Techniques for Effective Scientific Communication

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

In #datavisualization, the bar chart 📊 is one of the most popular chart types. It’s a staple in dashboards and reports and is often used to make precise comparisons. Bar charts are equally prevalent in #datastorytelling. However, assessing how we can reduce our audience's cognitive load is crucial—even with something as familiar as a bar chart. The accompanying example illustrates a common scenario: comparing two data series side-by-side in a grouped bar chart. Typically, this involves comparing actual values against forecasted, targeted, or budgeted figures. With the grouped bar chart, both actual sales and forecasts are clearly displayed, requiring the audience to simply compare the heights of the two bars. It's not too difficult to interpret, right? But with the bar chart with markers, the forecasts are overlaid on top of the actual sales. This streamlined approach reduces cognitive load, making it quicker and easier for your audience to identify discrepancies between actual performance and forecasts. While the effect may seem subtle, it can make a significant difference when multiplied across multiple charts in a data story. I’d rather my audience focus their mental effort on understanding key insights than deciphering charts. Simplifying comparisons is key to making visual information more accessible and impactful, especially in data stories. How often do you consider the cognitive load of your data visualizations? What other small changes do you use to reduce cognitive load? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7

I have a confession to make. I have been guilty of putting people to sleep during my presentations. Unfortunately, not once, but many times. I could blame it on the complexities of tech topics or the dryness of the subject. I could always console myself by saying that at least it's not as sleep-inducing as financial presentations (sorry, my friends in Finance). Deep down, though, I knew that even the most complicated and dry topics could come alive. As with anything, it's a skill and can be improved upon. Thus, I turned to my friend Christopher Chin, Communication Coach for Tech Professionals, for some much-needed advice. He shared these 5 presentation tips guaranteed to leave a lasting impression: 1/ Speak to Their Needs, Not Your Wants Don’t just say what you like talking about or what your audience wants to hear. Say what your audience needs to hear based on their current priorities and pain points: that sets your presentation up to be maximally engaging 2/ Slides Support, You Lead Slides are not the presentation. You are the presentation. Your slides should support your story and act as visual reinforcement rather than as the main star of the show.  Consider holding off on making slides until you have your story clear. That way, you don’t end up making more slides than you need or making slides more verbose than you need 3/ Start with a Bang, Not a Whisper The beginning of a presentation is one of the most nerve-wracking parts for you as the speaker and one of the most attention-critical parts for your audience. If you don’t nail the beginning, there’s a good chance you lose the majority of people. Consider starting with something that intrigues your audience, surprises them, concerns them, or makes them want to learn more. 4/ Think Conversation, Not Presentation One-way presentations where the speaker just talks “at” the audience lead to dips in attention and poorer reception of the material. Consider integrating interactive elements like polls and Q&A throughout a presentation (rather than just at the very end) to make it feel more like a conversation. 5/ Finish Strong with a Clear CTA We go through all the effort of preparing, creating, and delivering a presentation to cause some change in behavior. End with a powerful call to action that reminds your audience why they were in attendance and what they should do as soon as they leave the room. By integrating these, you won't just present; you'll captivate. Say goodbye to snoozing attendees and hello to a gripped audience. 😴 Repost if you've ever accidentally put someone to sleep with a presentation. We've all been there!

