How many citizen scientists in the world?

Since the development of the proposal for the Doing It Together Science project (DITOs), I have been using the “DITOs escalator” model to express the different levels of engagement in science, while also demonstrating that the higher level have fewer participants, which mean that there is a potential for people to move between levels of engagement – sometime towards deeper engagement, and sometime towards lighter one according to life stages, family commitments, etc. This is what the escalator, after several revisions, look like:

DitosEscalator7

I have an ongoing interest in participation inequality (the observation that very few participants are doing most of the work) and the way it plays out and influences citizen science projects. When you start attaching numbers to the different levels of public engagement in science, participation inequality is appearing in this area, too. Since writing the proposal in 2015, I have been looking for indications that will support the estimation of the number of participants. During the process of working on a paper that uses the escalator, I’ve done the research to identify sources of information to support these estimations. While the paper is starting its peer review journey, I am putting out the part that relates to these numbers so this part can get open peer review here. I have decided to use 2017 as a recent year for which we can carry out the analysis. As for geographical scale, I’m using the United Kingdom as a country with very active citizen science community as my starting point.

At the bottom of the escalator, Level 1 considers the whole population, about 65 million people. Because of the impact of science across society, the vast majority, if not all, will have some exposure to science – even if this is only in the form of medical encounters.

However, the bare minimum of engagement is to passively consume information about science through newspapers, websites, and TV and Radio programme (Level 2). We can gauge the number of people at this level from the BBC programmes Blue Planet II and Planet Earth II, both focusing on natural history, with viewing figures of 14 million and about 10 million, respectively. We can, therefore, estimate these “passive consumers” at about 25% of the population.

At the next level is active consumption of science – such as visits to London’s Science Museum (UK visitors in 2017 – about 1.3), or the Natural History Museum (UK visitors in 2017 – about 2.1m), so an estimation of participation at 10% of the population seem justified.

Next, we can look at active engagement in citizen science but to a limited degree. Here, the Royal Society for the Protection of Birds (RSPB) annual Big Garden Birdwatch requires the participants to dedicate a single hour in the year. The project attracted about 500,000 participants in 2017, and we can, therefore, estimate participation at this level at about 1% of the population. This should also include about 170,000 people who carried out a single task on Zooniverse and other online projects.

At the fifth level, there are projects that require remote engagement, such as volunteer thinking on the Zooniverse platform, or in volunteer computing on the IBM World Community Grid (WCG), in which participants download a software on their computer to allow processing to assist scientific research. The number of participants in WCG from the UK in 2017 was about 18,000. In Zooniverse about 74,0000 people carried out more than a single task in 2017, thus estimating participation at this level at 0.1% of the population (thanks to Grant Miller, Zooniverse and Caitlin Larkin, IBM for these details).

The sixth level requires the regular data collection, such as the participation in the British Trust of Ornithology Garden Birdwatch got about 6,500 active participants in 2017 (BTO 2018), while about 5000 contributed to the biodiversity recording system iRecord (thanks to Tom August, CEH) and it will be reasonable to estimate that the participation is about 0.01% of the population.

The most engaged level include those who are engaged in DIY Science, such as exploring DIY Bio, or developing their own sensors, etc. We can estimate that it represents 0.001% of the UK population at most (thanks to Philippe Boeing & Ilia Levantis).

We can see that as the level of engagement increases, the demand from participants increase and the number of participants drops. Not that this is earth-shattering, though what is interesting is that the difference between levels is in order of magnitude. We also know that the UK enjoys all the possible benefits that are needed to foster citizen science: a long history of citizen science activities, established NGOs and academic institutions that support citizen science, good technological infrastructure (broadband, mobile phone use), well-educated population (39.1% with tertiary education), etc. So we’re talking about a best-case scenario.

It is also important, already at this point, to note that UNESCO’s estimates of the percentage of UK population who are active scientists (working in research jobs), is 0.4% which is bigger than the 0.111 for levels 5,6 and 7.  

Let’s try to extrapolate from the UK to the world.

First, how many people we can estimate to have the potential of being citizen scientists? We want them to be connected and educated, with a middle-class lifestyle that gives them leisure time for hobbies and volunteering.

