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Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science

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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1126))

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Abstract

Studies have shown rising interest in scientific collaborations throughout the past decades. The challenges throughout various studies show an emerging need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors’ publications and showcasing them with citation scores. They neglect the possibility of creating a holistic assistance and collaborative approach that covers the entire scientific research process using adequate intelligence methods. We introduce in this chapter a novel approach to visual collaboration. Our approach covers the entire process of scientific paper writing through real-time visual recommendations. It combines on-the-fly similarity measurements, ideation assistance based on group constellations, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the integration of visual analytics that combines artificial intelligence with interactive visualizations. Thereby the interaction design and the ease of use are essential. Our approach presents a single-source AI-driven visual collaborative research platform for the entire research community.

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Acknowledgements

We thank Kjell Kunz, Viet Anh Ly, and Sascha Haas from the Darmstadt University of Applied Sciences and Jessica Bersch and Adrian Lumpe from our course Visual Trend Analytics at the Technische Universität Darmstadt, who contributed to this research. This work was conducted within the research group on Human-Computer Interaction and Visual Analytics at the Darmstadt University of Applied Sciences (https://vis.h-da.de).

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Authors and Affiliations

  1. Human-Computer Interaction and Visual Analytics, Darmstadt University of Applied Sciences, 64295, Darmstadt, Germany

    Midhad Blazevic, Lennart B. Sina, Cristian A. Secco & Kawa Nazemi

Authors
  1. Midhad Blazevic
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  2. Lennart B. Sina
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  3. Cristian A. Secco
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  4. Kawa Nazemi
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Corresponding author

Correspondence to Midhad Blazevic .

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Editors and Affiliations

  1. Dept. of Computer Science, Central Washington University, Ellensburg, WA, USA

    Boris Kovalerchuk

  2. Darmstadt University of Applied Sciences, Darmstadt, Germany

    Kawa Nazemi

  3. Dept. of Computer Science, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

  4. ISEL, Polytechnic Institute of Lisbon, Lisboa, Portugal

    Nuno Datia

  5. Department of Informatics, London South Bank University, London, UK

    Ebad Bannissi

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Blazevic, M., Sina, L.B., Secco, C.A., Nazemi, K. (2024). Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Bannissi, E. (eds) Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-031-46549-9_12

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