Towards enabling social analysis of scientific data

Flickr, Facebook, Yahoo! Pipes), which facilitate collaboration and sharing between users, are becoming increasingly popular. An important benefit of these sites is that they enable users to leverage the wisdom of the crowds. For example, in Flickr, users, in a mass collaboration approach, tag large volumes of pictures. These tags, in turn, help them to more easily find pictures they are looking for. In the (very) recent past, a new class of Web site has emerged that enables users to upload and collectively analyze many types of data (e.g., Many Eyes and Swivel). These are part of a broad phenomenon that has been called social data analysis". This trend is expanding to the scientific domain where a number of collaboratories are under development. As the cost of hardware decreases over time, the cost of people goes up as analyses get more involved, larger groups need to collaborate, and the volume of data manipulated increases. Science collaboratories aim to bridge this gap by allowing scientists to share, re-use and refine their computational tasks (workflows). In this position paper, we discuss the challenges and key components that are needed to enable the development of effective social data analysis (SDA) sites for the scientific domain.