SciBlend: Advanced data visualization workflows within Blender

Scientific data visualization is essential for analysis, communication, and storytelling in research. While Blender offers a powerful rendering engine and a flexible 3D environment, its steep learning curve and general-purpose interface can hinder scientific workflows. To address this gap, we present SciBlend, a Python-based toolkit that extends Blender for data visualization. It provides specialized add-ons for multiple computational data files import, annotation, shading, and scene composition, enabling both photorealistic (Cycles) and real-time (EEVEE) rendering of large-scale and time-varying data. By combining a streamlined workflow with physically based rendering, SciBlend supports advanced visualization tasks while preserving essential scientific attributes. Comparative evaluations across multiple case studies show improvements in rendering performance, clarity, and reproducibility relative to traditional tools. This modular and user-oriented design offers a robust solution for creating publication-ready visuals of complex computational data. • SciBlend advances data visualization through seamless Blender integration. • Enables publication-ready visuals of large, time-varying datasets. • Combines real-time and ray-traced rendering for advanced data workflows.