Scientific Writing Best Practices

If your paper is getting rejected, it isn’t necessarily the science that’s the problem (it’s likely the journal fit that’s off!). Here’s how you can be be strategic about journal selection. How do I choose the right scientific journal? ↳ Analyze your citation list and target relevant publications. Can impact factor really determine journal quality? ↳ Look beyond numbers, focus on specialized audience fit. How to avoid predatory journal publication traps? ↳ Verify journal reputation before submitting your research. Will editors help improve my manuscript? ↳ Follow author guidelines meticulously. Navigating the academic publication landscape can feel like traversing a complex maze. As a professor, I've learned that selecting the right journal is both an art and a science. Here's a game-changing approach I've developed: 1. Conduct a citation audit: Count journals you've referenced most frequently. These are likely your ideal publication targets. 2. Beyond Impact Factor: Don't get fixated on numbers. A lower-ranked journal with a specialized audience might be more valuable than a high-impact generic publication. 3. Beware of predatory journals: If an unsolicited email promises quick publication for a fee, run! Legitimate open-access journals conduct rigorous peer review. 4. Craft a strategic cover letter: Suggest credible reviewers, highlight your paper's novelty, and demonstrate professionalism. 5. Patience is key: Most journals reject approximately 50% of submissions. Don't be discouraged - each submission is a learning opportunity. Pro tip: Always read and follow the journal's specific author guidelines. This shows you're a detail-oriented, professional researcher. Have you ever struggled with selecting the right scientific journal for your research? What challenges have you encountered? #science #scientist #ScientificCommunication #publishing #phd #professor #research #postgraduate

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.

𝗪𝗲 𝘁𝗿𝘂𝘀𝘁 𝗼𝘂𝗿 𝗠𝗜𝗦 𝗿𝗲𝗽𝗼𝗿𝘁𝘀. After all, they’re “𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯,” right? But here’s the uncomfortable truth: 𝗘𝘅𝗰𝗲𝗹 𝘀𝗵𝗲𝗲𝘁𝘀 𝗰𝗮𝗻 𝗯𝗲 𝗯𝗶𝗮𝘀𝗲𝗱... 𝘀𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝘀𝘂𝗯𝘁𝗹𝘆, 𝘀𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝗱𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀𝗹𝘆. ➡️ 𝘚𝘦𝘭𝘦𝘤𝘵𝘪𝘷𝘦 𝘒𝘗𝘐s: Highlighting only the “feel-good” metrics while ignoring the ones that reveal cracks. ➡️ 𝘊𝘩𝘦𝘳𝘳𝘺-𝘱𝘪𝘤𝘬𝘦𝘥 𝘵𝘪𝘮𝘦𝘧𝘳𝘢𝘮𝘦𝘴: A report may look stellar if you show Q2, but not so much if you include Q1. ➡️ 𝘏𝘪𝘥𝘥𝘦𝘯 𝘧𝘪𝘭𝘵𝘦𝘳𝘴 𝘪𝘯 𝘗𝘪𝘷𝘰𝘵𝘛𝘢𝘣𝘭𝘦𝘴: A simple unchecked box can completely skew the story your data is telling. These aren’t just harmless quirks. They can 𝗱𝗶𝘀𝘁𝗼𝗿𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗲𝗮𝗹𝗶𝘁𝘆, leading to wrong calls. Whether it’s approving budgets, launching products, or restructuring teams. So, how do we keep reports honest? ✅ Cross-check KPIs against original objectives. ✅ Review multiple time horizons, not just the “best-looking” ones. ✅ Audit filters and slicers before presenting. ✅ Encourage a culture where bad news is not buried, but acted upon. Because at the end of the day, data doesn’t lie... but reporting can. The real question is: 𝘈𝘳𝘦 𝘸𝘦 𝘳𝘦𝘢𝘥𝘺 𝘵𝘰 𝘤𝘰𝘯𝘧𝘳𝘰𝘯𝘵 𝘵𝘩𝘦 𝘸𝘩𝘰𝘭𝘦 𝘵𝘳𝘶𝘵𝘩? 👉 𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙚𝙣𝙨𝙪𝙧𝙚 𝙣𝙚𝙪𝙩𝙧𝙖𝙡𝙞𝙩𝙮 𝙖𝙣𝙙 𝙩𝙧𝙖𝙣𝙨𝙥𝙖𝙧𝙚𝙣𝙘𝙮 𝙞𝙣 𝙩𝙝𝙚 𝙧𝙚𝙥𝙤𝙧𝙩𝙨 𝙮𝙤𝙪 𝙬𝙤𝙧𝙠 𝙬𝙞𝙩𝙝? #DataDrivenDecisionMaking #DataAnalytics #ExcelReports #DataTransparency #MISReporting

