Satellite: Quantifying Science

Thursday, October 1, 2015
8:30 am – 5:00 pm
Hilton DoubleTree:  Fiesta III

Satellite Organizers:

Dashun Wang, Pennsylvania State University, USA
James Evans, University of Chicago, USA

Abstract
Modern scientific research has grown exponentially over the past several decades. Growth in research articles, patents, preprints, white papers and informal content on the web (e.g., company product descriptions) has become “big data”, an information overload for scientists, engineers and also the government agencies, foundations and companies that support them. More than simply a change in scale, science increasingly constitutes a complex system: apparently complicated, involving strong interaction between components, and predisposed to emergent or unexpected collective outcomes. The growing number of global scientists and research teams, increasingly connected via multiple channels—international conferences, online publications, e-mail, and science blogs—have increased and multiplied this complexity. Moreover, intensifying specialization in science and engineering disciplines has made it more difficult for scientists to communicate and collaborate, and for evaluators to judge the promise and progress of research investments in those areas.

All of these changes in this 400 Billion dollar enterprise suggest the importance of quantitative and automated approaches to science that take into account the deluge of information that no individual scientist can master. The increasing availability of large-scale datasets that capture major activities in science—publications, patents, citations, grant proposals, as well as detailed meta-data associated with them—has created an unprecedented opportunity to explore in a quantitative manner the patterns of scientific production and reward. In contrast with standard bibliometric studies, the recent surge in quantitative studies of science is characterized by a few distinct flavors: (i) They typically rely on large-scale datasets to study science, ranging from hundreds of thousands to millions of authors, papers and their citations; (ii) Instead of evaluating metrics, they use models to more deeply probe the mechanisms driving science, from knowledge production to scientific impact, systematically distinguishing predictable from random patterns; (iii) More quantitative studies of science no longer hold the unique goal of evaluating and improving the system of science. Rather, researchers from a wide range of disciplines have begun to use science as an observatory to probe social phenomena that are more universal and widely applicable than the institutions of science themselves. As such, the tools and perspectives vary, involving social scientists, information and computer scientists, economists, physicists and mathematicians, with results published in venues with non-overlapping readership.

The goal of this satellite is to bring together leading researchers from various disciplines and form discussions on the proliferating subject of quantifying science.

Visit their very cool website at:  http://dashunwang.com/workshop/qs-ccs15

Speakers

Should We Stop Asking Authors to Suggest Reviewers?
Daniel Acuna, Rehabilitation Institute of Chicago and Northwestern University, USA
Misha Teplitskiy, University of Chicago, USA
James Evans, University of Chicago, USA
Konrad Kording, Rehabilitation Institute of Chicago and Northwestern University, USA

Quantifying Science: Sex-bias in Biomedical Research: a Bibliometric Perspective
Vetle I. Torvik, University of Illinois at Urbana-Champaign, USA
Laura G. Cruz, University of Illinois at Urbana-Champaign, USA

Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science
Misha Teplitskiy, University of Chicago, USA
Grace Lu, University of Chicago, USA
Eamon Duede, University of Chicago, USA

Will This Paper Increase Your h-index?
Nitesh Chawla, University of Notre Dame, USA
Reid Johnson, University of Notre Dame, USA
Yuxiao Dong, University of Notre Dame, USA

Evaluating the Predictive Power of Maps of Science
Miguel R. Guevara, MIT MediaLab, Universidad de Playa Ancha, Universidad Federico Santa María, Chile
Dominik Hartmann, MIT Media Lab, Germany
Manuel Aristaran, MIT Media Lab, USA
Marcelo Mendoza, Universidad Técnica Federico Santa María, Chile
César Hidalgo, MIT Media Lab, USA

The Unknown Known: Science, Social Learning, and Collective Intelligence
Jacob G. Foster, UCLA, USA

How International Scientific Collaboration Boosts National Research
Lingjiao Chen, Web Sciences Center, University of Electronic Science and Technology of China; Big Data Research Center, University of Electronic Science and Technology of China, China
Jin-Hu Liu, Web Sciences Center, University of Electronic Science and Technology of China; Big Data Research Center, University of Electronic Science and Technology of China, China
Hai-Xing Dai, Web Sciences Center, University of Electronic Science and Technology of China; Big Data Research Center, University of Electronic Science and Technology of China, China
Junming Huang, Center of Complex Network Research, Northeastern University; University of Electronic Science and Technology of China, USA
Zhihai Rong, Web Sciences Center, University of Electronic Science and Technology of China; Big Data Research Center, University of Electronic Science and Technology of China, China
Jiang Tian, Editorial department of Journal of University Of Electronic Science And Technology Of China, China
Jun Zhang, Editorial department of Journal of University Of Electronic Science And Technology Of China, China
Qian Song, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
Tao Zhou, Web Sciences Center, University of Electronic Science and Technology of China; Big Data Research Center, University of Electronic Science and Technology of China, China

Bounded Rationality in Scientific Knowledge Discovery
Kristina Lerman, USC, USA
Nathan Hodas, PNNL, USA
Hao Wu, USC, USA

Why Scientists Chase Hot Topics
Carl Bergstrom, University of Washington, USA
Yangbo Song, University of California Los Angeles, USA
Jacob Foster, University of California Los Angeles, USA

Evaluating Scientific Outcomes from the User Perspective
Yun Huang, Northwestern University, USA
Pramesh Singh, Northwestern University, USA
Raquel Asencio-Hodge, Georgia Institute of Technology, USA
Noshir Contractor, Northwestern University, USA
Leslie DeChurch, Georgia Institute of Technology, USA
Brian Uzzi, Kellogg School of Management, Northwestern University, USA