Make deforestation newsworthy. That’s a core principle behind much of the work we do at Mongabay. We're not as eye-catching as Greenpeace (pictured), but we doggedly report on it. I highlighted this as one of our key strategies during a talk on Friday at the International Society of Tropical Foresters (ISTF) conference hosted at the Yale School of the Environment. My talk covered 5 main themes, including the role of science communication in tropical forest governance. One of the biggest gaps in forest governance is communication. Policy change is nearly impossible if the right information doesn’t reach the right people at the right time. And let’s be honest—many of the conversations happening in expert circles aren’t resonating with the decision-makers and communities who matter most. Good communication isn’t a footnote in conservation. It’s fundamental to progress. Here are 7 ways science communication can strengthen forest governance: Raise awareness & drive engagement ↳ Well-communicated science makes deforestation, degradation, and ecosystem services more accessible. 🌳 e.g. Forests don’t just store carbon—they regulate the water cycle. Water security is tangible to people in ways CO₂ isn’t. Making that connection can shift priorities. Broaden the constituency for forests ↳ Messaging tailored to local contexts builds public demand for better governance. 🔥 e.g. Environmental crises are becoming personal for more people. When science is clear and credible, it expands the base of those who care. Shape policy & promote transparency ↳ Translating scientific data into actionable insights helps leaders make informed decisions. 📉 e.g. Satellite imagery in the Amazon helped drive policies that contributed to a sharp drop in deforestation in Brazil. Foster cross-sector collaboration ↳ Effective communication aligns scientists, policymakers, businesses, and NGOs toward shared goals. 🐘 e.g. Emerging research links biodiversity loss to reduced carbon storage in forests—bringing two historically separate fields together. Build trust & navigate complexities ↳ Accessible, transparent communication increases public buy-in for science-based policies. 🤝 e.g. People are more likely to support solutions when they understand the science behind them. Facilitate behavioral change ↳ Science can influence consumer and corporate decisions by showing the real-world impact of unsustainable practices. 🌴 e.g. Data on deforestation for palm oil fueled campaigns that led to corporate zero-deforestation commitments—and a significant decline in forest clearing for the crop in Indonesia 🇮🇩. Inspire new ideas & innovation ↳ Stories of success empower people. Solutions can give them something to act on. 🌈 e.g. “Bad news drains me. Solutions make me feel like I can do something.” This shift in framing fuels creativity and action. The takeaway? If we want better forest governance, we need better science communication.

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.

What I learned from my first science slam workshop Less than two weeks ago, I gave my first science slam — and I was instantly hooked. I absolutely loved the experience. Wanting to take things further, I joined a science slam workshop. It might seem ironic to do the workshop after the performance, but sometimes learning sticks better once you've had a taste. Here are the takeaways I’ll carry with me into the next round: 🔑 Less is more. Ten minutes is the official limit. If you’re worried that your talk won’t fit into ten minutes, trust that it definitely won’t work in twelve. Aim for 9:30. Keep it tight, clear, and punchy. 🎭 Authenticity beats perfection. You don’t have to be the funniest person in the room or a natural performer. But you do have to show up as yourself. If the audience can feel why your topic matters to you, it has a real chance of mattering to them too. 🧵 Structure it: Know – Feel – Do. Every strong talk has a clear thread. Ask yourself: What should people know by the end? What should they feel? And what might they be inspired to do? 💬 Start with “Why” — and go deeper. When preparing your talk, ask “Why?” not just once, but four or five times, like a curious three-year-old might. That’s when you move past the surface of your topic and reach the heart of your message. 🎯 Clarify your message. What is the core insight you want the audience to remember — not just right after the talk, but three weeks later, maybe even at 3 a.m.? That’s your north star. Everything else should support it. 🔔 Nail your opening and your ending. Audiences remember beginnings and endings more than anything else. Avoid a generic closing like “That’s it — thanks for listening.” And don’t just wing it — memorize your opening, closing, and transitions. They’re your anchors. 📚 Stories connect more than concepts. People connect more than things. If you want people to care, tell stories. Wrap your ideas in a narrative arc — not because it’s cute, but because it’s how our brains are wired to understand the world. 🧠 Storytelling is problem-solving. A good story reveals a challenge, a journey, and a resolution. Make that path visible. And don’t be afraid to show your own vulnerability — it’s the glue that holds everything together. I’m excited to keep exploring this format. It combines what I love most: research, storytelling, connection, and comedy. You could say: It’s passion in motion.

