How to cite this paper
Muharam, R., Rusli, B., Nurasa, H & Muhtar, E. (2023). Seven clusters of data visualization articles in Scopus using social network analysis.International Journal of Data and Network Science, 7(3), 1333-1340.
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Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R. A. (2021). Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Research, 4(January), 63. https://doi.org/10.12688/wellcomeopenres.15191.2
Bhatia, V. N., Perlman, D. H., Costello, C. E., & McComb, M. E. (2009). Software tool for researching annotations of proteins: Open-source protein annotation software with data visualization. Analytical Chemistry, 81(23), 9819–9823. https://doi.org/10.1021/ac901335x
Bishop, C. M., & Tipping, M. E. (1998). A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 281–293. https://doi.org/10.1109/34.667885
Buja, A., Swayne, D. F., Littman, M. L., Dean, N., Hofmann, H., & Chen, L. (2008). Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics, 17(2), 444–472. https://doi.org/10.1198/106186008X318440
Childs, H., Brugger, E., Bonnell, K., Meredith, J., Miller, M., Whitlock, B., & Max, N. (2005). A contract based system for large data visualization. Proceedings of the IEEE Visualization Conference, 25. https://doi.org/10.1109/VIS.2005.3
Claessen, J. H. T., & Van Wijk, J. J. (2011). Flexible linked axes for multivariate data visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2310–2316. https://doi.org/10.1109/TVCG.2011.201
Djorgovski, S. G., Donalek, C., Lombeyda, S., Davidoff, S., & Amori, M. (2018). Immersive and Collaborative Data Visualization and Analytics Using Virtual Reality. American Geophysical Union, Fall Meeting 2018, Abstract #IN53B-01, 609–614. http://adsabs.harvard.edu/abs/2018AGUFMIN53B..01D
Francisco, A. P., Vaz, C., Monteiro, P. T., Melo-Cristino, J., Ramirez, M., & Carriço, J. A. (2012). PHYLOViZ: Phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinformatics, 13(1). https://doi.org/10.1186/1471-2105-13-87
Gatto, L., & Lilley, K. S. (2012). Msnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics, 28(2), 288–289. https://doi.org/10.1093/bioinformatics/btr645
Lee, D. (2020). Bibliometric Analysis of Korean Journals in Arts and Kinesiology – from the Perspective of Authorship. Journal of Information Science Theory and Practice, 8(3), 15–29. https://doi.org/10.1633/JISTaP.2020.8.3.2
Li, K. C. (1992). On principal Hessian directions for data visualization and dimension reduction: Another application of Stein’s lemma. Journal of the American Statistical Association, 87(420), 1025–1039. https://doi.org/10.1080/01621459.1992.10476258
Marcus, D. S., Harms, M. P., Snyder, A. Z., Jenkinson, M., Wilson, J. A., Glasser, M. F., Barch, D. M., Archie, K. A., Burgess, G. C., Ramaratnam, M., Hodge, M., Horton, W., Herrick, R., Olsen, T., McKay, M., House, M., Hileman, M., Reid, E., Harwell, J., … Van Essen, D. C. (2013). Human Connectome Project informatics: Quality control, database services, and data visualization. NeuroImage, 80, 202–219. https://doi.org/10.1016/j.neuroimage.2013.05.077
Mifrah, S. (2020). Toward a Semantic Graph of Scientific Publications: A Bibliometric Study. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 3323–3330. https://doi.org/10.30534/ijatcse/2020/129932020
Nolte, H., MacVicar, T. D., Tellkamp, F., & Krüger, M. (2018). Instant Clue: A Software Suite for Interactive Data Visualization and Analysis. Scientific Reports, 8(1), 6–13. https://doi.org/10.1038/s41598-018-31154-6
Omoregbe, O., Mustapha, A. N., Steinberger-Wilckens, R., El-Kharouf, A., & Onyeaka, H. (2020). Carbon capture technologies for climate change mitigation: A bibliometric analysis of the scientific discourse during 1998–2018. Energy Reports, 6, 1200–1212. https://doi.org/10.1016/j.egyr.2020.05.003
