8 July, 2013
The term ‘Citizen Science’ is clearly gaining more recognition and use. It is now get mentioned in radio and television broadcasts, social media channels as well as conferences and workshops. Some of the clearer signs for the growing attention include discussion of citizen science in policy oriented conferences such as UNESCO’s World Summit on Information Society (WSIS+10) review meeting discussion papers (see page ), or the Eye on Earth users conference (see the talks here) or the launch of the European Citizen Science Association in the recent EU Green Week conference.
Another aspect of the expanding world of citizen science is the emerging questions from those who are involved in such projects or study them about the efficacy of the term. As is very common with general terms, some reflections on the accuracy of the term are coming to the fore – so Rick Bonney and colleagues suggest to use ‘Public Participation in Scientific Research‘ (significantly, Bonney was the first to use ‘Citizen Science’ in 1995); Francois Grey coined Citizen Cyberscience to describe projects that are dependent on the Internet; recently Chris Lintott discussed some doubts about the term in the context of Zooniverse; and Katherine Mathieson asks if Citizen Science is just a passing fad. In our own group, there are also questions about the correct terminology, with Cindy Regalado suggestions to focus on ‘Publicly Initiated Scientific Research (PIScR)‘, and discussion on the meaning of ‘Extreme Citizen Science‘.
One way to explore what is going on is to consider the evolution of the ‘hype’ around citizen science through ‘Gartner’s Hype Cycle‘ which can be seen as a way to consider the way technologies are being adopted in a world of rapid communication and inflated expectations from technologies. leaving aside Gartner own hype, the story that the model is trying to tell is that once a new approach (technology) emerges because it is possible or someone reconfigured existing elements and claim that it’s a new thing (e.g. Web 2.0), it will go through a rapid growth in terms of attention and publicity. This will go on until it reaches the ‘peak of inflated expectations’ where the expectations from the technology are unrealistic (e.g. that it will revolutionize the way we use our fridges). This must follow by a slump, as more and more failures come to light and the promises are not fulfilled. At this stage, the disillusionment is so deep that even the useful aspects of the technology are forgotten. However, if it passes this stage, then after the realisation of what is possible, the technology is integrated into everyday life and practices and being used productively.
So does the hype cycle apply to citizen science?
If we look at Gartner cycle from last September, Crowdsourcing is near the ‘peak of inflated expectations’ and some descriptions of citizen science as scientific crowdsourcing clearly match the same mindset.
There is a growing evidence of academic researchers entering citizen science out of opportunism, without paying attention to the commitment and work that is require to carry out such projects. With some, it seems like that they decided that they can also join in because someone around know how to make an app for smartphones or a website that will work like Galaxy Zoo (failing to notice the need all the social aspects that Arfon Smith highlights in his talks). When you look around at the emerging projects, you can start guessing which projects will succeed or fail by looking at the expertise and approach that the people behind it take.
Another cause of concern are the expectations that I noticed in the more policy oriented events about the ability of citizen science to solve all sort of issues – from raising awareness to behaviour change with limited professional involvement, or that it will reduce the resources that are needed for activities such as environmental monitoring, but without an understanding that significant sustained investment is required – community coordinator, technical support and other aspects are needed here just as much. This concern is heightened by statements that promote citizen science as a mechanism to reduce the costs of research, creating a source of free labour etc.
On the other hand, it can be argued that the hype cycle doesn’t apply to citizen science because of history. Citizen science existed for many years, as Caren Cooper describe in her blog posts. Therefore, conceptualising it as a new technology is wrong as there are already mechanisms, practices and institutions to support it.
In addition, and unlike the technologies that are on Gartner chart, academic projects within which citizen science happen benefit from access to what is sometime termed patient capital without expectations for quick returns on investment. Even with the increasing expectations of research funding bodies for explanations on how the research will lead to an impact on wider society, they have no expectations that the impact will be immediate (5-10 years is usually fine) and funding come in chunks that cover 3-5 years, which provides the breathing space to overcome the ‘through of disillusionment’ that is likely to happen within the technology sector regarding crowdsourcing.
And yet, I would guess that citizen science will suffer some examples of disillusionment from badly designed and executed projects – to get these projects right you need to have a combination of domain knowledge in the specific scientific discipline, science communication to tell the story in an accessible way, technical ability to build mobile and web infrastructure, understanding of user interaction and user experience to to build an engaging interfaces, community management ability to nurture and develop your communities and we can add further skills to the list (e.g. if you want gamification elements, you need experts in games and not to do it amateurishly). In short, it need to be taken seriously, with careful considerations and design. This is not a call for gatekeepers , more a realisation that the successful projects and groups are stating similar things.
