Science Data Visualization Methods

Visualizing Quantum Mechanics Through Art and Geometry I recently came across a fascinating project that visualizes hydrogenoid atom quantum states using Blender and analytical solutions of the Schrödinger equation. Instead of representing electron probability densities as traditional volumetric clouds, the creator used families of concentric toroidal structures to reveal the hidden geometry of quantum states. What makes this approach especially compelling is how it transforms abstract mathematics into something almost architectural. Each quantum state becomes a unique geometric structure shaped by radial nodes and angular symmetries. The result feels less like a physics simulation and more like generative digital art driven by pure mathematics. The project rendered 35 quantum states with principal quantum numbers up to , combining computational physics, topology, and 3D visualization into a striking visual experience. It is a powerful reminder that scientific understanding often advances not only through equations, but also through the way we visualize complex systems. This intersection of physics, computation, and design is exactly where scientific storytelling becomes most exciting. #QuantumMechanics #ScientificVisualization #Physics #QuantumPhysics #Blender3D #ComputationalPhysics #DataVisualization #GenerativeArt #ScienceCommunication #Innovation #STEM #HydrogenAtom

Modern AI research workflows are becoming increasingly autonomous, with agents now handling literature surveys, experimental design, and code execution. However, one critical and time intensive task has remained manual: the creation of publication ready academic illustrations. This bottleneck is now being addressed with PaperBanana: a new agentic framework from researchers at Peking University and Google Cloud AI Research. It introduces a fully automated pipeline for generating methodology diagrams and statistical plots that meet the aesthetic standards of top tier conferences. The system coordinates five specialized agents. A Retriever fetches relevant reference diagrams. A Planner translates method descriptions into a detailed visual plan. A Stylist applies learned academic style guidelines covering color palettes, shapes, and layouts. A Visualizer renders the image. A Critic then iteratively refines the output through self critique. For evaluation, the team built PaperBananaBench, a benchmark of 292 methodology diagrams extracted from NeurIPS 2025 papers. Results show the framework consistently outperforms leading baselines, achieving significant gains in conciseness, readability, and aesthetics, while also improving faithfulness. The implications are substantial. This work democratizes high quality scientific visualization and can accelerate the research dissemination cycle. Important questions remain regarding the balance between style standardization and creative diversity, and the essential role of human oversight for factual accuracy. PaperBanana marks a shift in focus from automating discovery to automating the communication of discovery. The final barrier between research completion and publication is beginning to fade. #AI #AGI #ComputerScience #DeepLearning #NeuralNetworks #DataScience #VisualAnalytics #TechInnovation #Research #Science #Automation #GenerativeAI #AIResearch #MachineLearning #AcademicPublishing #ScientificCommunication #ResearchTools #NeurIPS #Visualization #PaperBanana

Ever felt your data visuals don’t do justice to your insights? Meet Plotly - where data storytelling meets interactivity, aesthetics, and depth. In today’s data-driven world, static charts no longer cut it. Teams across AI, analytics, and business intelligence are turning to 3D, geographic, and interactive dashboards to transform raw data into living insights that drive impact. Here’s how you can level up your dashboards: 3D Plots – Spot hidden patterns and clusters in complex ML data. Geo Maps – Visualize trends across regions using Choropleth or Scattergeo maps. Interactive Dashboards – Build real-time dashboards with Plotly Dash for smarter decisions. Animation + Interactivity – Bring time-series data to life with dynamic, user-driven visuals. Customization Power – Create visuals that truly align with your brand narrative. Pro tip: Use dcc.Graph to integrate live-updating charts that engage users effortlessly. Advanced dashboards don’t just show data they tell stories. How do you currently bring your data stories to life - static visuals or interactive dashboards? #datascience #machinelearning #artificialintelligence #datavisualization #plotly #analytics #insightforge #data #dashboards #aiinnovation #datastorytelling

In academia, we often publish groundbreaking research that remains confined to journals—what if a few extra steps could amplify its impact and visibility? It's very common to generate valuable datasets, maps, and models, publishing our findings in peer-reviewed journals. However, these contributions often remain within the academic community. By taking additional steps—such as creating interactive visualizations and sharing them publicly—we can significantly increase the reach and impact of our research. This realization led me to develop two interactive tools based on the study "Integrated Socio-environmental Vulnerability Assessment of Coastal Hazards Using Data-driven and Multi-criteria Analysis Approaches" by a colleague of mine Ahad Hasan Tanim, published in Nature, Scientific Reports. Coastal Vulnerability Index StoryMap: https://lnkd.in/dTCrmgrq An interactive narrative that visualizes the study's findings, allowing users to explore various vulnerability categories across the region. Coastal Vulnerability Dashboard: https://lnkd.in/dJ7p24zA A dynamic dashboard that provides in-depth analysis and visualization of the coastal vulnerability data, facilitating informed decision-making. These projects were initially a way for me to apply and reinforce the skills I acquired from an ESRI course earlier this year. However, they also serve a deeper purpose: to enhance the visibility and impact of our academic work. Research indicates that sharing data and visualizations can lead to higher citation rates and broader dissemination of findings. Moreover, open access to research outputs fosters greater transparency and collaboration, accelerating scientific progress. I hope these tools inspire fellow researchers to consider how we can make our work more accessible and impactful. A few extra steps can transform our research from a published paper into a resource that benefits a wider audience. #CoastalResilience #OpenScience #DataVisualization #GIS #AcademicImpact #ClimateChange #PublicEngagement #visualization #dataViz #GISvisualization #vulnerabilityMap #coastalVulnerability #interactiveMap #ModernGIS