Peng, W., Ward, M. O., & Rundensteiner, E. A. (2004). Clutter reduction in multi-dimensional data visualization using dimension reordering. Proceedings - IEEE Symposium on Information Visualization, INFO VIS, 89–96. https://doi.org/10.1109/INFVIS.2004.15
Putera, P. B., Suryanto, S., Ningrum, S., & Widianingsih, I. (2020). A bibliometric analysis of articles on innovation systems in Scopus journals written by authors from Indonesia, Singapore, and Malaysia. Science Editing, 7(2), 177–183. https://doi.org/10.6087/KCSE.214
Saravanan, G., & Dominic, J. (2014). A Ten-year Bibliometric Analysis of Research Trends in Three Leading Ecology Journals during 2003-2012. Journal of Information Science Theory and Practice, 2(3), 40–54. https://doi.org/10.1633/jistap.2014.2.3.4
Swayne, D. F., Temple Lang, D., Buja, A., & Cook, D. (2003). GGobi: Evolving from XGobi into an extensible framework for interactive data visualization. Computational Statistics and Data Analysis, 43(4), 423–444. https://doi.org/10.1016/S0167-9473(02)00286-4
Thorvaldsdóttir, H., Robinson, J. T., & Mesirov, J. P. (2013). Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178–192. https://doi.org/10.1093/bib/bbs017
Van Wijk, J. J. (1991). Spot noise texture synthesis for data visualization. Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1991, 25, 309–318. https://doi.org/10.1145/122718.122751
Wang, C., Yu, H., & Ma, K. L. (2008). Importance-driven time-varying data visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1547–1554. https://doi.org/10.1109/TVCG.2008.140
Wang, Y. Q. (2014). MeteoInfo: GIS software for meteorological data visualization and analysis. Meteorological Applications, 21(2), 360–368. https://doi.org/10.1002/met.1345
Williamson, B. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments. Journal of Education Policy, 31(2), 123–141. https://doi.org/10.1080/02680939.2015.1035758
Akhavan, P., Ebrahim, N. A., Fetrati, M. A., & Pezeshkan, A. (2016). Major trends in knowledge management research: a bibliometric study. Scientometrics, 107(3), 1249–1264. https://doi.org/10.1007/s11192-016-1938-x
Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R. A. (2021). Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Research, 4(January), 63. https://doi.org/10.12688/wellcomeopenres.15191.2
Bhatia, V. N., Perlman, D. H., Costello, C. E., & McComb, M. E. (2009). Software tool for researching annotations of proteins: Open-source protein annotation software with data visualization. Analytical Chemistry, 81(23), 9819–9823. https://doi.org/10.1021/ac901335x
Bishop, C. M., & Tipping, M. E. (1998). A hierarchical latent variable model for data visualization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 281–293. https://doi.org/10.1109/34.667885
Buja, A., Swayne, D. F., Littman, M. L., Dean, N., Hofmann, H., & Chen, L. (2008). Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics, 17(2), 444–472. https://doi.org/10.1198/106186008X318440
Childs, H., Brugger, E., Bonnell, K., Meredith, J., Miller, M., Whitlock, B., & Max, N. (2005). A contract based system for large data visualization. Proceedings of the IEEE Visualization Conference, 25. https://doi.org/10.1109/VIS.2005.3
Claessen, J. H. T., & Van Wijk, J. J. (2011). Flexible linked axes for multivariate data visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2310–2316. https://doi.org/10.1109/TVCG.2011.201
Djorgovski, S. G., Donalek, C., Lombeyda, S., Davidoff, S., & Amori, M. (2018). Immersive and Collaborative Data Visualization and Analytics Using Virtual Reality. American Geophysical Union, Fall Meeting 2018, Abstract #IN53B-01, 609–614. http://adsabs.harvard.edu/abs/2018AGUFMIN53B..01D