Which bring us back to the issue of the definition of citizen science and terminology. I have been following terminology arguments in my own discipline for over 20 years. I have seen people arguing about a data storage format for GIS and should it be raster or vector (answer: it doesn’t matter). Or arguing if GIS is tool or science. Or unhappy with Geographic Information Science and resolutely calling it geoinformation, geoinformatics etc. Even in the minute sub-discipline that deals with participation and computerised maps that are arguments about Public Participation GIS (PPGIS) or Participatory GIS (PGIS). Most recently, we are debating the right term for mass-contribution of geographic information as volunteered geographic information (VGI), Crowdsourced geographic information or user-generated geographic information.
It’s not that terminology and precision in definition is not useful, on the contrary. However, I’ve noticed that in most cases the more inclusive and, importantly, vague and broad church definition won the day. Broad terminologies, especially when they are evocative (such as citizen science), are especially powerful. They convey a good message and are therefore useful. As long as we don’t try to force a canonical definition and allow people to decide what they include in the term and express clearly why what they are doing is falling within citizen science, it should be fine. Some broad principles are useful and will help all those that are committed to working in this area to sail through the hype cycle safely.
The talk, which is titled ‘Science for everyone by everyone – the re-emergence of citizen science‘ covered the area of citizen science and explained what we are trying to achieve within the Extreme Citizen Science research group.
Because the lunch hour lectures are open to all, I preferred not to assume any prior knowledge of citizen science (or public participation in scientific research) and start by highlighting that public participation in scientific research is not new. After a short introduction to the history and to the fact that many people are involved in scientific activities in their free time, from bird watching to weather or astronomical observations and that this never stopped, there is a notable difference in the attention that is paid to citizen science in recent years.
Therefore, I covered the trends in education and technology that are ushering in a new era of citizen science – access to information through the internet, use of location aware mobile devices, growth in social knowledge creation web-based systems, increased in education and the ability to deal with abstract ideas (Flynn effect is an indicator of this last point). The talk explored the current trends and types of citizen science, and demonstrate a model for extreme citizen science, in which any community, regardless of their literacy, can utilise scientific methods and tools to understand and control their environment. I have used examples of citizen science activities from other groups at UCL, to demonstrate the range of topics, domains and activities that are now included in this area.
The talk was recorded, and is available on YouTube and below
5 December, 2012
Recently, I attended a meeting with people from a community that is concerned with vibration and noise caused by a railway near their homes. We have discussed the potential of using citizen science to measure the vibrations that pass the sensory threshold and that people classify as unpleasant, together with other perceptions and feeling about these incidents. This can form the evidence to a discussion with the responsible authorities to see what can be done.
As a citizen science activity, this is not dissimilar from the work carried out around Heathrow to measure the level of noise nuisance or air pollution monitoring that ExCiteS and Mapping for Change carried out in other communities.
In the meetings, the participants felt that they need to emphasise that they are not against the use of the railway or the development of new railway links. Like other groups that I have net in the past, they felt that it is important to emphasise that their concern is not only about their locality – in other words, this is not a case of ‘Not In My Back Yard’ (NIMBY) which is the most common dismissal of local concerns. The concern over NIMBY and citizen science is obvious one, and frequently come up in questions about the value and validity of data collected through this type of citizen science.
During my masters studies, I was introduced to Maarten Wolsink (1994) analysis of NIMBY as a compulsory reading in one of the courses. It is one of the papers that I keep referring to from time to time, especially when complaints about participatory work and NIMBY come up.
Inherently, what Wolsink is demonstrating is that the conceptualisation of the people who are involved in the process as selfish and focusing on only their own area is wrong. Through the engagement with environmental and community concerns, people will explore issues at wider scales and many time will argue for ‘Not in Anyone’s Back Yard’ or for a balance between the needs of infrastructure development and their own quality of life. Studies on environmental justice also demonstrated that what the people who are involved in such activities ask for are not narrow, but many times mix aspects of need for recognition, expectations of respect, arguments of justice, and participation in decision-making (Schlosberg 2007).
In other words, the citizen science and systematic data collection are a way for the community to bring to the table evidence that can enhance arguments beyond NIMBY, and while it might be part of the story it is not the whole story.
For me, these interpretations are part of the reason that such ‘activism’-based citizen science should receive the same attention and respect as any other data collection, most notably by the authorities.