✍🏽 How To Write Better To Help People Read. With practical guidelines on how to help readers scan content more efficiently and understand it better ↓ ✅ Users rarely read on the web: they mostly scan. ✅ Chunks of unformatted text cause F-Shape scanning. 🤔 Users miss large chunks of content and skip key details. ✅ They read ~20% of a page; longer page → less reading. ✅ They spend 80% of time viewing the left half of a page. 🤔 When we use longer words, users skip shorter words. 🚫 Avoid long walls of text → max. 50 words/paragraph. 🚫 Avoid long sentences → max. 20 words/sentence. ✅ Write for mobile first: brief, clear, concise — prioritize. ✅ Leave room for translation: text might grow by 40%. ✅ Map your voice and tone against impact and purpose. ✅ Choose your words depending on the tone to match. ✅ Include a plain language summary, even for legal docs. ✅ Use Inverted Pyramid: key insights first, details below. ✅ If it doesn’t sound right, it doesn’t read right either. 🚫 Nothing is more effective than removing waste/fluff. On the web, people scan pages at incredible speeds. They jump from headings to bold keywords to bullet points. They puzzle together pieces of content. They seek insights and answers in unstructured and poorly written walls of text. And too often words are generic, technical, formal, long and overcomplicated. Plain language always works better. Shorter sentences are easier to read. Simpler words are easier to understand. It holds true for everyone, including domain experts and specialists who typically have the most to read. Yet too often, words are chosen almost mindlessly — along with repetitive phrases, unnecessary details and confusing jargon. A great way to avoid it is to test your writing. Read aloud critical parts of your messaging. If it doesn’t sound right, it most likely doesn’t read right either. Ask people to highlight parts that they find most useful. Use Cloze test to check comprehension. And: prioritize what matters, and declutter what doesn’t. ✤ Content Design in Design Systems Atlassian: https://lnkd.in/eGpzQqm4 Amplitude: https://lnkd.in/eaB85T7n 👍 DHL: https://lnkd.in/eF494fkT Girlguiding: https://lnkd.in/eZ8zMyC3 👍 Gov.uk: https://lnkd.in/ekRadXad 👍 Intuit: https://lnkd.in/eGyBUrZ2 👍 JSTOR: https://lnkd.in/eAnyrtcu 👍 MetLife: https://lnkd.in/evVE8sqf 👍 Monzo: https://lnkd.in/edVV8QWz Progressive’s: https://lnkd.in/evx_8bzY 👍 Schibsted: https://lnkd.in/et_BXg6R Shopify: https://lnkd.in/eAKgEHNW Skrill: https://lnkd.in/e2HGTq4q 👍 Slack: https://lnkd.in/ejZ2QtJa Zendesk: https://lnkd.in/euxijT5m 👍 Wise: https://lnkd.in/eWk-Mvf9 ✤ Useful resources: Plain Language Guidelines https://lnkd.in/eV2sxSyJ How To Write Good Interface, by Nick DiLallo https://lnkd.in/edwTaKcQ Content Testing Guidelines, by Intuit https://lnkd.in/ewZSVT3i Voice and Tone In UX Writing (+ PDF Worksheets) https://lnkd.in/e6r4cC8Y #ux #writing

