Untangling pair synergy in the evolution of collaborative scientific impact
Abstract Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scie...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-12-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-023-00439-w |
| _version_ | 1827399601354702848 |
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| author | Gangmin Son Jinhyuk Yun Hawoong Jeong |
| author_facet | Gangmin Son Jinhyuk Yun Hawoong Jeong |
| author_sort | Gangmin Son |
| collection | DOAJ |
| description | Abstract Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scientists and 1,026,196 pairs of scientists. We quantify pair synergy by extracting the non-additive effects of collaboration on scientific impact, which are not confounded by prior collaboration experience or luck. We employ a network inference methodology with the stochastic block model to investigate the mechanism of pair synergy and its connection to individual attributes. The inferred block structure, derived solely from the observed types of synergy, can anticipate an undetermined type of synergy between two scientists who have never collaborated. This suggests that synergy arises from a suitable combination of certain, yet unidentified, individual characteristics. Furthermore, the most relevant to pair synergy is research interest, although its diversity does not lead to complementarity across all disciplines. Our results pave the way for understanding the dynamics of collaborative success in science and unlocking the hidden potential of collaboration by matchmaking between scientists. |
| first_indexed | 2024-03-08T19:48:29Z |
| format | Article |
| id | doaj.art-89a073b545624959a2b3021107b9d6a5 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-03-08T19:48:29Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-89a073b545624959a2b3021107b9d6a52023-12-24T12:12:06ZengSpringerOpenEPJ Data Science2193-11272023-12-0112111410.1140/epjds/s13688-023-00439-wUntangling pair synergy in the evolution of collaborative scientific impactGangmin Son0Jinhyuk Yun1Hawoong Jeong2Department of Physics, KAISTSchool of AI Convergence, Soongsil UniversityDepartment of Physics, KAISTAbstract Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scientists and 1,026,196 pairs of scientists. We quantify pair synergy by extracting the non-additive effects of collaboration on scientific impact, which are not confounded by prior collaboration experience or luck. We employ a network inference methodology with the stochastic block model to investigate the mechanism of pair synergy and its connection to individual attributes. The inferred block structure, derived solely from the observed types of synergy, can anticipate an undetermined type of synergy between two scientists who have never collaborated. This suggests that synergy arises from a suitable combination of certain, yet unidentified, individual characteristics. Furthermore, the most relevant to pair synergy is research interest, although its diversity does not lead to complementarity across all disciplines. Our results pave the way for understanding the dynamics of collaborative success in science and unlocking the hidden potential of collaboration by matchmaking between scientists.https://doi.org/10.1140/epjds/s13688-023-00439-wScience of scienceTeam scienceNetwork inference |
| spellingShingle | Gangmin Son Jinhyuk Yun Hawoong Jeong Untangling pair synergy in the evolution of collaborative scientific impact EPJ Data Science Science of science Team science Network inference |
| title | Untangling pair synergy in the evolution of collaborative scientific impact |
| title_full | Untangling pair synergy in the evolution of collaborative scientific impact |
| title_fullStr | Untangling pair synergy in the evolution of collaborative scientific impact |
| title_full_unstemmed | Untangling pair synergy in the evolution of collaborative scientific impact |
| title_short | Untangling pair synergy in the evolution of collaborative scientific impact |
| title_sort | untangling pair synergy in the evolution of collaborative scientific impact |
| topic | Science of science Team science Network inference |
| url | https://doi.org/10.1140/epjds/s13688-023-00439-w |
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