Wolsink, M. (1994) Entanglement of Interests and Motives: Assumptions Behind the NIMBY-Theory on Facility Siting, Urban Studies, 31(6), pp. 851-866.
Scholsberg, D. (2007) Defining Environmental Justice: Theories, Movements, and Nature. Oxford University Press, 2007
8 August, 2012
On the 4th and 5th August, Portland, OR, was the gathering place for 300 participants that came to the workshop on Public Participation in Scientific Research. The workshop was timed just before the annual meeting of the Ecological Society of America, and therefore it was not surprising that the workshop focused on citizen science projects that are linked to ecology and natural environments monitoring. These projects are some of the longest running citizen science activities, that are now gaining recognition and attention.
The workshop was organised as a set of thematic talks interlaced with long poster sessions. This way, the workshop included over 180 presentations in a day and a half. That set the scene for a detailed discussion at the end of the second day, to explore what is the way forward to the field of PPSR/Citizen Science/Civic Science etc., with attention to sharing lessons, developing and supporting new activities, considering codes of ethics, etc.
I presented the last talk of the workshop, describing Extreme Citizen Science and arguing for the potential of public participation to go much deeper in terms of engagement. The presentation is provided below, together with an interview that was conducted with me shortly after it.
And the interview,
The London Citizen Cyberscience Summit ran in the middle of February, from 16th (Thursday) to 18th (Saturday). It marked the launch of the UCL Extreme Citizen Science (ExCiteS) group, while providing an opportunity for people who are interested in different aspects of citizen science to come together, discuss, share ideas, consider joint projects and learn from other people. The original idea for the summit, when the first organisational meeting took place in October last year, was to set a programme that would include academics who research citizen science or develop citizen science projects; practitioners and enthusiasts who are developing technologies for citizen science activities; and people who are actively engaged in citizen science.Therefore, we included a mix of talks, workshops and hack days and started approaching speakers who would cover the range of interests, backgrounds and knowledge.
The announcement about the summit came out only in late December, so it was somewhat surprising to see the level of interest in the topic of citizen science. Considering that the previous summit, in 2010, attracted about 60 or 70 participants, it was pleasing to see that the second summit attracted more than 170 people.
To read about what happened in the summit there is plenty of material online. Nature news reported it as ‘Citizen science goes extreme‘. The New Scientist blog post discussed the ‘Intelligent Maps’ project of ExCiteS in ‘Interactive maps help pygmy tribes fight back‘, which was also covered by the BBC World Service Newshour programme (around 50 minutes in) and the Canadian CBC Science Shift programme. Le Monde also reported on ‘Un laboratoire de l’extrême‘.
Another report in New Scientist focused on the Public Laboratory for Open Technology and Science (PLOTS) development of a thermal flashlight in ‘Thermal flashlight “paints” cold rooms with colour‘. The China Dialogue ‘Scientists and Citizens‘ provided a broader review of the summit.
In terms of blogs, there are summaries on the GridCast blog (including some video interviews), and a summary by one of the speakers, Andrea Wiggins, of day 1, day 2 and day 3. Nicola Triscott from the Arts Catalyst provides another account of the summit and her Arctic Perspective Initiative linkage. Another participant, Célya Gruson-Daniel, discussed the summit in French at MyScienceWork, which also provided a collection of social media from the first day at http://storify.com/mysciencework/london-citizen-cyberscience-summit-16-18th-februar.
The talks are available to view again on the LiveStream account of ExCiteS at http://www.livestream.com/excites and there are also summaries on the ExCiteS blog http://uclexcites.wordpress.com/ and on the conference site http://cybersciencesummit.org/blog/ . Flickr photos from MyScienceWork and UCL Engineering (where the image on the right is from) are also available.
For me, several highlights of the conference included the impromptu integration of different projects during the summit. Ellie D’Hondt and Matthias Stevens from BrusSense and NoiseTube used the opportunity of the PLOTS balloon mapping demonstration to extend it to noise mapping; Darlene Cavalier from SciStarter discussed with the Open Knowledge Foundation people how to use data about citizen science projects; and the people behind Xtribe at the University of Rome considered how their application can be used for Intelligent Maps – all these are synergies, new connections and new experimentation that the summit enabled. The enthusiasm of people who came to the summit contributed significantly to its success (as well as the hard work of the ExCiteS team).