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

12 Ways to Build Trust When Nobody Believes You Trust isn't won by being perfect. It's won by being real. Here's how smart leaders build it: 1. Never pretend to know everything. Say "we don't know yet" instead of faking certainty. Smart leaders admit gaps in knowledge and share updates as they learn. "We're still learning" builds more trust than "the science is settled." 2. Show your work, not just conclusions. Don't just announce decisions. Share the debate, data, and trade-offs that led there. "Transparency isn't weakness — it's leadership." 3. Drop the corporate robot speak. Nobody trusts a press release. Speak like a human who cares. Say "we messed up" not "inconsistencies were identified." "If lawyers love your message, the public won't." 4. Embrace emotion, don't dismiss it. Validated feelings build bridges. Start with "We hear you" before jumping to facts. "Empathy isn't soft — it's strategic." 5. Own changes before rumors do. Don't hide policy shifts. Explain them fast and loud. Context kills conspiracy theories. "People don't hate changes. They hate being confused." 6. Make risks relatable. "0.000043% chance" means nothing. "100x safer than aspirin" clicks instantly. "Data without context is just noise." 7. Face the public heat. Town halls forge credibility. Let people vent. Answer honestly. "Trust is earned in sunlight, not shadow." 8. Open your books. Share sources, math, and methods. Let people fact-check you. Transparency beats PR every time. "If you're not willing to be audited, you can't be trusted." 9. Admit failures first. Beat the watchdogs to it. Own mistakes before they own you. "People forgive errors. They punish coverups." 10. Bring critics inside. Include opposing views early. Prevention beats damage control. "Diversity isn't politics — it's protection against blindness." 11. Explain the 'no' pile. Show what you rejected and why. Make people part of the process. "Explaining 'why not' matters as much as 'why.'" 12. Teach bullshit detection. Don't just fact-check. Show how to spot lies. Give people your tools. "The best defense against lies is teaching truth." Smart leaders know: Trust is earned through radical honesty. Even when it hurts. Which of these would rebuild your trust? Share your thoughts 👇 ♻️ Repost if this resonated with you!

If I could offer one piece of data viz advice it would be this: Keep it simple What is the ONE takeaway for your chart? How can you use *less* color? What can you remove? Now I know, easier said than done. There’s a reason Blaise Pascal famously said, “I would have written a shorter letter, but did not have the time.” It’s harder to be more concise. It’s harder to pick just one takeaway. But your audience is bombarded with information all day long. They don’t care about all the work you did, every insight you found, and all the caveats behind it. They just want to know what’s critical, and what they should do about it. Here’s how I approach this: ➤ Write down the ONE message I want to communicate ➤ Sketch chart ideas on a piece of paper (this helps me explore options without being limited by a tool's default settings) ➤ Make all design decisions through the lens of: how can I most effectively convey my message? ➤ Refine and simplify: do both axes need labels, or is labeling the data series clearer? Am I using color with purpose? Can I make the header more concise? Remember: clear is kind.  Make your visuals so simple that anyone can grasp the main point in under 30 seconds. —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Everyone loves a good story. You should be using your data to tell one every chance you get. The importance of narrative in scientific communication cannot be understated. And that includes communication in traditionally technical environments! One thing that gets beaten into you in graduate school is that a scientific presentation is a technical affair. Communicating science is fact based, it's black and white, here's the data, this is the conclusion, do you have any questions? Actually, I do. Did you think about what story your data could tell before you put your slides together? I know this is a somewhat provocative question because a lot of scientists overlook the importance of telling a story when they present results. But if you want to keep your audience engaged and interested in what you have to say, you should think about your narrative! This is true for a presentation at 'The Mountain Lake Lodge Meeting on Post-Initiation Activities of RNA Polymerases,' the 'ACMG Annual Clinical Genetics Meeting,' or to a class of 16 year old AP Biology Students. The narrative doesn't need to be the same for all of those audiences, BUT IT SHOULD EXIST! There is nothing more frustrating to me than seeing someone give a presentation filled with killer data only to watch them blow it by putting the entire audience to sleep with an arcane technical overview of the scientific method. Please. Tell. A. Story. With. Your. Data. Here's how: 1. Plot - the series of events that drive the story forward to its resolution. What sets the scene, the hypothesis or initial observation? How can the data be arranged to create a beginning, middle, and end? 2. Theme - Good vs Evil, Human vs Virus, Day in the life of a microbe? Have fun with this (even just as a thought experiment) because it makes a big difference. 3. Character development - the team, the protein, gene, or model system 4. Conflict - What were the blockers and obstacles? Needed a new technique? Refuting a previous finding? 5. Climax - the height of the struggle. Use your data to build to a climax. How did one question lead to another and how were any problems overcome? 6. Resolution - What's the final overall conclusion and how was the conflict that was setup in the beginning resolved by what you found? By taking the time to work through what story you can tell, you can engage your entire audience and they'll actually remember what you had to say!