Francisco, A. P., Vaz, C., Monteiro, P. T., Melo-Cristino, J., Ramirez, M., & Carriço, J. A. (2012). PHYLOViZ: Phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinformatics, 13(1). https://doi.org/10.1186/1471-2105-13-87
Gatto, L., & Lilley, K. S. (2012). Msnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics, 28(2), 288–289. https://doi.org/10.1093/bioinformatics/btr645
Lee, D. (2020). Bibliometric Analysis of Korean Journals in Arts and Kinesiology – from the Perspective of Authorship. Journal of Information Science Theory and Practice, 8(3), 15–29. https://doi.org/10.1633/JISTaP.2020.8.3.2
Li, K. C. (1992). On principal Hessian directions for data visualization and dimension reduction: Another application of Stein’s lemma. Journal of the American Statistical Association, 87(420), 1025–1039. https://doi.org/10.1080/01621459.1992.10476258
Marcus, D. S., Harms, M. P., Snyder, A. Z., Jenkinson, M., Wilson, J. A., Glasser, M. F., Barch, D. M., Archie, K. A., Burgess, G. C., Ramaratnam, M., Hodge, M., Horton, W., Herrick, R., Olsen, T., McKay, M., House, M., Hileman, M., Reid, E., Harwell, J., … Van Essen, D. C. (2013). Human Connectome Project informatics: Quality control, database services, and data visualization. NeuroImage, 80, 202–219. https://doi.org/10.1016/j.neuroimage.2013.05.077
Mifrah, S. (2020). Toward a Semantic Graph of Scientific Publications: A Bibliometric Study. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 3323–3330. https://doi.org/10.30534/ijatcse/2020/129932020
Nolte, H., MacVicar, T. D., Tellkamp, F., & Krüger, M. (2018). Instant Clue: A Software Suite for Interactive Data Visualization and Analysis. Scientific Reports, 8(1), 6–13. https://doi.org/10.1038/s41598-018-31154-6
Omoregbe, O., Mustapha, A. N., Steinberger-Wilckens, R., El-Kharouf, A., & Onyeaka, H. (2020). Carbon capture technologies for climate change mitigation: A bibliometric analysis of the scientific discourse during 1998–2018. Energy Reports, 6, 1200–1212. https://doi.org/10.1016/j.egyr.2020.05.003
Peng, W., Ward, M. O., & Rundensteiner, E. A. (2004). Clutter reduction in multi-dimensional data visualization using dimension reordering. Proceedings - IEEE Symposium on Information Visualization, INFO VIS, 89–96. https://doi.org/10.1109/INFVIS.2004.15
Putera, P. B., Suryanto, S., Ningrum, S., & Widianingsih, I. (2020). A bibliometric analysis of articles on innovation systems in Scopus journals written by authors from Indonesia, Singapore, and Malaysia. Science Editing, 7(2), 177–183. https://doi.org/10.6087/KCSE.214
Saravanan, G., & Dominic, J. (2014). A Ten-year Bibliometric Analysis of Research Trends in Three Leading Ecology Journals during 2003-2012. Journal of Information Science Theory and Practice, 2(3), 40–54. https://doi.org/10.1633/jistap.2014.2.3.4
Swayne, D. F., Temple Lang, D., Buja, A., & Cook, D. (2003). GGobi: Evolving from XGobi into an extensible framework for interactive data visualization. Computational Statistics and Data Analysis, 43(4), 423–444. https://doi.org/10.1016/S0167-9473(02)00286-4
Thorvaldsdóttir, H., Robinson, J. T., & Mesirov, J. P. (2013). Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178–192. https://doi.org/10.1093/bib/bbs017
Van Wijk, J. J. (1991). Spot noise texture synthesis for data visualization. Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1991, 25, 309–318. https://doi.org/10.1145/122718.122751
Wang, C., Yu, H., & Ma, K. L. (2008). Importance-driven time-varying data visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1547–1554. https://doi.org/10.1109/TVCG.2008.140
Wang, Y. Q. (2014). MeteoInfo: GIS software for meteorological data visualization and analysis. Meteorological Applications, 21(2), 360–368. https://doi.org/10.1002/met.1345
Williamson, B. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments. Journal of Education Policy, 31(2), 123–141. https://doi.org/10.1080/02680939.2015.1035758