Designing Clear Graphs for Scientific Data

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.

A clean chart always beats a complicated one. Fancy visuals might look impressive, but they often hide the story instead of revealing it. Before adjusting colors, fonts, or effects, strip the chart down to its essentials. Start by asking yourself what’s truly needed for someone to understand the data fast. Here’s how to simplify effectively: - Remove extra gridlines and borders that don’t add meaning. - Keep only the most relevant labels and data points. - Sort data so the trend or comparison jumps out naturally. - Use consistent spacing and alignment to keep the layout steady. Save design tweaks for clarity, not decoration. When the clutter is gone, the insight shines through. Simplicity gives your audience less to look at and more to understand. Clean visuals aren’t plain, they’re powerful.

Rule II of Effective Dataviz: Clear Meaning A strong data visualization doesn’t just present numbers — it tells a story. The key is clarity. Even if your analysis is rock solid, a poorly designed chart can leave your audience confused, forcing them to guess the insight instead of getting it. Here’s how to use common chart elements to ensure your dataviz communicate exactly what you intend: - Use clear titles & headlines: Your title should answer “What am I looking at?” and your headline should answer “What does this chart say?” Don’t make your audience work to figure it out. - Ditch legends, use direct labeling: Labels should be placed on the chart, not in a separate key. Make it easy for viewers to process information without extra effort. - Add annotations for context: A well-placed note can highlight key takeaways and provide essential background info. - Leverage visual cues: Use arrows, boxes, or subtle shading to direct attention — just don’t overdo it. Too many cues, and nothing stands out. The best data visualizations guide the audience effortlessly to the insight, freeing their minds up actually hear the story you're telling them. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Most charts get ignored. Great ones get remembered. If your data doesn’t spark clarity, it won’t drive action. You don’t need louder visuals. You need smarter storytelling. Here are 7 shifts to help your charts inform, engage, and stick: 1️⃣ Focus on what matters ➟ Cut out clutter and extras. ➟ Use only what drives understanding. 2️⃣ Remove visual noise ➟ Ditch the 3D, shadows, and flashy backgrounds. ➟ Keep attention on the message. 3️⃣ Make complex info simple ➟ Use clear layouts. ➟ Break things down, step by step. 4️⃣ Use color with purpose ➟ Choose colors for contrast, not decoration. ➟ Be mindful of accessibility. 5️⃣ Lead with the point ➟ Use the Pyramid Principle. ➟ Start with the insight, support it underneath. 6️⃣ Annotate the story ➟ Add callouts or notes to guide attention. ➟ Connect the dots for the viewer. 7️⃣ Keep your style consistent ➟ Fonts, layout, and colors should flow. ➟ Design is clarity, not decoration. The takeaway: Every graph, chart, and slide is a chance to lead through insight. Use structure to show the story—and make it stick. What’s one data mistake you see all the time? Drop it below. Let’s help each other improve our slides. 📌 Save this before your next presentation 🔁 Share with your team to sharpen their storytelling 👤 Follow Jay Mount for high-trust tips on data, clarity, and communication that moves people.

5 simple steps to create better charts (that you can implement immediately) You’ve gathered your data, run your analyses, and created your charts. But… looking at your charts… you feel like they could be better. The data is there, the information is accurate, but the key message doesn’t “pop.” How can you make your message more clear and compelling? Let’s walk through an example: Below is a chart showing U.S. users’ daily engagement with top social media platforms in 2023. Follow along to see how we can make it more attention-grabbing. 1) Begin with your standard chart -- Start with grayscale. -- Avoid using color just for decoration - color should be added intentionally later to highlight key information. 2) Arrange the categories from highest to lowest value -- Improve visual flow by guiding your audience's eyes. -- Note: if the categories follow a natural sequence (e.g., age ranges or months of the year), maintain their original order. 3) Remove the axes and place value labels directly on the chart -- Improve readability and reduce visual clutter. -- Note: This works best if the exact values are important and the chart has a manageable number of categories. 4) Use color to emphasize the key category -- Apply color strategically to draw attention to the most important category (or categories). -- All other categories should remain gray. 5) Include a brief explanation highlighting the main insight -- Don’t make your audience guess what's most important. -- Ensure your message is clear by including a brief explanation of the main insight. Voilà! —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Data science or data analytics without storytelling is void. You can do all the SQL, all the Python, all the modeling — but if the final insight is not communicated in the right visual form, the value is lost. This cheat sheet is a perfect reminder that choosing the right chart is not decoration — it is part of analysis. It breaks the decision down by purpose of insight: 1) Composition Waterfall, Progress bar, Pie, Gauge — great when you want to show parts contributing to a whole or target progress. 2) Comparison Bar charts, Row charts, Line charts, Combo charts — useful when comparing categories or trends over time. 3) Distribution & Relationship Histogram and Scatter plot — when you want to show how values are spread or how two variables interact. 4) Stage Analysis Sankey and Funnel — ideal for visualizing drop-offs or flow across process stages. 5) Single Value KPIs Number & Trend cards — best for dashboards where one metric needs to stand out with context. The skill is not in plotting a chart — the skill is in selecting the correct one for the question being asked. Your analysis is only as powerful as the clarity of how you present it. cc Metabase #DataAnalytics #DataScience #DataVisualization #StorytellingWithData #BI #Metabase #DashboardDesign #DecisionMaking

