Revision Techniques for Grant Applications

🚀 Grantwriting post 🚀 Imagine your reviewer or panel/committee member. It's 9 PM, the night before the review meeting. They've got a toddler on their lap, a glass of wine in one hand, and your proposal in the other. They've been interrupted 5 times while reading and they need to get through 9 more proposals before going to sleep. Can they understand what you want to do? No? Make. It. Simpler. Over the past few months I've been reviewing grants for Cancer Research UK (CRUK), the Research Council of Finland | Suomen Akatemia, and the Lung Cancer Research Foundation. Submitting a grant takes an enormous amount of effort, but so many fail simply because **the reviewers can't understand what you want to do.** A few suggestions applicable to almost any grant ➡️ 1️⃣ Include a sentence saying, "The goal of this project is ________." Ideally make it the first sentence of your abstract and your proposal, but at minimum put it in the first paragraph. If you can't state your goal in one sentence, refine it until you can. 2️⃣ Remove all acronyms. If it's less recognizable than "HIV" or "DNA," then spell it out every time. Acronyms can hopelessly muddle an otherwise strong proposal. 3️⃣ Start with *simplicity* then add *complexity.* First, write a half-length version of your proposal in an extremely simple way that an educated layperson could understand, and test it on a few people. When that's done, use what you wrote as header sentences/paragraphs, and add all the technical details below them. Now, each reviewer can easily skim past the parts outside their field, while still understanding the big picture. 4️⃣ List the central elements of your proposal, then REPEAT THEM. This is important for methods, e.g. - humans or mice? study design? case and control definition? recruitment strategy? sample size? statistical approach? These key facts (without details) can be repeated in the abstract, end of background, methods (here, include the details), and assessment of threats/weaknesses. If you repeat them with perfect consistency, your reviewer will understand what you want to do, and feel reassured that you have a clear plan. Happy grantwriting ✍ 😊

When they said, "Just revise and resubmit (your proposal), you will be fine," I am certain that they didn't mean this ⬇ 🙃 Revising and resubmitting a rejected proposal requires careful planning, consideration, and improvement of various aspects. Here are some tips. 1. Review feedback thoroughly: Start by thoroughly reviewing the feedback provided by the reviewers. Understand their suggestions, as this will be the basis for the revisions. 2. Address reviewer comments: Read each comment and criticism thoroughly from the reviewers (not in the R&R but internally to plan). Think about how you will incorporate their suggestions or why you choose not to if you believe your original approach is sound. 3. Improve clarity and organization: Ensure that the proposal is clear and well-organized. Make your goals, objectives, activities, methods, and significance of the project explicit and easy to understand. Include a TOC and/or LM. 4. (If NSF) Strengthen the Intellectual Merit and Broader Impacts: Emphasize the IM and BI of your project. Explain how your project advances scientific knowledge and benefits society. 5. Update data: Include new preliminary data or results that strengthen your case. This can demonstrate the progress or the feasibility of your project. 6. Highlight collaborations: If applicable, highlight any new collaborations or partnerships that have developed since your initial submission. 7. Revisit budget and timeline: Review them to ensure they are realistic and well-justified. 8. Seek external input: Consider seeking feedback from colleagues, mentors, or peers who were not involved in the initial submission. Fresh perspectives can be valuable. 9. Follow guidelines: Ensure that you follow all solicitation guidelines and formatting requirements to the letter. This includes adherence to page limits, font sizes, and other formatting rules. 10. Resubmit strategically: Timing is important. Don't rush to resubmit immediately after receiving feedback. Take the time needed to plan and make substantial improvements. 11. Write a stronger project summary: Pay attention to your project summary or abstract. It should provide a clear and compelling overview of your project. 12. Stay positive: Remember that rejection is common in the world of grant funding. Be persistent, maintain a positive attitude, and keep refining your proposal with each resubmission. 13. Maybe consider a different program: If your proposal is repeatedly rejected, it may be worth exploring whether another program might be a better fit. 14. Consult w/Program Officers: Reach out to the POs for guidance. Attend office hours/webinars. 15. Peer review: Consider having your revised proposal reviewed by colleagues/mentors who have experience with similar grants. Yes, the resubmission process can be challenging and time-consuming, but it's also a good opportunity to strengthen the proposal and increase the chances for success. 0 submission = 0 success.

When I talk to my colleagues and graduate students about how they are using AI tools, I realized that they are missing out on some important use cases that I've found extremely valuable. I wanted to share some of these below and look forward to hearing your thoughts on other unconventional ways you've applied these tools! ✅ Iterative Proposal Refinement – Used ChatGPT to evaluate a revised grant proposal in the context of reviewer comments, identifying gaps, strengthening arguments, and ensuring all weaknesses were addressed. This mimics an outside reviewer’s perspective before submission. ✅ Logic and Flow Checks – AI can analyze argument coherence, detect missing connections, and suggest clearer phrasing in technical documents, making research papers and proposals more compelling and concise. I will prompt to ask for what information is missing to enhance understanding or to identify areas that were unclear and need more explanation. ✅ Cutting the Fluff – Academics love long paragraphs, but reviewers don’t. I ask the LLMs to identify areas of redundancy or areas of varying detail between different parts of a proposal. ✅ Comparative Feedback Analysis – Given multiple drafts, ChatGPT can compare versions, pinpointing what improved and what still needs work—saving time in manual cross-referencing. ✅ Visualization Gaps & Idea Generation – Beyond writing, LLMs can help brainstorm visualization strategies, high priority areas where figures can benefit understanding, or suggest charts or tables to ease understanding. Happy to share prompting strategies I've been using that have been successful - please feel free to leave a comment. 💡 How are you using LLMs in your research? Would love to hear about unconventional ways you've integrated AI tools into your academic workflow!

