Developing Innovative Research Projects

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

🔍 Why Your Paper Gets Rejected Immediately — Without Even Going to Review (Lack of Novelty Is Often the Reason) As an Editor-in-Chief, I frequently receive manuscripts that are technically adequate — but they are desk-rejected immediately after submission. The most common reason? Lack of real novelty. Many young researchers do not have a deep understanding of what novelty truly means. For example, some mistakenly assume that using an unstudied material, such as a new plant extract, automatically makes the work novel. This is a common misunderstanding. 💡 Based on my experience, true novelty in scientific research includes: 🔴 1. Developing new methodologies, models, or analytical frameworks. e.g., a new deep learning architecture for nondestructive food quality prediction. 🔴 2. Borrowing technologies or approaches from distant disciplines. e.g., applying algorithms from aerospace engineering to optimize cold-chain temperature control in food logistics. 🔴 3. Revealing previously unknown scientific mechanisms or interactions. e.g., discovering how microstructure affects ice crystal formation during frozen storage. 🔴 4. Achieving specific, measurable performance breakthroughs compared to existing methods. e.g., reducing analysis time from over 2 hours to under 2 seconds in real-world applications. 🔴 5. Raising important new questions or addressing neglected real-world variables. e.g., while temperature and humidity in cold chains have been well studied, few ask: “What about vibration, light exposure, or air composition during transport?” Identifying such overlooked factors can open novel directions. 🔴 6. Integrating known tools into a novel, system-level solution. e.g., using a smartphone camera, colorimetric sensor, and lightweight AI model to create a portable freshness detector for small retailers. If no one has combined these components before in this context, that system integration itself is novel. 🔴 7. Challenging prevailing assumptions or widely accepted models. e.g., demonstrating that a standard microbial growth model fails under high-humidity cold-chain conditions and proposing a corrected one. 📌 Therefore, before submitting your manuscript to any journal, ask yourself: ➡️ “What am I truly contributing to scientific knowledge?” ➡️ Not just “Has anyone done this exact material or setting before?” In publishing, novelty is not about being different for the sake of it — it’s about making a genuine, original contribution that advances your field. #AcademicPublishing #EditorInChiefInsights #ResearchTips #NoveltyInResearch #DeskRejection #ScientificWriting #CrossDisciplinary #PhDAdvice #FoodEngineering #DaWenSun

I have spent over a decade coordinating some of the largest health surveys; NDHS, PHIA, IBBS, and more. The one document that makes or breaks these massive projects is the research protocol. Please permit me to demystify the process for you in my simple, step-by-step guide to developing a competitive research protocol. 1. Identify the Research Problem: Start with a clear, pressing gap in knowledge you want to address. E.g Why do adolescents in rural areas have lower ART adherence compared to urban peers? 2. Justify the Problem (Rationale): Explain why it matters. Is it a public health priority? Does it affect policy or practice? Use stats, reports, or lived experiences to show urgency. 3. Conduct a Literature Review: Summarize what’s known and, more importantly, what’s missing. 4. Define the Goal and Objectives: Your goal is the broad purpose; objectives are the specific, measurable steps. Example of a goal is “Improve adolescent HIV treatment outcomes” while Specific, measurable steps can be to “Assess adherence barriers in three rural states”. 5. Frame Research Questions or Hypotheses: Turn your objectives into clear questions or testable statements. Question example: “What factors influence ART adherence among adolescents in rural Nigeria?” Hypothesis example: “Adolescents with peer-support programs will have higher adherence rates.” 6. Choose a Study Design: Cross-sectional, cohort, randomized trial? Pick what best fits your question. 7. Outline Research Methodology: Detail your population, sample size, data collection tools, and analysis plan. Keep it replicable so that someone else should be able to follow in your footsteps. 8. Define Expected Outcomes: What do you hope to find or demonstrate? Example: “We expect to identify key structural and social factors driving adherence gaps.” 9. Plan Dissemination and Publication: How will you share your findings? Think of conferences, journals, and policy briefs. You can refer to my previous posts for practical tips here>> https://lnkd.in/d9rzRVHK 10. Address Ethical Considerations: Plan for IRB approval, informed consent, and participant confidentiality. This is non-negotiable. 11. Draft a Timeline: Break the project into phases with clear deadlines. Use Gantt charts or even simple month-by-month tables 12. Anticipate Problems: Think ahead about potential delays or logistical issues. A mitigation plan shows you're prepared. 13. Prepare a Budget: Be realistic and detailed about costs for personnel, logistics, and dissemination. Reviewers can spot under- or over-estimates quickly. ✅ Final Tip: A strong protocol is like a strong CV—it tells your story, proves you’re prepared, and convinces reviewers you can deliver. I’ve seen how well-written protocols open doors for funding, collaboration, and global recognition. I hope this roadmap helps you build your confidence. #Research #Protocol #Data #Epidemiology #ResearchCoordinator

