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Awards and Fellowships

Thanks to support from the Alfred P. Sloan Foundation, IRIS distributed annual funding awards from 2018 to 2020 to researchers who used the IRIS UMETRICS dataset to address questions about the social and economic returns to investments in research.

The grants supported innovative use of the IRIS UMETRICS dataset to address open issues in the study of science and technology and in science policy. Bios of the winners and descriptions of their projects are listed below.


James Evans is the Director of Knowledge Lab, Professor of Sociology, Faculty Director of the Computational Social Science program, and member of the Committee on Conceptual and Historical Studies of Science at the University of Chicago. He is an External Professor at the Santa Fe Institute. His research is centered on collective systems of thinking and knowing: the origin of ideas, the accumulation of certainty, and the social structure of innovation.His research team on this project consists of Donghyun Kang, a PhD student focused on the social processes of dissonance and consensus in interdisciplinary research, and post-docs Clara del Junco and Simon Shachter.
Award Year: 2020-2021
Research Title: Impact of strategic funding on research type and content
Abstract: Academic ecologies need a diverse set of research programs: programs that receive few citations, but publish important research with long‐term effects; that publish anchoring paradigmatic work, or conversely, disruptive innovative work; programs driving interdisciplinary exchange. All of these qualities are important, even if sometimes at the cost of a lower H‐index or less immediate exposure. Simplified scalar measures fail to capture these multidimensional cultures of success. Stakeholders need richer signals so they can allocate strategic funding in a manner sensitive to existing institutional and organizational strengths. We propose, first, to refine an innovative framework rooted directly in published textual products, with multiple dimensions of measurement that can better accommodate diverse strategies for research impact. Linguistic influence within the broader publication record is a crucial and overlooked measure of research outcomes. To measure these implicit flows, we propose to map the semantic geography of collective academic production by compressing encodings of published abstracts, using state‐of‐the‐art deep contextual embedding methods. Our team has already deployed these methods at scale and validated their sensitivity to semantic structure—sensitivity not just to individual words, but to their composed, contextual meaning. Second, using our measures based on typologies within semantic space, we will measure significant factors at the coupling between funding portfolios and research outcomes. Finally, adapting methods to study causal influence in biological networks employed by members of our team, we will filter measured associations to identify a set of likely causal factors by which distinct funding portfolios support archetypical research strategies and outcomes.


Ran Xu is a Postdoctoral Research Associate in the Grado Department of Industrial and System Engineering at the Virginia Polytechnic Institute and State University. He holds a Bachelor’s degree in Mathematics from the Hefei University of Technology and a PhD in Measurement and Quantitative Methods from Michigan State University. His research interests include the application of quantitative methods and computational social science to the study of networks, innovation and workforce development.
Award Year: 2019-2020
Research Title: How Funding Facilitates Interdisciplinary Research for Early Career Scientists: Evidence from Neuroscience Research
Abstract:  Complex problems often cut across the borders of disciplines, and scientists often need to conduct interdisciplinary research that combines knowledge and concepts from multiple disciplines to fully understand the mechanisms and find solutions. However, there are also considerable risks associated with conducting interdisciplinary research (e.g., less likely to get funded or be published in top journals), especially for early career scientists, who face greater consequences from conducting interdisciplinary research. From a science policy perspective, it is vital to gain a better understanding of the institutional factors (e.g., funding, infrastructure) that facilitate/inhibit the production of interdisciplinary research for early career scientists, as well as the mechanisms through which they function.
In this study, we utilize the recent blossom of interdisciplinary neuroscience research and investigate how funding facilitates the production of interdisciplinary neuroscience research for early career scientists. The primary aims of this project are to investigate (1) the types of funding that facilitate the production of interdisciplinary neuroscience dissertations, and (2) the mechanisms through which these funding facilitate the production of interdisciplinary neuroscience dissertations. More specifically, we hypothesize that funding from industries and NIH institutes (especially those from institutes other than the National Institute of Neurological Disorders and Stroke (NINDS)) facilitate the production of interdisciplinary neuroscience dissertations, and they support such research by establishing interdisciplinary research centers, creating interdisciplinary research teams, and strengthening connection with industries. Together these findings will contribute to a systematic development of theories about how institutions can lead and support interdisciplinary research for early career scientists.