Especially interesting, because of the wide-ranging overview of examples and case studies, is how the activity is conceptualised in different ways across the spectrum of DIY citizen science to structured observations that are managed by professional scientists. This is also apparent in the reports about the summit. I have commented in earlier blog posts about the need to understand citizen science as a different way of producing scientific knowledge. What might be helpful is a clear ‘code of ethics’ or ‘code of conduct’ for scientists who are involved in such projects. As Francois Taddei highlighted in his talk at the summit, there is a need to value the shared learning among all the participants, and not to keep the rigid hierarchies of university academics/public in place. There is also a need to allow for the creativity, exploration and development of ideas that we have seen during the summit to blossom – but only happen when all the sides that are involved in the process are open to such a process.
The previous post focused on citizen science as participatory science. This post is discussing the meaning of this differentiation. It is the final part of the chapter that will appear in the book:
The typology of participation can be used across the range of citizen science activities, and one project should not be classified only in one category. For example, in volunteer computing projects most of the participants will be at the bottom level, while participants that become committed to the project might move to the second level and assist other volunteers when they encounter technical problems. Highly committed participants might move to a higher level and communicate with the scientist who coordinates the project to discuss the results of the analysis and suggest new research directions.
This typology exposes how citizen science integrates and challenges the way in which science discovers and produces knowledge. Questions about the way in which knowledge is produced and truths are discovered are part of the epistemology of science. As noted above, throughout the 20th century, as science became more specialised, it also became professionalised. While certain people were employed as scientists in government, industry and research institutes, the rest of the population – even if they graduated from a top university with top marks in a scientific discipline – were not regarded as scientists or as participants in the scientific endeavour unless they were employed professionally to do so. In rare cases, and following the tradition of ‘gentlemen/women scientists’, wealthy individuals could participate in this work by becoming an ‘honorary fellow’ or affiliated to a research institute that, inherently, brought them into the fold. This separation of ‘scientists’ and ‘public’ was justified by the need to access specialist equipment, knowledge and other privileges such as a well-stocked library. It might be the case that the need to maintain this separation is a third reason that practising scientists shy away from explicitly mentioning the contribution of citizen scientists to their work in addition to those identified by Silvertown (2009).
However, similarly to other knowledge professionals who operate in the public sphere, such as medical experts or journalists, scientists need to adjust to a new environment that is fostered by the Web. Recent changes in communication technologies, combined with the increased availability of open access information and the factors that were noted above, mean that processes of knowledge production and dissemination are opening up in many areas of social and cultural activities (Shirky 2008). Therefore, some of the elitist aspects of scientific practice are being challenged by citizen science, such as the notion that only dedicated, full-time researchers can produce scientific knowledge. For example, surely it should be professional scientists who can solve complex scientific problems such as long-standing protein-structure prediction of viruses. Yet, this exact problem was recently solved through a collaboration of scientists working with amateurs who were playing the computer game Foldit (Khatib et al. 2011). Another aspect of the elitist view of science can be witnessed in interaction between scientists and the public, where the assumption is of unidirectional ‘transfer of knowledge’ from the expert to lay people. Of course, as in the other areas mentioned above, it is a grave mistake to argue that experts are unnecessary and can be replaced by amateurs, as Keen (2007) eloquently argued. Nor is it suggested that, because of citizen science, the need for professionalised science will diminish, as, in citizen science projects, the participants accept the difference in knowledge and expertise of the scientists who are involved in these projects. At the same time, the scientists need to develop respect towards those who help them beyond the realisation that they provide free labour, which was noted above.
Given this tension, the participation hierarchy can be seen to be moving from a ‘business as usual’ scientific epistemology at the bottom, to a more egalitarian approach to scientific knowledge production at the top. The bottom level, where the participants are contributing resources without cognitive engagement, keeps the hierarchical division of scientists and the public. The public is volunteering its time or resources to help scientists while the scientists explain the work that is to be done but without expectation that any participant will contribute intellectually to the project. Arguably, even at this level, the scientists will be challenged by questions and suggestions from the participants and, if they do not respond to them in a sensitive manner, they will risk alienating participants. Intermediaries such as the IBM World Community Grid, where a dedicated team is in touch with scientists who want to run projects and a community of volunteered computing providers, are cases of ‘outsourcing’ the community management and thus allowing, to an extent, the maintenance of the separation of scientists and the public.
