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Introduction of the Inaugural Issue of the Journal of Data and Information Science

multidimensional approach of examining raw data with the purpose of discovering meaningful patterns, analyzing and communicating the obtained information to specific target groups. Data analytics relies on the simultaneous application of mathematics, statistics, computer programming, and operations research. It makes use of techniques for explanatory research, seeks to identify underlying factors, and performs conceptual modeling, often leading to data visualization to communicate insights. As a consequence we hope that this journal will play an important role in the scholarly

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Big Data and Data Science: Opportunities and Challenges of iSchools

science, security and privacy issues of big data, visualization, and data curation. The coverage of these topics in computer science or business school curricula is not as extensive as those of iSchools, however. The iSchool curriculum has strong advantages in the user-based data science education by training students who understand the importance of requirement modeling, know the roles of metadata and utilize them, design and develop systems with human-centered usability in mind, consider security and privacy of data in all the stages of the data science lifecycle

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Smart Data for Digital Humanities

mining/text mining , archives & repositories , literary studies , and data visualization . The 2017 count, which separated topics from disciplines, shows that those top topics are joined by interdisciplinary collaboration and corpora and corpus activities . The disciplines that have more than 100 submissions are: computer science , literary studies , library and information science , cultural studies , and historical studies . A notable finding is that submissions from film and media studies have greatly increased compared to previous years, as have other

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Science Mapping: A Systematic Review of the Literature

impact indicators ( Waltman, 2016 ) 2016 0.9235 0.0019 0.3407 0.0842 0 How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014) ( Kim, Zhu, & Chen, 2016 ) 2016 0.8207 0.0017 0.0640 0.0447 2 A bibliometric analysis of 20 years of research on software product lines ( Heradio et al., 2016 ) 2015 1.7498 0.0073 0.0380 0.0786 0 Global ontology research progress: A bibliometric analysis ( Zhu et al., 2015 ) 2015 1.9873 0.0052 0.0397 0

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A comprehensive review of plus-minus ratings for evaluating individual players in team sports

/minus metric for individual soccer player performance. Journal of Sports Analytics , 4, 121–131. Sill, J. (2010). Improved NBA adjusted +/− using regularization and out-of-sample testing. Proceedings of the 2010 MIT Sloan Sports Analytics Conference. Sisneros, R. & Van Moer, M. (2013). Expanding plus-minus for visual and statistical analysis of NBA box-score data. In: Proceedings of IEEE Vis Workshop on Sports Data Visualization. Sittl, R. & Warnke, A. (2016). Competitive balance and assortative matching in the German Bundesliga. Discussion Paper No. 16

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RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release

and adaptive networks. Technical report, Royal Signals and Radar Establishment Malvern (United Kingdom), 1988. [20] Andreas Buja, Dianne Cook, and Deborah F. Swayne. Interactive high-dimensional data visualization. Journal of computational and graphical statistics , 5(1):78–99, 1996. [21] Mark Bun, Jonathan Ullman, and Salil Vadhan. Fingerprinting codes and the price of approximate differential privacy. In Proceedings of the forty-sixth annual ACM symposium on Theory of computing , pages 1–10. ACM, 2014. [22] John Burkardt. Normal

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