SciBlend

SciBlend is an extensible, Python-based toolkit developed to facilitate advanced scientific visualization workflows within Blender. It integrates Blender's rendering capabilities (Cycles & EEVEE) with functionalities for processing and visualizing complex computational data, aiming to bridge the gap often found between specialized data analysis tools and general-purpose 3D creation suites.

🧩 Components included in SciBlend

• Advanced Core: scientific data import for static and animations (.x3d, .vtk, .vtu, .pvtu, .vtp, .pvtp, .nc, .nc4, .shp)
• Shader Generator: scientific colormaps and shader creation
• Legend Generator: compositor-based legends
• Grid Generator: coordinate grids and guides
• Shapes Generator: compositor shapes
• Notes Generator: 3D annotations
• Compositor: camera, formats, and render helpers

Note: Each addon is also maintained and can be obtained from its individual repository in Github.

🔄 Workflow Overview

SciBlend is designed to integrate into scientific visualization pipelines as follows:

1. Data Preparation (External, Optional): Tools such as ParaView can be used for initial data processing, filtering, and preparation of large datasets. SciBlend includes ParaView macros to aid data export.
2. Import & Setup (SciBlend Advanced Core / Core): Import static or time-varying datasets into Blender.
3. Material Application (SciBlend Shader Generator): Apply or define shaders and colormaps based on data attributes.
4. Contextualization & Annotation (SciBlend Grid, Shapes, Notes Generators): Incorporate coordinate systems and annotations.
5. Legend Generation (SciBlend Legend Generator): Create legends to correlate visual elements with data values.
6. Scene Composition & Rendering (SciBlend Compositor): Adjust camera settings, lighting, and render parameters using Cycles or EEVEE.

📚 Tutorials (In Development)

Get started with SciBlend by following our video tutorials. These guides cover everything from installation to advanced features. Please note that these tutorials are currently under development. Watch them at: https://github.com/SciBlend/SciBlend

Total Course Duration: Approximately 35 minutes and 48 seconds.

📜 Citing SciBlend

If SciBlend or its components are used in research or publications, please include the following citations:

1. The SciBlend Paper:

@article{marin2025,
title = {SciBlend: Advanced data visualization workflows within Blender},
journal = {Computers & Graphics},
volume = {130},
pages = {104264},
year = {2025},
issn = {0097-8493},
doi = {https://doi.org/10.1016/j.cag.2025.104264},
url = {https://www.sciencedirect.com/science/article/pii/S0097849325001050},
author = {José Marín and Tiffany M.G. Baptiste and Cristobal Rodero and Steven E. Williams and Steven A. Niederer and Ignacio García-Fernández},
keywords = {Scientific visualisation, Blender, Paraview, Data rendering, Visual storytelling, Scientific communication}
}

3. The Software Suite:

@software{sciblend2025,
doi = {10.5281/ZENODO.15420392},
url = {https://zenodo.org/doi/10.5281/zenodo.15420392},
author = {José Marín},
title = {SciBlend: Advanced Data Visualization Workflows within Blender - Software},
publisher = {Zenodo},
year = {2025},
copyright = {Creative Commons Attribution 4.0 International}
}