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International workshop on data-driven science of science

- Bu, Yi, Liu, Meijun, Zhai, Yujia, Ding, Ying, Xia, Feng, Acuña, Daniel, Zhang, Yi

  • Authors: Bu, Yi , Liu, Meijun , Zhai, Yujia , Ding, Ying , Xia, Feng , Acuña, Daniel , Zhang, Yi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, USA, 14-18 August 2022, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining p. 4856-4857
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  • Description: Citation data, along with other bibliographic datasets, have long been adopted by the knowledge and data discovery community as an important direction for presenting the validity and effectiveness of proposed algorithms and strategies. Many top computer scientists are also excellent researchers in the science of science. The purpose of this workshop is to bridge the two communities (i.e., the knowledge discovery community and the science of science community) together as the scholarly activities become salient web and social activities that start to generate a ripple effect on broader knowledge discovery communities. This workshop will showcase the current data-driven science of science research by highlighting several studies and constructing a community of researchers to explore questions critical to the future of data-driven science of science, especially a community of data-driven science of science in Data Science so as to facilitate collaboration and inspire innovation. Through discussion on emerging and critical topics in the science of science, this workshop aims to help generate effective solutions for addressing environmental, societal, and technological problems in the scientific community. © 2022 Owner/Author.

Early-stage reciprocity in sustainable scientific collaboration

- Wang, Wei, Ren, Jing, Alrashoud, Mubarak, Xia, Feng, Mao, Mengyi, Tolba, Amr

  • Authors: Wang, Wei , Ren, Jing , Alrashoud, Mubarak , Xia, Feng , Mao, Mengyi , Tolba, Amr
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Informetrics Vol. 14, no. 3 (2020), p.
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  • Description: Scientific collaboration is of significant importance in tackling grand challenges and breeding innovations. Despite the increasing interest in investigating and promoting scientific collaborations, we know little about the collaboration sustainability as well as mechanisms behind it. In this paper, we set out to study the relationships between early-stage reciprocity and collaboration sustainability. By proposing and defining h-index reciprocity, we give a comprehensive statistical analysis on how reciprocity influences scientific collaboration sustainability, and find that scholars are not altruism and the key to sustainable collaboration is fairness. The unfair h-index reciprocity has an obvious negative impact on collaboration sustainability. The bigger the reciprocity difference, the less sustainable in collaboration. This work facilitates understanding sustainable collaborations and thus will benefit both individual scholar in optimizing collaboration strategies and the whole academic society in improving teamwork efficiency. © 2020 Elsevier Ltd.
  • Description: The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP-78. This work is partially supported by China Postdoctoral Science Foundation ( 2019M651115 ).

The dominance of big teams in china’s scientific output

- Liu, Linlin, Yu, Jianfei, Huang, Junming, Xia, Feng, Jia, Tao

  • Authors: Liu, Linlin , Yu, Jianfei , Huang, Junming , Xia, Feng , Jia, Tao
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Quantitative Science Studies Vol. 2, no. 1 (2021), p. 350-362
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  • Description: Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams. © 2020 Linlin Liu, Jianfei Yu, Junming Huang, Feng Xia, and Tao Jia. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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