The connectivity gives us a large number – according to ITU, 3.5 Billion people are using the Internet. The estimation of the size of middle-class is a bit smaller, at 3.2 Billion people.  However, we know that participants in most citizen science projects which use passive inclusiveness, where everyone is welcome without an active effort in outreach to under-represented groups, tend to be from people with higher education (a.k.a tertiary education). There is actually data about it – here is the information from Wikipedia about tertiary educational attainment. According to UNESCO’s statistics, there were over 672 million people with a form of tertiary education in 2017. Let’s say that not everyone in citizen science is with tertiary education (which is true) so our potential starting number is 1 Billion.

I’ll assume the same proportion of the UK, ignoring that it present for us the best case. So about 250 million of these are passive consumers of science (L2), and 100 million are active consumer (e.g. going to science museums) (L3). We can then have 10 million people that participate in the once a year events (L4); 1 million that are active in online citizen science (this is more than a one-off visit or trial) (L5); about 100,000 who are the committed participants (mostly nature observers) and about 10,000 DIY bio, makers, and DIY science people (L6 and L7).

Are these numbers make sense? Looking at the visits to science/natural history museums on Wikipedia, level 3 seems about right. Level 4 looks very optimistic – in addition to Big Garden Birdwatch, there were about 17,000 people participating in City Nature Challenge, and 73,000 participants in the Christmas Bird Count, and about 888,000 done a single task on Zooniverse – it looks like that a more realistic number is 3 million or 4 million. Level 5 is an underestimate – IBM Word Community Grid have 753,000 members, and there are other volunteer computing projects which will make it about 1 million, then there were about 163,000 global Zooniverse contributors (thanks to the information from Grant Miller), 130,000 Wikipedians, 50,000 active contributors in OpenStreetMap, and other online projects such as EyeWire etc. So let’s say that it’s about 1.5 Million. At level 6, again the number is about right – e.g. eBird reports 20,000 birders in their peak day. For the sake of the argument, let’s say that it’s double the number – 200,000. Level 7 also seems right, based on estimations of biohackers numbers in Europe.

Now let’s look at the number of scientists globally: in 2013 there were 7.3 million researchers worldwide. With the estimation of “serious” citizen scientists (levels 5,6 and 7) at about 1.7 million, we can see the issue of crowdsourcing here: the potential crowdsourcer community is, at the moment, much bigger than the volunteers.

Something that is important to highlight here is the amazing productivity of citizen scientists in terms of their ability to analyse, collect information, or inventing tools – we know from participation inequality that this tiny group of participants are doing a huge amount of work – the 50,000 OSM volunteers are mapping the world or the 73,000 Christmas Bird Count participants provided 56,000,000 observations or the attention impact of the Open Insulin Project. So numbers are not the only thing that we need to think about.

Moreover, this is not a reason to give up on increasing the number of citizen scientists. Look at the numbers of Google Local Guides – out of 1 Billion users, a passive crowdsourcing approach reached 50 million single time contributors, and 465,000 in the equivalent of levels 5 to 7. Therefore, citizen science has the potential of reaching much larger numbers. At the minimum, there is the large cohort of people with tertiary education, with at least 98 million people with Masters and PhD in the world.

Therefore, to enable a wider and deeper public engagement with science, apart from the obvious point of providing funding, institutional support, and frameworks to scale up citizen science, we can think of an “escalator” like process, which makes people aware of the various levels and assists them in moving up or down the engagement level. For example, due to a change in care responsibilities or life stages, people can become less active for a period of time and then chose to become more active later. With appropriate funding, support, and attention, growing the global citizen science should be possible. 

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Citizen Science for Observing and Understanding the Earth

Since the end of 2015, I’ve been using the following mapping of citizen science activities in a range of talks:

Range of citizen science activities
Explaining citizen science

The purpose of this way of presentation is to provide a way to guide my audience through the landscape of citizen science (see examples on SlideShare). The reason that I came up with it, is that since 2011 I give talks about citizen science. It started with the understanding that I can’t explain extreme citizen science when my audience doesn’t understand what citizen science is, and that turned into general talks on citizen science.

Similarly to Caren Cooper, I have an inclusive approach to citizen science activities, so in talks, I covered everything – from bird watching to DIY science. I felt that it’s too much information, so this “hierarchy” provides a map to go through the overview (you can look at our online course to see why it’s not a great typology). It is a very useful way to go through the different aspects of citizen science, while also being flexible enough to adapt it – I can switch the “long-running citizen science” fields according to the audience (e.g. marine projects for marine students).

