Simplifying Data with Visual Representations in Science

I was sitting in a meeting, and a graph popped up during the presentation. It had five different colors, two types of chart elements (bars and lines), and it told multiple stories. I didn’t know where to look. My eyes – and brain – eventually gave up. The five-second rule (not the one about dropping food on the ground!) came from user research, and it measures how effectively information is communicated to the audience within the first five seconds. Originally used for testing web pages, it is now a recommended guide for interactive visual images – like infographics, charts, etc. Before you insert a complex graph into a presentation, I beg you to step away from your Excel file and consider the following: ☑ Can an audience understand this in five seconds? ☑ Is there a better way to tell this in a narrative? ☑ Is the chart necessary? If so, how can it be simplified? Does it have a clear title? Easy elements to understand? Remember, the more data points you have in a visual, the harder it is for your audience to know where to focus. And, if they are trying to figure out an image, they aren’t listening to you! Also, you have the curse of knowledge. You’ve been staring at this data for longer than five seconds. You are assuming your audience will know more than they do! Data is only helpful IF your audience can understand it; otherwise, it’s a reason for them to tune out! What are your tricks for simplifying complex information in presentations? I break charts into one or two slides, and I tell a story with them. Your audience needs to know why this chart matters to them! (I also avoid all the fancy options like 3D and breaking up pie charts! Simplicity for the win!) #CommunicationTips Image credit: visme dot com

There's this assumption in bioinformatics that good EDA means exhaustive analysis. But here's the thing: the best exploratory data analysis isn't about doing more. It's about explaining less. The 3-slide test changes everything. Frame it as a 3-slide talk to a non-bioinformatician: Slide 1: What's in the dataset (samples, variables, source, structure) Slide 2: What patterns you see (clusters, gaps, batch effects, outliers) Slide 3: What actions to take (next steps, hypotheses, design flaws) For instance, when analyzing multi-omics data: Slide 1: "80 ovarian cancer samples, metastatic vs non-metastatic, with RNA-seq and DNA methylation data" (not technical pipeline details) Slide 2: "Found 1,200 differentially expressed genes, but only 180 overlap with methylation changes" (not exhaustive gene lists) Slide 3: "Focus on those 180 overlapping genes for biomarker validation" (not complex integration methods) This constraint forces you to simplify, clarify, and prioritize......fast. It cuts through analysis paralysis and gets straight to what matters. Because if you can't explain what you're seeing, you probably don't understand it yet. Try this on your next dataset. What story emerges when you strip away the complexity? #Bioinformatics #DataExploration #ScientificThinking #EDA #DataVisualization #CommunicationInScience #Omics #DataStorytelling #PrecisionMedicine #ComputationalBiology #ResearchTools

Choosing the right chart is half the battle in data storytelling. This one visual helped me go from “𝐖𝐡𝐢𝐜𝐡 𝐜𝐡𝐚𝐫𝐭 𝐝𝐨 𝐈 𝐮𝐬𝐞?” → “𝐆𝐨𝐭 𝐢𝐭 𝐢𝐧 10 𝐬𝐞𝐜𝐨𝐧𝐝𝐬.”👇 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐭𝐨 𝐜𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐜𝐡𝐚𝐫𝐭 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚: 🔹 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧? • Few categories → Bar Chart • Over time → Line Chart • Multivariate → Spider Chart • Non-cyclical → Vertical Bar Chart 🔹 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩? • 2 variables → Scatterplot • 3+ variables → Bubble Chart 🔹 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧? • Single variable → Histogram • Many points → Line Histogram • 2 variables → Violin Plot 🔹 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧? • Show part of a total → Pie Chart / Tree Map • Over time → Stacked Bar / Area Chart • Add/Subtract → Waterfall Chart 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩𝐬: • Don’t overload charts; less is more. • Always label axes clearly. • Use color intentionally, not decoratively. • 𝐀𝐬𝐤: What insight should this chart unlock in 5 seconds or less? 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: • Charts don’t just show data, they tell a story • In storytelling, clarity beats complexity • Don’t aim to impress with fancy visuals, aim to express the insight simply, that’s where the real impact is 💡 ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 14,000+ readers here → https://lnkd.in/dUfe4Ac6

Do you want your data to make a difference? Transform your numbers into narratives that drive action—follow these five key steps: 📌 STEP 1: understand the context Before creating any visual, ask: - Who is your audience? - What do they need to know? - How will they use this information? Getting the context right ensures your message resonates. 📊 STEP 2: choose an appropriate graph Different visuals serve different purposes: - Want to compare values? Try a bar chart. - Showing trends? Use a line graph. - Need part-to-whole context? A stacked bar may work. Pick the right tool for the job! 🧹 STEP 3: declutter your graphs & slides More isn’t better. Remove unnecessary elements (gridlines, redundant labels, clutter) to let your data breathe. Less distraction = clearer communication. 🎯 STEP 4: focus attention Not all elements on your graphs and slides are equal. Use: ✔️ Color ✔️ Annotations ✔️ Positioning …to guide your audience’s eyes to what matters most. Help them know where to look and what to see. 📖 STEP 5: tell a story Numbers alone don’t inspire action—stories do. Structure your communication like a narrative: 1️⃣ Set the scene 2️⃣ Introduce the conflict (tension) 3️⃣ Lead to resolution (insight or action) Make it memorable! THAT'S the *storytelling with data* process! ✨ Following these five steps will help you create clear, compelling data stories. What's your favorite tip or strategy for great graphs and powerful presentations? Let us know in the comments!