Pursuits. Washington, DC: National Academies Press.

Bone

Archer

Barraclough

Eggleton

Flight

Head

Jones

Scheib

Voulvoulis

(2012) Public participation in soil surveys: Lessons from a pilot study in England. Environmental Science & Technology 46(7): 3687–3696.

Bonney

Ballard

Jordan

McCallie

Phillips

Shirk

Wilderman

(2009) Public Participation in Scientific Research: Defining the Field and Assessing its Potential for Informal Science Education. Washington, DC: Center for Advancement of Informal Science Education (CAISE).

Burchell

(2007) Empiricist selves and contingent ‘others’: The performative function of the discourse of scientists working in conditions of controversy. Public Understanding of Science 16(2): 145–162.

Burchell

Franklin

Holden

(2009) Public Culture as Professional Science: Final Report of the ScoPE Project. London: LSE.

Citizen Science Alliance (no date) Why citizen science? Available at (accessed 22 May 2013): http://www.citizensciencealliance.org/philosophy.html

Citizen Science Central (2013) Defining citizen science. Available at (accessed 22 May 2013): www.birds.cornell.edu/citscitoolkit/about/definition/

Cohn

(2008) Citizen science: Can volunteers do real research? BioScience 58(3): 192–197.

10.

Cooper

Dickinson

Phillips

Bonney

(2007) Citizen science as a tool for conservation in residential ecosystems. Ecology and Society 12(2): 11.

11.

Cornwell

Campbell

(2012) Co-producing conservation and knowledge: Citizen-based sea turtle monitoring in North Carolina, USA. Social Studies of Science 42: 101–120.

12.

Crall

Jordan

Holfelder

Newman

Graham

Waller

(2012) The impacts of an invasive species citizen science training program on participant attitudes, behavior, and science literacy. Public Understanding of Science, doi: 10.1177/0963662511434894

13.

Cronje

Rohlinger

Crall

Newman

(2011) Does participation in citizen science improve scientific literacy? A study to compare assessment methods. Applied Environmental Education & Communication 10(3): 135–145.

14.

Curiale

(2010) America’s new glass ceiling: Unpaid internships, the Fair Labor Standards Act, and the urgent need for change. Hastings Law Journal 61: 1531–1549.

15.

Davies

Bell

JNB

Bone

Head

Hill

Howard

Hobbs

Jones

Power

Rose

Ryder

Seed

Stevens

Toumi

Voulvoulis

White

PCL

(2011) Open Air Laboratories (OPAL): A community-driven research programme. Environmental Pollution 159(8–9): 2203–2210.

16.

Davies

Gosling

Bachariou

Eastwood

Fradera

Manomaiudom

Robins

(eds) (2013) OPAL Community Environment Report: Exploring Nature Together. Imperial College London.

17.

Davies

(2008) Constructing communication: Talking to scientists about talking to the public. Science Communication 29(4): 413–434.

18.

Delaney

Sperling

Adams

Leung

(2008) Marine invasive species: Validation of citizen science and implications for national monitoring networks. Biological Invasions 10(1): 117–128.

19.

Epstein

(1996) Impure Science: AIDS, Activism and the Politics of Knowledge. Los Angeles: University of California Press.

20.

Evans

Abrams

Reitsma

Roux

Salmonsen

Marra

(2005) The Neighborhood Nestwatch program: Participant outcomes of a citizen-science ecological research project. Conservation Biology 19(3): 589–594.

21.

Forestry Commission (2011) Action Plan for Tree Health and Plant Biosecurity. Available at (accessed 22 May 2013): http://www.forestry.gov.uk/pdf/Action-Plan-Tree-health-and-plant-biosecurity.pdf/$FILE/Action-Plan-Tree-health-and-plant-biosecurity.pdf

22.

Gallo

Waitt

(2011) Creating a successful citizen science model to detect and report invasive species. BioScience 61: 459–465.

23.

Galloway

AWE

Tudor

van der Haegen

(2006) The reliability of citizen science: A case study of Oregon white oak stand surveys. Wildlife Society Bulletin 34(5): 1425–1429.

24.

Hemment

Ellis

Wynne

(2011) Participatory mass observation and citizen science. Leonardo 44(1): 62–63.

25.

Indicia (2013) The open source wildlife recording toolkit. Available at (accessed 22 May 2013): http://www.indicia.org.uk/

26.

Irwin

(1995) Citizen Science. London: Routledge.

27.

iSpot (no date) iSpot: Your place to share nature. Available at (accessed 22 May 2013): http://www.ispot.org.uk/

28.

Jensen

Holliman

(2009) Investigating science communication to inform science outreach and public engagement. In: Holliman

Whitelegg

Scanlon

Smidt

Thomas

(eds) Investigating Science Communication in the Information Age. Oxford: Oxford University Press, pp. 55–71.

29.

Jordan

Gray

Howe

Brooks

Ehrenfeld

(2011) Knowledge gain and behavioral change in citizen-science programs. Conservation Biology 25(6): 1148–1154.

30.

Lee

Quinn

Duke

(2006) Citizen, science, highways, and wildlife: Using a web-based GIS to engage citizens in collecting wildlife information. Ecology and Society 11(1): 11.

31.

Mankowski

Slater

(2011) An interpretive study of meanings citizen scientists make when participating in Galaxy Zoo. Contemporary Issues in Education Research 4(4): 25–42.

32.

OPAL (no date) OPAL: Explore your local environment. Available at (accessed 22 May 2013): http://www.opalexplorenature.org/

33.

Perlin

(2012) Indentures are a sign of universities’ decline into a factory system. Times Higher Education, 16 August.

34.

Poliakoff

Webb

(2007) What factors predict scientists’ intentions to participate in public engagement of science activities? Science Communication 29: 242–263.

35.

Porter

Williams

Wainwright

Cribb

(2012) On being a (modern) scientist: Potential risks of engaging the public in the UK interspecies embryo debate. New Genetics and Society 31(4): 408–423.

36.

Raddick

Szalay

Vandenberg

Bracey

Gay

Lintott

Murray

Schawinski

(2010) Galaxy Zoo: Exploring the motivations of citizen science volunteers. Astronomy Education Review 9(1): 18.

37.

Rowe

Frewer

(2004) Evaluating public-participation exercises: A research agenda. Science, Technology and Human Values 29(4): 512–556.

38.

Shirk

Ballard

Wilderman

Phillips

Wiggins

Jordan

McCallie

Minarchek

Lewenstein

Krasny

Bonney

(2012) Public participation in scientific research: A framework for deliberate design. Ecology and Society 17(2): 29.

39.

Silvertown

(2009) A new dawn for citizen science. Trends in Ecology & Evolution 24(9): 467–471.

40.

Stilgoe

(2009) Citizen Scientists: Reconnecting Science with Civil Society. London: Demos.

41.

Trumbull

Bonney

Bascom

Cabral

(2000) Thinking scientifically during participation in a citizen-science project. Science Education 84(2): 265–275.

42.

Wiggins

Crowston

(2011) From conservation to crowdsourcing: A typology of citizen science. In: Proceedings of the 44th Hawaii International Conference on System Sciences. IEEE, pp. 2764–2773.