I tested 12 AI writing tools over 3 months. Here's which ones actually pass journal editorial standards. Academic publishing is evolving rapidly. Yet, one thing remains unchanged: your reputation is everything. Recently, a talented PhD student approached me in panic. Her manuscript was desk rejected. Not for weak science, but for using an AI tool the wrong way. The message from journal editors is clear: They are vigilant. They are testing submissions. They are catching mistakes. One misstep can cost you more than a paper. It can cost you your future in academia. After months of conversations with editors and tracking submission outcomes, I have distilled the safest approach for researchers using AI: AI Tools Editors Accept: Grammarly (with full disclosure) - Polishes language to professional standards - Detects subtle errors - Must be acknowledged in your paper Quillbot (for light paraphrasing only) - Refines awkward sentences - Avoids unintentional plagiarism - Never use for entire paragraphs Hemingway Editor - Enhances clarity and readability - Editors appreciate concise writing - No risk of detection DeepL (for translation) - Maintains academic tone - Outperforms standard translators - Essential for non-native English speakers Notion AI (for planning and structure) - Helps organise arguments and sections - Should never draft your content - Use for outlining only AI Tools That Put Your Career at Risk: ChatGPT or similar for full text generation - Editors spot AI-generated prose instantly - Immediate grounds for rejection Claude for academic writing - Text may appear polished, but patterns are detectable - Editors are trained to notice Perplexity without transparency - Lacks proper referencing - Raises ethical red flags Any undisclosed AI-generated content - Breaches publication ethics - Can result in permanent blacklisting My advice to every researcher: If an AI tool creates content, always disclose it. If it checks or organises, you are generally safe. When uncertain, err on the side of caution. Academic integrity is non-negotiable. No shortcut is worth your career. Which AI tool have you found most useful or surprising in your writing process?

When a business grows rapidly, the cracks in your processes start to show. That’s exactly what happened to us As our team scaled, it became clear: not everyone understood the hypothesis-generation process in the same way. This caused confusion, inconsistent problem-solving, and slowed down decision-making So, we developed a clear format to align everyone, newcomers and veterans alike, around structured, high-impact hypotheses. It starts with identifying the bottleneck In ecommerce, this might mean noticing that users drop off before completing a purchase The first instinct? "Add trust badges at checkout" But that’s too vague Is the real issue trust? A confusing checkout? Delivery costs? We learned to dig deeper: Problem: Low checkout conversion because users lack trust Action: Add trust badges (e.g., privacy policy, money-back guarantees) Expected result: Increase conversion from 20% to 40% 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 + 𝗔𝗰𝘁𝗶𝗼𝗻 + 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗥𝗲𝘀𝘂𝗹𝘁 This structure keeps our hypotheses focused and testable We prioritize using the ICE framework (Impact, Confidence, Ease). Doesn’t matter if we sum or multiply the values; the important part is consistent prioritization Then, we hold regular meetings: 1) Prepare hypotheses with a defined problem and goal 2) Refine and discuss existing ideas 3) Only brainstorm new ones when we’ve addressed the current list The result? A ready-to-implement hypothesis that’s documented from start to finish. This documentation becomes gold when reviewing what worked and what didn’t Fast growth demands clarity. Rebuilding internal processes isn’t just helpful, it’s necessary What’s your go-to method for hypothesis generation?

𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐂𝐡𝐚𝐫𝐭𝐬: 𝐀 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Data visualization is a powerful tool for 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 and 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐧𝐠 insights from data. Different types of charts serve different purposes. Let's explore some common types of charts and their applications: 1️⃣ 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 📊: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Comparing categorical data or showing changes over time. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Comparing values of different categories, such as sales by product category or revenue by month. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Bar chart comparing monthly sales for different products. 2️⃣ 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭 📈: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Showing trends and changes over time. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing continuous data over a period, such as stock prices over months or temperature variations over days. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Line chart showing the trend of website traffic over a year. 3️⃣ 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭 🥧: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Displaying parts of a whole and illustrating proportions. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Showing the composition of a categorical variable, such as market share by product or distribution of expenses by category. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Pie chart illustrating the distribution of budget allocation for different departments. 4️⃣ 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 📊: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Representing the distribution of continuous data. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing the frequency distribution of numerical data, such as age distribution of survey respondents or distribution of exam scores. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Histogram showing the distribution of heights among a sample population. 5️⃣ 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭 📈: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Examining relationships between two continuous variables. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Identifying patterns and correlations between variables, such as the relationship between temperature and ice cream sales or the correlation between advertising spending and sales revenue. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Scatter plot depicting the relationship between hours studied and exam scores for students. 6️⃣ 𝐁𝐨𝐱 𝐏𝐥𝐨𝐭 📊: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Summarizing the distribution of numerical data and identifying outliers. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing the spread and skewness of data, comparing distributions, and identifying anomalies. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Box plot comparing the distribution of salaries for different job roles within a company. 7️⃣ 𝐇𝐞𝐚𝐭𝐦𝐚𝐩 🔥: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Displaying the magnitude of a variable in a matrix format. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing relationships and patterns in large datasets, such as correlation matrices or user engagement matrices. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Heatmap showing customer engagement levels across different demographics and products. Remember to choose the appropriate chart type based on the nature of your data and the insights you want to convey. #dataanalysis #visualization #charts #insights #analysis #eda Follow Sneha Vijaykumar for more... 😊

