Search (14 results, page 1 of 1)

  • × author_ss:"Ding, Y."
  1. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.00
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    Date
    22. 8.2014 16:52:04
  2. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.00
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  3. Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012) 0.00
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  4. Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015) 0.00
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  5. Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014) 0.00
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    Date
    29. 1.2014 16:40:41
  6. 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) 0.00
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    Abstract
    Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. To uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (a) syntactic complexity, including measurements of sentence length and sentence complexity; and (b) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.
  7. Bu, Y.; Ding, Y.; Liang, X.; Murray, D.S.: Understanding persistent scientific collaboration (2018) 0.00
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    Abstract
    Common sense suggests that persistence is key to success. In academia, successful researchers have been found more likely to be persistent in publishing, but little attention has been given to how persistence in maintaining collaborative relationships affects career success. This paper proposes a new bibliometric understanding of persistence that considers the prominent role of collaboration in contemporary science. Using this perspective, we analyze the relationship between persistent collaboration and publication quality along several dimensions: degree of transdisciplinarity, difference in coauthor's scientific age and their scientific impact, and research-team size. Contrary to traditional wisdom, our results show that persistent scientific collaboration does not always result in high-quality papers. We find that the most persistent transdisciplinary collaboration tends to output high-impact publications, and that those coauthors with diverse scientific impact or scientific ages benefit from persistent collaboration more than homogeneous compositions. We also find that researchers persistently working in large groups tend to publish lower-impact papers. These results contradict the colloquial understanding of collaboration in academia and paint a more nuanced picture of how persistent scientific collaboration relates to success, a picture that can provide valuable insights to researchers, funding agencies, policy makers, and mentor-mentee program directors. Moreover, the methodology in this study showcases a feasible approach to measure persistent collaboration.
  8. Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014) 0.00
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  9. Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017) 0.00
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  10. Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009) 0.00
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  11. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.00
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    Date
    22. 1.2011 13:02:21
  12. Ding, Y.: Scholarly communication and bibliometrics : Part 1: The scholarly communication model: literature review (1998) 0.00
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    Source
    International forum on information and documentation. 23(1998) no.2, S.20-29
  13. Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018) 0.00
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    Date
    29. 9.2018 13:24:10
  14. Liu, M.; Bu, Y.; Chen, C.; Xu, J.; Li, D.; Leng, Y.; Freeman, R.B.; Meyer, E.T.; Yoon, W.; Sung, M.; Jeong, M.; Lee, J.; Kang, J.; Min, C.; Zhai, Y.; Song, M.; Ding, Y.: Pandemics are catalysts of scientific novelty : evidence from COVID-19 (2022) 0.00
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    Abstract
    Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.