Search (5 results, page 1 of 1)

  • × author_ss:"Ding, Y."
  1. 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) 0.01 
    0.0057466435 = product of:
  0.040226504 = sum of:
    0.040226504 = weight(_text_:computer in 663) [ClassicSimilarity], result of:
      0.040226504 = score(doc=663,freq=4.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.28550854 = fieldWeight in 663, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=663)
  0.14285715 = coord(1/7)
    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.
  2. Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019) 0.00 
    0.0048761885 = product of:
  0.03413332 = sum of:
    0.03413332 = weight(_text_:computer in 5387) [ClassicSimilarity], result of:
      0.03413332 = score(doc=5387,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.24226204 = fieldWeight in 5387, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=5387)
  0.14285715 = coord(1/7)
    Abstract
    This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high-, medium-, and low-cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium- and low-cited publications. Moreover, as N increases, the difference in receiving new citations among high-, medium-, and low-cited publications increases quite significantly.
  3. Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018) 0.00 
    0.004063491 = product of:
  0.028444434 = sum of:
    0.028444434 = weight(_text_:computer in 4012) [ClassicSimilarity], result of:
      0.028444434 = score(doc=4012,freq=2.0), product of:
        0.14089422 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.038553525 = queryNorm
        0.20188503 = fieldWeight in 4012, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4012)
  0.14285715 = coord(1/7)
    Abstract
    Scientific collaboration is vital to many fields, and it is common to see scholars seek out experienced researchers or experts in a domain with whom they can share knowledge, experience, and resources. To explore the diversity of research collaborations, this article performs a temporal analysis on the scientific careers of researchers in the field of computer science. Specifically, we analyze collaborators using 2 indicators: the research topic diversity, measured by the Author-Conference-Topic model and cosine, and the impact diversity, measured by the normalized standard deviation of h-indices. We find that the collaborators of high-impact researchers tend to study diverse research topics and have diverse h-indices. Moreover, by setting PhD graduation as an important milestone in researchers' careers, we examine several indicators related to scientific collaboration and their effects on a career. The results show that collaborating with authoritative authors plays an important role prior to a researcher's PhD graduation, but working with non-authoritative authors carries more weight after PhD graduation.
  4. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.00 
    0.0026117351 = product of:
  0.018282145 = sum of:
    0.018282145 = product of:
      0.03656429 = sum of:
        0.03656429 = weight(_text_:22 in 4188) [ClassicSimilarity], result of:
          0.03656429 = score(doc=4188,freq=2.0), product of:
            0.13500787 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038553525 = queryNorm
            0.2708308 = fieldWeight in 4188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4188)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)
    Date
    22. 1.2011 13:02:21
  5. 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 
    0.0022386303 = product of:
  0.015670411 = sum of:
    0.015670411 = product of:
      0.031340823 = sum of:
        0.031340823 = weight(_text_:22 in 1521) [ClassicSimilarity], result of:
          0.031340823 = score(doc=1521,freq=2.0), product of:
            0.13500787 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.038553525 = queryNorm
            0.23214069 = fieldWeight in 1521, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1521)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)
    Date
    22. 8.2014 16:52:04

Authors

Years

Themes