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Encounters with Self-Monitoring Data on ICT Use

-Gartland, B. & Neff, G. (2015). Communication, mediation, and the expectations of data: Data valences across health and wellness communities. International Journal of Communication , 9: 1466-1484. Kennedy, H. & Hill, L. R. (2017). The feeling of numbers: Emotions in everyday engagements with data and their visualisation. Sociology , 1-19, pre-publication. Kennedy, H., Hill, R. L., Allen, W. & Kirk, A. (2016). Engaging with (big) data visualizations: Factors that affect engagement and resulting new definitions of effectiveness. First Monday , 21

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Think big: learning contexts, algorithms and data science

ISTAT. Retrieved from http://www.istat.it/it/files/2015/05/Big-Data-Visualization-ForumPA2015-finale1.pdf De Mauro, A. & Greco, M. & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics, AIP Conference Proceedings, 1644, 97-104. http://dx.doi.org/10.1063/1.4907823 DeLillo, D. (2003). Cosmopolis: A novel. New York: Scribner. Elliott M. (2013). Big learning data. Alexandria, VA: ASTD Press. Frické, M. (2009). The knowledge pyramid: a critique of the DIKW

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Heuristic algorithms for optimization of task allocation and result distribution in peer-to-peer computing systems

., Asioli, S., Celetto, L., Izquierdo, E. and Rovati, F. (2011). Peer-to-peer streaming of scalable video in future Internet applications, IEEE Communications Magazine 49 (3): 128-135, DOI: 10.1109/MCOM.2011.5723810. Samanta, R., Funkhouser, T. and Li, K. (2001). Parallel rendering with k-way replication, in S.N. Spencer (Ed.), Proceedings of the IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics, PVG’01 , IEEE Press, Piscataway, NJ, pp. 75-84. Shen, X., Yu, H., Buford, J. and Akon, M. (Eds.) (2009). Handbook of

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Estimation of Copper Intake in Moderate Wine Consumers in Croatia

multielement composition - A comparison with vineyard soil. Food Chem 2005;91:157-65. Ražić S, Čokeša Đ, Sremac S. Multivariate data visualization methods based on elemental analysis of wines by atomic absorption spectrometry. J Serb Chem Soc 2007;72:1487-92. Suturović ZJ, Marjanović NJ. Determination of zinc, cadmium, lead and copper in wines by potentiometric stripping analysis. Nahrung 1998;42:36-8. Banović M, Kirin J, Ćurko N, Kovačević Ganić K. Influence of vintage on Cu, Fe, Zn and Pb content

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Local dependency in networks

the 9th SIAM International Conference on Data Mining, Sparks, NV, USA, pp. 1111-1122. Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R. and Kadie, C. (2001). Dependency networks for inference, collaborative filtering, and data visualization, The Journal of Machine Learning Research 1: 49-75. Kahanda, I. and Neville, J. (2009). Using transactional information to predict link strength in online social networks, Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM), San Jose, CA, USA, pp. 74

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Storylab Lessons
A Collaborative Project Between Courses in Journalism and Media Technology

.183-193 in Kotilainen, Sirkku & Kupiainen, Reijo (eds.). Reflections on Media Education Futures: Contributions to the conference Media Education Futures in Tampere, Finland 2014. Gothenburg: International Clearinghouse on Children, Youth and Media, Nordicom, University of Gothenburg. Storsul, Tanja & Krumsvik, Arne H. (eds.) (2013). Media Innovations: A Multidisciplinary Study of Change . Gothenburg: Nordicom. Weber, Wibke & Rall, Hannes (2012). Data Visualization in Online Journalism and Its Implications for the Production Process. Paper presented at

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Acoustic analysis assessment in speech pathology detection

applications, in A.N. Gorban et al. (Eds.), Principal Manifolds for Data Visualization and Dimension Reduction, Springer, Berlin/Heidelberg, pp. 44-67. Scholz, M. and Vigário, R. (2002). Nonlinear PCA: A new hierarchical approach, 10th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pp. 439-444. Skalski, A., Zielinski, T. and Deliyski, D. (2008). Analysis of vocal folds movement in high speed videoendoscopy based on level set segmentation and image registration, International Conference on Signals and Electronic

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Prevalence of antibodies to Aujeszky’s disease virus in wild boar in Poland, between 2011 and 2014: a retrospective study

boar: data visualization as an aid to understanding disease dynamics. Prev Vet Med 2005, 68, 35–48. 42. Van der Leek M.L., Gibbs E.P.J.: Aujeszky’s disease virus infections in wild swine in the USA. 12 th International Pig Veterinary Society Congress, the Hague, the Netherlands 1992, p. 86. 43. Vengust G., Valencak Z., Bidovec A.: Presence of antibodies against Aujeszky’s disease virus in wild boar ( Sus scrofa ) in Slovenia. J Wildl Dis 2005, 41, 800–802. 44. Vengust G., Valencak Z., Bidovec A.: A serological survey of selected pathogens in wild

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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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Optimization of the Maximum Likelihood Estimator for Determining the Intrinsic Dimensionality of High–Dimensional Data

Žilinskas, J. (2013). Multidimensional Data Visualization: Methods and Applications , Optimization and Its Applications, Vol. 75, Springer-Verlag, New York, NY. Einbeck, J. and Kalantan, Z. (2013). Intrinsic dimensionality estimation for high-dimensional data sets: New approaches for the computation of correlation dimension, Journal of Emerging Technologies in Web Intelligence 5 (2): 91–97. Elgammal, A. and su Lee, C. (2004a). Inferring 3d body pose from silhouettes using activity manifold learning, IEEE Computer Society Conference on Computer Vision and

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