- Title
- International workshop on data-driven science of science
- Creator
- Bu, Yi; Liu, Meijun; Zhai, Yujia; Ding, Ying; Xia, Feng; Acuña, Daniel; Zhang, Yi
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/192196
- Identifier
- vital:17949
- Identifier
-
https://doi.org/10.1145/3534678.3542891
- Identifier
- ISBN:9781450393850 (ISBN)
- Abstract
- 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.
- Publisher
- Association for Computing Machinery
- 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
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- © 2022 Copyright is held by the owner/author(s)
- Subject
- Data science; Quantitative methods; Science of science
- Reviewed
- Funder
- National Science Foundation, NSF National Institutes of Health, NIH Defense Advanced Research Projects Agency, DARPA Alfred P. Sloan Foundation, APSF European Commission, EC Australian Research Council, ARC
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