As we move up the ladder to a higher level of participation, the need for direct engagement between the scientist and the public increases. At the highest level, the participants are assumed to be on equal footing with the scientists in terms of scientific knowledge production. This requires a different epistemological understanding of the process, in which it is accepted that the production of scientific insights is open to any participant while maintaining scientific standards and practices such as systematic observations or rigorous statistical analysis to verify that the results are significant. The belief that, given suitable tools, many lay people are capable of such endeavours is challenging to some scientists who view their skills as unique. As the case of the computer game that helped in the discovery of new protein formations (Khatib et al. 2011) demonstrated, such collaboration can be fruitful even in cutting-edge areas of science. However, it can be expected that the more mundane and applied areas of science will lend themselves more easily to the fuller sense of collaborative science in which participants and scientists identify problems and develop solutions together. This is because the level of knowledge required in cutting-edge areas of science is so demanding.
Another aspect in which the ‘extreme’ level challenges scientific culture is that it requires scientists to become citizen scientists in the sense that Irwin (1995), Wilsdon, Wynne and Stilgoe (2005) and Stilgoe (2009) advocated (Notice Stilgoe’s title: Citizen Scientists). In this interpretation of the phrase, the emphasis is not on the citizen as a scientist, but on the scientist as a citizen. It requires the scientists to engage with the social and ethical aspects of their work at a very deep level. Stilgoe (2009, p.7) suggested that, in some cases, it will not be possible to draw the line between the professional scientific activities, the responsibilities towards society and a fuller consideration of how a scientific project integrates with wider ethical and societal concerns. However, as all these authors noted, this way of conceptualising and practising science is not widely accepted in the current culture of science.
Therefore, we can conclude that this form of participatory and collaborative science will be challenging in many areas of science. This will not be because of technical or intellectual difficulties, but mostly because of the cultural aspects. This might end up being the most important outcome of citizen science as a whole, as it might eventually catalyse the education of scientists to engage more fully with society.
27 November, 2011
This post continues to the theme of the previous one, and is also based on the chapter that will appear next year in the book:
The post focuses on the participatory aspect of different Citizen Science modes:
Against the technical, social and cultural aspects of citizen science, we offer a framework that classifies the level of participation and engagement of participants in citizen science activity. While there is some similarity between Arnstein’s (1969) ‘ladder of participation’ and this framework, there is also a significant difference. The main thrust in creating a spectrum of participation is to highlight the power relationships that exist within social processes such as urban planning or in participatory GIS use in decision making (Sieber 2006). In citizen science, the relationship exists in the form of the gap between professional scientists and the wider public. This is especially true in environmental decision making where there are major gaps between the public’s and the scientists’ perceptions of each other (Irwin 1995).
In the case of citizen science, the relationships are more complex, as many of the participants respect and appreciate the knowledge of the professional scientists who are leading the project and can explain how a specific piece of work fits within the wider scientific body of work. At the same time, as volunteers build their own knowledge through engagement in the project, using the resources that are available on the Web and through the specific project to improve their own understanding, they are more likely to suggest questions and move up the ladder of participation. In some cases, the participants would want to volunteer in a passive way, as is the case with volunteered computing, without full understanding of the project as a way to engage and contribute to a scientific study. An example of this is the many thousands of people who volunteered to the Climateprediction.net project, where their computers were used to run global climate models. Many would like to feel that they are engaged in one of the major scientific issues of the day, but would not necessarily want to fully understand the science behind it.
Therefore, unlike Arnstein’s ladder, there shouldn’t be a strong value judgement on the position that a specific project takes. At the same time, there are likely benefits in terms of participants’ engagement and involvement in the project to try to move to the highest level that is suitable for the specific project. Thus, we should see this framework as a typology that focuses on the level of participation.
At the most basic level, participation is limited to the provision of resources, and the cognitive engagement is minimal. Volunteered computing relies on many participants that are engaged at this level and, following Howe (2006), this can be termed ‘crowdsourcing’. In participatory sensing, the implementation of a similar level of engagement will have participants asked to carry sensors around and bring them back to the experiment organiser. The advantage of this approach, from the perspective of scientific framing, is that, as long as the characteristics of the instrumentation are known (e.g. the accuracy of a GPS receiver), the experiment is controlled to some extent, and some assumptions about the quality of the information can be used. At the same time, running projects at the crowdsourcing level means that, despite the willingness of the participants to engage with a scientific project, their most valuable input – their cognitive ability – is wasted.
The second level is ‘distributed intelligence’ in which the cognitive ability of the participants is the resource that is being used. Galaxy Zoo and many of the ‘classic’ citizen science projects are working at this level. The participants are asked to take some basic training, and then collect data or carry out a simple interpretation activity. Usually, the training activity includes a test that provides the scientists with an indication of the quality of the work that the participant can carry out. With this type of engagement, there is a need to be aware of questions that volunteers will raise while working on the project and how to support their learning beyond the initial training.