An invitation for Pierre-Philippe Mathieu (European Space Agency) in 2015 was an opportunity to turn this mapping and presentation into a book chapter. The book is dedicated to “Earth Observation Open Science and Innovation and was edited by Pierre-Philippe and Christoph Aubrecht.

When I got to writing the chapter, I contacted two researchers with further knowledge of citizen science and Earth Observation – Suvodeep Mazumdar and Jessica Wardlaw. I was pleased that they were happy to join me in the effort.

Personally, I’m very pleased that we could include in the chapter the story of the International Geophysical Year, (thank Alice Bell for this gem), with Moonwatch and Sputnik monitoring.

The book is finally out, it is open access, and you can read our chapter, “Citizen Science for Observing and Understanding the Earth” for free (as well as all the other chapters). The abstract of the paper is provided below:

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges.

 

Citizen Cyberlab Summit (day 2)

DSCN1165The second day of the Citizen Cyberlab Summit followed the same pattern of the first day: Two half day sessions, in each one short presentations from guest speakers from outside the project consortium, followed by two demonstrations of specific platform, tool, pilot or learning, and ending with discussion in groups, which were then shared back.

The first session started with History of Citizen Sciences – Bruno Strasser (Uni Geneva) – looking at both practical citizen science and the way it is integrated into the history of science. The Bioscope is a place in Geneva that allowing different public facing activities in the medical and life science: biodiversity, genetic research etc. They are developing new ways of doing microscopy – a microscope which is sharing the imagery with the whole room so it is seen on devices and on turning the microscope from solitary experience to shared one. They are involved in biodiversity research that is aimed to bar-coding DNA of different insects and animals. People collect data, extract DNA and sequence it, and then share it in a national database. Another device that they are using is a simple add-on that turns a smartphone can be turned into powerful macro camera, so children can share images on instagram with bioscope hashtag. They also do ‘Sushi night’ where they tell people what fish you ate if at all…
This link to a European Research Council (ERC) project  – the rise of citizen sciences – on the history of the movement. Is there something like ‘citizen sciences’? From history of science perspective, in the early 20c the amateur scientist is passing and professionals are replacing it. He use a definition of citizen science as amateurs producing scientific knowledge – he is not interested in doing science without the production of knowledge. He noted that there are a lot of names that are used in citizen science research. In particular, the project focus is on experimental sciences – and that because of the laboratory revolution of the 1930s which dominated the 20th century. The lab science created the divide between the sciences and the public (Frankenstein as a pivotal imagery is relevant here). Science popularisation was trying to bridge the gap to the public, but the rise in experimental sciences was coupled with decline of public participation. His classification looks at DIYbio to volunteer computing – identifying observers, analysers etc. and how they become authors of scientific papers. Citizen science is taken by the shift in science policy to science with and for society. Interest in the promises that are attached to it: scientific, educational (learning more about science) and political (more democratic). It’s interesting because it’s an answer to ‘big data’, to the contract of science and society, expertise, participation and democratisation. The difference is demonstrated in the French response following Chernobyl in 1986, with presentation by a leading scientists in France that the particle will stop at the border of France, compared that to Deep Horizon in 2010 with participatory mapping through public lab activities that ‘tell a different story’. In the project, there are 4 core research question: how citizen science transform the relationship between science and society? who are the participants in the ‘citizen sciences’ – we have some demographic data, but no big picture – collective biography of people who are involved in it. Next, what is the ‘moral economies’ that sustain the citizen sciences? such as the give and take that people get out of project and what they want. Motivations and rewards. Finally, how do citizen sciences impact the production of knowledge? What is possible and what is not. He plan to use approaches from digital humanities process. He will build up the database about the area of citizen science, and look at Europe, US and Asia. He is considering how to run it as participatory project. Issues of moral economies are demonstrated in the BOINC use in commercial project. 

