A Perspective View on Data Visualization Libraries Used in Data Analytics

Öz

In today’s data-driven world, one of the cornerstones of data science is data visualization. Data visualization makes information more understandable by presenting complex data sets through graphs, tables, and other visual tools. Thus, trends, relationships, and anomalies in the data can be easily detected. In this way, more intuitive and meaningful results are obtained from the data. In addition, decision makers are able to make the right decisions faster thanks to these visualization tools. Graphics libraries created and used in programming languages are used intensively to perform these operations. This article explores programming language-based data visualization libraries, which are widely utilized in the field of data science, and examines the key features and functionalities of these libraries. In this context, 76 visualization libraries were examined and evaluated in terms of customization level, interactive features, and the data types they support, and presented separately in tables according to programming languages. The study areas of the articles in which these libraries were used in the literature and the results obtained from the studies were also presented. In addition, the directions of future studies in this field were added to the conclusion.

Anahtar Kelimeler

• Data Visualization
• Visualization Libraries
• Data Visualization Libraries
• Data Analytics

Veri Analitiğinde Kullanılan Veri Görselleştirme Kütüphanelerine İlişkin Perspektif Görünümü

Öz

Günümüzün veri odaklı dünyasında, veri biliminin temel taşlarından biri veri görselleştirmedir. Veri görselleştirme, grafikler, tablolar ve diğer görsel araçlar aracılığıyla karmaşık veri kümeleri sunarak bilgileri daha anlaşılabilir hale getirir. Böylece, verilerdeki eğilimler, ilişkiler ve anomaliler kolayca tespit edilebilir. Bu şekilde verilerden daha sezgisel ve anlamlı sonuçlar elde edilir. Ayrıca, karar vericiler bu görselleştirme araçları sayesinde doğru kararları daha hızlı verebilirler. Programlama dillerinde oluşturulan ve kullanılan grafik kütüphaneleri, bu işlemleri gerçekleştirmek için yoğun bir şekilde kullanılır. Bu makale, veri bilimi alanında yaygın olarak kullanılan ve bu kütüphanelerin temel özelliklerini ve işlevlerini inceleyen programlama dil tabanlı veri görselleştirme kütüphanelerini araştırmaktadır. Bu bağlamda, 76 görselleştirme kütüphanesi, özelleştirme düzeyi, etkileşimli özellikler ve destekledikleri veri türleri açısından incelenmiş ve değerlendirilmiştir ve programlama dillerine göre tablolarda ayrı olarak sunulmuştur. Bu kütüphanelerin literatürde kullanıldığı makalelerin çalışma alanları ve çalışmalardan elde edilen sonuçlar da sunulmuştur. Buna ek olarak, bu alanda gelecekteki çalışmaların talimatları sonuca eklenmiştir.

Anahtar Kelimeler

• Veri Görselleştirme
• Veri Analitiği
• Veri Görselleştirme Kütüphanaleri
• Görselleştirme Kütüphaneleri

Kaynakça

1. [1] I. H. Sarker, ‘Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective’, SN Computer Science, vol. 2, no. 5, p. 377, Sep. 2021, doi: 10.1007/s42979-021-00765-8.
2. [2] X. Qin, Y. Luo, N. Tang, and G. Li, ‘Making Data Visualization More Efficient and Effective: A Survey’, The VLDB Journal, vol. 29, no. 1, pp. 93–117, Jan. 2020, doi: 10.1007/s00778-019-00588-3.
3. [3] J. Nayak et al., ‘Intelligent Computing in IoT-Enabled Smart Cities: A Systematic Review’, in Green Technology for Smart City and Society, R. Sharma, M. Mishra, J. Nayak, B. Naik, and D. Pelusi, Eds., in Lecture Notes in Networks and Systems. Singapore: Springer, 2021, pp. 1–21. doi: 10.1007/978-981-15-8218-9_1.
4. [4] I. H. Sarker, ‘Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions’, SN computer science, vol. 2, no. 6, p. 420, 2021, doi: 10.1007/s42979-021-00815-1.
5. [5] V. Mathur, Y. Saini, V. Giri, V. Choudhary, U. Bharadwaj, and V. Kumar, ‘Weather Station Using Raspberry Pi’, in 2021 Sixth International Conference on Image Information Processing (ICIIP), Shimla, India: IEEE, Nov. 2021, pp. 279–283. doi: 10.1109/ICIIP53038.2021.9702687.
6. [6] D. Demirol, R. Das, and D. Hanbay, ‘A Key Review on Security and Privacy of Big Data: Issues, Challenges, and Future Research Directions’, Signal, Image and Video Processing, pp. 1–9, Sep. 2022, doi: 10.1007/s11760-022-02341-w.