Performing research gap analysis helps you prioritise your work, answer research questions that actually need answering, and grow your career (here are 7 reasons why). Performing research gap analysis: → Helps you identify unexplored areas in your field → Trains you in critical academic thinking → Gets you closer to meaningful scientific contributions → Helps you understand current research limitations → Trains you in comprehensive scholarly investigation → Can win you research grants and funding opportunities → Elevates your academic credibility Discovering a truly novel research gap takes time, but it is time well invested in your career development. But as researchers, we often struggle to determine whether our proposed research area is genuinely innovative. Here's my approach to validating the novelty of the research gap before I decide to work on it: 1. Conduct exhaustive literature reviews 2. Use advanced academic databases 3. Analyse recent publications in this domain 4. Identify existing research limitations 5. Assess potential theoretical or practical contributions Pro tip: A novel research gap isn't just about finding an untouched topic, but about offering a unique perspective or methodology that significantly advances knowledge. Would you add anything? #Science #Scientist #Professor #Research #PhD #Postgraduate #Postdoc #Publishing #Innovation

Finding Research Gaps: Here’s How You Can Do It! As researchers, one of our key tasks is to identify those hidden gaps in knowledge. Here’s how you can find them: 1. Dive Into the Literature Read as much as you can, especially recent papers in top journals. Ask yourself: • What’s been studied to depth? • What’s still missing or unexplored? And don’t forget about the “future work” or “limitations” sections those are goldmines for new ideas! 2. Think About Overlooked Areas Are there topics that just haven’t gotten the attention they deserve? Maybe there are regions or populations that haven’t been studied enough. For example, healthcare access might be well-studied in cities, but what about rural communities? 3. Spot Contradictions If you come across studies with conflicting results, that’s a sign! Dig deeper what’s causing the difference? This could be a gap just waiting for someone to explore. 4. Look at What’s Happening in the Real World Research gaps often exist where industries are struggling. What’s on the horizon new tech, policies, or global shifts? Take AI, for instance. The rise of AI has opened up so many questions around ethics, bias, and regulation. There’s plenty of room for research here! 5. Use the Right Tools Tools like Connected Papers, Research Rabbit, and Scite can help you see connections between studies and spot trends. Citation trackers (like Google Scholar, Web of Science) are also great for finding fresh angles. And if you haven’t tried Rayyan for literature screening, it’s a lifesaver! 6. Don’t Forget About Interdisciplinary Approaches Sometimes the best ideas come from combining fields. Imagine blending AI with healthcare there’s a whole world of research gaps in ethics and personalized medicine waiting to be uncovered. ✨ My tip: Look beyond your own field, collaborate with others, and chat with people in different disciplines. These conversations can spark new, innovative ideas that you might not have thought about before. Follow for more Dr.Kawsalya Maharajan Got any tools or tips you swear by? Drop them in the comments, let’s share! #ResearchGaps #Innovation #AI #Collaboration #FutureOfResearch

𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝗼𝗰𝗰𝘂𝗿 𝗶𝗻 𝗶𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻. Instead, many impactful innovations emerge from creatively combining existing ideas and methods—a process known as 𝗿𝗲𝗰𝗼𝗺𝗯𝗶𝗻𝗮𝗻𝘁 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻. A recent paper titled "Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations" demonstrates that Large Language Models (LLMs) can effectively assist this process. The authors propose a framework that systematically identifies promising problem-method combinations using LLM-driven reasoning, supported by objective evaluation metrics (𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝘃𝗲 𝗜𝗻𝗱𝗲𝘅). How does it work in practice? Consider battery recycling: Given a new challenge in sustainable recycling technologies, we first retrieve existing methods (chemical separation, mechanical sorting, enzymatic treatments), evaluates potential innovative combinations through quantitative metrics, and iteratively refines the solutions using an optimization algorithm. This framework exemplifies how AI-powered tools can amplify our natural ability to innovate by recombining existing knowledge effectively. https://lnkd.in/eyNMrjRz

𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼𝗺𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗮𝗺𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘀𝘁𝗮𝗹𝗹—𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗷𝘂𝘀𝘁 𝗮𝘀 𝗵𝗮𝗿𝗱? In research conducted with Johnathan Cromwell, Kevin J. Johnson, and Amy Edmondson, we studied more than 160 innovation teams—including those in a Fortune Global 500 company—and found that it's not just how much teams learn that matters, but when and how they learn. We identified four core modes of team learning: 𝗥𝗲𝗳𝗹𝗲𝘅𝗶𝘃𝗲 — assessing goals, roles, and strategies 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 — brainstorming, prototyping, testing new ideas 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 — scanning the environment for trends, signals, and shifts 𝗩𝗶𝗰𝗮𝗿𝗶𝗼𝘂𝘀 — drawing lessons from others who’ve done similar work The most effective teams didn’t try to do everything at once. They began and ended with reflexive learning, anchoring their work in shared understanding. They placed exploratory learning (experimental and contextual) in the middle. This rhythm—reflection → exploration → reflection—helped them reduce friction, integrate insights, and build real momentum. We also found that vicarious learning can be combined with reflexive learning in the same project phase with positive results. But when teams mixed reflexive with experimental or contextual learning in the same phase, performance suffered. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Innovation doesn’t thrive on more learning. It thrives on structured learning. Teams that sequence and separate their learning activities make faster, clearer progress. We’ve summarized the findings from our research, published in Administrative Science Quarterly—a leading journal in organizational research—in this new Harvard Business Review article. Link in comments.

Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans...... Read full article: https://lnkd.in/gQ_z759q Paper: https://lnkd.in/gDz8XwX4 GitHub Page: https://lnkd.in/ganKnXzc