International Workshop on Data-driven Science of Science

Yi Bu; Meijun Liu; Yujia Zhai; Ying Ding; Feng Xia; Daniel E. Acuña; Yi Zhang

doi:10.1145/3534678.3542891

International Workshop on Data-driven Science of Science

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

Research output: Chapter in Book/Entry/Poem › Conference contribution

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.

Original language	English (US)
Title of host publication	KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	4856-4857
Number of pages	2
ISBN (Electronic)	9781450393850
DOIs	https://doi.org/10.1145/3534678.3542891
State	Published - Aug 14 2022
Externally published	Yes
Event	28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States Duration: Aug 14 2022 → Aug 18 2022

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/Territory	United States
City	Washington
Period	8/14/22 → 8/18/22

Keywords

data science
quantitative methods
science of science

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3534678.3542891

Cite this

Bu, Y., Liu, M., Zhai, Y., Ding, Y., Xia, F., Acuña, D. E., & Zhang, Y. (2022). International Workshop on Data-driven Science of Science. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4856-4857). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3534678.3542891

International Workshop on Data-driven Science of Science. / Bu, Yi; Liu, Meijun; Zhai, Yujia et al.
KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2022. p. 4856-4857 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Bu, Y, Liu, M, Zhai, Y, Ding, Y, Xia, F, Acuña, DE & Zhang, Y 2022, International Workshop on Data-driven Science of Science. in KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 4856-4857, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, United States, 8/14/22. https://doi.org/10.1145/3534678.3542891

Bu Y, Liu M, Zhai Y, Ding Y, Xia F, Acuña DE et al. International Workshop on Data-driven Science of Science. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2022. p. 4856-4857. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3534678.3542891

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title = "International Workshop on Data-driven Science of Science",

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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author = "Yi Bu and Meijun Liu and Yujia Zhai and Ying Ding and Feng Xia and Acu{\~n}a, {Daniel E.} and Yi Zhang",

note = "Funding Information: Daniel Acu{\~n}a is an Assistant Professor in the School of Information Studies at Syracuse University, Syracuse. The goal of his current research is to understand decision-making in science— from helping hiring committees to predict future academic success to removing the potential biases that scientists and funding agencies commit during peer review. He has grants from NSF, DDHS, Sloan Foundation, and DARPA and his work has been featured in Nature News, Nature Podcast, The Chronicle of Higher Education, NPR, and the Scientist. Before joining Syracuse University, Acu{\~n}a studied a Ph.D. in Computer Science at the University of Minnesota - Twin Cities and was a postdoctoral researcher at Northwestern University and the Rehabilitation Institute of Chicago. Funding Information: Yi Zhang serves as a Senior Lecturer at the Australian Artificial Intelligence Institute, University of Technology Sydney, Australia. He is an Associate Editor for Technological Forecasting & Social Change, and Scientometrics, and the Advisory Board Member of the International Center for the Study of Research, Elsevier. He was awarded the 2019 Discovery Early Career Researcher Award by the Australian Research Council. Funding Information: Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. Before that, she was a professor and director of graduate studies for data science program at School of Informatics, Computing, and Engineering at Indiana University. She has led the effort to develop the online data science graduate program for Indiana University. She has been involved in various NIH, NSF and European-Union funded projects. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies. Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 ; Conference date: 14-08-2022 Through 18-08-2022",

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N1 - Funding Information: Daniel Acuña is an Assistant Professor in the School of Information Studies at Syracuse University, Syracuse. The goal of his current research is to understand decision-making in science— from helping hiring committees to predict future academic success to removing the potential biases that scientists and funding agencies commit during peer review. He has grants from NSF, DDHS, Sloan Foundation, and DARPA and his work has been featured in Nature News, Nature Podcast, The Chronicle of Higher Education, NPR, and the Scientist. Before joining Syracuse University, Acuña studied a Ph.D. in Computer Science at the University of Minnesota - Twin Cities and was a postdoctoral researcher at Northwestern University and the Rehabilitation Institute of Chicago. Funding Information: Yi Zhang serves as a Senior Lecturer at the Australian Artificial Intelligence Institute, University of Technology Sydney, Australia. He is an Associate Editor for Technological Forecasting & Social Change, and Scientometrics, and the Advisory Board Member of the International Center for the Study of Research, Elsevier. He was awarded the 2019 Discovery Early Career Researcher Award by the Australian Research Council. Funding Information: Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. Before that, she was a professor and director of graduate studies for data science program at School of Informatics, Computing, and Engineering at Indiana University. She has led the effort to develop the online data science graduate program for Indiana University. She has been involved in various NIH, NSF and European-Union funded projects. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies. Publisher Copyright: © 2022 Owner/Author.

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ER -

International Workshop on Data-driven Science of Science

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this