7. [7] Z. Il-Agure and J. Dempere, ‘Review of Data Visualization Techniques in IoT Data’, in 2022 8th International Conference on Information Technology Trends (ITT), May 2022, pp. 167–171. doi: 10.1109/ITT56123.2022.9863948.
8. [8] R. B. Haber and D. A. McNabb, ‘Visualization Idioms: A Conceptual Model for Scientific Visualization Systems’, Visualization in Scientific Computing, vol. 74, p. 93, 1990.

9. [9] N. Boukhelifa, J. C. Roberts, and P. J. Rodgers, ‘A coordination model for exploratory multiview visualization’, in Proceedings International Conference on Coordinated and Multiple Views in Exploratory Visualization - CMV 2003 -, Jul. 2003, pp. 76–85. doi: 10.1109/CMV.2003.1215005.
10. [10] S. dos Santos and K. Brodlie, ‘Gaining Understanding of Multivariate and Multidimensional Data Through Visualization’, Computers & Graphics, vol. 28, no. 3, pp. 311–325, Jun. 2004, doi: 10.1016/j.cag.2004.03.013.
11. [11] M. Sülü and R. Daş, ‘Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks’, Balkan Journal of Electrical and Computer Engineering, vol. 10, no. 3, pp. 300–306, Jul. 2022, doi: 10.17694/bajece.1090145.
12. [12] ‘A Survey of Scholarly Data Visualization | IEEE Journals & Magazine | IEEE Xplore’. Accessed: Nov. 26, 2024. [Online]. Available: https://ieeexplore.ieee.org/document/8314667/similar#similar
13. [13] I. Soric, D. Dinjar, M. Stajcer, and D. Orescanin, ‘Efficient social network analysis in big data architectures’, in 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), May 2017, pp. 1397–1400. doi: 10.23919/MIPRO.2017.7973640.
14. [14] Z. Wang, N. Ferreira, Y. Wei, A. S. Bhaskar, and C. Scheidegger, ‘Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets’, IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 681–690, Jan. 2017, doi: 10.1109/TVCG.2016.2598694.
15. [15] Y. Ye et al., ‘Generative AI for visualization: State of the art and future directions’, Visual Informatics, vol. 8, no. 2, pp. 43–66, Jun. 2024, doi: 10.1016/j.visinf.2024.04.003.
16. [16] Y. Hou, M. Yang, H. Cui, L. Wang, J. Xu, and W. Zeng, ‘C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model’, in Proceedings of the CHI Conference on Human Factors in Computing Systems, May 2024, pp. 1–18. doi: 10.1145/3613904.3642224.
17. [17] L. Shi, Z. Gao, M. Wang, and A. Liu, ‘A mainframe CPU capacity prediction system based on the mathematical modeling’, in ICSSSM12, Jul. 2012, pp. 537–540. doi: 10.1109/ICSSSM.2012.6252295.
18. [18] X. Zhang, Y. Cao, and X. Mu, ‘The Dynamic Retrieval Tree Menu Based on Dojo’, in 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, Sep. 2011, pp. 202–205. doi: 10.1109/ICM.2011.116.
19. [19] L. Zhuhadar, S. R. Kruk, and J. Daday, ‘Semantically enriched Massive Open Online Courses (MOOCs) platform’, Computers in Human Behavior, vol. 51, pp. 578–593, Oct. 2015, doi: 10.1016/j.chb.2015.02.067.
20. [20] Z. Chu and H. Wang, ‘An investigation of hotlinking and its countermeasures’, Computer Communications, vol. 34, no. 4, pp. 577–590, Apr. 2011, doi: 10.1016/j.comcom.2010.05.007.
21. [21] M. del P. Salas-Zárate, G. Alor-Hernández, R. Valencia-García, L. Rodríguez-Mazahua, A. Rodríguez-González, and J. L. López Cuadrado, ‘Analyzing best practices on Web development frameworks: The lift approach’, Science of Computer Programming, vol. 102, pp. 1–19, May 2015, doi: 10.1016/j.scico.2014.12.004.
22. [22] M. Bostock and J. Heer, ‘Protovis: A Graphical Toolkit for Visualization’, IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1121–1128, Nov. 2009, doi: 10.1109/TVCG.2009.174.
23. [23] R. D. Saxton et al., ‘HILIGT, upper limit servers I-Overview’, Astronomy and Computing, vol. 38, p. 100531, 2022, doi: https://doi.org/10.1016/j.ascom.2021.100531.
24. [24] R. R. Zhang et al., ‘The web-based user interface for EAST plasma control system’, Fusion Engineering and Design, vol. 89, no. 5, pp. 558–562, May 2014, doi: 10.1016/j.fusengdes.2014.02.070.
25. [25] D. Han, J. Pan, X. Zhao, and W. Chen, ‘NetV.js: A web-based library for high-efficiency visualization of large-scale graphs and networks’, Visual Informatics, vol. 5, no. 1, pp. 61–66, Mar. 2021, doi: 10.1016/j.visinf.2021.01.002.
