Collaboration Capacity: Measuring the Impact of Cyberinfrastructure Enabled Collaboration Networks

Published on October 17, 2018


This paper reports a study of the incremental impact of evolving cyberinfrastructure (CI)-enabled collaboration networks on scientific capacity and knowledge diffusion. While ample research shows how collaboration contributes to greater productivity, higher-quality scientific outputs, and increased probability of breakthroughs, it is unclear how the early stages of collaboration on data creation supports knowledge generation and diffusion. Further, it is not known whether the ability to garner larger inputs increases collaboration capacity and subsequently accelerates the rate of knowledge diffusion. Given that the collaboration capacity of a science team is largely dependent upon the Scientific and Technical (S&T) Human Capital, the greater a researcher’s S&T human capital, the greater the opportunity to collaborate and access resources. We use “Collaboration Capacity” to refer to this measure of S&T human capital.

In this study, we collected metadata for molecular sequences in GenBank from 1990-2013. The data contain details about sequences, submission date, submitter(s), and associated publications and authors. Based on the collaboration capacity framework (Figure 1), we focused on the relationship between collaboration network size and research productivity and the role of CI-enabled data repositories in accelerating collaboration capacity. Our preliminary results show that the size of CI-enabled collaboration networks at data creation stage was positively related to research productivity as measured by sequence data production, and the extent and rate of knowledge diffusion, represented by patent applications. Shrinking time gaps between data submissions and patent applications support the hypothesis that CI-enabled data repositories are an accelerating factor in incremental collaboration capacity.


Jian Qin

Jian Qin is Professor at the iSchool, Syracuse University. The areas of her research interest include metadata, knowledge and data modeling, scientific communication, research networks, and research data management. She received funding from IMLS to develop an eScience librarianship curriculum and from NSF for the Science Data Literacy project. Her recent research projects include metadata modeling for gravitational wave research data management and big metadata analytics using GenBank metadata records for molecular sequences, both with funding from NSF. She also collaborated with a colleague to develop a Capability Maturity Model for Research Data Management funded by a grant from the Interuniversity Consortium for Political and Social Research (ICPSR). She was a visiting scholar at the Online Computer Library Center (OCLC), where she developed the learning object vocabulary project. Jian Qin has published widely in national and international research journals. She was the co-author of the book Metadata and co-editor for several special journal issues on knowledge discovery in databases and knowledge representation.

Jian Qin holds a Master of Library and Information Science from the University of Western Ontario andPh.D. from University of Illinois at Urbana-Champaign.

Jian Qin’s contact information can be found from the faculty directory at School of Information Studies, Syracuse University. Her profile is also available on orcid-logo and View Jian Qin’s profile on LinkedIn.

Sarah Bratt

Sarah Bratt is a doctoral student at Syracuse University’s School of Information Studies. She holds a B.S. in Philosophy at Ithaca College, and M.S. in Library and Information Science with a Data Science certificate from Syracuse University. Her research focuses on scientific communication, data science, and new technologies for organizing scientific communities. Current projects include (1) big metadata analytics of NCBI’s GenBank scientific collaboration network metadata to understand the practices and processes involved in distributed science work; and (2) calculating research dataset use and circulation to understand how different types of scientific interactions impact research productivity and knowledge diffusion.

Jeff Hemsley

An Assistant Professor at the School of Information Studies at Syracuse University. He is co-author of the book Going Viral (Polity Press, 2013 and winner of ASIS&T Best Science Books of 2014 Information award and selected by Choice magazine as an Outstanding Academic Title for 2014), which explains what virality is, how it works technologically and socially, and draws out the implications of this process for social change. You can see Jeff talk about researching viral events on YouTube. You can also see his Benefunder Profile. He is a founding member of the Behavior, Information, Technology and Society Laboratory (BITS lab) here at the Syracuse iSchool.

Jeff earned his Ph.D. from the University of Washington’s Information School, where he was a founding member of the Social Media Lab at the University of Washington. The lab received RAPID and INSPIRE awards from NSF, an Amazon Web Services in Education research grant award, and a gift from Microsoft Research. His research has appeared in journals like Policy & Internet, American Behavioral Scientist and the Journal of Organizational Computing and Electronic Commerce.

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