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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.

The gene of scientific success

- Kong, Xiangjie, Zhang, Jun, Zhang, Da, Bu, Yi, Ding, Ying, Xia, Feng

  • Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
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  • Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.
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