🔬 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝟯𝗗 𝗺𝗶𝗰𝗿𝗼𝘀𝗰𝗼𝗽𝘆: 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘀𝗶𝗻𝗴 𝗱𝗶𝗲𝗹𝗲𝗰𝘁𝗿𝗼𝗽𝗵𝗼𝗿𝗲𝘀𝗶𝘀 𝗶𝗻 𝗮 𝗾𝘂𝗮𝗱𝗿𝘂𝗽𝗼𝗹𝗲 𝘁𝗿𝗮𝗽 Our multiplane microscope enables fast 3D visualisation, which we recently used to map dielectrophoresis in 3D (see comments ). 3D imaging often yields amazing views and insights into samples However, for publication, it poses two problems: 1) Representing 3D data in 2D is always complicated and is not fully representative of the data 2) Datasets are usually huge and complicated to share, and have open access due to limited storage 🎯 𝗛𝗼𝘄 𝘄𝗲 𝘀𝗼𝗹𝘃𝗲𝗱 𝘁𝗵𝗶𝘀: We’ve put our DEP results and the associated calculation into an interactive 3D viewer you can explore in the browser: 🔗 https://lnkd.in/eqFEA2S3 The tool was built by Pablo Diez Silva using PyVista library in Python. Data was acquired together with Flip de Jong. 🔧 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼: 🔸 Spin/zoom full-3D scenes (mouse or touch). 🔸Toggle negative vs. positive DEP conditions. 🔸See particle trajectories and speed/flow fields for both simulations and experiments. This work is part of the FastComet (funded by EIC - European Innovation Council) project on information storage with nanoparticles (colloidal memory). 🔄 𝗦𝘁𝗮𝘁𝘂𝘀: The time plot for experimental runs is temporarily disabled (work in progress). Everything else is interactive; feedback welcome! 🎯 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Besides the results, this is a nice way to make data visualisation accessible: anyone can inspect the data in 3D, not just read static figures. If you try it, any feedback is appreciated; we are still improving it as the paper is under review. Acknowledgement: Research Foundation Flanders - FWO #Dielectrophoresis #DEP #Microfluidics #Colloids #DataVisualization #PyVista #Python #OpenScience #SciComm #EICPathfinder #FastCOMET #Nanoparticles #ScientificVisualization

How do we build data visualization tools for large-scale biomedical data to help scientists surface insights quickly? In my invited keynote talk at ISMB BioVis, I discussed the challenges and opportunities in creating integrated, composable, scalable, and interactive BioVis tools. At Ozette, we embrace these principles to build insightful and intelligent data visualization systems, enabling rapid identification of cellular biomarkers from large-scale single-cell data. Key takeaways from my talk: - Integrating BioVis tools into the compute and data ecosystem is essential for ensuring that insights can be gained fast. - BioVis tools should be composable as complex analyses often require multiple visualizations to explain patterns. - Scalability is essential for handling the vast amounts of data generated in biomedical research. - Bidirectional interactivity is key to creating intelligent visualizations that offer AI/ML-guidance during exploration. **In case you missed it, feel free to browse through my slides. The recording will be available on ISMB’s YouTube channel soon. Some notable software tools that make it easier than ever to build integrated, composable, scalable, and (bidirectionally) interactive software are: • Anywidget: Custom Jupyter widgets made easy. (https://anywidget.dev) • Jupyter Scatter: Explore datasets with millions of data points in Jupyter. (https://lnkd.in/e2ncaxVb) • Comparative Embedding Visualization: Compare two embeddings with shared labels. (https://lnkd.in/eUTxtWbU) • HiGlass: Explore and compare genomic contact matrices. (http://higlass.io) • Gosling: Scalable linked interactive nucleotide graphics. (https://lnkd.in/ex83JQBT) • GenomeSpy: GPU-accelerated rendering for genomic data. (https://genomespy.app) • Upset: Visualize intersecting sets effectively. (https://upset.app) • Vitessce: Explore spatial single-cell experiment data. (http://vitessce.io) • Viv: Interactive visualization of high-resolution bioimaging datasets. (https://lnkd.in/eHqprqDP) • ipylangchat: Serverless Jupyter chat UI for LangChain conversational AIs. (https://lnkd.in/e8wqpcyR) • CandyGraph: Fast 2D plotting for huge datasets. (https://lnkd.in/ekU3NvTA) • Deck.gl: GPU-powered framework for large dataset analysis. (https://deck.gl) • DataShader: Accurate rendering of the largest data. (https://datashader.org) • Mosaic: Extensible framework for scalable data visualization. (https://idl.uw.edu/mosaic/) This list is not exhaustive. If you know other great BioVis tools, please share them! Last but not least, huge shoutouts to Trevor Manz, Ashley Wilson, Nezar Abdennur, PhD, and Arpan Neupane for their feedback on my talk and help with the examples and demos. 🙏 #ISMB #BioVis #SingleCell #Visualization #DataVis #ML #Python #JavaScript #DataExploration #Jupyter #AI