Data visualizations Creating compelling visuals is both an art and a science.  Let's unravel some key tips to elevate your data storytelling and captivate your audience. ➡️ Choose Colors Thoughtfully - Colors influence emotions and perceptions.  - Select schemes carefully to highlight key insights.  - Limit your palette to 6 colors for clarity and coherence. ➡️ Pay Attention to Placement - Strategic placement helps guide your viewer’s eye.  - Lead with your most important data points. ➡️ Avoid Distracting Fonts or Visuals - Simplicity is key. - Stick to clean, legible fonts that enhance, not distract. ➡️ Ensure Axis Labels are Clear - Confusion kills insight. - Clear labels allow viewers to quickly grasp your data. ➡️ Gather Relevant Data - Trustworthy insights stem from relevant data. - Curate carefully for maximum impact. ➡️ Remove Unnecessary Elements - Less is often more.  - Strip away clutter to focus on what matters. ➡️ Use Consistent Colors for Similar Data - Consistency builds familiarity.  - Anchor your audience with a uniform coding system. ➡️ Pick the Chart that Fits - Not all charts are created equal. - Choose the right type for your story—bar, line, pie, or scatter. ➡️ Begin with Clear Questions - Frame your visualization with purpose. - What story do you want to tell? ➡️ Design for Easy Understanding - Your audience should grasp insights at a glance. - Keep it straightforward. ➡️ Prioritize Visualization Efficiency - Aim for efficiency in both design and interpretation. - Leave time for deeper analysis. ➡️ Keep Titles Concise and Clear - Your title sets the stage.  - Make it informative yet succinct. ➡️ Convey the Complete Narrative - Every visualization should tell a story.  - Link elements to convey the overall message seamlessly. 🌟 I help technical professionals build impactful career brands on LinkedIn. 👉 { https://lnkd.in/g7Gp68cV } Follow Ashish Sahu for more tech content

Data analysts: if your visualizations look more complicated than your data, it's time for a reset! New data analysts often get caught up in creating fancy, overcomplicated visuals that can cloud the message. While fancy charts might look impressive, a straightforward bar chart often delivers insights much more clearly. Tips to Overcome Overcomplexity: 1. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗖𝗹𝗮𝗿𝗶𝘁𝘆: Ask yourself, “Does this chart help my stakeholders understand the data better?”     2. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗦𝗶𝗺𝗽𝗹𝗲: Bar or line charts will be the best fit for 80% of your use cases.     3. 𝗧𝗲𝘀𝘁 𝗬𝗼𝘂𝗿 𝗩𝗶𝘀𝘂𝗮𝗹𝘀: Get feedback. If the audience struggles to interpret your chart, it’s time to simplify it further. Your goal as an analyst is to present your results in an easily digestible form to your stakeholders so that they can make informed decisions. What’s your strategy for keeping your charts clear and impactful? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you've seen too many complex charts. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #datavisualization #simplicity #careergrowth

So many chart types available for data analysis, How do you choose the right one? Here's a simple framework to guide your decision: 𝟏. 𝐃𝐞𝐟𝐢𝐧𝐞 𝐲𝐨𝐮𝐫 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: → 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧: Showing how parts make up a whole (e.g., market share, age distribution).    ○ 𝑪𝒐𝒏𝒔𝒊𝒅𝒆𝒓: Pie charts, stacked bar charts, treemaps. → Distribution: Understanding the spread of a single variable.    ○ 𝑪𝒐𝒏𝒔𝒊𝒅𝒆𝒓: Histograms, density plots, box plots. → Relationship: Exploring the connection between two or more variables    ○ 𝑪𝒐𝒏𝒔𝒊𝒅𝒆𝒓: Scatter plots, line charts, bar charts (for comparisons). → Change Over Time: Tracking trends and fluctuations over time.    ○ 𝑪𝒐𝒏𝒔𝒊𝒅𝒆𝒓: Line charts, area charts. 𝟐. 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫 𝐭𝐡𝐞 𝐍𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬: → One variable: Use histograms, bar charts, or pie charts to visualize distribution. → Two variables: Explore relationships with scatter plots, line charts, or bar charts. → Three or more variables: Consider bubble charts, stacked area charts, or 3D charts (use with caution!). 𝟑. 𝐓𝐡𝐢𝐧𝐤 𝐀𝐛𝐨𝐮𝐭 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞: → Categorical data: Use bar charts, pie charts, or stacked bar charts. → Numerical data: Use histograms, box plots, scatter plots, or line charts. → Time-series data: Use line charts or area charts. Remember... Choose a chart that is clear, concise, and avoids misleading interpretations. Your goal is to communicate your insights effectively and efficiently to your audience. P.S Here's a chart chooser to make your life and understanding of the framework easier. ---------------------- Follow me - I'm Varun Sagar Theegala, posting weekly from my professional experience in Data Analytics & as a Masters student in Data Science. Tags : Shashank Singh 🇮🇳 | Munna Das | Leon Jose | Ayan Khan | Thodupunuri Bharath | Venkata Naga Sai Kumar Bysani | AMAN KUMAR | Adapala Naveen Kumar | Simala Om Prakash | Neil B. | Preeti Moolani | Neha Panjabi | Bigyandutt P. | Ashvini Patil #datavisualization #dataviz #datascience #charts #dataanalysis #visualizationtips