How to Communicate Science Ideas Across Disciplines

🧩 Soft Skills for Materials/Mechanical Engineers: Communicating with Non-Tech Teams We spend years mastering tensile curves, phase diagrams, and microstructures… But how often do we practice explaining those to designers, marketers, or managers? 🔍 In industry and research, it’s not just about what you know, but how well you explain it to people who don’t speak your technical language. Here’s what helped me communicate better with non-technical teams: 💡 1. Translate without dumbing down Instead of "yield strength drops at high temperatures," try: "This material might lose strength when exposed to engine heat, leading to part failure." 💬 2. Focus on impact Instead of “precipitation hardening increases tensile strength,” say: "This treatment makes the metal stronger, so it lasts longer in service." 🎯 3. Be visual Engineers love graphs. Others? Not always. A simple sketch or before/after photo = instant clarity. 🤝 4. Practice active listening Sometimes the question is not “what’s the modulus?” but “can this fail in real-world use?” Listen beyond the words. 🔗 Whether you’re collaborating with procurement, sales, or HR, your ability to bridge the knowledge gap is a career-defining skill. 👂Have you ever had to explain a complex material concept to a non-engineer? How did you do it? 👇Let’s share tactics because soft skills are hard-earned too. #MaterialsScience #EngineeringCommunication #SoftSkills #CareerTips #InterdisciplinaryTeams #TechnicalCommunication #LeadershipInSTEM #PhDLife #WomenInSTEM #MaterialsEngineer

Communication between science and IT teams is hampered by technical jargon. An effective strategy to facilitate alignment is to define a boundary of understanding and the sphere of what one cares to control. In a simplified view, imagine a line with IT and science at the opposite ends. The boundary of understanding is the middle point where the teams meet. This is as far as IT can comfortably understand the science and vice versa. When communicating to a partner team, details beyond their boundary of understanding should be abstracted away. You may encounter teams that have true or perceived understanding of another’s area of expertise. The question to pose is - “What is important for you to control? Why?”. Defining the sphere of control gives teams authority to move fast. Avoid unnecessary negotiations. If you are a science team, think of all computational work as software operating on data in a sequence of steps. The scientific questions need to be abstracted away. Think tools, files, speed and costs. Meet your IT team at their boundary of understanding. For IT teams, ask about software, process, user experience, performance and cost. Here is a made up research project - “We use FancyTool for protein folding to understand structural implications of genomic variants of the ABC3 gene identified by NGS implicated in disease X”. Interesting but hard to comprehend for all teams. Let’s restate the same in terms that both teams understand and care about - “We generate data at the lab. Output is in FASTA format up to 100GB per experiment. Data are processed with a community pipeline from nf-core. We manually inspect each step on our laptops. The pipeline must complete in < 12 hrs. We will submit each file to FancyTool using Jupyter Notebooks. We use StructureViewer to examine the output on our laptops. FancyTool must be always available and we want to get the fastest possible turnaround. Cost is not an issue“. Now that is a great starting point! #cloud #research #computationalbiology #IT #collaboration #science

𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗳𝗲𝗲𝗹𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸 𝗜’𝗺 𝗱𝗼𝗶𝗻𝗴 𝗻𝗼𝘄. For the first time, I’m not just analyzing raw data without knowing what’s behind it. I’m working closely with the wet lab team - understanding how the samples were processed, how the study was designed, and why the data was generated in the first place. And that has changed everything. Instead of jumping straight into code, I’m learning to pause and ask: 👉 What was the experiment trying to uncover? 👉 What should I be looking for - and what might I be missing? 👉 How do we make sure the insights we generate are actually meaningful? I’m not just answering questions anymore - I’m helping shape them. And being the person on the team who connects the data back to the biology has been… really fulfilling. It’s easy to think bioinformatics is just about pipelines and plots. But what I’m learning is that it’s also about context. 🧬 Translating complexity. 💬 Communicating clearly. 🤝 Collaborating across disciplines. I’ve started to think of bioinformatics as a bridge - Between the bench and the result. Between raw reads and real meaning. And being trusted to carry ideas across that bridge - from one side to the other - has been one of the most rewarding parts of this journey. 📸 𝘛𝘩𝘪𝘴 𝘤𝘰𝘮𝘪𝘤 𝘪𝘴 𝘢 𝘧𝘶𝘯 𝘳𝘦𝘮𝘪𝘯𝘥𝘦𝘳 - 𝘸𝘦 𝘢𝘭𝘭 𝘱𝘭𝘢𝘺 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘰𝘭𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘴𝘵𝘰𝘳𝘺. And when the lab meets the laptop, that’s where the science really moves forward. If you’re early in your bioinformatics career: Yes, keep learning the tools. But also… 📚 Learn how the data is generated. 💡 Ask better questions. 💬 And learn how to explain what you found in a way that makes people care. Because bioinformatics isn’t just about analyzing data. It’s about helping people understand it.