Lifelong learning & DIY AFM – En-Te Hwu (Edwin) from Academia Sinica, Taiwan). There are different ways of doing microscopy at different scales – in the past 100 years, we have the concept of seeing is believing, but what about things that we can’t see because of the focused light of the microscope – e.g. under 1 micron. This is possible with scanning electron microscope which costs 500K to 2M USD, and can use only conductive samples, which require manipulation of the sample. The Atomic Force Microscope (AFM) is more affordable 50K to 500K USD but still out of reach to many. This can be used to examine nanofeatures – e.g. carbon nanotubes – we are starting to have higher time and spatial resolution with the more advanced systems. Since 2013, the LEGO2NANO project started – using the DVD head to monitor the prob and other parts to make the AFM affordable. They put an instructable prototype that was mentioned by the press and they called it DIY AFM. They created an augmented reality tool to guide people how to put the device together, and it can be assembled by early high school students – moving from the clean room to the class room.  The tool is being used to look at leafs, CDs – area of 8×8 microns and more. The AFM data can be used with 3D printing – they run a summer school in 2015 and now they have a link to LEGO foundation. They are going through a process of reinventing the DIY AFM, because of patenting and intellectual property rights (IPR) – there is a need to rethink how to do it. They started to rethink the scanner, the control and other parts. They share the development process (using building process platform of MIT media lab). There is a specific application of using the AFM for measuring air pollution at PM2.5. using a DVD – exposing the DVD by removing the protection layer, exposing it for a period of time and then bringing it and measuring the results. They combined the measurements to crowdcrafting for analysis. The concept behind the AFM is done by using LEGO parts, and scanning the Lego points as a demonstration, so students can understand the process. 

wpid-wp-1442566370890.jpgThe morning session included two demonstrations. First, Creativity in Citizen Cyberscience – Charlene Jennett  (UCLIC, UCL) – Charlene is interested in psychological aspects of HCI. Creativity is a challenge in the field of psychology. Different ideas of what is creativity – one view is that it’s about eureka moment as demonstrated in Foldit breakthrough. However, an alternative is to notice everyday creativity of doing thing that are different, or not thought off original. In cyberlab, we are looking at different projects that use technologies and different context. In the first year, the team run interviews with BOINC, Eyewire, transcribe Bentham, Bat Detective, Zooniverse and Mapping for Change – a wide range of citizen science projects. They found many examples  – volunteers drawing pictures of the ships that they were transcribing in Old Weather, or identifying the Green Peas in Galaxy zoo which was a new type of galaxy. There are also creation of chatbots about their work -e.g. in EyeWire to answer questions, visualisation of information, creating dictionaries and further information. The finding showed that the link was about motivation leading to creativity to help the community or the project. They created the model of linking motivation, learning through participation, and volunteer identity that lead to creativity. The tips for projects include: feedback on project progress at individual and project level, having regular communication – forum and social media, community events – e.g. competitions in BOINC, and role management – if you can see someone is doing well, then encourage them to take more responsibility. The looked at the different pilots of Cyberlab – GeoTag-X, Virtual Atom Smasher, Synthetic Biology through iGEM and Extreme Citizen Science. They interview 100 volunteers. Preliminary results – in GeoTag-X, the design of the app is seen as the creative part, while for the analysts there are some of the harder tasks – e.g. the georeferencing of images and sharing techniques which lead to creative solutions. In the iGEM case they’ve seen people develop games and video. in the ExCiteS cases, there is DIY and writing of blog posts and participants being expressive about their own work. There are examples of people creating t-Shirt, or creating maps that are appropriate for their needs.They are asking questions about other projects and how to design for creativity. It is interesting to compare the results of the project to the definition of creativity in the original call for the project. The cyberlab project is opening up questions about creativity more than answering them. 