The next level, which is especially relevant in ‘community science’ is a level of participation in which the problem definition is set by the participants and, in consultation with scientists and experts, a data collection method is devised. The participants are then engaged in data collection, but require the assistance of the experts in analysing and interpreting the results. This method is common in environmental justice cases, and goes towards Irwin’s (1995) call to have science that matches the needs of citizens. However, participatory science can occur in other types of projects and activities – especially when considering the volunteers who become experts in the data collection and analysis through their engagement. In such cases, the participants can suggest new research questions that can be explored with the data they have collected. The participants are not involved in detailed analysis of the results of their effort – perhaps because of the level of knowledge that is required to infer scientific conclusions from the data.
Finally, collaborative science is a completely integrated activity, as it is in parts of astronomy where professional and non-professional scientists are involved in deciding on which scientific problems to work and the nature of the data collection so it is valid and answers the needs of scientific protocols while matching the motivations and interests of the participants. The participants can choose their level of engagement and can be potentially involved in the analysis and publication or utilisation of results. This form of citizen science can be termed ‘extreme citizen science’ and requires the scientists to act as facilitators, in addition to their role as experts. This mode of science also opens the possibility of citizen science without professional scientists, in which the whole process is carried out by the participants to achieve a specific goal.
This typology of participation can be used across the range of citizen science activities, and one project should not be classified only in one category. For example, in volunteer computing projects most of the participants will be at the bottom level, while participants that become committed to the project might move to the second level and assist other volunteers when they encounter technical problems. Highly committed participants might move to a higher level and communicate with the scientist who coordinates the project to discuss the results of the analysis and suggest new research directions.
20 July, 2011
As part of the Volunteered Geographic Information (VGI) workshop that was held in Seattle in April 2011, Daniel Sui, Sarah Elwood and Mike Goodchild announced that they will be editing a volume dedicated to the topic, published as ‘Crowdsourcing Geographic Knowledge‘ (Here is a link to the Chapter in Crowdsourcing Geographic Knowledge)
My contribution to this volume focuses on citizen science, and shows the links between it and VGI. The chapter is currently under review, but the following excerpt discusses different types of citizen science activities, and I would welcome comments:
“While the aim here is not to provide a precise definition of citizen science. Yet, a definition and clarification of what the core characteristics of citizen science are is unavoidable. Therefore, it is defined as scientific activities in which non-professional scientists volunteer to participate in data collection, analysis and dissemination of a scientific project (Cohn 2008; Silvertown 2009). People who participate in a scientific study without playing some part in the study itself – for example, volunteering in a medical trial or participating in a social science survey – are not included in this definition.
While it is easy to identify a citizen science project when the aim of the project is the collection of scientific information, as in the recording of the distribution of plant species, there are cases where the definition is less clear-cut. For example, the process of data collection in OpenStreetMap or Google Map Maker is mostly focused on recording verifiable facts about the world that can be observed on the ground. The tools that OpenStreetMap mappers use – such as remotely sensed images, GPS receivers and map editing software – can all be considered scientific tools. With their attempt to locate observed objects and record them on a map accurately, they follow the footsteps of surveyors such as Robert Hooke, who also carried out an extensive survey of London using scientific methods – although, unlike OpenStreetMap volunteers, he was paid for his effort. Finally, cases where facts are collected in a participatory mapping activity, such as the one that Ghose (2001) describes, should probably be considered a citizen science only if the participants decided to frame it as such. For the purpose of the discussion here, such a broad definition is more useful than a limiting one that tries to reject certain activities.
Notice also that, by definition, citizen science can only exist in a world in which science is socially constructed as the preserve of professional scientists in academic institutions and industry, because, otherwise, any person who is involved in a scientific project would simply be considered a contributor and potentially a scientist. As Silvertown (2009) noted, until the late 19th century, science was mainly developed by people who had additional sources of employment that allowed them to spend time on data collection and analysis. Famously, Charles Darwin joined the Beagle voyage, not as a professional naturalist but as a companion to Captain FitzRoy. Thus, in that era, almost all science was citizen science albeit mostly by affluent gentlemen scientists and gentlewomen. While the first professional scientist is likely to be Robert Hooke, who was paid to work on scientific studies in the 17th century, the major growth in the professionalisation of scientists was mostly in the latter part of the 19th and throughout the 20th centuries.