26. [26] D. Li et al., ‘ECharts: A declarative framework for rapid construction of web-based visualization’, Visual Informatics, vol. 2, no. 2, pp. 136–146, Jun. 2018, doi: 10.1016/j.visinf.2018.04.011.
27. [27] G. Buchanan and R. Tashakkori, ‘A Web-App for Analysis of Honey Bee Hive Data’, in 2019 SoutheastCon, Apr. 2019, pp. 1–6. doi: 10.1109/SoutheastCon42311.2019.9020286.
28. [28] A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer, ‘Vega-Lite: A Grammar of Interactive Graphics’, IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 341–350, Jan. 2017, doi: 10.1109/TVCG.2016.2599030.
29. [29] R. Lu and S. Cao, ‘Highcharts-based implementation of the micro video big data visualization system’, in 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), Sep. 2017, pp. 1–5. doi: 10.1109/CIAPP.2017.8167049.
30. [30] M. Bostock, V. Ogievetsky, and J. Heer, ‘D3 Data-Driven Documents’, IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2301–2309, Dec. 2011, doi: 10.1109/TVCG.2011.185.
31. [31] T. B. Chandra and A. K. Dwivedi, ‘Chapter 10 - Data visualization: existing tools and techniques’, in Advanced Data Mining Tools and Methods for Social Computing, S. De, S. Dey, S. Bhattacharyya, and S. Bhatia, Eds., in Hybrid Computational Intelligence for Pattern Analysis. , Academic Press, 2022, pp. 177–217. doi: 10.1016/B978-0-32-385708-6.00017-5.
32. [32] M. Bostock, ‘D3.js - Data-Driven Documents’. Accessed: Nov. 25, 2022. [Online]. Available: https://d3js.org/
33. [33] https://www.ibm.com/cloud/learn/data-visualization, ‘What is Data Visualization?’ Accessed: Dec. 08, 2022. [Online]. Available: https://www.ibm.com/cloud/learn/data-visualization
34. [34] https://www.chartjs.org/, ‘Chart.js \textbar Open source HTML5 Charts for your website’. Accessed: Nov. 25, 2022. [Online]. Available: https://www.chartjs.org/
35. [35] J. Wang, W. Dou, C. Gao, and J. Wei, ‘JSTrace: Fast reproducing web application errors’, Journal of Systems and Software, vol. 137, pp. 448–462, Mar. 2018, doi: 10.1016/j.jss.2017.06.038.
36. [36] https://dojotoolkit.org/, ‘Dojo Toolkit’. Accessed: Dec. 15, 2022. [Online]. Available: https://dojotoolkit.org/
37. [37] A. Papantoniou, V. Loumos, M. Poulizos, and G. Rigas, ‘A Framework for Visualizing the Web of Data: Combining DBpedia and Open APIs’, in 2011 15th Panhellenic Conference on Informatics, 2011, pp. 240–244. doi: 10.1109/PCI.2011.32.
38. [38] prototypejs, ‘Prototype JavaScript framework: a foundation for ambitious web applications’. Accessed: Dec. 09, 2022. [Online]. Available: http://prototypejs.org/
39. [39] Prototype, ‘Prototype’. Accessed: Dec. 15, 2022. [Online]. Available: https://github.com/prototypejs/prototype
40. [40] ‘Frameworks for RIAs Development’, in https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-4666-6437-1.ch002, IGI Global, 1AD. Accessed: Nov. 27, 2024. [Online]. Available: https://www.igi-global.com/chapter/frameworks-for-rias-development/www.igi-global.com/chapter/frameworks-for-rias-development/117376
41. [41] EChartsJS, ‘EChartsJS \textbar Home Page’. Accessed: Dec. 29, 2022. [Online]. Available: https://www.echartsjs.com/index.html
42. [42] Idera Inc Company, ‘Fusioncharts’. Accessed: Nov. 25, 2022. [Online]. Available: https://www.fusioncharts.com
43. [43] https://c3js.org/, ‘C3.js \textbar D3-based reusable chart library’. Accessed: Nov. 30, 2022. [Online]. Available: https://c3js.org/
44. [44] X. Li, A. Kuroda, H. Matsuzaki, and N. Nakajima, ‘Advanced Aggregate Computation for Large Data Visualization’, in 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), Oct. 2015, pp. 137–138. doi: 10.1109/LDAV.2015.7348086.
45. [45] O. Laursen, ‘Flot: Attractive JavaScript plotting for jQuery’. Accessed: Mar. 20, 2023. [Online]. Available: https://www.flotcharts.org/
46. [46] vega.github.io/, ‘Vega: A Visualization Grammar’, Vega. Accessed: Jan. 29, 2023. [Online]. Available: https://vega.github.io/
47. [47] Z. Fang et al., ‘A Java web application based MDSplus data analysis and real-time monitoring system for EAST’, Fusion Engineering and Design, vol. 147, p. 111215, 2019, doi: https://doi.org/10.1016/j.fusengdes.2019.05.034.
