Scientific Data Visualizations - Data Doesn't Care What You Believe. Not random. **Scientific Data Visualizations - Data Doesn't Care What You Believe** In science, data is the ultimate arbiter of truth—objective, unemotional, and indifferent to human biases or beliefs. Scientific data visualization transforms raw numbers into intuitive visual narratives, revealing patterns, anomalies, and truths that might otherwise remain hidden. Whether mapping the structure of a protein, simulating climate change scenarios, or visualizing quantum states, these representations make complex phenomena accessible while rigorously preserving accuracy. The phrase *"Data doesn't care what you believe"* underscores a fundamental principle: visualizations must convey reality as measured, not as wished for. From Florence Nightingale’s mortality charts to modern COVID-19 dashboards, history shows that when politics, opinions, or dogma clash with data, it’s the visualizations grounded in evidence that ultimately guide progress. Effective scientific visualization doesn’t persuade—it *reveals*, forcing us to confront inconvenient truths and innovate solutions. In an era of misinformation, this discipline remains a bulwark for integrity, proving that while beliefs may sway, data—when clearly visualized—speaks for itself.

1. Scientific & Simulation
Visualizations
Data doesn't care what you believe.
Data Visualization & Analytics

2. Introduction
•The power of visualizing complex scientific data
•How simulations help us understand the world
•Key techniques for effective visualization
•Real-world applications and case studies
•Future trends in scientific visualization

3. Recommend Reading
101 Data Visualization and
Analytics Projects (Paperback)
WebGPU Data
Visualization Cookbook
(2nd Edition)

4. Why Visualize Scientific Data?
• Human brain processes visuals 60,000x faster than text
• We can spot patterns in images that algorithms miss
• Complex relationships become intuitively understandable
• Critical for communicating findings to diverse audiences
• Fun Fact: The first scientific visualization was likely star charts
drawn by ancient astronomers

5. Types of Scientific Visualizations
1. Volume Rendering (CT scans, MRI)
2. Flow Visualization (wind, water, plasma)
3. Molecular Modeling
4. Astronomical Visualizations
5. Geospatial Data Mapping
6. Multivariate Data Displays

6. Simulation Visualization Techniques
• Particle Systems (fluid dynamics)
• Vector Fields (electromagnetic fields)
• Isosurfaces (medical imaging)
• Streamlines & Pathlines (aerodynamics)
• Time-series Animation (climate models)
• Fun Fact: Weather forecasting uses over 100 million
observations daily in its simulations

7. The Science of Color in Visualization
• Color maps must accurately represent data
• Avoid rainbow color maps - they distort perception
• Perceptually uniform colormaps are ideal
• Consider colorblind-friendly palettes
• Use color to highlight, not decorate
• Fun Fact: The Viridis colormap was specifically designed for
accurate scientific visualization

8. Making the Invisible Visible
• Techniques to visualize high-dimensional data:
• Principal Component Analysis (PCA)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• UMAP (Uniform Manifold Approximation)
• Parallel Coordinates
• Radar/Spider Charts
• Fun Fact: The human brain can intuitively understand up to 3
dimensions, but these techniques help us 'see' 100+ dimensions

9. Case Study: Climate Modeling
• Petabyte-scale datasets from global simulations
• Visualizing temperature gradients, ocean currents
• Time animations showing decades in seconds
• Ensemble modeling shows probability distributions
• Critical for policy decisions and public understanding
• Example: NASA's 'Perpetual Ocean' visualization of ocean
currents

10. Medical Imaging Breakthroughs
• 3D reconstructions from CT/MRI scans
• Real-time surgical navigation systems
• Virtual dissection of digital cadavers
• Blood flow simulations for stroke prediction
• Tumor growth modeling
• Fun Fact: The Visible Human Project dataset is over 15GB for
just one complete human body

11. Seeing the Unseeable
• Black hole accretion disks (Event Horizon Telescope)
• Galaxy formation simulations
• Cosmic microwave background maps
• Exoplanet atmosphere modeling
• Gravitational wave visualizations
• Fun Fact: The first image of a black hole required processing
5 petabytes of data (equivalent to 5,000 years of MP3 audio)

12. Molecular Dynamics Visualizations
• Protein folding simulations
• Drug binding interactions
• DNA transcription processes
• Nanomaterial behavior
• Virus structure modeling
• Example: COVID-19 spike protein visualizations helped
vaccine development

13. Fluid Dynamics Simulations
• Aerodynamic testing (aircraft, cars)
• Ocean current modeling
• Blood flow in arteries
• Combustion chamber dynamics
• Weather pattern formation
• Fun Fact: A single detailed fluid simulation can require over 1
billion grid points

