CSCW Principles to Support Citizen Science

Citizen science changes the way scientific research is pursued. It opens up data collection and analysis to the general public, to the wisdom of crowds. In this emerging area, there is much research to be done to better understand how we can develop citizen science infrastructure and continue the democratization of science. In creating such systems, there is much we can learn from principles that have emerged out of computer-supported cooperative work (CSCW) research. In this paper, I use a nine-step framework to highlight where CSCW knowledge can contribute. CSCW PRINCIPLES FOR CITIZEN SCIENCE Citizen science has two main purposes: first, to aid in scientific research and secondly, to provide education to the public, as successful citizen science projects should ideally engage the community in science in addition to producing scientific results. In order to succeed in reaching these goals as well as the goals of scientific education and involvement, systems to support citizen science need to be evaluated in certain ways to continue improving design. In this paper, I explore how cyberinfrastructures for citizen science have been and can be evaluated and discuss some design recommendations and the methods by which these systems can be evaluated and refined. A Nine Step Framework The Cornell Lab on Ornithology (CLO) has a model for how citizen science projects should be designed and implemented so that they can fulfill both research and education goals [3]. This 9-step model serves as a good framework to analyze both the benefits and challenges of collaborative science systems. For each step, I will present the most pertinent CSCW topics and discuss implications for design. 1Choose a scientific question In choosing a scientific question that can be addressed through cyberinfrastructure, it is important to think about how a particular scientific question can be translated and deand re-contextualized in a way that the participant can understand. Though the research itself is highly complex, it must be able to be broken down into shared elements of understanding. Boundary objects work because they are understandable by both parties without lots of unnecessary or unhelpful additional information. [1] A second area of concern is the often widelydistributed geography and timespan of citizen science projects. Coupling is key. Only work that can be loosely coupled can be done successfully through citizen science crossing numerous locations. [5]