International Workshop on Data-driven Science of Science

Bu, Y; Liu, M; Zhai, Y; Ding, Y; Xia, F; Acuña, DE; Zhang, Y

International Workshop on Data-driven Science of Science

Bu, Y Liu, M Zhai, Y Ding, Y Xia, F Acuña, DE Zhang, Y

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2022, pp. 4856-4857
Issue Date:: 2022-08-14

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Field	Value	Language
dc.contributor.author	Bu, Y
dc.contributor.author	Liu, M
dc.contributor.author	Zhai, Y
dc.contributor.author	Ding, Y
dc.contributor.author	Xia, F
dc.contributor.author	Acuña, DE
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.date.accessioned	2023-03-17T06:03:35Z
dc.date.available	2023-03-17T06:03:35Z
dc.date.issued	2022-08-14
dc.identifier.citation	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2022, pp. 4856-4857
dc.identifier.isbn	9781450393850
dc.identifier.uri	http://hdl.handle.net/10453/167497
dc.description.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.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
dc.relation.ispartof	Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
dc.relation.isbasedon	10.1145/3534678.3542891
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	International Workshop on Data-driven Science of Science
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-17T06:03:34Z
pubs.publication-status	Published

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.

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http://hdl.handle.net/10453/167497