Being both a storyteller and a data investigator, I always felt these two parts of what I do are very different and are two parallel tracks that continue on never to meet. This is also reinforced by how we traditionally view analysts and writers as people requiring completely different skillsets with no overlap. But a recent story in New Scientist (https://lnkd.in/d7DzeDsW) that looked at storytelling from a scientific perspective got me thinking that may not be so.     Here are my three key takeaways from it that explain what I mean:   1. Stories were the first way to manage data Before the advent of writing or paper or computers and modern information management systems, the only way for us to manage and remember information (landscapes, good foraging grounds, weather patterns, animal movement, etc.) was in our heads. Telling stories about these facts ensured survival and ensured the data was passed down generations and through the community.   2. Stories make key facts and events memorable I can recite and show numbers and facts from countless hours of research to fill hours and thick books. The majority of it is promptly forgotten. But plot twists or something unexpected, gets us neural activity going, helping us remember this better and for a long time, making all the insights from analyzing data stick in the minds of recipients.   3. Stories help bring a group together and build consensus So many times in our day and week we meet friends, colleagues, at the family dinner table where the talk revolves around the latest episodes of whatever series you are (binge?) watching. We argue about the characters, the plot and ultimately come to a similar conclusion on what we think was good or bad and what will happen next. That is a group coming together to build consensus. And it is the stories in the episodes we watch or the books we read that brings us to similar views on how we interpret the world around us.   How can this help you?   ✅ When you are drowning in information and struggling to make sense of it, think about what story can you tell about it ✅ Framing that story in your mind helps you convey the facts and data to others such that it is memorable ✅ Making things memorable helps create a strategic path forward   Ultimately, what happens around us is because of the stories we tell. #storytelling #DataStory #Dareation

Communicate like a human. This is what we call the 'barbecue chat' - how would you explain something or talk about what you do with people at a backyard barbecue? Would you recommend someone "buy an apple" or "procure the round green fruit"? In the corporate world - and the academic world - we often fall into the trap of writing in this flowery, jargon-heavy language because we think it gives a more professional (or, dare I say it, more intelligent) impression. The reality is, this language comes across as spin, fluff, or corporate BS. If your communication is unclear, hard to understand, or frustrating to engage with, you won't get the outcome you want. You're talking to humans, so write for humans. Not for robots. Remember: keeping it simple is not dumbing it down. Keeping it simple is smart. #WritingTips #CommunicationSkills #Communication [Image description: Pink and white text on a dark blue background. The text reads - How we think we should write: 'Procure the round green fruit.' How we should write: 'Buy an apple'.

I used to think using complex terminology demonstrated expertise. That "leveraging synergies" and "utilising frameworks" showed I belonged in business conversations. The reality? No one was impressed. They were just confused. 💡 The breakthrough came when I started writing exactly how I think not how I speak. This transformed my newsletter engagement (open rates jumped over 30%) Here's why writing how you think (not how you speak) works: 1. Authenticity cuts through noise - Your natural thought stands out in a sea of corporate-speak - Readers sense when you're being genuine vs. performing - Trust builds faster with authentic communication 2. Simplicity enables action - Clear instructions get implemented - Complex directions get abandoned - Young entrepreneurs especially value directness 3. Relatable language builds connection - Industry jargon creates outsiders - Conversational tone creates community - Speaking their language shows you understand their world 📊 In marketing specifically: - Conversational emails see 17% higher click-through rates - Simple language in sales pages increases conversion by 2.1x - Readability improvements can boost engagement by 58% ➡️ Your readers aren't stupid. They're busy. They want to understand your point in seconds, not decode your buzzword bingo. When writing for my newsletter, pitching to investors, or speaking to young entrepreneurs, I constantly remind myself: "If my 16-year-old self wouldn't understand it, it needs a rewrite." Clear writing shows clear thinking. So next time you write anything: If a shorter word works, use it If you wouldn't say it in casual conversation, don't write it If it sounds like a "business robot," start over Simple, isn't it?