wpid-wp-1442679548581.jpgPreliminary Results from creativity and learning survey – Laure Kloetzer (university of Geneva). One of the aims of Citizen Cyberlab was to look at different aspects of creativity. The project provided a lot of information from a questionnaire about learning and creativity in citizen science. The general design of the questionnaire was to learn the learning outcomes. Need to remember that out of the whole population, small group participate in citizen science – and within each project, there is a tiny group of people that do most of the work (down to 16 in Transcribed Bentham) and the question of how people turn from the majority, who do very little work to highly active participants is unknown, yet. In Citizen Cyberlab we carried out interviews with participants in citizen science projects, which led to a typology of learning outcomes – which are lot wider than those that are usually expected or discussed in the literature – but they didn’t understand what people actually learn. The hypothesis is that people who engage with the community can learn more than those that doesn’t – the final questionnaire of the project try to quantify learning outcomes (informal learning in citizen science – ILICS survey). The questionnaire was tested in partial pilot. Sent to people in volunteer computing, volunteer thinking and others types. They had about 700 responses, and the analysis only started. Results – age group of participants is diverse from 20-70, but need to analyse it further according to projects. Gender – 2/3 male, third female, and 20% of people just have high school level of education, with 40% with master degree or more – large minority of people have university degree. They got people from 64 countries – US, UK, Germany and France are the main ones (the survey was translated to French). Science is important to most, and a passion for half, and integrated in their profession (25% of participants). Time per week – third of people spend less than 1 hour, and 70% spend 1-5 hours – so the questionnaire captured mostly active people. Results on learning – explore feeling, what people learn, how they learn and confidence (based on the typology from previous stages of the project). The results show that – people who say that they learn something to a lot, and most people accept that they learn on-topic knowledge (about the domain itself – 88%), scientific skills (80%), technological skills (61%), technical skills (58%), with political, collaboration skills and communication skills in about 50% of the cases. The how question – people learn most from project documentation (75%) but also by external resources (70%). Regarding social engagement, about 11% take part in the community, and for 61% it’s the first time in their life that they took such a role. There are different roles – translation, moderating forums with other things in the community that were not recognised in the questionnaire. 25% said that they met people online to share scientific interests – opportunity to share and meet new people. Learning dimensions and types of learners – some people feel that they learn quite a lot about various things, while others focus on specific types of learning. wpid-wp-1442679528037.jpgPrincipal Component Analysis show that learner types correlate with different forms of engagement – more time spent correlate to specific type of learner. There are different dimensions of learning that are not necessarily correlate. The cluster analysis show about 10 groups – people who learn a lot on-topic and about science with increase self-confidence. Second group learn on topic but not much confidence. Group 3, like 2 but less perception of learning. Group 4 don’t seem to learn much but prefer looking at resources. 5 learn somewhat esp about computers. 6 learn through other means. 7 learn by writing and communicating, collaborating and some science. 8 learn only about tools, but have general feeling of learning. 9 learn on topic but not transferable and 10 learn a lot on collaboration and communication – need to work more on this, but these are showing the results and the raw data will be shared in December. 

DSCN1160Following the presentation, the group discussion first explored examples of creativity from a range of projects. In crowdcrafting, when people are not active for a month, they get email with telling them that they will be deleted – one participant created activities that link to the project – e.g. tweeting from a transcriptions from WW I exactly 100 years after it happen. In Cornell Lab of Ornithology, volunteers suggest new protocols and tasks about the project – new ways of modifying things. In the games of ScienceatHome are targeted specifically to explore when problem solving become creative – using the tools and explaining to the researchers how they solve issues. In WCG one volunteered that create graphics from the API that other volunteers use and expect now to see it as part of the project. There is a challenge to project coordinators what to do with such volunteers – should they be part of the core project?
Next, there are questions about roles – giving the end users enough possibilities is one option, while another way is to construct modularising choices, to allow people to combine them in different ways. In ScienceatHome they have decided to put people into specific modes so consciously changing activities. There is wide variety of participants – some want to be fairly passive and low involvement, while other might want to do much more. Also creativity can express itself in different forms, which are not always seem linked to the project. The learning from Citizen Cyberlab is that there isn’t simple way of linking creativity and capture it in computer software, but that you need organisational structure and most importantly, awareness to look out for it and foster it to help it develop. Having complementarity – e.g. bringing game people and science people to interact together is important to creativity. Another point is to consider is to what degree people progress across citizen science projects and type of activities – the example of Rechenkraft.net that without the hackspace it was not possible to make things happen. So it’s volunteers + infrastructure and support that allow for creativity to happen. There are also risks – creating something that you didn’t know before – ignorance – in music there isn’t much risk, but in medical or synthetic biology there can be risks and need to ask if people are stopping their creativity when they see perceived risks.

