Integrating citizen science

The concept has many names: networked science, crowd-sourced science, crowd science, civic science, or citizen science. All these terms emphasise a specific aspect, and all those aspects play a more or less important role in the overall concept. Let’s see:

• Networked science – science beyond the ivory tower, it requires interaction with others to progress; the emphasis is on the technology dimension of collaboration ;
• Crowd-sourced science – focus on a specific funding mechanism, which may well be employed (though it’s not a must) ;
• Crowd science – science in the crowd, by the crowd; a more or less unstructured group of people ;
• Civic science – science embedded in civil society ;
• Citizen science – in essence very similar to civic, but now with focus on the individual, with a personal appeal; this is not only inspiring, but truly empowering: “Every citizen can do science, so I can do science.“

My preference clearly is for citizen science, as this term is –to my mind– the most attractive and inclusive of them all. Why do I think this is important? Because I believe that citizen science will shape the future of science. The question just is: how?

I don’t foresee that citizen science could actually replace professional science, nor do I see any need for that. Hence it is not a question of either this or that, it’s a situation of both this and that: citizen science should be employed to augment professional science in order to expand the capacity, reach, and speed of the scientific endeavour.

The beauty of citizen science, and its major benefit, is its breadth and reach. Citizen science engages a far greater number of contributors, it gains access to an otherwise untapped intellectual resource by casting a much wider net further than professional science could. Yet that bigger catch comes at a price: the heterogeneous composition of the dramatically enlarged group of scientists. We’ll have to deal with wide range of motivations, expectations, skills, experience, training and education, within and beyond science. Hence established paradigms that build the common basis for collaboration amongst professional scientists cannot be taken for granted as the accepted, understood, or even known standard when engaging citizen scientists. All that to say that we’ll certainly need to develop some kind of structured approach to maximise the contributions of citizen science.

We need to acknowledge that while citizen science is good for solving some types of problems, it is inefficient or even useless for some other types. Hence there is no point in provoking a competition between citizen science and professional science. Rather, there is a more than sufficient number of puzzles and problems that professional could not solve yet. And for these, citizen science presents a promising alternative approach, provided that the specific puzzle can be formulated in a way that facilitates the engagement of citizen scientists. I can see two types of problems that are suitable for citizen science.

Topics for citizen science – Type 1

In order to engage citizen scientists, the respective problem must be broken down in bite-sized chunks that an individual can handle without formal training. They’ll need to have some basic introduction to the overall problem, but their specific task should be pretty straightforward. This simplicity of the concrete task is a prerequisite to reach a large number of citizen scientists; that’s what scales up the resource.

But there’s a second, even more important aspect: many scientific problems can be split up in subtasks and subsubtasks, and even further to almost atomic levels. And their majority can be handled by some mechanism or algorithm by just repeating the same action over and over again in some automated routine. Though citizen science may well achieve the same result, it would be far less efficient than such automation. Therefore, citizen science is best suited for the type of problems that require a specific human skill, which cannot be replicated by machines, mechanisms, or algorithms. Think for example about pattern recognition: despite significant technological progress, the human ability to see patterns in images, in behaviours, even in sequences of numbers, is still unrivalled. There is no other resource than a human brain that can handle this type of problem. Hence, if your problem entails lots of such tasks, then citizen science is the way to go.

Topics for citizen science – Type 2

The other type of topic that citizen science is very well suited for is in the area of cross-domain or cross-disciplinary problems. Professional science has become compartmentalised and almost segregated in disciplines and subdisciplines. This is very useful to efficiently solve scientific puzzles that fall within these compartments, but poses severe challenges for those problems that don’t fit. And especially for those problems that cut across many different compartments, conventional scientific wisdom struggles.

The difference that citizen science can make for such problems is in the broad access to micro-expertise on and off the beaten track. It is the density and numerousity of random encounters of ideas, a randomness that is difficult to obtain within the established circles of a given scientific paradigm. Instead, it is more likely to achieve that outside and beyond the professional circles. Thus casting the net wider and further through citizen science is a promising approach. Michael Nielsen calls it designed serendipity: making sure that the broadest range of potentially useful ideas can be expressed, encouraging the exchange of seemingly unrelated thoughts that inspire yet further ideas, which go beyond that conventional concepts and approaches that are employed within a scientific paradigm. Solutions are thus built from component elements that are usually considered as totally incompatible, and citizen science is the tool to create the conditions for this serendipity to occur.

The role of professional scientists

Citizen science does not challenge the existence of professional science. Rather, it provides an additional resource that is a healthy augmentation to our scientific endeavour. For citizen science to succeed, it actually needs the engagement of professional scientists.

In order to make a scientific problem accessible to the citizen scientists, this problem needs to be broken down into ever smaller pieces, blocks, modules, which an individual can handle. This modularisation is essential for the success of the exercise; and it’s equally important to construct these modules already with an understanding for how the results can be integrated again to develop the overarching solution to the problem. Modularisation and integration, as well as the planing of these activities, are a task that requires deep and solid understanding on the state of the knowledge in a given science domain. Therefore, modularisation and integration are the interfaces between professional and citizen science, and they are unthinkable without the strong engagement of professional scientists.

In addition, the validation and documentation of results (regardless whether they originate from citizen science or from professional science) require formal scientific training and education to ensure that standards are met in order to keep the  body of scientific knowledge growing, curated, and accessible. But it is also the part that is considered most boring, least appreciated. Citizen scientists are far less likely to invest their time in this perceivedly less noble task. And professional scientists are likely to maintain some scepticism over the work of laymen.

Challenges

For those reason, ensuring that the results of citizen science are accepted as valid contributions and incorporated in mankind’s body of scientific knowledge will be the main challenge for us in order to make citizen science succeed. And this challenge has aspects of basic science education, both in terms of training professional scientists to understand and appreciate the contributions of citizen science and in terms of preparing citizens better to engage in science. As a result, this challenge has elements of science literacy as well, for the citizens’ view of science and for the scientists’ views of society and their role therein.

A second challenge is in funding. Most likely, the individual citizen scientist is motivated by personal interest and belief in the relevance of the work. However, he makes his living in some way that is not directly dependent on solving your scientific problem; otherwise he’d be a professional scientist. Therefore, and despite his best intentions, he can only invest a very small share of his (spare) time in contributing to solve your problem. This personal economic constraint for the individual citizen scientist poses a fundamental limitation on the present feasibility and long-term sustainability of networked science.

If we want to harvest the contributions from citizen science to augment the work of professional scientists in order to propel our scientific endeavour forward, then I believe we must find innovative ways to incentivise and to remunerate the work of the volunteer citizen willing to engage in science. Otherwise we’d fail to attract the experts’ attention and keep it focused on some of our most pressing problems.

The first challenge is comparatively easy to overcome, though it will require some time to gain more experience. It is the second challenge that poses the bigger obstacle. Maybe there’s a hybrid construct of professionalising the funding for citizen science, while maintaining its strengths; maybe there are other approaches to solve this economic challenge. At this stage, I have no solution to offer; but I’ll keep this question on my radar. And of course your ideas are welcome …