AI in Data Visualization

Data visualization is an essential facet of science, facilitating the digestion and dissemination of complex data through visual representations. As datasets become increasingly large and intricate, traditional data visualization techniques struggle to keep pace with the demand for clarity and insight. Artificial Intelligence (AI) offers transformative potential in this space by enhancing the capacity, speed, and interpretative power of data visualization tools. With AI, dynamic, real-time visualizations can be generated, uncovering patterns and insights that might escape the human eye. Machine learning algorithms can automate the generation of visualizations tailored to the specific nuances of the data, while natural language processing enables more intuitive interaction with data through conversational interfaces. As the field of AI in data visualization continues to evolve, it presents opportunities and challenges, especially concerning interpretability, ethical considerations, and user engagement. Harnessing AI promises not only more informative visuals but also democratizes access to complex insights by abstracting the underlying complexity through accessible interfaces.

This Research Topic aims to explore the intersection of AI and data visualization, seeking to understand how AI can be leveraged to create more effective, insightful, and accessible visual representations of data. By bringing together scholars, practitioners, and developers, the aim is to foster collaboration and innovation that push the boundaries of what is possible in AI-assisted data visualization.

Submissions are welcomed across a broad spectrum of topics under the umbrella of AI in data visualization. We welcome original research articles, reviews, case studies, technology and code, methodologies, and perspective that might explore but are not limited to:

1. AI Automated Visualization Creation: Exploring algorithms and models that autonomously generate visual representations from raw data.
2. AI-Powered Interactive Visualizations: Development of AI-powered tools that facilitate interactive engagement and exploration of datasets.
3. Visualization in AI Interpretability: Using visualization techniques to enhance the understanding and explainability of AI models.
4. AI-Driven Aesthetic Design: Investigating how AI can optimize the aesthetic quality of visualizations to improve user experience.
5. AI-Assisted Multi-Modal Data Visualization: Addressing challenges and opportunities in visualizing complex, multi-modal datasets with AI.
6. Ethical and Bias Considerations: Examining potential biases introduced by AI in visualizations and developing frameworks for ethical visualization practices.
7. AI-Powered Real-Time Data Visualization: Innovations enabling live, updating visualizations powered by AI techniques.

By addressing these areas, this research endeavor seeks to highlight cutting-edge advancements, practical applications, and future directions in AI-enhanced data visualization, ultimately fostering a deeper understanding and broader application in various sectors.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: AI, Visualization, Interactive, Automation, Interpretability, Real-Time Analysis, Machine Learning, Data Insights, Ethics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.