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Data Science Altmetrics

for data mining to look for international, temporal or disciplinary differences. For example, one study showed that articles tended to be read more often by people from the same country as the authors ( Thelwall & Maflahi, 2015 ). Large scale altmetric data can be used to assess the validity of specific altmetrics (altmetric) by investigating the extent to which the altmetric correlates with citation counts ( Sud & Thelwall, 2014 ). Although some correlations of this type have already been calculated, it is important to calculate more correlations for different

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Science Mapping: A Systematic Review of the Literature

Research Council (UK), and the Library and Information Commission (UK). Dr. Chen has designed and developed the widely used visual analytics software CiteSpace for visualizing and analyzing structural and temporal patterns in scientific literature. 1 Introduction Science mapping is a generic process of domain analysis and visualization. The scope of a science mapping study can be a scientific discipline, a field of research, or topic areas concerning specific research questions. In other words, the unit of analysis in science mapping is a domain of

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Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

/3/DataScienceBook1_1.pdf . Stanton J.M. 2012 Introduction to data science Syracuse University Retrieved on June 6, 2017, from https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf Sugimoto, S., Li, C., Nagamori, M., & Greenberg, J. (2016). Permanence and temporal interoperability of metadata in the linked open data environment. In Proceedings of the International Conference on Dublin Core and Metadata Applications 2016 (pp. 45–54). Retrieved on June 28, 2017, from http://dcevents.dublincore.org/IntConf/dc-2016/paper/view/430 . Sugimoto S. Li C

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Measuring and Visualizing Research Collaboration and Productivity

subjects (and for the comparison groups) Group—benchmarking iUTAH results against two suitable comparison groups. For the temporal comparisons, we used 2010–2012 as the Before period and 2014–2016 as After. We set aside 2013 as ambiguous with respect to research publications that are apt to reflect participation in the iUTAH project. Lacking a randomly assigned control group equivalent to iUTAH researchers, we worked to develop reasonable “comparison groups.” Our first comparison group consisted of participants in two Utah-based university centers comparable in

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Predictive Characteristics of Co-authorship Networks: Comparing the Unweighted, Weighted, and Bipartite Cases

network is known as link prediction ( Liben-Nowell & Kleinberg, 2007 ). We may distinguish between two types of link prediction applications ( Guns, 2014 ) that have sometimes been confounded in the literature: Network evolution prediction, and Network reconstruction. Network evolution prediction ( Liben-Nowell & Kleinberg, 2007 ) concerns the situation where one is given a temporal snapshot of an evolving network. The task is to predict a future state of the network. Network reconstruction ( Guimerà & Sales-Pardo, 2009 ), on the other hand, concerns the

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Factors Influencing Cities’ Publishing Efficiency

’s Press. Kealey T. 1996 The Economic Laws of Scientific Research New York St. Martin’s Press Kim, H., Yoon, J. W., & Crowcroft, J. (2012). Network analysis of temporal trends in scholarly research productivity. Journal of Informetrics, 6(1), 97−110. 10.1016/j.joi.2011.05.006 Kim H. Yoon J. W. Crowcroft J. 2012 Network analysis of temporal trends in scholarly research productivity Journal of Informetrics 6 1 97 110 King, D. A. (2004). The scientific impact of nations. What different countries get for their research spending. Nature

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Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps

biological sciences and less linked to chemistry? The basemaps appear to evolve slowly as shown by the fact that the underlying 2010 and 2015 citation matrices among WCs are very similar (QAP correlation r = 0.937; p < 0.001) in spite of considerable changes in WoS journal inclusion over that period. This justifies their use for overlays over a certain temporal range. In stepping through the case analyses, we have pointed to a variety of appealing applications for the science overlay mapping. We believe the enhanced clustering of the WCs, improved visualization

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A Multilayered Self-Learning Spiking Neural Network and its Learning Algorithm Based on ‘Winner-Takes-More’ Rule in Hierarchical Clustering

References Bohte S.M., Kok J.N., La Poutre H. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks // IEEE Transactions on Neural Networks. - 13 (March, 2002), P. 426-435. Bodyanskiy Ye., Dolotov A., Viktorov Ye. Heterogeneous spiking neural network in clustering problem // Bulletin of Lviv Polytechnic National University. Computer Science and Information Technologies. - 604 (2008), P. 84-90. Image clustering with spiking neuron network

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No Place to Hide: Inadvertent Location Privacy Leaks on Twitter

, 2013. [29] C. Li and A. Sun. Fine-grained location extraction from tweets with temporal awareness. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval , SIGIR ’14, pages 43–52, New York, NY, USA, 2014. ACM. [30] J. Lingad, S. Karimi, and J. Yin. Location extraction from disaster-related microblogs. In 22nd international conference on World Wide Web companion International World Wide Web Conferences Steering Committee , 2013. [31] Z. Liu and Y. Huang. Where are you tweeting?: A context

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Expectation-Maximization Tensor Factorization for Practical Location Privacy Attacks

user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(1):129–142 [46] Zhang S, Wang W, Ford J, Makedon F (2006) Learning from incomplete ratings usig non-negative matrix factorization. In: Proceedings of the 6th SIAM International Conference on Data Mining (SDM’06), pp 548–552 [47] Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web

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