I am often asked why it takes so long to edit a manuscript or grant proposal. Here's why... With every sentence I read in the document, I ask: 🔹 Does the sentence support the core idea and story of the document? 🔹 Does the sentence align with the topic of the paragraph? 🔹 Does the sentence make the point the author intends? 🔹 What does the reader already know and need to know at this point? 🔹 What do I (or the author) want the reader to think after reading this sentence? 🔹 What do I (or the author) want the reader to feel after reading this sentence? 🔹 Are the sentences organized logically? 🔹 How does this sentence connect to the preceding and following sentences? 🔹 Would the sentence be better placed somewhere else in the text? 🔹 Is the sentence parallel internally and with neighboring sentences? 🔹 Is the subject of the sentence clear? 🔹 Is the verb strong and accurate? 🔹 Does the verb agree with the subject? 🔹 Is the verb close to the subject? 🔹 Are the terms consistent with those used elsewhere in the text? 🔹 Is the sentence too long or overloaded with information? 🔹 Does the sentence contain unnecessary nominalizations that can be simplified? 🔹 Does the sentence contain unnecessary noun strings that can be broken up? 🔹 Does the sentence contain any long words that can be changed to shorter words? 🔹 Does the sentence contain the most accurate words for the intended meaning? 🔹 Does the sentence contain wordiness than can be condensed? 🔹 Does the sentence contain redundant or repetitive details? 🔹 Does the sentence use inclusive language? 🔹 Are the words spelled correctly and consistently? 🔹 Is the grammar correct? 🔹 Is the punctuation correct or confusing? 🔹 Could the sentence be written more persuasively? 🔹 Is the tone, formality, and readability appropriate for the intended readers? 🔹 Does the change I want to make maintain the same meaning or strengthen the intended meaning? 🔹 If I make a change to this part of the text, will that change affect another part of the text? 🔹 Is the formatting consistent? 🔹 Does the text align with the required style guide? And all these questions do not include the critical thinking needed to analyze the data, frame the interpretations, and clarify the conclusions made in the text. Editors carry a tremendous cognitive load as they review and revise text. This load takes time, energy, and attention to process and refine. What would you add to this list? #editing #medicalwriting #science

5 Common Mistakes I See When Reviewing Funding Proposals As someone who reviews many grant applications, I often see the same mistakes over and over. These mistakes can hurt your chances of getting the funding you need. The good news is that you can identify and fix these mistakes. Here are five common ones: 1. Unclear Story A successful proposal has a clear explanation of the problem you're addressing and why it matters. Many proposals fail because they don't clearly explain what they aim to solve. Think of your proposal like a story — start simple by explaining the problem, why it's important, and how your project will solve it. Make sure anyone, regardless of their background, can understand what you plan to do, how you'll do it, and the project's timeline. 2. Lots of Fancy Words Using technical jargon can confuse people who are not experts in your field. While it's good to show your expertise, using too many specialized terms can make your proposal hard to follow. Remember: the reviewer may not be familiar with jargon specific to your field. Use simple language whenever possible, and clearly explain any technical terms you must use. This makes your proposal easier to understand for everyone, which increases your chances of getting funding. 3. Unrealistic Promises Avoid making promises that are too big or unrealistic. It's tempting to talk about the huge impact your project could have, but this can make reviewers skeptical. Instead, focus on realistic goals that you can achieve with the funding and time available. Show that you have a good plan with achievable steps. This boosts your proposal’s credibility. 4. Lack of Preliminary Data Many proposals don't include enough initial data to show that the project is feasible. Preliminary data can prove that your project is based on solid methods and initial results are promising. Without this, reviewers might doubt your project’s viability. Include relevant initial data to show that your project has a good chance of success. 5. Poor Budget Justification An unclear or excessive budget request can be a major problem. Often, proposals request more funding than seems necessary without explaining why. Be detailed in explaining how the funding will be spent and why it’s necessary for your project. A clear and well-justified budget can greatly enhance your proposal’s credibility. Conclusion When writing a funding proposal, remember these five tips: tell a simple and clear story, avoid complex language, make realistic promises, include preliminary data, and provide a detailed budget explanation. By following these guidelines, you increase the chances that reviewers will understand and support your project, making it more likely that you'll receive funding. #engineering #science #research #mtvconsortium #funding #writing #proposal Nuclear Engineering and Radiological Sciences—University of Michigan University of Michigan College of Engineering