Kevin Kniffin is an Assistant Professor of Management and Organizations at the Dyson School of Applied Economics and Management in the S. C. Johnson College of Business at Cornell University. Kniffin has contributed original research to outlets including American Psychologist, Group & Organization Management, Human Performance, Journal of Organizational Behavior, and The Leadership Quarterly. His research interests focus on athletic and scientific teams, including topics such as the relevance of leadership, and academic career paths, within different organizational structures.
Award Year: 2019-2020
Research Title: Antecedents and Consequences of Sponsored Teaming among STEM Doctoral Graduates
Abstract: The current proposal aims to address a set of Research Questions (RQs) that will advance knowledge on the antecedents and consequences of teaming within the domain of sponsored research in Science, Technology, Engineering, and Math (STEM) fields. More specifically, we will engage the IRIS dataset to examine the degree to which team structures (e.g., team size) influence productivity. Benefits of the research include a deeper understanding of the nature of teamwork (e.g., who tend to participate in team science) along with appreciation for the role of teamwork in relation to boundary-spanning (across disciplinary divides). In addition to advancing knowledge on the people who participate in team science alongside a consideration of team products (focusing on patents), we will also seek to gain approval for increased linkages between current data available through IRIS and related datasets such as the Survey of Earned Doctorates (SED) with which we have been working for more than five years.


Douglas Guilbeault is a PhD student in the Annenberg School for Communication at the University of Pennsylvania. He holds a Bachelor’s degree in Philosophy, Rhetoric, and Cognitive Science from the University of Waterloo and a Master’s degree in Linguistics from the University of British Columbia. His research interests include the application of network science and online experiments to the study of collective creativity and how new ideas emerge and spread as a result of interdisciplinary research.
Award Year: 2018-2019
Research Title: The Network Dynamics of Interdisciplinary Research
Abstract: Research institutions face a paradox of incentives concerning interdisciplinary research. On the one hand, there are incentives to push for interdisciplinarity, because diverse teams are said to be more capable of innovation, due to their ability to link information across social networks. On the other hand interdisciplinary research has consistently lower funding and publication success. Evidence shows that innovative ideas are difficult to market if they are too new, because audiences prefer familiar material that fits their existing categories. The most successful scientific publications strike a balance between conventional and novel citations within a research community. However, interdisciplinary teams can struggle to maintain conventional categories, because higher diversity can prevent groups from converging on shared practices and terminologies. As a member of the Network Dynamics Group at the University of Pennsylvania, my dissertation uses quantitative network analysis and online experiments to study how certain social network structures are more effective at enabling diverse teams to integrate conventional and novel ideas, so they can harness creativity, while also procuring grants and high‐ impact publications. My hypothesis is that the most effective teams are those that optimally balance two key network parameters: brokerage and closure. Brokerage refers to the number of people in a network who connect otherwise unconnected networks. Closure refers to the density of interconnection within a local community. I plan to use IRIS data to develop network‐based measures to predict the creativity and productivity of interdisciplinary teams, with the aim of informing the optimal design of scientific institutions.


Charu Gupta is a PhD student in the Wharton School of Business at the University of Pennsylvania specializing in Managerial Science and Applied Economics. She holds a Bachelor’s degree in Economics and International Relations from Brown University and a Master’s degree in Economics from the London School of Economics and Political Science. Her research interests include the economics of innovation, knowledge complementarity and productivity, and the adoption and diffusion of new medical technologies and pharmaceuticals.
Award Year: 2018-2019
Research Title: The role of collaboration networks in science productivity
Abstract: Recent decades have seen a shift toward collaborative research in both academic and commercial science, yet little is known regarding the structure or actual impact of teams in these settings. Using the IRIS data, I propose to address the following research questions: (1) Are researchers with larger collaboration networks more successful? (2) How does the relatedness of scientific knowledge within teams influence overall productivity and innovative output? In doing so, I develop metrics for academic productivity, industry innovation, and knowledge relatedness and explore how collaboration networks evolve over time. In order to make causal inferences, I control for unobserved heterogeneity with fixed effects and consider potential instrumental variable approaches. This research relates to the growing science of science policy and has important implications for the optimal allocation of public R&D funds and the design of research incentives for innovation.


Elan Segarra is a PhD student in the Department of Economics at the University of Wisconsin-Madison. He holds a Bachelor’s degree in Mathematics from Harvey Mudd College and a Master’s degree in Economics from San Francisco State University. His research interests include microeconomics, applied econometrics, economics of innovation, and agricultural economics.
Award Year: 2018-2019
Research Title: Describing Heterogeneity in Research Projects Via Classification of Expenditure Profiles
Abstract: In 2017 alone the federal government is planning on investing over $140 billion in R&D across a diverse range of agencies and knowledge pursuits. While most research on the returns to investment in science have limited their focus to measuring the effects of pecuniary inputs on simple outcomes, these results offer limited policy recommendations aside from increasing funding levels. One aspect that has yet to be fully investigated is heterogeneity in how these funds are spent across different fields and types of research. What a research project spends its funding on (post‐docs, travel, test tubes, land, surveying, or x‐ray machines) tells a story about the type of research being conducted, and these expenditure profiles can be leveraged to classify projects (eg. labor intensive or capital intensive). Having both standardized expenditure categories (SECs) as well as project classifications inferred from these expenditure profiles will both further illuminate the heterogeneity of the research process as well as provide a new dimension along with heterogeneous returns to investments can be studied in future research.

