| name | scientific-visualization |
| description | Publication-aware scientific figure planning, generation, and review that preserves data meaning, provenance, uncertainty, accessibility, and reproducibility. Use for plots, schematics, dashboards, or figure redesign. |
Scientific Visualization
Preserve scientific meaning before optimizing appearance.
- State the data, transformation, comparison, audience, destination, and intended inference.
- Keep raw data, exclusions, missing-value codes, analysis code, normalization, binning, image adjustments, and random seeds traceable.
- Choose encodings that match the variable types and uncertainty. Use labels, units, legends, accessible colors, and redundant cues; avoid decorative distortion and unexplained dual axes.
- Keep static, interactive, and machine-readable deliverables distinct. Provide a data table or accessible fallback when needed.
- Inspect the rendered output at the actual delivery size and audit captions, annotations, source references, and numerical consistency.
Never hide inconvenient observations, connect missing points without justification, or claim that DPI, a palette, or an automated export makes a figure journal-compliant. Verify the target journal and article type before applying venue-specific rules.
This is a ScanSci adaptation of the public K-Dense scientific-visualization guidance.
