Scientific Visualization Strategies

Figures help communicate your research findings better. But they must be designed with clarity and integrity to avoid misinterpretation. Here are some key principles: ✅ 1. Figures aren't just for duplicating what's in tables or text—they're a powerful tool for highlighting visually compelling insights. In any manuscript, results can be presented in four places: the main text, tables, figures, or online supplemental materials. 👁️🗨️ 2. Figures Should Stand Alone With many journals now displaying figures independently online, it's important that a reader can understand the figure without having to consult the full manuscript. Include a descriptive title with key elements: person, place, and time. Add clear footnotes to define terms, measures, or abbreviations used. 📏 3. Use Scales Appropriately For percentages, your Y-axis should run from 0 to 100. If the data points are small and you need to truncate the axis, indicate this with two slashes (//) to show that the full range is not depicted. 🎨 4. Design for Black and White Assume your figure may be printed in grayscale. Use color AND patterns (e.g., hatching, stripes, dots) to differentiate data points clearly—ensuring your visualization is effective in both color and monochrome formats. 📉 5. Less Is More Avoid squeezing too much into one figure. If you need to show results for multiple demographic breakdowns, it’s better suited for a table, not a figure. Use figures, for example, when you’re presenting: Overall estimates for multiple outcomes , or Stratified estimates for one or two outcomes by a key demographic (e.g., education). 🧾 6. Always Include a Legend If your figure includes multiple outcomes or variables, include a legend. If it shows just one single outcome, make sure that outcome is clearly stated in the title. 🧭 7. Label Your Axes Clearly Both X and Y axes must be labeled with units, where applicable. This helps orient your audience. 📌 Pro tip: When presenting a figure live, begin by walking your audience through the axes: “This figure shows X. The horizontal axis represents [variable], and the vertical axis represents [variable]...” Give them a moment to get oriented before diving into the interpretation. 🧹 8. Minimize Clutter Avoid gridlines—they make your figure look messy. Only label bars or data points when essential, especially if space is tight. 🖼️ 9. Submit High-Resolution Figures Minimum resolution: 300 DPI (dots per inch). If using Excel: paste your chart into PowerPoint, save the slide as a PDF, then convert that PDF to an image at 300 DPI using tools like IrfanView (https://www.irfanview.com/). ✍️ 10. Use Consistent Footnote Symbols Use a recurring set of symbols in this order: *, †, ‡, § Then repeat with double marks: **, ††, etc. Alternatively, use superscript letters (a–z) or numbers. Keep it clean and consistent. By following these principles, you ensure your results are clear, credible, and impactful—getting the attention they deserve.

Visualizing Uncertainty in Machine Learning with Gaussian Process Regression I've been reflecting on how Gaussian Process Regression (GPR) visualizations provide one of the most intuitive ways to understand uncertainty in machine learning models. What makes these visualizations so powerful is how they transform abstract statistical concepts into immediate visual insight: 🔍 Uncertainty as space: The confidence interval (typically shown as a shaded region) visually represents where the model believes the true function might lie. It's uncertainty made tangible. 📊 Data-driven confidence: Watching how uncertainty narrows precisely at locations where data exists, while remaining wide in unexplored regions, creates an immediate "aha!" moment about how models learn. 📈 Correlation intuition: Seeing how adding a single point affects predictions in neighboring regions helps build intuition about the fundamental concept of correlation in probabilistic models. 🧠 Prior knowledge visualization: GPR visualizations elegantly show how prior assumptions about smoothness and variation influence predictions in regions with sparse data. I find these visualizations particularly valuable when explaining complex concepts like Bayesian reasoning, active learning, and the exploration-exploitation tradeoff to stakeholders without technical backgrounds. What I appreciate most is how a simple curve with a shaded region conveys a sophisticated mathematical concept: that our models aren't just making predictions; they're expressing degrees of confidence that systematically decrease as we gather more evidence. Have you found other visualization approaches that make complex ML concepts more intuitive? I'd love to hear your thoughts! #MachineLearning #DataScience #Visualization #UncertaintyQuantification #GaussianProcesses #BayesianML

Flying through 3D brain cell electron microscopy data in Unreal Engine with an Xbox controller in real-time: featuring open data from the IARPA MICrONS dataset. The video shows a selection of 250 mouse neurons, originally reconstructed in 3D from electron microscopy data by a large team of researchers from the MICrONS consortium. The rendering is a real-time capture of a 3D scene I set up in the game engine Unreal, using the 3D reconstruction data that has been generously made publicly available by the MICrONS consortium: https://lnkd.in/gDP5rpKv This cortical cubic millimeter data set spans a section of a mouse brain containing about 200,000 cells— so this 250-cell random subset represents less than 1% of the cell density in the original tissue. Each cell mesh is richly detailed, and even this sparse selection of cells is densely packed with data. The dataset has been a great resource for me to stress-test features in Unreal Engine for visualizing dense scientific spatial data. Unreal Engine recently released a new system for handling 3D scenes with dense geometry: Nanite. The idea behind Nanite is for geometry to automatically adapt in detail depending on how far it is from viewer, representing only the amount of detail that is actually perceptible from a given distance. As you get close, the representation becomes more detailed, and as you move away, it is simplified, freeing up resources for more critical parts of the scene. In this scene shown here, this method gives at least a 10-fold improvement in performance, transforming something that is barely workable to a fluid experience. These rendering approaches were originally developed for video games, which are constantly facing demands for increasing depth, scale, and immersion. But we’re facing similar challenges in scientific visualization: an explosion of depth and scale of scientific data and a need to find new ways to represent and engage with that data to make sense of it. ---------------------- References and acknowledgements: The data used for this visualization were produced by a consortium of labs led by members of the Allen Institute for Brain Science, Princeton University, and Baylor College of Medicine (the MICrONS Consortium). I was not involved in studies that generated the data. The dataset is described in the following publication: MICrONs Consortium, J. Alexander Bae, Chi Zhang, et al. Functional connectomics spanning multiple areas of mouse visual cortex. bioRxiv 2021.07.28.454025; doi: 10.1101/2021.07.28.454025 More information about the study, the Microns program, and these data can be found at: https://lnkd.in/gDP5rpKv This post was not sponsored or endorsed by the MICrONS Consortium, or any of its affiliated institutions or researchers.