The Fundamentals of Predictability of Scientific Success
Technical Report,01 Feb 2015,31 Jan 2019
Northeastern University Boston United States
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During 2018, our team has been focusing on understanding scientific success in a couple of aspects, including scientific ability, gender, social network, team, country, discipline, move, etc. To be specific, as we discuss next, we tackled several key questions, aiming to propose quantitative and predictive models to understand, explain and predict scientific success. 1. Ongoing Data curation and disambiguation This is one of the most critical tasks that facilitates the implementation of the whole line of work, by providing more accurate and expansive datasets. Tackling the author name ambiguity problem, we developed a method to identify authors with high accuracy, collaborating with a group from University of Massachusetts Amherst. Specifically, we employed several layers of information, from an authors affiliation to email address and the list of hisher collaborators, allowing us to uniquely identify each scientist. This method outperforms previous efforts indisambiguating the Web of Science dataset by providing a higher accuracy and covering a broader extent of data. 2. Ongoing Impact of network on scientific success We have started a new line of research to separate the social impacts of the scientific community on the perceived success of a scientist. Our analysis assumes that the impact of a scientist is strongly determined by the structure of her professional interaction network, defined as the list of scientists with whom one has collaborated or worked at the same institution or got to know them through a common collaborator. During last year our effort has focused on extracting the pertinent interaction network of scientists, which provides the primary information to understand the effect of the interaction network on scientific success.
- Test Facilities, Equipment and Methods