Why Citizen Science?
The Citizen Science Alliance’s mission is to create online citizen science projects to involve the public in academic research. We believe that by doing this we can not only help everyone share in the excitement of discovery, but that such projects are a necessary response to the flood of data facing researchers in many fields.
As data sets have expanded in size due to the rapid decline in cost of computing, detectors, bandwidth and storage, so traditional modes of research have struggled to cope. While machine learning and computing have been able to take up some of the slack, they are not always adequate replacement for human abilities. After all, our brains have evolved to be extremely good at pattern recognition and we should try and take advantage of this ability where possible.
In the early years of this data flood, researchers were able to cope by recruiting more willing collaborators and students, but in many fields this proved to be only a stopgap. We need a much larger workforce than any academic department can provide. Luckily, the web provides a means of reaching a much larger audience, willing to devote their free time to projects which can add to our knowledge of the world and the Universe.
We call this method of involving the public ‘citizen science’, and we believe working this way has several advantages, a few of which are listed below.
- The ability to cope with extremely large data sets – in its first six months Galaxy Zoo provided the same number of classifications as would a graduate student working round the clock for 3.5 years.
- Unlike work by a small number of experts, our ability to gather multiple independent interactions with the data provides quantitative estimates of error. This is an essential part of the ‘wisdom of crowds’, allowing us to understand the accuracy of the data we provide.
- Citizen science data sets naturally provide large and powerful training sets for machine learning approaches to classification problems. This is an essential part of our future; as data sets continue to grow we will need to hand off more and more of the routine tasks to machines; by doing citizen science today we can help train them.
- Serendipitous discovery is a natural consequence of exposing data to large numbers of users, and is something that is very difficult to program into automatic routines. Humans are naturally programmed to keep an eye out for the weird and the odd, even while sorting most objects into more mundane categories.
- While the primary goal of our projects is to produce academic research, by their very nature they are also outreach projects. As it involves our volunteers directly in the process of research, citizen science is a powerful tool for both formal and informal education. Unlike traditional education programs, from the moment users first interact with one of our project, they are not only learning but also contributing to science.
Why work together?
A key part of our plans is to provide a home for citizen science projects from a host of different disciplines, rather than have them scattered about the web. For those developing projects, there are obvious logistical advantages; we have an infrastructure that has been purpose built to support massively distributed citizen science, and which can cope with the rigours of hosting the most popular projects. Many tools and features, whether in the user interface, the backend or the analysis of data can be shared between projects, too. As the world leaders in online data analysis with citizen science methods, we can make sure that the researchers we work with just have to worry about the results. This philosophy is exemplified in our Project Builder tool which allows any researcher to build their own citizen science data analysis project through a set of templates.
Access to our existing community of citizen scientists allows us to market new projects directly to about 1.5 million people; while media attention is always nice, if we can enthuse our audience they can and have become advocates for our projects. Paradoxically, this allows us to carry out projects on a smaller scale; completing classifications for smaller data sets within a weekend or a few weeks is now plausible.
In providing new projects, we are also providing a pathway through a citizen science career for our volunteers. Those who turn up to look at the Moon may discover a passion for galaxies, or oceanography or ancient papyri. Those who start with the simplest parts of Galaxy Zoo or Solar Stormwatch may graduate to harder or more abstract projects, or to more detailed interaction with the data behind each of our projects. This opportunity - to enable real science, online, by volunteers and non-specialists - is one of the most rewarding parts of citizen science.
To get involved, click here.