wpid-wp-1442679513070.jpgThe final session of the summit was dedicated to Evaluation and Sustainability. Starting with The DEVISE project – Tina Philips (Cornell Lab of Ornithology). Tina is involved in the public engagement part of Cornell Lab of Ornithology . Starting from the work on the 2009 of the Public Participation in Scientific Research (PPSR) report – the finding from the CAISE project that scarcity of evaluations, higher engagement suggested deeper learning, and need for a more sensitive measures and lack of overall finding that relate to many projects. The DEVISE project (Developing, Validating, and Implementing Situated Evaluation Instruments) focused on evaluation in citizen science overall – identifying goals and outcomes, building professional opportunities for people in the field of informal learning, and creating a community of practice around this area. Evaluation is about improving the overall effectiveness of programmes and projects. Evaluation is different from research as it is trying to understand strengths and weaknesses of the specific case and is less about universal rules – it’s the localised learning that matter. In DEVISE, they particularly focused on individual learning outcomes. The project used literature review, interviews  with participants, project leaders and practitioners to understand their experience. They looked at a set of different theories of learning. This led to a framework for evaluating PPSR learning outcomes. The framework includes aspects such as interest in science & the environment, self efficacy, motivation, knowledge of the nature of science, skills of science inquiry, and behaviour & stewardship. They also develop scales – short surveys that allow to examine specific tools – e.g. survey about interest in science and nature or survey about self-efficacy for science. There is a user guide for project evaluators that allow to have plan, implement and share guidance. There is a logic model for evaluation that includes Inputs, activities, outputs, short-term and long-term impacts. It is important to note that out of these, usually short and long terms outcomes are not being evaluated. Tina’s research looked at citizen science engagement, and understand how they construct science identity. Together with Heidi Ballard, they looked at contributory, collaborative and co-created projects – including Nestwatch, CoCoRaHS, and Global Community Monitor. They had 83 interviews from low , medium and high contributors and information from project leaders. The data analysis is using qualitative analysis methods and tools (e.g. Nvivo). The interview asked about engagement and what keep participants involved and asking about memorable aspects of their research involvement. There are all sort of extra activities that people bring into interviews – in GCM people say ‘it completely changes the way that they respond to us and actually how much time they even give us because previously without that data, without something tangible’ – powerful experiences through science. The interviews that were coded show that data collection, communicating with others and learning protocols are very common learning outcomes. About two-third of interviewees are also involved in exploring the data, but smaller group analyse and interpret it. Majority of people came with high interest in science, apart of the people who are focused on local environmental issues of water or air quality. Lower engagers tend to feel less connected to the project – and some crave more social outlets. The participants have a strong understanding of citizen science and their role in it. Data transparency is both a barrier and facilitator – participants want to know what is done with their data. QA/QC is important personally and organisationally important. Participants are engaged in wide range of activities beyond the project itself. Group projects may have more impact than individual projects.
Following the presentation, the discussion explore the issue of data – people are concerned about how the data is used, and what is done with it even if they won’t analyse it themselves. In eBird, you can get your raw data, and checking the people that used the data there is the issue of the level in which those who download the data understand how to use it in an appropriate way. 

wpid-wp-1442679499689.jpgThe final guest presentation was Agroecology as citizen science – Peter Hanappe (Sony Computer Science Lab, Paris).  Peter is interested in sustainability, and in previous projects he was involved in working on accessibility issues for people who use wheelchair, the development of NoiseTube, or porting ClimatePrediction BOINC framework to PlayStation, and reducing energy consumption in volunteer computing. In his current work he looks at sustainability in food systems. Agroecology is the science of sustainable agriculture, through reducing reliance on external inputs – trying to design productive ecosystems that produce food. Core issues include soil health and biodiversity, with different ways of implementing systems that will keep them productive. The standard methods of agriculture don’t apply, and need to understand local conditions and the practice of agroecology is very knowledge intensive. Best practices are not always studied scientifically – with many farms in the world that are small (below 2 hectares, 475 millions farms across the world). There are more than 100M households around the world that grow food.  This provide the opportunity for citizen science – each season can be seen as an experiment, with engaging more people and asking them to share information so the knowledge slowly develops to provide all the needed details. Part of his aim is to develop new, free tools and instruments to facilitate the study of agroecology. This can be a basic set with information about temperature and humidity or more complex. The idea to have local community and remote community that share information on a wiki to learn how to improve. Together with a group of enthusiasts that he recruited in Paris, they run CitizenSeeds where they tried different seeds in a systematic way – for example, with a fixed calendar of planting and capturing information People took images and shared information online. The information included how much sunlight plants get and how much humidity the soil have. on p2pfoodlab.net they can see information in a calendar form. They had 80 participants this year. Opportunity for citizen science – challenges include community building, figuring out how much of it is documentation of what worked, compared to experimentation – what are the right way to carry out simple, relevant, reproducible experiments. Also if there is focus on soil health, we need multi-year experiments.  