Even with the rise of the professional scientist, the role of volunteers has not disappeared, especially in areas such as archaeology, where it is common for enthusiasts to join excavations, or in natural science and ecology, where they collect and send samples and observations to national repositories. These activities include the Christmas Bird Watch that has been ongoing since 1900 and the British Trust for Ornithology Survey, which has collected over 31 million records since its establishment in 1932 (Silvertown 2009). Astronomy is another area where amateurs and volunteers have been on par with professionals when observation of the night sky and the identification of galaxies, comets and asteroids are considered (BBC 2006). Finally, meteorological observations have also relied on volunteers since the early start of systematic measurements of temperature, precipitation or extreme weather events (WMO 2001).
This type of citizen science provides the first type of ‘classic’ citizen science – the ‘persistence’ parts of science where the resources, geographical spread and the nature of the problem mean that volunteers sometimes predate the professionalisation and mechanisation of science. These research areas usually require a large but sparse network of observers who carry out their work as part of a hobby or leisure activity. This type of citizen science has flourished in specific enclaves of scientific practice, and the progressive development of modern communication tools has made the process of collating the results from the participants easier and cheaper, while inherently keeping many of the characteristics of data collection processes close to their origins.
A second set of citizen science activities is environmental management and, even more specifically, within the context of environmental justice campaigns. Modern environmental management includes strong technocratic and science oriented management practices (Bryant & Wilson 1998; Scott & Barnett 2009) and environmental decision making is heavily based on scientific environmental information. As a result, when an environmental conflict emerges – such as a community protest over a local noisy factory or planned expansion of an airport – the valid evidence needs to be based on scientific data collection. This aspect of environmental justice struggle is encouraging communities to carry out ‘community science’ in which scientific measurements and analysis are carried out by members of local communities so they can develop an evidence base and set out action plans to deal with problems in their area. A successful example of such an approach is the ‘Global Community Monitor’ method to allow communities to deal with air pollution issues (Scott & Barnett 2009). This is performed through a simple method of sampling air using plastic buckets followed by analysis in an air pollution laboratory, and, finally, the community being provided with instructions on how to understand the results. This activity is termed ‘Bucket Brigade’ and was used across the world in environmental justice campaigns. In London, community science was used to collect noise readings in two communities that are impacted by airport and industrial activities. The outputs were effective in bringing environmental problems to the policy arena (Haklay, Francis & Whitaker 2008). As in ‘classic’ citizen science, the growth in electronic communication has enabled communities to identify potential methods – e.g. through the ‘Global Community Monitor’ website – as well as find international standards , regulations and scientific papers that can be used together with the local evidence.
However, the emergence of the Internet and the Web as a global infrastructure has enabled a new incarnation of citizen science: the realisation of scientists that the public can provide free labour, skills, computing power and even funding, and, the growing demands from research funders for public engagement all contributing to the motivation of scientists to develop and launch new and innovative projects (Silvertown 2009; Cohn 2008). These projects utilise the abilities of personal computers, GPS receivers and mobile phones to double as scientific instruments.
This third type of citizen science has been termed ‘citizen cyberscience’ by Francois Grey (2009). Within it, it is possible to identify three sub-categories: volunteered computing, volunteered thinking and participatory sensing.
Volunteered computing was first developed in 1999, with the foundation of SETI@home (Anderson et al. 2002), which was designed to distribute the analysis of data that was collected from a radio telescope in the search for extra-terrestrial intelligence. The project utilises the unused processing capacity that exists in personal computers, and uses the Internet to send and receive ‘work packages’ that are analysed automatically and sent back to the main server. Over 3.83 million downloads were registered on the project’s website by July 2002. The system on which SETI@home is based, the Berkeley Open Infrastructure for Network Computing (BOINC), is now used for over 100 projects, covering Physics, processing data from the Large Hadron Collider through LHC@home; Climate Science with the running of climate models in Climateprediction.net; and Biology in which the shape of proteins is calculated in Rosetta@home.
While volunteered computing requires very little from the participants, apart from installing software on their computers, in volunteered thinking the volunteers are engaged at a more active and cognitive level (Grey 2009). In these projects, the participants are asked to use a website in which information or an image is presented to them. When they register onto the system, they are trained in the task of classifying the information. After the training, they are exposed to information that has not been analysed, and are asked to carry out classification work. Stardust@home (Westphal et al. 2006) in which volunteers were asked to use a virtual microscope to try to identify traces of interstellar dust was one of the first projects in this area, together with the NASA ClickWorkers that focused on the classification of craters on Mars. Galaxy Zoo (Lintott et al. 2008), a project in which volunteers classify galaxies, is now one of the most developed ones, with over 100,000 participants and with a range of applications that are included in the wider Zooniverse set of projects (see http://www.zooniverse.org/) .
