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Nobel Laureates in Physiology or Medicine who received the Prize between 1969 and 2011 are compared to a matched group of scientists to examine productivity, impact, coauthorship and international collaboration patterns embedded within research networks. After matching for research domain, h-index, and year of first of publication, we compare bibliometric statistics and network measures. We find that the Laureates produce fewer papers but with higher average citations. The Laureates also produce more sole-authored papers both before and after winning the Prize. The Laureates have a lower number of coauthors across their entire careers than the matched group, but are equally collaborative on average. Further, we find no differences in international collaboration patterns. The Laureates coauthor network reveals significant differences from the non-Laureate network. Laureates are more likely to build bridges across a network when measuring by average degree, density, modularity, and communities. Both the Laureate and non-Laureate networks have “small world” properties, but the Laureates appear to exploit “structural holes” by reaching across the network in a brokerage style that may add social capital to the network. The dynamic may be making the network itself highly attractive and selective. These findings suggest new insights into the role "star scientists" in social networks and the production of scientific discoveries.

Key to accurate bibliometric analyses is the ability to correctly link individuals to their corpus of work, with an optimal balance between precision and recall. We have developed an algorithm that does this disambiguation task with a very high recall and precision. The method addresses the issues of discarded records due to null data fields and their resultant effect on recall, precision and F-measure results. We have implemented a dynamic approach to similarity calculations based on all available data fields. We have also included differences in author contribution and age difference between publications, both of which have meaningful effects on overall similarity measurements, resulting in significantly higher recall and precision of returned records. The results are presented from a test dataset of heterogeneous catalysis publications. Results demonstrate significantly high average F-measure scores and substantial improvements on previous and stand-alone techniques.

Bibliometric studies often measure and compare scholarly performance, but they rarely investigate why universities, departments, and research groups do have different performance. In this paper we try to explain differences in scholarly performance of research groups in terms of organizational variables. In order to do this, we extensively review the relevant literature, and develop a model using two theoretical approaches. A multivariate analysis shows which of the independent variables do play a role in the various scholarly performance dimensions. The study shows what organizational strategies may help in optimizing performance in various dimensions. Implications are discussed.