Editors:
Provides a complete guide to analyzing scholarly communication and the informetrics used for the assessment of scholarly impact
Consolidates techniques and technologies for measuring scholarly impact from the fields of statistical science, scientific visualization, network analysis, text mining and information retrieval
Equips data scientists with the ability to apply these techniques and technologies to other social network analyses and metrics-related research
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Table of contents (14 chapters)
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Front Matter
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The Science System
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Front Matter
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Statistical and Text-Based Methods
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Front Matter
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Visualization
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Front Matter
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Back Matter
About this book
Keywords
- ISI
- Page Rank
- Thomson Reuters
- bibliometrics
- citations
- community detection
- discrete choice models
- impact factor
- informetrics
- knowledge integration and diffusion
- network dynamics
- percentiles and effect size
- scholarly impact
- scientometrics
- system life cycle
- text mining
- topic modeling
- visualization
Editors and Affiliations
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School of Informatics and Computing, Indiana University, Bloomington, USA
Ying Ding
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University of Antwerp, Antwerp, Belgium
Ronald Rousseau
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University of Wisconsin-Milwaukee, Milwaukee, USA
Dietmar Wolfram
Bibliographic Information
Book Title: Measuring Scholarly Impact
Book Subtitle: Methods and Practice
Editors: Ying Ding, Ronald Rousseau, Dietmar Wolfram
DOI: https://doi.org/10.1007/978-3-319-10377-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2014
Hardcover ISBN: 978-3-319-10376-1Published: 19 November 2014
Softcover ISBN: 978-3-319-34863-6Published: 23 August 2016
eBook ISBN: 978-3-319-10377-8Published: 06 November 2014
Edition Number: 1
Number of Pages: XIV, 346
Number of Illustrations: 21 b/w illustrations, 68 illustrations in colour
Topics: Information Storage and Retrieval, Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy, Data Mining and Knowledge Discovery, Artificial Intelligence, Data and Information Visualization