Participatory sensing is the final and most recent type of citizen science activity. Here, the capabilities of mobile phones are used to sense the environment. Some mobile phones have up to nine sensors integrated into them, including different transceivers (mobile network, WiFi, Bluetooth), FM and GPS receivers, camera, accelerometer, digital compass and microphone. In addition, they can link to external sensors. These capabilities are increasingly used in citizen science projects, such as Mappiness in which participants are asked to provide behavioural information (feeling of happiness) while the phone records their location to allow the linkage of different locations to wellbeing (MacKerron 2011). Other activities include the sensing of air-quality (Cuff 2007) or noise levels (Maisonneuve et al. 2010) by using the mobile phone’s location and the readings from the microphone.”
7 March, 2011
Challenging Engineering is an EPSRC programme aimed at supporting individuals in building a research group and to ‘establish themselves as the future leaders of research’. As can be imagined, this is a both prestigious and well-funded programme – it provides enough resources to establish a group, recruit postdoctoral and PhD researchers, visit external laboratories and run innovative research activities.
The process of selecting the UCL candidates started in mid-May 2010, with the final interviews at the end of December, just before the Christmas break. Therefore, it was very satisfying to open the email from EPSRC while at a visit to the Technion and see that my application will be funded.
The proposal itself focused on Citizen Science – the participation of amateurs, volunteers and enthusiasts in scientific projects – which is not new, given activities such as the Christmas Bird Count or the British Trust for Ornithology Survey, in which volunteers observe birds and report to a national repository. Such projects date back to the early 20th century, and many of the temperature records used in climate modelling today have been collected by amateur enthusiasts operating their own weather stations.
Over the past decade, Web 2.0 technologies have led to the proliferation of Citizen Science activities, from SETI@Home, where people volunteer their unused computer processing power, to Galaxy Zoo, where amateur astronomers suggest interpretations of images from the Hubble telescope, to the Pepys Estate in Deptford, London, where residents carried out community noise monitoring for six weeks to challenge the activities of a local scrapyard operator.
However, the current range of Citizen Science projects is limited in several respects. First, in most instances the participants are trusted only as passive participants (by donating CPU cycles), or as active participants but limited to basic observation and data collection. They do not participate in problem definition or in the scientific analysis itself. Second, there is an implicit assumption that participants will have a relatively advanced level of education. Third, and largely because of the educational requirements, Citizen Science occurs mostly in affluent places, and therefore most of the places that are critical for encouraging biodiversity conservation, and where population growth is most rapid, are effectively excluded.
The new research group will challenge this current mode of Citizen Science by suggesting the establishment of an interdisciplinary team that will focus on ‘Extreme’ Citizen Science (ExCiteS). ExCiteS is extreme in three ways: first, it aims to develop the theories and methodologies to allow any community to start a Citizen Science project that will deal with the issues that concern them – from biodiversity to food production; second, it will provide a set of tools that can be used by any user, regardless of their level of literacy, to collect, analyse and act on information by using established scientific methods; finally, it aims to use the methodologies of Citizen Science around the globe, by developing a technology, through collaborative activities, that can involve communities from housing estates in London to hunter-gatherers and forest villagers in the Congo Basin. The underlying technology is intended to be universal and to provide the foundations for many other projects and activities.
The technology that will be developed will rely on spatial and geographical representations of information. The reason for focusing on this mode of representation is that, as a form of human communication, geographical representations predate text, and are likely to be accessible by many people with limited reading and technology literacy.
ExCiteS has the transformative potential to deal with some of the major sustainability challenges involved in using science and Information and Communication Technologies in a hot (due to climate change), flat (due to globalisation) and crowded (due to population increase) world, by creating tools that will help communities understand their environment as it changes, and manage it by using scientific modelling and management methods.
The proposal focuses not only on the development of ExCiteS as a practice, but, significantly, on developing a fundamental understanding of Citizen Science by studying the motivation of participants and their incentives, identifying patterns of data collection, and dealing with the uncertainty and validity of data collected in this way.
The activities of the ExCiteS group will officially start in May, and I will be working closely with Dr Jerome Lewis, at UCL Anthropology, to develop the area of Extreme Citizen Science. We are going to start by recruiting a postdoctoral fellow and 2 PhD students – so if you are interested in this type of challenge, get in touch.