The increasing complexity of science means that it is increasingly important to pull together teams in order to produce high quality research. This means that it is important to develop technologies that identify researchers with the right set of skills so that such teams can be expeditiously formed. This is particularly true in biomedical science, where the translation of research into clinical care often requires the expeditious convening of the right group of researchers, clinicians, and consumers. A variety of different technologies have evolved so that scientists can profile their skills; however it has been extremely difficult to incentivize researchers to adopt and use these technologies. This research explores the specific value that academic profiles may provide, how that value may be maximized in these larger research coordination networks, and what motivates researcher adoption and use.
Intellectual Merit: This research advances understanding of what features of profiling tools can be used to incentivize the scientific community to generate social capital in the sciences. Many examples of features are prevalent on the social networking sites that are currently popular on the Web. For instance, Facebook's initial success was attributed to incentivizing features such as exclusivity, relationship status, poking mechanisms, and directory services. For Twitter, the incentivizing feature is the availability of diverse usage channels (professional/private) and multi-client applications. The incentivizing feature for LinkedIn has been the development of professional networks and discourse only. Almost nothing is known on what incentivizes scientists. This research fills the gap by collecting an unprecedented set of data in both a biomedical and a multidisciplinary environment on what incentivizes profile adoption, influences self-curation, and increases the social capital of profiles through research networking tools. It assesses and disseminates the impact of using the data sources on the accuracy and comprehensiveness of data matched to investigators. It assesses the impact of auto-population, new data sources, and value-added features on usage and engagement. It also assesses the impact of tools on industry-academic alliances through the assessment by survey and focus groups of those actively engaged by the University of San Francisco's Office of Innovation, Technology and Alliances
Broader Impacts: Findings from this experiment can have direct impact on the progress and development of partnership methods and systems. The broad availability of wellcurated profiles should advance the quality of both science and innovation. The research advances understanding in all areas of science where collaborative efforts are required to maintain and progress the capacity for innovation.