14. Quantum Mechanics Made Visible
• Wavefunction probability densities
• Orbital shapes and electron clouds
• Quantum entanglement diagrams
• Qubit state representations
• Feynman diagrams of particle interactions
• Challenge: Visualizing concepts that have no classical analog

15. Big Data Visualization Challenges
• Data sizes exceeding GPU memory
• Real-time rendering of streaming data
• Occlusion in dense datasets
• Multi-resolution representations
• Distributed rendering techniques
• Fun Fact: The Large Hadron Collider produces about 1PB/sec
(only a fraction is stored)

16. Immersive Visualization
• VR for molecular docking
• Astronomical scale exploration
• Surgical planning in 3D space
• Architectural fluid dynamics
• Geological formation exploration
• Example: NASA uses VR to train astronauts for spacewalks

17. AI-Enhanced Visualization
• Neural networks for feature detection
• GANs for super-resolution rendering
• Automated colormap optimization
• Dimensionality reduction with autoencoders
• Predictive visualization of simulations
• Future Trend: AI as co-pilot for scientific discovery

18. Historical Perspective
• 17th century: First microscopic drawings
• 1854: John Snow's cholera map (first epidemiological viz)
• 1950s: First computer-generated scientific plots
• 1987: First volume rendering algorithm
• 2000s: GPU-accelerated visualization
• Fun Fact: Florence Nightingale was a pioneer in statistical
visualization

19. Popular Visualization Tools
• Open Source:
• ParaView (large data)
• VisIt (HPC simulations)
• Matplotlib/Seaborn (Python)
• Commercial:
• Tableau (business analytics)
• IDL (astronomy)
• Amira (bioimaging)

20. Scientific Visualization Best Practices
1. Never distort the data
2. Make the visualization self-documenting
3. Use appropriate visual encodings
4. Include uncertainty representations
5. Optimize for the target medium
6. Provide interactive exploration when possible

21. Ethical Considerations
• Avoid misleading representations
• Clearly show limitations and uncertainty
• Consider how visualizations might be misinterpreted
• Be transparent about data processing
• Protect sensitive information
• Example: Climate change deniers often misuse temperature
visualizations

22. Future Trends
• Exascale visualization
• AI-generated visual narratives
• Holographic displays
• Collaborative VR visualization
• Real-time streaming visualization
• Brain-computer interface visualization

23. Data Doesn't Care What You Believe
• Visualizations make objective truths visible
• Science depends on reproducible results
• Good visualization prevents cherry-picking
• The map is not the territory
• All models are wrong, but some are useful
George Box

24. Interactive Visualization Demos
• Try these amazing online demos:
• NASA's Eyes on the Solar System
• Protein Data Bank Molecule of the Month
• NOAA's Historical Hurricane Tracks
• WebGL Fluid Simulation
• COVID-19 Data Dashboard (Johns Hopkins)

25. Visualization in Crisis Response
• Pandemic tracking dashboards
• Wildfire spread simulations
• Earthquake damage predictions
• Flood risk mapping
• Refugee movement patterns
• Example: COVID-19 visualizations informed public health
policies worldwide

26. Engaging the Public
• Galaxy Zoo (classifying galaxies)
• FoldIt (protein folding game)
• Zooniverse (multiple projects)
• EyeWire (neuron mapping)
• Floating Forests (kelp tracking)
• Fun Fact: FoldIt players solved an AIDS-related enzyme
structure that stumped scientists for 15 years

27. Common Visualization Mistakes
• Using 3D when 2D would suffice
• Improper axis scaling
• Over-smoothing noisy data
• Hiding uncertainty
• Too many visual elements
• Choosing form over function

28. Educational Applications
• Virtual labs and experiments
• Interactive textbooks
• Scientific concept animations
• Historical experiment recreations
• Complex system simulators
• Example: PhET Interactive Simulations (used by millions)

29. Cross-Disciplinary Impact
• Medical diagnostics
• Materials science
• Climate research
• Aerospace engineering
• Financial modeling
• Archaeology and cultural heritage

30. Key Takeaways
1. Visualization turns abstract data into understanding
2. Effective visualization requires both technical and design
skills
3. The best visualizations tell truthful stories
4. New technologies are expanding what's possible
5. Visualization is becoming essential across all sciences
6. Data doesn't care what you believe - good viz shows the
truth

31. Assignment
Work through the practical projects on Ethics, Testing, and Deployment
in 101 Data Visualization and Analytics Projects (Paperback).
Your work must be available to view on GitHub – and should be able to
run on the web (i.e., any browser connected to the internet with
WebGPU enabled).
Demonstrate in the Lab!

32. Summary
Scientific visualization:
• Makes the invisible visible
• Reveals patterns in complexity
• Bridges disciplines
• Communicates truth
• Drives discovery

33. Thank you!
Questions?