Tutorial for IC2S2 2019

Computational Social Science of Science

The science of science is an emerging field that uses computational tools to quantify the patterns underlying scientific relationships and dependencies from the explosion of data in science. In this tutorial, we aim to provide a comprehensive overview of the field, including recent studies on scientific ideas, individual careers and teams from the perspective of computational social science.

Tutorial outline

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This tutorial will cover three components that probe three critical, interconnected pillars of the scientific enterprise: the science of careers, the science of collaborations, and science of knowledge. The outline is subject to change.

The Science of Careers

Considering the complex and apparently unpredictable nature of a scientific career, we will answer the following questions: are there reproducible patterns underlying an individual career in science? What are the fundamental mechanisms that drive the success of a scientist? Accurate quantitative answers to these questions are the aim of this tutorial, as they can affect both the way we train scientists and the way we acknowledge and reward scientific excellence.

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The Science of Collaboration

Scientists do not work alone. The modern science has been shifted toward big science dominated by team productions. In this tutorial, we will rely on a rich body of literature that is often called the Science of Team Science (SciTS), and discuss how scientists collaborate and work together in teams. We will address the questions like why are some collaborations so fruitful, lasting, and impactful while others fail, at times, spectacularly? What are the factors that help and hinder the effectiveness of teams?

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The Science of Knowledge

New scientific ideas do not emerge in vacuum. They inherently build on previous work by other scientists. In return, researchers give credit to body of work their ideas built on, giving rise to what we call today citations. As such, modeling the emergence and spreading of scientific ideas through citation networks has been widely studied in recent years. This part of the tutorial will discuss recent studies on the dynamics of scientific ideas through citation networks and the underlying mechanisms.

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Organizers

Lu Liu is a Ph.D. candidate at the College of Information Science and Technology, the Pennsylvania State University. She is also a visiting scholar at the Kellogg School of Management, Northwestern University and Northwestern Institute on Complex Systems (NICO). Her research interests are science of science and computational social science. Her initial progress on the subject has been published recently in Nature.

Dashun Wang is an Associate Professor of Management and Organizations at the Kellogg School of Management, Northwestern University, where he directs the Social Complexity group. He is also a core faculty at the Northwestern Institute on Complex Systems (NICO) and an affiliated faculty of Industrial Engineering & Management Sciences at the McCormick School of Engineering. Prior to joining Northwestern, he was an Assistant Professor of Information Sciences and Technology at the Pennsylvania State University and before that, a Research Staff Member at the IBM T.J. Watson Research Center. His research has been published in journals as Nature, Science and PNAS as well as top specialized venues in computer science and physics, his opinion pieces have been published in the Wall Street Journal, Harvard Business Review, and Forbes, and his work has been featured in the New York Times, the Economist, the Guardian, the Washington Post, the Boston Globe, among other major global media outlets. He is a recipient of the AFOSR Young Investigator Award (2016).