🧬 What if students could “see” proteins interact instead of only memorizing pathways from textbooks? Today in our lab/classroom, we explored the fascinating world of protein–ligand docking visualization — where a moving 3D protein structure reveals how small molecules interact with biological targets in real time. Watching these molecular interactions dynamically helps students understand: 🔹 Protein structure & function 🔹 Binding pockets and active sites 🔹 Drug–target interactions 🔹 Molecular stability and conformational changes 🔹 The power of bioinformatics & computational biology in modern life sciences. This is exactly how we can make Biochemistry, Molecular Biology, Bioinformatics, and Proteomics more interactive and exciting for students. Instead of learning biology as static diagrams, students begin to think like researchers and drug designers. 🚀 As educators, integrating AI tools, molecular docking, and visualization platforms into teaching can bridge the gap between theory and real-world scientific applications. Future biology students will not only work in wet labs — they will also work with: 💻 AI 🧠 Computational biology 🧬 Structural bioinformatics 🔬 Drug discovery tools 📊 Omics data analysis This is the future of biological sciences education, and our students deserve exposure to it today. #Bioinformatics #ProteinDocking #ComputationalBiology #Biochemistry #Proteomics #MolecularBiology #DrugDiscovery #StructuralBiology #AIinBiology #STEMEducation #HigherEducation #Research #LifeSciences #StudentLearning #ScientificVisualization #Biotech #TeachingInnovation

BrAVe: Unifying the Brain Across Scales and Species The quest to understand the brain’s structure and function has long been limited by one barrier: the inability to integrate molecular, structural, and functional data across scales and species. A new open-source framework, BrAVe (BrainAtlas Viewer), aims to change that. BrAVe provides a 3D, species-agnostic, interactive platform for integrating multimodal brain atlas datasets, from gene expression and neuronal morphology to circuit connectivity and whole-brain activity. Supporting standardized data formats (.nrrd, .stl, .csv, .swc), it enables researchers to visualize and quantitatively analyze how molecular signatures align with neuronal structures and network wiring, across flies, fish, mice, and primates. At its core, BrAVe bridges three frontiers of brain research: ◾ Cross-modal integration: linking molecular, structural, and functional data in a unified coordinate space to identify molecularly defined neurons and their functional circuits. ◾ Cross-scale mapping: connecting light and electron microscopy datasets to match neuronal types and reconstruct synaptic networks. ◾ Cross-species alignment: enabling comparative analyses from invertebrates to non-human primates within a single framework. Technically, BrAVe combines intuitive 3D visualization with a distributed computing backend for high-performance analysis of large-scale datasets. Users can perform neuron morphology clustering, infer synaptic connectivity, and explore network motifs, all without code. It adheres to FAIR principles, promoting interoperability, reproducibility, and open science. From a translational perspective, this kind of integrative spatial biology platform has far-reaching implications: ▪️ For neuroscience, it means accelerating cell-type discovery and functional annotation. ▪️ For neurodegenerative and psychiatric disease research, it opens a path to correlate molecular changes with circuit-level dysfunction. ▪️ And for the broader life sciences, BrAVe’s architecture offers a template for multi-omic, spatially anchored analysis that could extend to other organs (heart, lung, or kidney) ushering in a next generation of organ-level reference atlases. The future of brain mapping will not be defined by one dataset or imaging modality, but by the integration of many. Tools like BrAVe move us toward an era where spatial registration is as foundational to biology as sequencing once was - linking molecules, cells, and networks into coherent, dynamic systems we can finally see and analyze as one. Read the full preprint: https://lnkd.in/eYBkY_UX #Neuroscience #BrainAtlas #SpatialBiology #Neuroinformatics #OpenScience