Using Visuals to Enhance Scientific Presentations

10 data visualization mistakes that confuse your audience (and what to do instead) Poor chart choices can distort meaning and reduce trust, even when your analysis is correct. (Save this!) 𝟏. 𝐔𝐬𝐢𝐧𝐠 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 ↳ More than 5 slices become hard to read ↳ Pie charts work best for showing simple parts of a whole → Use bar charts when comparing many categories 𝟐. 𝐌𝐢𝐬𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐘-𝐀𝐱𝐢𝐬 𝐒𝐜𝐚𝐥𝐞𝐬 ↳ Non-zero baselines exaggerate differences ↳ Can unintentionally mislead viewers → Start bar charts at zero or clearly indicate axis breaks 𝟑. 𝐑𝐚𝐢𝐧𝐛𝐨𝐰 𝐂𝐨𝐥𝐨𝐫 𝐒𝐜𝐡𝐞𝐦𝐞𝐬 ↳ Too many colors create visual noise ↳ Colors lose meaning without intention → Use 3–5 purposeful colors to highlight insights 𝟒. 𝟑𝐃 𝐂𝐡𝐚𝐫𝐭𝐬 𝐓𝐡𝐚𝐭 𝐃𝐢𝐬𝐭𝐨𝐫𝐭 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 ↳ Perspective makes comparisons inaccurate ↳ Especially problematic in pie charts → Stick to clean 2D visualizations 𝟓. 𝐖𝐫𝐨𝐧𝐠 𝐂𝐡𝐚𝐫𝐭 𝐓𝐲𝐩𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 ↳ Line charts for categories or bars for trends cause confusion → Line for trends over time → Bar for category comparisons 𝟔. 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐨𝐧 𝐎𝐧𝐞 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 ↳ Information overload reduces clarity ↳ Viewers don't know where to focus → Highlight 3–5 key metrics that tell a story 𝟕. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐂𝐨𝐥𝐨𝐫𝐛𝐥𝐢𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 ↳ Red–green combinations exclude many users → Use accessible palettes (blue–orange) plus labels or patterns 𝟖. 𝐂𝐡𝐚𝐫𝐭 𝐉𝐮𝐧𝐤 & 𝐔𝐧𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐃𝐞𝐜𝐨𝐫𝐚𝐭𝐢𝐨𝐧𝐬 ↳ Shadows, gradients, borders, and clip art distract from insights → Remove anything that doesn't add informational value 𝟗. 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐋𝐚𝐛𝐞𝐥𝐬 ↳ Charts without titles, units, or axes create confusion → Ensure visuals are understandable without explanation 𝟏𝟎. 𝐍𝐨𝐭 𝐓𝐞𝐥𝐥𝐢𝐧𝐠 𝐚 𝐒𝐭𝐨𝐫𝐲 ↳ Data without narrative loses impact → Use insight-driven titles and annotations that answer "So what?" 𝐐𝐮𝐢𝐜𝐤 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐡𝐚𝐫𝐢𝐧𝐠: → Right chart type → Honest scale → Accessible colors → Clear labels & context → One clear takeaway ⚡𝐏𝐫𝐨 𝐭𝐢𝐩: Show your visualization to someone unfamiliar with the data. If they need an explanation, simplify the chart. Which of these mistakes have you seen (or made)? ♻️Repost to help someone level up their data viz game Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