48. [48] N. Mukhtar, M. A. Khan, and N. Chiragh, ‘Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains’, Telematics and Informatics, vol. 35, no. 8, pp. 2173–2183, Dec. 2018, doi: 10.1016/j.tele.2018.08.003.
49. [49] N. Manick, A. Appigadoo, and B. Z. Cadersaib, ‘Enterprise Architecture for the Fire Rescue Services’, in 2019 7th International Conference on Information and Communication Technology (ICoICT), Jul. 2019, pp. 1–6. doi: 10.1109/ICoICT.2019.8835254.
50. [50] A. Alarcón-Paredes, V. Francisco-García, I. P. Guzmán-Guzmán, J. Cantillo-Negrete, R. E. Cuevas-Valencia, and G. A. Alonso-Silverio, ‘An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi’, Applied Sciences, vol. 9, no. 15, Art. no. 15, Jan. 2019, doi: 10.3390/app9153046.
51. [51] R. R. Laher, ‘Thoth: Software for data visualization & statistics’, Astronomy and Computing, vol. 17, pp. 177–185, Oct. 2016, doi: 10.1016/j.ascom.2016.09.003.
52. [52] L. N. SHORNA, ‘Printing and signing ECG leads in PDF files’, Jul. 2012, Accessed: Feb. 09, 2025. [Online]. Available: https://www.politesi.polimi.it/handle/10589/56923
53. [53] Y. Zhou, Y. Yang, B. Xu, H. Leung, and X. Zhou, ‘Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study’, Information and Software Technology, vol. 56, no. 2, pp. 220–237, Feb. 2014, doi: 10.1016/j.infsof.2013.09.003.
54. [54] A. N. Privalov and V. A. Smirnov, ‘Detection of Fake Educational Sites Using Fuzzy String Match’, in 2022 2nd International Conference on Technology Enhanced Learning in Higher Education (℡E), May 2022, pp. 31–36. doi: 10.1109/℡E55498.2022.9800955.
55. [55] S. Kelley, M. Goldberg, M. Magdon-Ismail, K. Mertsalov, W. Wallace, and M. Zaki, ‘graphOnt: An ontology based library for conversion from semantic graphs to JUNG’, in 2009 IEEE International Conference on Intelligence and Security Informatics, Jun. 2009, pp. 170–172. doi: 10.1109/ISI.2009.5137292.
56. [56] J. Chen, Y. Li, and J. Song, ‘Research on Interactive Information Visualization System in Special Academic Discussion’, in 2012 Fourth International Symposium on Information Science and Engineering, Dec. 2012, pp. 59–63. doi: 10.1109/ISISE.2012.22.
57. [57] D. Hägele, ‘Visualizing Optimization Trajectories’, masterThesis, 2019. Accessed: Feb. 09, 2025. [Online]. Available: https://elib.uni-stuttgart.de/handle/11682/10841
58. [58] P. Ambure, R. B. Aher, A. Gajewicz, T. Puzyn, and K. Roy, ‘“NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling’, Chemometrics and Intelligent Laboratory Systems, vol. 147, pp. 1–13, Oct. 2015, doi: 10.1016/j.chemolab.2015.07.007.
59. [59] A. Renduchintala, F. Jahan, R. Khanna, and A. Y. Javaid, ‘A comprehensive micro unmanned aerial vehicle (UAV/Drone) forensic framework’, Digital Investigation, vol. 30, pp. 52–72, Sep. 2019, doi: 10.1016/j.diin.2019.07.002.
60. [60] H. Kassogué, A. Bernoussi, M. Maâtouk, and M. Amharref, ‘A two scale cellular automaton for flow dynamics modeling (2CAFDYM)’, Applied Mathematical Modelling, vol. 43, pp. 61–77, Mar. 2017, doi: 10.1016/j.apm.2016.10.034.
61. [61] M. Li, X. Feng, Y. Han, and X. Liu, ‘Mobile augmented reality-based visualization framework for lifecycle O&M support of urban underground pipe networks’, Tunnelling and Underground Space Technology, vol. 136, p. 105069, Jun. 2023, doi: 10.1016/j.tust.2023.105069.
62. [62] R. R. Laher, ‘Thoth: Software for data visualization & statistics’, Astronomy and Computing, vol. 17, pp. 177–185, Oct. 2016, doi: 10.1016/j.ascom.2016.09.003.
63. [63] C. Song et al., ‘Collection of patient-generated health data with a mobile application and transfer to hospital information system via QR codes’, Computer Methods and Programs in Biomedicine Update, vol. 3, p. 100099, Jan. 2023, doi: 10.1016/j.cmpbup.2023.100099.
