Center for Knowledge Infrastructures

Big data gets all the attention but little data are the norm in most fields. Scientists, social scientists, and humanities scholars alike tend to work in small groups and on projects of a year or two in length. The resulting datasets tend to be small, local, and not easily shared. The talk will characterize the problem of long tail data and identify factors that determine how well data can be transferred between contexts. These include provenance, metadata, documentation, and features of the data and of the research methods. Case studies of astronomy and sensor networked science are presented and compared. Video available at: http://www.youtube.com/watch?v=LrgwWqc8_tI&feature;=youtu.be&noredirect;=1