DATA VISUALIZATION AS A FORM OF SCULPTURAL ART
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6863Keywords:
Data Physicalization, Generative Design, Computational Sculpture, Aesthetic Information Mapping, Digital FabricationAbstract [English]
Data visualization, the interconnection of information and sculptural art is a new paradigm where information moves out of the digital screens and finds space in the physical world to express itself. This paper examines the conceptual, aesthetic and technological systems that allow the data to be physically realized as three dimensional artworks. It investigates how datasets are transformed algorithmically and modeled computationally into forms that provoke sensory, emotional and intellectual interaction. The paper contextualizes the current practices in the context of the historical path of abstract visualization and data physicalization to identify the connection of the modern practice with the information design and artistic tradition. The research methodologically describes the following processes: the choice of the dataset, semantic-geometric mapping, application of digital fabrication tools, including 3D printing, CNC, and laser cutting. Machine learning adds value to the technological ecosystem by supporting pattern extraction, generative modeling through Grasshopper and Houdini, and previews of the sculpture in real-time. The aesthetic inquiry is concerned with the ability of the physicalized information to tell visual tales, mediate perception, balance between science and creative interpretation. It is further discussed with regards to issues of material constraints, sustainability and representation of multidimensional datasets. This study, finally, makes the data-driven sculpture be both an art and an epistemological practice, i.e. the way of turning data into an embodied and spatial conversation of information, materiality and human experience.
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Copyright (c) 2025 Manivannan Karunakaran, Praney Madan, Sachin Pratap Singh, Dr. Peeyush Kumar Gupta, Mohd Faisal, Fazil Hasan, Mahesh Kurulekar
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