64. [64] R. Mayerle et al., ‘Development of a coastal information system for the management of Jeddah coastal waters in Saudi Arabia’, Computers & Geosciences, vol. 89, pp. 71–78, Apr. 2016, doi: 10.1016/j.cageo.2015.12.006.
65. [65] T. W. Fredian and J. A. Stillerman, ‘Web based electronic logbook and experiment run database viewer for Alcator C-Mod’, Fusion Engineering and Design, vol. 81, no. 15, pp. 1963–1967, Jul. 2006, doi: 10.1016/j.fusengdes.2006.04.009.
66. [66] A. Sopan, A. S.-I. Noh, S. Karol, P. Rosenfeld, G. Lee, and B. Shneiderman, ‘Community Health Map: A geospatial and multivariate data visualization tool for public health datasets’, Government Information Quarterly, vol. 29, no. 2, pp. 223–234, Apr. 2012, doi: 10.1016/j.giq.2011.10.002.
67. [67] S. Zhang, Y. Li, T. Zhang, and Y. Peng, ‘An integrated environmental decision support system for water pollution control based on TMDL – A case study in the Beiyun River watershed’, Journal of Environmental Management, vol. 156, pp. 31–40, Jun. 2015, doi: 10.1016/j.jenvman.2015.03.021.
68. [68] T. J. Overbye and J. Weber, ‘Smart Grid Wide-Area Transmission System Visualization’, Engineering, vol. 1, no. 4, pp. 466–474, Dec. 2015, doi: 10.15302/J-ENG-2015098.
69. [69] P. Rajendrakumar, ‘The Sorghum Genome: Current Status and Future Prospects’, 2021. doi: 10.1007/978-981-15-8249-3_20.
70. [70] P. Horovčák, J. Terpák, and B. Stehlíková, ‘Selected thermochemical properties of substances and their graphic web presentation’, in Proceedings of the 13th International Carpathian Control Conference (ICCC), May 2012, pp. 221–226. doi: 10.1109/CarpathianCC.2012.6228643.
71. [71] H. González-Díaz et al., ‘Generalized lattice graphs for 2D-visualization of biological information’, Journal of Theoretical Biology, vol. 261, no. 1, pp. 136–147, Nov. 2009, doi: 10.1016/j.jtbi.2009.07.029.
72. [72] M. Mora-Cantallops, S. Sánchez-Alonso, and E. García-Barriocanal, ‘A complex network analysis of the Comprehensive R Archive Network (CRAN) package ecosystem’, Journal of Systems and Software, vol. 170, p. 110744, Dec. 2020, doi: 10.1016/j.jss.2020.110744.
73. [73] G. Longo-Minnolo et al., ‘Delineating citrus management zones using spatial interpolation and UAV-based multispectral approaches’, Computers and Electronics in Agriculture, vol. 222, p. 109098, Jul. 2024, doi: 10.1016/j.compag.2024.109098.
74. [74] C. Stoiber et al., ‘VisAhoi: Towards a library to generate and integrate visualization onboarding using high-level visualization grammars’, Visual Informatics, vol. 8, no. 3, pp. 1–17, 2024, doi: https://doi.org/10.1016/j.visinf.2024.06.001.
75. [75] A. S. Moorthy and H. J. Eberl, ‘compuGUT: An in silico platform for simulating intestinal fermentation’, SoftwareX, vol. 6, pp. 237–242, Jan. 2017, doi: 10.1016/j.softx.2017.06.004.
76. [76] Z. K. Mundargi, K. Patel, A. Patel, R. More, S. Pathrabe, and S. Patil, ‘Plotplay: An Automated Data Visualization Website using Python and Plotly’, in 2023 International Conference for Advancement in Technology (ICONAT), 2023, pp. 1–4. doi: 10.1109/ICONAT57137.2023.10079977.
77. [77] M. K. Nematchoua, J. A. Orosa, and M. Afaifia, ‘Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries’, Ecological Informatics, vol. 69, p. 101643, Jul. 2022, doi: 10.1016/j.ecoinf.2022.101643.
78. [78] M. A. Perkins and J. W. Carrier, ‘Protocol for programing a degree-to-careers dashboard in R using Posit and Shiny’, STAR Protocols, vol. 5, no. 1, p. 102902, Mar. 2024, doi: 10.1016/j.xpro.2024.102902.
79. [79] M. Figueira, D. Conesa, and A. López-Quílez, ‘A shiny R app for spatial analysis of species distribution models’, Ecological Informatics, vol. 80, p. 102542, May 2024, doi: 10.1016/j.ecoinf.2024.102542.
80. [80] J. J. Castillo et al., ‘The Development of the Davis Food Glycopedia—A Glycan Encyclopedia of Food’, Nutrients, vol. 14, no. 8, p. 1639, Apr. 2022, doi: 10.3390/nu14081639.