Specifically, this work will produce two sets of deliverables:
1. A list of SECs together with mappings between every member university’s idiosyncratic expenditure categories and the SECs, and a framework (both an automatic method with restricted PCA and a manual pipeline) to create mappings for new universities.
2. Descriptive statistics of expenditure profiles across multiple dimensions (funding agency, discipline, and time) as well as project archetypes inferred from clustering the expenditure profiles (eg. labor intensive vs capital intensive).


Jason Coupet is an Assistant Professor of Public Administration in the School of Public and International Affairs at North Carolina State University. He holds a Bachelor’s degree in Economics from the University of Michigan, and a PhD in Management from the University of Illinois at Chicago. His research emphasizes both efficiency measurement and the econometric investigation of the role of external funding on the efficiency of public and nonprofit organizations, with a particular emphasis on higher education.
Award Year: 2018-2019
Research Title: Measuring the Efficiency of the Research Enterprise
Abstract: This project emphasizes measurement of the efficiency and productivity of research funding. To try to capture the complexity of the research funding process and the many inputs and outputs involved, this project proposes using a combination of operations management and organizational economics to estimate the productivity of the university and public agency grant infrastructures. As the IRIS data grows in size and influence, this study hopes to contribute meaningfully complex measures of grant productivity able to handle the longitudinal and complex nature of research funding and production.


Jonathon Mote is an Assistant Professor in the Department of Organization Sciences and Communication at the George Washington University. He holds Bachelor’s degrees in Economics and History from the University of Iowa, Master’s degrees in Economics and Historical Studies from the New School for Social Research, and a PhD in Sociology from the University of Pennsylvania. His research interests focus primarily on the interrelationship between organizational environments and networks of science and innovation.
Award Year: 2018-2019
Research Title: Using UMETRICS to Assess Collaboration Among Universities and National Laboratories
Abstract: This proposal seeks to explore the feasibility of utilizing the UMETRICS dataset to measure the extent and shape of collaboration between member universities and the seventeen national laboratories funded and overseen by the Department of Energy. This proposed effort would identify DOE grants to member universities, as well as subcontract and vendor awards to the national laboratories, to assess collaboration patterns. In particular, the effort would initially focus on two key outcomes: training of STEM personnel and utilization of user facilities at national laboratories. 


Lisa Cook is an Associate Professor of Economics and International Relations at Michigan State University. She holds a Bachelor’s degree in Philosophy from Spelman College and a Bachelor’s degree in Philosophy, Politics and Economics from Oxford University, and a PhD in Economics from the University of California-Berkeley. Her primary research interests include economic growth and development, innovation, and financial institutions and markets.
Award Year: 2018-2019
Research Title: The Idea Gap in Pink and Black
Abstract: Recent studies have shown that since 1963, rates of commercialization of ideas by women and African Americans have lagged those of U.S. inventors. Using new techniques of gender and ethnic identification and USPTO data, Cook and Kongchareon (2010) show that the commercialization gap has closed at the very top, at the largest firms that are publicly traded. Simultaneously, the new data show that the gap is still wide for broad commercialization activity and much wider for patent activity. The 2010 findings have been further corroborated using data from the NSF Survey of Earned Doctorates in Cook and Yang (2017), who also provide evidence on implications of gender and racial disparities in applying for, obtaining, and commercializing patents. What accounts for observed differences at each stage of the innovation process: training and research, application, patenting and commercialization? To explain mechanisms underlying these and other results, we propose to use UMETRICS data from IRIS, which contain transaction‐level data on research grants, employees paid by grants, and businesses paid by grants at 19 universities between 2001 and 2016. These data have been linked to census, USPTO, and NSF‐award data, which makes accounting for a wide range of factors affecting invention and innovation, including field of study, demographic characteristics, research funding, and patent‐ or research‐team membership, possible. Given the important progression from basic research to invention to commercialization of ideas to higher living standards, it would be critical to identify sources of breaks in this sequence.

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