-
Ding, Y.: Scholarly communication and bibliometrics : Part 1: The scholarly communication model: literature review (1998)
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- Abstract
- State of the art review of models of the process of scholarly communication and the role that bibliometrics can play in studying this process
- Source
- International forum on information and documentation. 23(1998) no.2, S.20-29
-
Ding, Y.; Foo, S.: Ontology research and development : part 1 - a review of ontology generation (2002)
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- Date
- 12. 8.2005 14:25:23
-
Ding, Y.; Foo, S.: Ontology research and development : part 2 - a review of ontology mapping and evolving (2002)
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- Date
- 12. 8.2005 14:26:23
-
Yan, E.; Ding, Y.: Weighted citation : an indicator of an article's prestige (2010)
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- Date
- 23. 7.2010 13:12:05
-
Yan, E.; Ding, Y.: Discovering author impact : a PageRank perspective (2011)
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- Date
- 23. 1.2011 17:07:35
-
Ding, Y.: Visualization of intellectual structure in information retrieval : author cocitation analysis (1998)
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- Source
- International forum on information and documentation. 23(1998) no.1, S.25-36
-
Ding, Y.: Topic-based PageRank on author cocitation networks (2011)
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- Date
- 17. 3.2011 18:08:23
-
Ni, C.; Shaw, D.; Lind, S.M.; Ding, Y.: Journal impact and proximity : an assessment using bibliographic features (2013)
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- Date
- 23. 3.2013 13:07:39
-
Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022)
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- Abstract
- Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
-
Lu, C.; Bu, Y.; Wang, J.; Ding, Y.; Torvik, V.; Schnaars, M.; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model (2019)
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- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.5, S.462-475
-
Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019)
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- Date
- 5. 9.2019 19:03:52
-
He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.5, S.831-845
-
Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1007-1017
-
Lu, C.; Zhang, Y.; Ahn, Y.-Y.; Ding, Y.; Zhang, C.; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations (2020)
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- Abstract
- Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known about how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 articles published in PLoS journals. Given an article, we define 3 types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the 5 most common tasks as expected, such as "data analysis" and "performing experiments." The specialists and versatiles are more prevalent than expected by our designed 2 null models. Versatiles tend to be senior authors associated with funding and supervision. Specialists are associated with 2 contrasting roles: the supervising role as team leaders or marginal and specialized contributors.
-
Ding, Y.: Applying weighted PageRank to author citation networks (2011)
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- Date
- 22. 1.2011 13:02:21
-
Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014)
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- Date
- 22. 8.2014 16:52:04