81. [81] A. Severinsen and Ø. Myrland, ‘ShinyRBase: Near real-time energy saving models using reactive programming’, Applied Energy, vol. 325, p. 119798, Nov. 2022, doi: 10.1016/j.apenergy.2022.119798.
82. [82] M. Owkes, K. Coblentz, A. Eriksson, T. Kammerzell, and P. S. Stewart, ‘Biofilm.jl: A fast solver for one-dimensional biofilm chemistry and ecology’, Computer Physics Communications, vol. 293, p. 108890, Dec. 2023, doi: 10.1016/j.cpc.2023.108890.
83. [83] X. Pu and M. Kay, ‘How Data Analysts Use a Visualization Grammar in Practice’, in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, in CHI ’23. New York, NY, USA: Association for Computing Machinery, Apr. 2023, pp. 1–22. doi: 10.1145/3544548.3580837.
84. [84] A. Yamashita, S. Berg, and E. Figgemeier, ‘Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance’, Energy and AI, vol. 17, p. 100409, Sep. 2024, doi: 10.1016/j.egyai.2024.100409.
85. [85] I. Morales Becerra, E. Anguiano Mondragón, I. Morales Becerra, and E. Anguiano Mondragón, ‘Matías Romero Avendaño ve 19. yüzyılın sonlarında Meksika’nın Oaxaca kentindeki kahve üretimi’, Accessed: Feb. 09, 2025. [Online]. Available: https://www.scielo.sa.cr/scielo.php?pid=S1409-469X2022000100117&script=sci_arttext&tlng=en
86. [86] M. Bitar, ‘Rust and Julia for Scientific Computing’, Computing in Science & Engineering, vol. 26, no. 1, pp. 72–76, Jan. 2024, doi: 10.1109/MCSE.2024.3369988.
87. [87] H. N, P. P. Teggi, M. S. Bharadwaj, and R. K, ‘Detection of Brain Disorders Using Ocular Responses’, in 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Oct. 2024, pp. 304–308. doi: 10.1109/DISCOVER62353.2024.10750701.
88. [88] M. T. Zonin and M. Sperandio, ‘Methodology for Reducing the Connection Possibilities in Transmission Network Expansion Planning’, in 2023 15th Seminar on Power Electronics and Control (SEPOC), Oct. 2023, pp. 1–5. doi: 10.1109/SEPOC58810.2023.10322600.
89. [89] H. J. Hadi, Y. Cao, S. Li, N. Ahmad, and M. A. Alshara, ‘FCG-MFD: Benchmark function call graph-based dataset for malware family detection’, Journal of Network and Computer Applications, vol. 233, p. 104050, Jan. 2025, doi: 10.1016/j.jnca.2024.104050.
90. [90] S. K. A. Fahad and A. E. Yahya, ‘Big Data Visualization: Allotting by R and Python with GUI Tools’, in 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Jul. 2018, pp. 1–8. doi: 10.1109/ICSCEE.2018.8538413.
91. [91] M. Molero-Armenta, U. Iturrarán-Viveros, S. Aparicio, and M. G. Hernández, ‘Optimized OpenCL implementation of the Elastodynamic Finite Integration Technique for viscoelastic media’, Computer Physics Communications, vol. 185, no. 10, pp. 2683–2696, Oct. 2014, doi: 10.1016/j.cpc.2014.05.016.
92. [92] G. Ulian and G. Valdrè, ‘SEISMIC, a Python-based code of the quantas package to calculate the phase and group acoustic velocities in crystals’, Computers & Geosciences, vol. 188, p. 105615, Jun. 2024, doi: 10.1016/j.cageo.2024.105615.
93. [93] N. Kovač, K. Ratković, H. Farahani, and P. Watson, ‘A practical applications guide to machine learning regression models in psychology with Python’, Methods in Psychology, vol. 11, p. 100156, Dec. 2024, doi: 10.1016/j.metip.2024.100156.
94. [94] J. Lym, G. R. Wittreich, and D. G. Vlachos, ‘A Python Multiscale Thermochemistry Toolbox (pMuTT) for thermochemical and kinetic parameter estimation’, Computer Physics Communications, vol. 247, p. 106864, Feb. 2020, doi: 10.1016/j.cpc.2019.106864.
95. [95] T. Koutsellis et al., ‘In-Cognitive: A web-based Python application for fuzzy cognitive map design, simulation, and uncertainty analysis based on the Monte Carlo method’, SoftwareX, vol. 23, p. 101513, Jul. 2023, doi: 10.1016/j.softx.2023.101513.
96. [96] G. Castellazzi, N. Lo Presti, A. M. D’Altri, and S. de Miranda, ‘Cloud2FEM: A finite element mesh generator based on point clouds of existing/historical structures’, SoftwareX, vol. 18, p. 101099, Jun. 2022, doi: 10.1016/j.softx.2022.101099.