🍩 Practical Guide To Accessible Data Visualization. With useful pointers on how to design accessible charts and tables ↓ 🚫 Don’t rely on colors alone to communicate your data. ✅ Consider patterns or textures to distinguish bars and lines. ✅ For line charts, use different widths/dashes to set them apart. ✅ Place labels on lines, areas and pie charts directly. ✅ Make interactive visualization keyboard-accessible. 🚫 Don't rely on hover effects for explanations. ✅ Allow users to turn off animation and movements. ✅ Test in various screen sizes and zoom levels. ✅ Duplicate data from charts to the table format. ✅ Provide a text summary of the visualization. 🚫 Don’t mix red, green and brown together. 🚫 Don’t mix pink, turquoise and grey together. 🚫 Don’t mix purple and blue together. 🚫 Don’t use green and pink if you use red and blue. 🚫 Don’t mix green with orange, red or blue of the same lightness. ✅ Use any 2 colors as long as they vary by lightness. The safest bet is to never rely on colors alone to communicate data. Use labels, icons, shapes, rectangles, triangles, stars to indicate differences and show relationships. Be careful when combining hues and patterns: the pattern changes how bright or dark colors will be perceived. Use lightness to build gradients, not just hue. Make all interactive components accessible via keyboard. Add an option to explore data in a data table format. And always include people with accessibility needs not just in usability testing but in the design process. ✤ Useful resources Free Online Course On DataViz Accessibility (11 modules) https://lnkd.in/ejFYw5iA Intro To Accessible DataViz, by Sarah Fossheim https://lnkd.in/dEzvCsdP Data Viz Accessibility Resources, by Silvia Canelón, PhD Full list: https://lnkd.in/eM27dp7e Summary: https://lnkd.in/eGFKh4dk Colorblindness In DataViz, by Lisa Charlotte Muth https://lnkd.in/evn95YBp Accessibility-First Charts, by Kent Eisenhuth, Kai Salmon Chang https://lnkd.in/dnE2bfzZ Guidelines for DataViz Accessibility, by Øystein Moseng https://lnkd.in/epq5jwe6 Accessible Alternatives To Complex Charts, by Sheri Byrne-Haber (disabled) https://lnkd.in/eTJgvBWH Data Visualization Design Systems + Guidelines https://lnkd.in/dgADUDcz ✤ Tools For Accessible DataViz Highcharts: https://www.highcharts.com Datawrapper: https://www.datawrapper.de Viz-Palette: https://lnkd.in/e-JxgwHh Visa Charts: https://lnkd.in/e675Fsgr #ux #dataviz #accessibility

Most plots fail before they even leave the notebook. Too much clutter. Too many colors. Too little context. I have a stack of visualization books that teach theory, but none of them walk through the tools. In Effective Visualizations, I aim to fix that. I introduce the CLEAR framework—a simple checklist to rescue your charts from confusion and make them resonate: Color: Use color sparingly and intentionally. Highlight what matters. Avoid rainbow palettes that dilute your message. Limit plot type: Just because you can make a 3D exploding donut chart doesn’t mean you should. The simplest plot that answers your question is usually the best. Explain plot: Add clear labels, titles. Remove legends! If you need a decoder ring to read it, you’re not done. Audience: Know who you’re talking to. Executives care about different details than data scientists. Tailor your visuals accordingly. References: Show your sources. Data without provenance erodes trust. All done in the most popular language data folks use today, Python! When you build visuals with CLEAR in mind, your plots stop being decorations and start being arguments—concise, credible, and persuasive.

Data storytelling isn’t just about showing data, it’s about showing it right. If you truly want to level up your storytelling skills, this guide is a masterclass in visual thinking, 100 different ways to visualize data depending on the message you want to communicate. Every dataset tells a story, but the wrong chart can silence it. Here’s how to think about visualization like a professional: • Show trends with line, area, or stream charts. • Reveal relationships using scatter or bubble plots. • Explain composition through pie, stacked bar, or waffle charts. • Highlight ranking or comparison with bar, bullet, or lollipop charts. • Uncover distribution using histograms, density, or violin plots. • Map geospatial data with choropleths, grids, or bubble maps. • Tell time-based stories through timelines, Gantt, or horizon charts. • Explore structure and hierarchy using trees, dendrograms, and sunbursts. • Detect anomalies or behaviors with time series and funnel charts. Good visualization isn’t decoration — it’s data communication. Every chart is a design decision, and every design choice shapes understanding. If you’re serious about becoming a stronger data storyteller, study this chart until you can choose visuals instinctively, not by habit, but by purpose. #DataVisualization #DataStorytelling #DataScience #Analytics #BusinessIntelligence #DashboardDesign #InformationDesign #StorytellingWithData

I often stress this in workshops: if you want people to actually read the explanation of your visual, place the information next to the visual, NOT separate from it. And this morning (I woke up way too early) I stumbled onto a great eye-tracking study that shows some evidence for this. The researchers compared two layouts of the same figure: Separated – text far away from the visuals. Readers read the title, skipped the text, and jumped straight to the figure without context. Integrated – text and visuals placed together. Readers were far more likely to read the explanation and connect it with the visual. The results show that integrating text with visuals helps the reader also read the accompanying text. Yet, in peer-reviewed papers, subsidy proposals and reports, we often do the opposite [e.g. "See figure 3, three pages down"]. So when you're designing a figure, infographic, or diagram, make sure that the explanation of the visual is integrated into the visual, and not presented separately. Otherwise, your explanation might be ignored, or worse misunderstood. Reference: Holsanova, J., Holmberg, N., & Holmqvist, K. (2009). Reading information graphics: The role of spatial contiguity and dual attentional guidance. Applied Cognitive Psychology, 23(9), 1215–1226. doi.org/10.1002/acp.1525 #infographics #makingsciencesexy

