Problem with data
We live in an era when vast amounts of data are being generated from a lot of sources. An example are satellites which photograph planetary surfaces in great detail. The problem is that these satellites such as ESA's Mars Express transfer such amounts of data back to earth that the relative small amount of involved researchers. This means there isn't enough capacity to process all available data so important discoveries might remain hidden.
Diagram: In general the problem is that we have a lot of data which due to a
limited amount of research capacity doesn't reach its full knowledge potential.
The solution would be to reinforce the researchers so more of the available data gets processed. This solution is exactly what the Cerberus platform brings. Cerberus offers additional processing capacity in the form of a 'crowdsourcing' serious computer game which presents the to be processed data to its players. By doing this Cerberus significantly increases research output which then automatically leads to more knowledge.
Diagram: The solution is that the Cerberus platform reinforces the research
capacity which transforms the bottle neck in a rapid data analysis engine. This
could result in an enormous boost of new knowledge.
Crodsourcing vs. E-learning
The best known form of a serious game such as Cerberus is where the game teaches new knowledge to its players. In the case of Cerberus however, the players are actually responsible for the creation of new knowledge. The fact is, however, before we can enable our players to generate the new knowledge out of the satellite photographs Cerberus first must teach the players about what to find. This way Cerberus acts both as a crowdsourcing and an e-learning platform. See the diagram for a short clarification.
Diagram: In the case of Cerberus as an e-learning platform we transfer new knowledge to our players. In the case of Cerberus as a crowdsourcing platform the people generate new knowledge out of the data. These two factors are in most Cerberus versions combined.
Besides the new Cerberus platform there also exist other examples where crowdsourcing is successfully used. Here are some examples.
A first example of a serious crowdsourcing game used for science is Foldit which enables players to create new protein-chains.. The goal is to contribute to cancer research. Players are stimulated to play the game by earning points and the possibility to join in "the scientific glory" (Viņas, Baker, & Popovic, 2008; The Economist, 2008).
A second example is Galaxy Zoo. This is an initiative where galaxies and their behaviors are being classified by users (Darg, et al., 2009). It has been shown that the research capacity of the available astronomers can only cover a small portion of the amount of data which needs to be analyzed. For this reason the astronomers only examine data which is prioritized while possible new discoveries may remain hidden. To compensate for this Galaxy Zoo was developed. Galaxy Zoo succeeds in the delivery of reliable analysis by letting multiple contributors analyze photographs together. This way each user analysis makes the database of galaxies a more and more reliable representation of the reality. The results prove to be just as accurate as when the analysis would have been done by expert astronomers.
By the year 2009 over 220 000 people have participated in this project and already have contributed in the discovery of a new type of galaxy. Galaxy Zoo has an active social network where users share knowledge and opinions about objects they have seen (Charles, Arkel, & Schwawinski, 2009).