97. [97] A. Peñuela, C. Hutton, and F. Pianosi, ‘An open-source package with interactive Jupyter Notebooks to enhance the accessibility of reservoir operations simulation and optimisation’, Environmental Modelling & Software, vol. 145, p. 105188, Nov. 2021, doi: 10.1016/j.envsoft.2021.105188.
98. [98] M. Hatamzad, G. C. P. Pinerez, and J. Casselgren, ‘Intelligent cost-effective winter road maintenance by predicting road surface temperature using machine learning techniques’, Knowledge-Based Systems, vol. 247, p. 108682, Jul. 2022, doi: 10.1016/j.knosys.2022.108682.
99. [99] G. Singh, D. K. Misra, C. Tanwar, and P. Narang, ‘Visualization and Analysis of Populations’, in 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), May 2022, pp. 364–368. doi: 10.1109/COM-IT-CON54601.2022.9850886.
100. [100] A. Lavanya, P. Darsha, P. Akhil, J. Lloret, and N. Yogeshwar, ‘A Real-Time Human Mobility Visualization of Covid-19 Spread from East Asian Countries’, in 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS), Gandia, Spain: IEEE, Dec. 2021, pp. 1–8. doi: 10.1109/SNAMS53716.2021.9732103.
101. [101] ‘Rainfall Forecasting Using Machine Learning Algorithms for Localized Events’, Computers, Materials and Continua, vol. 71, no. 3, pp. 6333–6350, Jan. 2022, doi: 10.32604/cmc.2022.023254.
102. [102] A. Fathima, G. S. Devi, and M. Faizaanuddin, ‘Improving distributed denial of service attack detection using supervised machine learning’, Measurement: Sensors, vol. 30, p. 100911, Dec. 2023, doi: 10.1016/j.measen.2023.100911.
103. [103] S. Hooda and P. Mondal, ‘Predictive modeling of plastic pyrolysis process for the evaluation of activation energy: Explainable artificial intelligence based comprehensive insights’, Journal of Environmental Management, vol. 360, p. 121189, Jun. 2024, doi: 10.1016/j.jenvman.2024.121189.
104. [104] I. Sadek and B. Abdulrazak, ‘A comparison of three heart rate detection algorithms over ballistocardiogram signals’, Biomedical Signal Processing and Control, vol. 70, p. 103017, Sep. 2021, doi: 10.1016/j.bspc.2021.103017.
105. [105] J. Kerexeta-Sarriegi et al., ‘Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach’, International Journal of Medical Informatics, vol. 188, p. 105466, Aug. 2024, doi: 10.1016/j.ijmedinf.2024.105466.
106. [106] R. De Filippo and D. Schmitz, ‘Transcriptomic mapping of the 5-HT receptor landscape’, Patterns, vol. 5, no. 10, p. 101048, Oct. 2024, doi: 10.1016/j.patter.2024.101048.
107. [107] S. Willems, E. Voytik, P. Skowronek, M. T. Strauss, and M. Mann, ‘AlphaTims: Indexing Trapped Ion Mobility Spectrometry–TOF Data for Fast and Easy Accession and Visualization’, Molecular & Cellular Proteomics, vol. 20, p. 100149, Jan. 2021, doi: 10.1016/j.mcpro.2021.100149.
108. [108] H. Martin, Y. Hong, N. Wiedemann, D. Bucher, and M. Raubal, ‘Trackintel: An open-source Python library for human mobility analysis’, Computers, Environment and Urban Systems, vol. 101, p. 101938, Apr. 2023, doi: 10.1016/j.compenvurbsys.2023.101938.
109. [109] X. Chen et al., ‘Simulation study on water yield service flow based on the InVEST-Geoda-Gephi network: A case study on Wuyi Mountains, China’, Ecological Indicators, vol. 159, p. 111694, 2024, doi: https://doi.org/10.1016/j.ecolind.2024.111694.
110. [110] J. Liu, T. Tang, W. Wang, B. Xu, X. Kong, and F. Xia, ‘A Survey of Scholarly Data Visualization’, IEEE Access, vol. 6, pp. 19205–19221, 2018, doi: 10.1109/ACCESS.2018.2815030.
111. [111] P. Paschalis et al., ‘Online application for the barometric coefficient calculation of the NMDB stations’, New Astronomy, vol. 19, pp. 10–18, 2013, doi: https://doi.org/10.1016/j.newast.2012.08.003.