I opened the last two Demonstrations of the session with a description of the 
Extreme Citizen Science pilots – starting similarly to the first presentation of the day, it is useful to notice the three major period in science (with regard to public participation). First, the early period of science when you needed to be wealthy to participate – although there are examples like Mary Anning, who. for gender, religion and class reasons was not accepted within the emerging scientific establishment as an equal, and it is justified to describe her as citizen scientists, although in full time capacity. However, she’s the exception that point to the rule. More generally, not only science was understood by few, but also the general population had very limited literacy, so it was difficult to engage with them in joint projects. During the period of professional science, there are a whole host of examples for volunteer data collection – from phenology to meteorology and more. As science became more professional, the role of volunteered diminished, and scientists looked for automatic sensors as more reliable mean to collect information. At the same time, until the late 20th century, most of the population had limited education – up to high school mostly, so the tasks that they were asked to perform were limited to data collection. In the last ten years, there are many more people with higher education – especially in industrialised societies, and that is part of the opening of citizen science that we see now. They can participate much more deeply in projects.
Yet, with all these advances, citizen science is still mostly about data collection and basic analysis, and also targeted at the higher levels of education within the population. Therefore, Extreme Citizen Science is about the extremities of citizen science practice – engage people in the whole scientific process, allow them to shape data collection protocols, collect and analyse the data, and use it in ways that suit their goals. It is also important to engage people from all levels of literacy, and to extend it geographically across the world.
The Extreme Citizen Science (ExCiteS) group is developing methodologies that are aimed at facilitating this vision. Tool like GeoKey, which is part of the Cyberlab project, facilitate community control over the data and decision what information is shared and with whom. Community Maps, which are based on GeoKey are way to allow community data collection and visualisation, although there is also a link to EpiCollect, so mobile data collection is possible and then GeoKey managed the information.
These tools can be used for community air quality monitoring, using affordable and accessible methods (diffusion tubes and borrowed black carbon monitors), but also the potential of creating a system that will be suitable for people with low level of literacy. Another pilot project that was carried out in Cyberlab included playshops and exploration of scientific concepts through engagement and play. This also include techniques from Public Lab such as kite and balloon mapping, with potential of linking the outputs to community maps through GeoKey. 

 Finally, CCL Tracker was presented by Jose Luis Fernandez-Marquez (CERN) – the motivations to create the CCL tracker is the need to understand more about participants in citizen cyberscience projects and what they learn. Usual web analytics  provide information about who is visiting the site, how they are visiting and what they are doing. Tools like Google analytics – are not measuring what people do on websites. We want to understand how the 20% of the users doing 80% of the work in citizen cyberscience projects and that require much more information. Using an example of Google Analytics from volunteer computing project, we can see about 16K sessions, 8000 users, from 108 countries and 400 sessions per day. Can see that most are males – we can tell which route they arrived to the website, etc. CCL tracker help to understand the actions performed in the site and measure participants contribution. Need to be able to make the analytics data public and create advanced data aggregation – clustering it so it is not disclosing unwanted details about participants. CCL tracker library work together with Google tag manager and Google analytics. There is also Google Super Proxy to share the information. 

Beyond quantification: a role for citizen science and community science in a smart city

Arduino sensing in MaltaThe Data and the City workshop will run on the 31st August and 1st September 2015, in Maynooth University, Ireland. It is part of the Programmable City project, led by Prof Rob Kitchin. My contribution to the workshop is titled Beyond quantification: a role for citizen science and community science in a smart city and is extending a short article from 2013 that was published by UCL’s Urban Lab, as well as integrating concepts from philosophy of technology that I have used in a talk at the University of Leicester. The abstract of the paper is:

“When approaching the issue of data in Smart Cities, there is a need to question the underlying assumptions at the basis of Smart Cities discourse and, especially, to challenge the prevailing thought that efficiency, costs and productivity are the most important values. We need to ensure that human and environmental values are taken into account in the design and implementation of systems that will influence the way cities operate and are governed. While we can accept science as the least worst method of accumulating human knowledge about the natural world, and appreciate its power to explain and act in the world, we need to consider how it is applied within the city in a way that does leave space for cultural, environmental and religious values. This paper argues that a specific form of collaborative science – citizen science and community science – is especially suitable for making Smart Cities meaningful and democratic. The paper use concepts from Albert Borgmann’s philosophy of technology – especially those of the Device Paradigm and Focal Practices, to identify the areas were sensing the city can gain meaning for the participants.”

The paper itself can be accessed here.