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

Data visualization is no longer just a nice-to-have skill; it’s essential. Across every industry and profession, and especially in engineering, the ability to transform raw data into clear, visual insights is critical for effective communication, decision-making, and problem-solving. As engineers, researchers, analysts, and educators, we deal with large and complex datasets such as material properties, traffic volumes, energy consumption, climate risk, project schedules, and more. However, data is only valuable when people can understand it, and too often, insights get buried in spreadsheets or generic bar charts that fail to communicate the real story. This is where strong data visualization skills make a difference. A well-designed graph, map, or dashboard bridges the gap between data and action. It lets stakeholders, from technical experts to policymakers and the general public, grasp key findings quickly, ask better questions, and make smarter choices. But visualization alone is not enough. What we really need is "Storytelling with Data". A table full of numbers or a complex plot might contain all the right information, but without context, focus, and narrative structure, the message is lost. Storytelling with data means: - Framing the problem clearly - Choosing the right visual elements to highlight what matters - Guiding the audience through the data in a logical and engaging way - Making it easier to connect the data to real-world decisions In engineering, this becomes even more important. Whether you are presenting pavement condition trends to a city council, showing risk levels in a floodplain study, or summarizing construction performance metrics, your ability to tell a story with data can be the difference between getting buy-in and getting ignored. If you are in any technical field and have not yet invested in improving your data visualization skills, this is the time. It is a professional edge and a communication superpower that every expert should have. (Visualization examples from Vox) #DataVisualization #DataStorytelling #EngineeringCommunication #Analytics #DataDrivenDecisions #ProfessionalSkills #CivilEngineering #STEMEducation #AIandData #InsightDriven

Most dashboards look polished. But if they shut people out, they’re not doing their job. This post based on Dr. Angela Young's talk breaks down how inaccessible design quietly reinforces exclusion and how small shifts in layout, labeling, and storytelling can turn data into shared insight. Accessible data design isn’t just about compliance. It’s about clarity, equity, and trust. When teams build accessibility into every chart, caption, and filter, they stop gatekeeping information and start inviting participation. That’s not just good design. It’s good leadership. If your visuals rely on color alone, hover-only filters, or cluttered layouts, you might be losing your audience before they even start. This piece offers practical fixes and real-world examples that show how inclusive storytelling transforms decision-making. https://buff.ly/oQF0PqQ #Data #Accessibility Image: Angela Young is in front of the room with a top monitor showing their slides and a bottom monitor showing the ASL interpreter and captions.

Scientific visualization is not just a tool, it’s an experience.
The first time you see vortical structures roll up, shocks intersect, or instabilities amplify, you realize: we’re not just solving equations, we’re uncovering physics. Scientific visualization sits at a unique intersection of discovery and communication. As engineers and researchers, we rely on it to decode complex, high-dimensional datasets. It also plays a critical role in translating those insights beyond our niche communities. This is exemplified through efforts like the #APS Division of Fluid Dynamics Gallery of Fluid Motion. They don’t just showcase fluid physics—they make it accessible. Recent entries, including hypersonic and reentry flow visualizations, reveal shock structures, transition mechanisms, and energy pathways in ways that both experts and non-experts can intuitively grasp. In hypersonics, this dual role is especially important. Reentry flows involve extreme heating, nonequilibrium effects, and tightly coupled physics often beyond direct intuition. Visualization becomes the bridge: enabling analysis for specialists and understanding for a broader audience. From my experience in CFD, turbulence modeling, and engaging with visualization communities, one thing stands out:
the most valuable insight often comes not from convergence, but from what you see. So here’s a thought:
Are we fully leveraging visualization, not just to analyze science, but to truly communicate it? Link for Gallery of Fluid Motion - https://gfm.aps.org Bow shock instability during hypersonic reentry - https://lnkd.in/g-Uf4mza (Illustration adapted from V. Sharma et al., 2024, for conceptual demonstration.) #CFD #Hypersonics #Turbulence #EntryDescentLanding #Aerospace #SpaceTechnology #SciViz #APS #DFD #GFM #Visualization #ScientificCommunication