112. [112] https://plotly.com/, ‘Plotly: Low-Code Data App Development’. Accessed: Nov. 29, 2022. [Online]. Available: https://plotly.com/
113. [113] M. L. Waskom, ‘seaborn: statistical data visualization’. Accessed: Feb. 02, 2023. [Online]. Available: https://joss.theoj.org/papers/10.21105/joss.03021
114. [114] J. VanderPlas et al., ‘Altair: Interactive Statistical Visualizations for Python’, Journal of Open Source Software, vol. 3, no. 32, p. 1057, Dec. 2018, doi: 10.21105/joss.01057.
115. [115] F. M. / Kozea, ‘Pygal: A Python Svg Graph Plotting Library’. Accessed: Nov. 29, 2022. [Online]. Available: https://www.pygal.org/
116. [116] Bokeh, ‘Bokeh Documentation’. Accessed: Nov. 29, 2022. [Online]. Available: https://docs.bokeh.org/en/latest/index.html
117. [117] M. J. MacLeod et al., ‘Invited Review: A Position on the Global Livestock Environmental Assessment Model (gleam)’, Animal, vol. 12, no. 2, pp. 383–397, Jan. 2018, doi: 10.1017/S1751731117001847.
118. [118] https://matplotlib.org, ‘Matplotlib 3.6.3 documentation’. Accessed: Feb. 04, 2023. [Online]. Available: https://matplotlib.org/stable/index.html
119. [119] J. D. Hunter, ‘Matplotlib: A 2D Graphics Environment’, Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007, doi: 10.1109/MCSE.2007.55.
120. [120] G. Singh, D. K. Misra, C. Tanwar, and P. Narang, ‘Visualization and Analysis of Populations’, in 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), May 2022, pp. 364–368. doi: 10.1109/COM-IT-CON54601.2022.9850886.
121. [121] Folium, ‘Folium 0.14.0 documentation’. Accessed: Feb. 05, 2023. [Online]. Available: https://python-visualization.github.io/folium/
122. [122] M. S. Kahil, A. Bouramoul, and M. Derdour, ‘Multi Criteria-Based Community Detection and Visualization in Large-Scale Networks Using Label Propagation Algorithm’, in 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI), Sep. 2021, pp. 1–6. doi: 10.1109/ICRAMI52622.2021.9585964.
123. [123] MIT, ‘https://github.com/plotly/plotly.py’. Accessed: Feb. 05, 2023. [Online]. Available: https://github.com/plotly/plotly.py
124. [124] J. Heer, S. K. Card, and J. A. Landay, ‘prefuse: a toolkit for interactive information visualization’, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’05. New York, NY, USA: Association for Computing Machinery, Apr. 2005, pp. 421–430. doi: 10.1145/1054972.1055031.
125. [125] Blokt, ‘Flare \textbar Data Visualization for the Web’, Blokt - Privacy, Tech, Bitcoin, Blockchain & Cryptocurrency. Accessed: Dec. 02, 2022. [Online]. Available: https://blokt.com/tool/prefuse-flare
126. [126] X. Zhang, C. Zhao, and X. Wang, ‘A Survey on Knowledge Representation in Materials Science and Engineering: An Ontological Perspective’, Computers in Industry, vol. 73, pp. 8–22, Oct. 2015, doi: 10.1016/j.compind.2015.07.005.
127. [127] S. Purushe, S. K. Anbalagan, and G. Grinstein, ‘Development of an Interactive Ramachandran Plot in Weave’, in 2011 15th International Conference on Information Visualisation, Jul. 2011, pp. 232–236. doi: 10.1109/IV.2011.109.
128. [128] C. Dempsey, ‘Weave - Open Source Data Visualization’, GIS Lounge. Accessed: Dec. 23, 2022. [Online]. Available: https://www.gislounge.com/weave-open-source-data-visualization/
129. [129] A. Baumann, S. Shams, M. Ross, W. Mass, and G. Grinstein, ‘Enhancing STEM classes using weave: A collaborative web-based visualization environment’, in 2011 Integrated STEM Education Conference (ISEC), Apr. 2011, pp. 2A-1-2A – 4. doi: 10.1109/ISECon.2011.6229637.
130. [130] R. Agrawal, A. Kadadi, X. Dai, and F. Andres, ‘Challenges and Opportunities with Big Data Visualization’, in Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems, Caraguatatuba Brazil: ACM, Oct. 2015, pp. 169–173. doi: 10.1145/2857218.2857256.
131. [131] L. Wilkinson, The Grammar of Graphics. Springer Science & Business Media, 2013.
132. [132] N. Kruchten, A. M. McNutt, and M. J. McGuffin, ‘Metrics-Based Evaluation and Comparison of Visualization Notations’, IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 1, pp. 425–435, 2024, doi: 10.1109/TVCG.2023.3326907.
133. [133] S. G, A. Swaminathan, J. B. J, S. R, and L. Nelson, ‘IPL Data Analysis and Visualization for Team Selection and Profit Strategy’, in 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), 2023, pp. 592–598. doi: 10.1109/ICCMC56507.2023.10083736.