Other papers from the same workshop that are already available include:

Rob Kitchin: Data-Driven, Networked Urbanism

Gavin McArdle & Rob Kitchin: Improving the Veracity of Open and Real-Time Urban Data

Michael Batty: Data About Cities: Redefining Big, Recasting Small

More details on the workshop will appear on the project website

Volunteer computing, engagement and enthusiasm

This post is about perceptions, engagement and the important of ‘participant-observation‘ approach in citizen science research.IBM World Community Grid

It start with a perception about volunteer computing. The act of participating in scientific project by downloading and installing software that will utilise unused processing cycles of your computer is, for me, part of citizen science. However, in different talks and conversations I have noticed many people dismiss it as ‘not real citizen science’. I suspect that this is because of the assumption that the engagement of the participant is very low – just downloading a piece of software and not much beyond.

Until few weeks ago, I was arguing that there are many participants who are much more engaged – joining teams, helping others, attending webinars – and quietly accepting that it might be difficult to justify that people who ‘just download software’ are active citizen scientists.

Until this:

A bit of background – the day after the first Citizen Cyberscience Summit in 2010, I’ve joined IBM World Community Grid, as a way to experience volunteer computing on my work desktop, laptops, and later on my smartphone, while contributing the unused processing cycles to scientific projects. Out of over 378,000 participants in the project, I’m in the long tail – ranking 20,585. My top contributions are for FightAIDS@Home and Computing for Clean Water.

I notice the screen saver on my computers, and pleased with seeing the IBM World Community Grid on my smartphone in the morning, knowing that it used the time since it was fully charged for some processing. I also noticed it when I reinstall a computer, or get a new one, and remember that I need to set it going. I don’t check my ranking, and I don’t log-in more than twice a year to adjust the projects that I’m contributing to. So all in all, I self-diagnosed myself to be a passive contributor in volunteer computing.

But then came the downtime of the project on the 28th February. There was an advanced message, but I’ve missed it. So looking at my computer during the afternoon of the day, I’ve noticed a message ‘No Work Available to Process’. After a while, it bothered me enough to go on and check the state of processing on the smartphone, which also didn’t process anything. Short while after that, I was searching the internet to find out what is going on with the system, and after discovering that the main site was down, I continued to look around until I found the twitter message above. Even after discovering that it is all planned, I couldn’t stop looking at the screen saver from time to time, and was relieved when processing resumed.

What surprised me about this episode was how much I cared about it. The lack of processing annoyed me  enough to spend over half an hour on discovering the reason. From the work of Hilary Geoghagen, I know about technology enthusiasm, but I didn’t expected that I would care about downtime the way I did.

This changed my view on volunteer computing – there must be more people that are engaged in a project and care about it than what usually is perceived. This is expressed in the survey the IBM run in 2013 when 15,627 people cared enough about the World Community Grid project to complete a survey. I guess that I’m not alone…

The final note is about the importance of ‘participant-observation‘. As a researcher, participatory action research is a core methodology that I’ve been using for a long while, and I advocated it to others, for example as a necessary research method for those who are researching OpenStreetMap. Participant-observation require you to get a deeper understanding about the topic that you are researching by actively participating in it, not just analysing interviews or statistics about participation. The episode above provide for me a demonstration for the importance of this methodology. For over 4 years, my participation in volunteer computing was peripheral, but eventually, it provided me with an insight that is important to my understanding of the topic and the emotional attachment of those who are participating in it.

Francois Grey’s 7 myths of citizen science

Over the Air 2012 event was a wonderful event – it’s a 36 hours event, dedicated to mobile development and it is based on Bletchley park. This year, Citizen Science was a theme of the event. The final talk was given by Francois Grey from the Citizen Cyberscience Centre . Francois’ interest is on volunteer computing – the type of citizen science were people donate the unused cycles on the computers through software such as BOINC – as well as the wider range of citizen science project. Based on his experience from talks with scientists around the world about citizen science, he developed the 7 myths of citizen science which he covered in his talk (see it below). He suggest them as point of views that are expressed by scientists when citizen science is suggested to them. They are:

  1. It doesn’t produce real science
  2. It doesn’t work for my science – it is too complex to engage people in it
  3. Nobody will be interested in my area of science
  4. You can’t trust the results from ordinary people if you involve them in something other than automatic processing
  5. Volunteer computing is energetically hugely wasteful when compared to computer clusters
  6. It doesn’t really engage people in science
  7. One day we will run out of volunteers

Interestingly, the myths are covering the practice of science (energy consumption, validation), social practices (number of volunteers) and the educational aspects of science (interest, engagement). It is worth thinking about these myths and what they mean for various projects – as well as remembering that they are based on scientists’ views.