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Peng H, Qiu HS, Fosse HB, Uzzi B. Promotional language and the adoption of innovative ideas in science. Proc Natl Acad Sci U S A 2024; 121:e2320066121. [PMID: 38861605 PMCID: PMC11194578 DOI: 10.1073/pnas.2320066121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/01/2024] [Indexed: 06/13/2024] Open
Abstract
How are the merits of innovative ideas communicated in science? Here, we conduct semantic analyses of grant application success with a focus on scientific promotional language, which may help to convey an innovative idea's originality and significance. Our analysis attempts to surmount the limitations of prior grant studies by examining the full text of tens of thousands of both funded and unfunded grants from three leading public and private funding agencies: the NIH, the NSF, and the Novo Nordisk Foundation, one of the world's largest private science funding foundations. We find a robust association between promotional language and the support and adoption of innovative ideas by funders and other scientists. First, a grant proposal's percentage of promotional language is associated with up to a doubling of the grant's probability of being funded. Second, a grant's promotional language reflects its intrinsic innovativeness. Third, the percentage of promotional language is predictive of the expected citation and productivity impact of publications that are supported by funded grants. Finally, a computer-assisted experiment that manipulates the promotional language in our data demonstrates how promotional language can communicate the merit of ideas through cognitive activation. With the incidence of promotional language in science steeply rising, and the pivotal role of grants in converting promising and aspirational ideas into solutions, our analysis provides empirical evidence that promotional language is associated with effectively communicating the merits of innovative scientific ideas.
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Affiliation(s)
- Hao Peng
- Department of Management & Organizations, Kellogg School of Management, Northwestern University, Evanston, IL60208
- Northwestern Institute on Complex Systems, Evanston, IL60208
| | - Huilian Sophie Qiu
- Department of Management & Organizations, Kellogg School of Management, Northwestern University, Evanston, IL60208
- Northwestern Institute on Complex Systems, Evanston, IL60208
| | | | - Brian Uzzi
- Department of Management & Organizations, Kellogg School of Management, Northwestern University, Evanston, IL60208
- Northwestern Institute on Complex Systems, Evanston, IL60208
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Lin Z, Yin Y, Liu L, Wang D. SciSciNet: A large-scale open data lake for the science of science research. Sci Data 2023; 10:315. [PMID: 37264014 DOI: 10.1038/s41597-023-02198-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.
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Affiliation(s)
- Zihang Lin
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- School of Computer Science, Fudan University, Shanghai, China
| | - Yian Yin
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Lu Liu
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Dashun Wang
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
- Kellogg School of Management, Northwestern University, Evanston, IL, USA.
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
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3
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Liu L, Jones BF, Uzzi B, Wang D. Data, measurement and empirical methods in the science of science. Nat Hum Behav 2023:10.1038/s41562-023-01562-4. [PMID: 37264084 DOI: 10.1038/s41562-023-01562-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/17/2023] [Indexed: 06/03/2023]
Abstract
The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.
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Affiliation(s)
- Lu Liu
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA
| | - Benjamin F Jones
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- National Bureau of Economic Research, Cambridge, MA, USA
- Brookings Institution, Washington, DC, USA
| | - Brian Uzzi
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Dashun Wang
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
- Kellogg School of Management, Northwestern University, Evanston, IL, USA.
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
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Practical operation and theoretical basis of difference-in-difference regression in science of science: The comparative trial on the scientific performance of Nobel laureates versus their coauthors. JOURNAL OF DATA AND INFORMATION SCIENCE 2023. [DOI: 10.2478/jdis-2023-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
Abstract
Purpose
In recent decades, with the availability of large-scale scientific corpus datasets, difference-in-difference (DID) is increasingly used in the science of science and bibliometrics studies. DID method outputs the unbiased estimation on condition that several hypotheses hold, especially the common trend assumption. In this paper, we gave a systematic demonstration of DID in the science of science, and the potential ways to improve the accuracy of DID method.
Design/methodology/approach
At first, we reviewed the statistical assumptions, the model specification, and the application procedures of DID method. Second, to improve the necessary assumptions before conducting DID regression and the accuracy of estimation, we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention. Lastly, we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates, by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.
Findings
We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results. As a case study, we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.
Research limitations
This study ignored the rigorous mathematical deduction parts of DID, while focused on the practical parts.
Practical implications
This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.
Originality/value
This study gains insights into the usage of econometric tools in science of science.
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Shibuya Y, Lai CM, Hamm A, Takagi S, Sekimoto Y. Do open data impact citizens' behavior? Assessing face mask panic buying behaviors during the Covid-19 pandemic. Sci Rep 2022; 12:17607. [PMID: 36266321 PMCID: PMC9584957 DOI: 10.1038/s41598-022-22471-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/14/2022] [Indexed: 01/13/2023] Open
Abstract
Data are essential for digital solutions and supporting citizens' everyday behavior. Open data initiatives have expanded worldwide in the last decades, yet investigating the actual usage of open data and evaluating their impacts are insufficient. Thus, in this paper, we examine an exemplary use case of open data during the early stage of the Covid-19 pandemic and assess its impacts on citizens. Based on quasi-experimental methods, the study found that publishing local stores' real-time face mask stock levels as open data may have influenced people's purchase behaviors. Results indicate a reduced panic buying behavior as a consequence of the openly accessible information in the form of an online mask map. Furthermore, the results also suggested that such open-data-based countermeasures did not equally impact every citizen and rather varied among socioeconomic conditions, in particular the education level.
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Affiliation(s)
- Yuya Shibuya
- grid.26999.3d0000 0001 2151 536XCenter for Spatial Information Science, The University of Tokyo, Tokyo, Japan
| | - Chun-Ming Lai
- grid.265231.10000 0004 0532 1428Department of Computer Science, Tunghai University, Taichung City, Taiwan
| | - Andrea Hamm
- grid.6734.60000 0001 2292 8254Department for Electrical Engineering and Computer Science, Technical University Berlin, Berlin, Germany
| | - Soichiro Takagi
- grid.26999.3d0000 0001 2151 536XInterfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan
| | - Yoshihide Sekimoto
- grid.26999.3d0000 0001 2151 536XCenter for Spatial Information Science, The University of Tokyo, Tokyo, Japan
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Abstract
In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, and resources and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from a controlled surrogate. In particular, we find that highly productive scientists tend to have a higher fraction of single-topic collaborators, while highly cited-i.e., impactful-scientists have a higher fraction of multitopic collaborators. We also suggest a plausible mechanism for this distinction. Moreover, we investigate the cases where scientists involve existing collaborators in a new topic. We find that, compared to productive scientists, impactful scientists show strong preference of collaboration with high-impact scientists on a new topic. Finally, we validate our findings by investigating active scientists in different years and across different disciplines.
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