Bioinspired Communication & Ethics

Module 2: Proposal Writing & Review

This module provides training in research proposal development and evaluation. We begin with the research landscape — how breakthroughs happen and how funding works — then build proposal writing skills through practice, case studies, and peer review, before examining how AI is reshaping the research enterprise.

Student Profile (Mentimeter survey, n≈18): Most students are Year 2–3, preparing for qualifying exams. 10/14 require a written proposal; 8/16 do a formal proposal defense. Top concerns: novelty, clarity, and feasibility. Priority sections: Background & Significance (9), Research Design/Methods (9), Specific Aims/Hypotheses (7). Confidence in proposal writing: 3.4/5.

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Module Structure: 7 Lectures

#	Lecture	Key Topics
1	The Research Landscape & Research Framing	Nobel patterns, NSF directorate culture, hypothesis-driven framing, paragraph-level reframing
2	Funding Agencies & Your First Research Narrative	NSF vs. NIH, drafting challenge & objectives
3	Writing the Research Narrative	Field-specific best practices, NIH specific aims, revision workshop
4	Intellectual Merit, Broader Impacts & Case Studies	NSF review criteria, CAREER case study, review panel simulation
5	AI in the Research Enterprise	AI history, hypothesis generation debate, AI detection callback
6	Peer Review, Ethics & Responding to Critique	Mock panel review, AI evaluation limits, ethical framing
7	GCR Team Proposal Workshop	Growing Convergence Research proposal drafting & cross-team review

📝 Running Assignments

Two threads build across the module — one individual, one team-based:

Individual: Research Narrative Draft

Lecture	Milestone
2	Draft challenge statement (While…However) + 3 research objectives
2	Peer review in pairs (cross-discipline)
3	Revision workshop: strengthen challenge & sharpen objectives
3	“Elevator Test” — 90-second pitch of your narrative

This produces a draft challenge/objectives statement students can use for their qualifying exam proposals.

Team: GCR Convergence Proposal

Lecture	Milestone
7	Draft convergent research question + challenge + objectives + IM/BI
7	Cross-team peer review using simplified rubric
Post-L7	Final revised submission on Blackboard

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Lecture 1: The Research Landscape & Research Framing — How Breakthroughs Happen and How to Frame Them

Goal: Before writing proposals, understand what the research enterprise looks like — how discoveries are made, what patterns exist across fields, and how to frame your work strategically for different funding cultures.

📖 Pre-Class Reading

• 🔬 How Scientific Breakthroughs Really Happen: 125 Years of Nobel Prizes (presentation under revision — link temporarily removed)

  Analysis of Physics, Chemistry, and Physiology/Medicine Nobel Prizes (1901–2025). Key findings: Physiology/Medicine has the highest rate of serendipitous discoveries (~50%); Physics is most theory-driven (~45–50% hypothesis-driven); Chemistry has the strongest tool-building tradition. All fields are shifting toward more hypothesis-driven research over time.

• 📈 Research Methodology Patterns Across NSF Directorates (presentation under revision — link temporarily removed)

  Analysis of 494 funded grant abstracts (2024–2025) across BIO, PHY, CMMI, CHE, and CBET. Physics favors measurement (47%), Biology/Chemistry favor hypothesis testing (~50–60%), Engineering requires interdisciplinary integration (88% CMMI).

• 🎯 Hypothesis, Fundamental Research & Word Choice (presentation under revision — link temporarily removed)

  The critical connection between hypothesis-driven and fundamental research. Includes corrected word choice guidance (words aren’t “forbidden” — context is everything), the 2×2 matrix (hypothesis vs. discovery × fundamental vs. applied), and the NSF quote explicitly endorsing optimization and control as research topics.

🎯 Team Activity: “Classify Your Own Field” (~25 min)

Each team (12 min discussion + 13 min share-out):

1. Pick 3 landmark discoveries in your team members’ fields (can be Nobel-winning or not)
2. Classify each as hypothesis-driven, discovery-driven, or method-driven — and defend your choice
3. Identify one discovery that doesn’t fit neatly into any category. Why not?
4. Discuss: Is your field becoming more or less hypothesis-driven over time? What’s driving the shift?

This exercise forces students to apply the Nobel analysis framework to their own disciplines, rather than treating it as an abstract historical overview.

✍️ In-Class Practice: Paragraph-Level Reframing (~30 min)

Use the interactive exercises built into the Word Choice presentation (Slides 7–8). Teams work on the realistic paragraph-level prompts:

• Round 1 (12 min): Each team takes one exercise passage (biomedical, environmental/AI, or chemistry/materials). Reframe without changing the actual research.
• Round 2 (8 min): Teams swap and critique each other’s reframes — did they change the research or just the framing? Did they fabricate unsupported numbers?
• Share-out (10 min): Each team presents their best reframe. Class votes on which ones successfully led with the knowledge question without losing the applied context.

💬 Mentimeter Discussion & Quick Poll (~15 min)

• Where does your own dissertation research fall on the hypothesis–discovery–method spectrum?
• Has your field had its own “penicillin moment” — a breakthrough no one was looking for?
• Which of these words would most trigger “not fundamental” for a reviewer in your field? (Revisit the survey from last year — “Optimize” was #1 at 10/16)

💡 Key Takeaways

• Different fields have different discovery patterns — there’s no single “scientific method”
• The funding system’s preference for hypothesis-driven proposals creates structural tension with how breakthroughs actually happen
• “Hypothesis-driven” is a useful framing strategy, not a logical requirement for fundamental research
• Words need careful framing, not avoidance — match your framing to your target directorate’s culture
• Don’t fabricate specific predictions without evidence — reviewers will question unsupported claims

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Lecture 2: Funding Agencies & Your First Research Narrative

Goal: Understand the key differences between NSF and NIH (and other agencies), then immediately apply that knowledge by drafting your first challenge–objective statement.

🏛️ Mini-Lecture: Know Your Audience (~20 min)

NSF vs. NIH at a Glance:

Feature	NSF	NIH
Review criteria	Intellectual Merit + Broader Impacts	Significance, Investigators, Innovation, Approach, Environment
Scoring	Qualitative (E/VG/G/F/P)	Numerical 1–9 (lower is better)
Proposal length	15 pages (project description)	12 pages (research strategy, R01)
Specific aims	Integrated into narrative	Separate 1-page document (critical)
Preliminary data	Helpful but not required	Essentially required for R01
Resubmission	No formal response to reviews	1-page Introduction responding to prior reviews
Broader impacts	Required, weighted equally	Not a separate criterion
Fundamental vs. applied	Strongly favors fundamental framing	Accepts translational and clinical framing

Other key agencies (brief overview): DOE Office of Science, DARPA, private foundations (Sloan, Gates, CZI). Fellowships: NSF GRFP, NIH F31, Ford, Hertz.

Growing Convergence Research (GCR): NSF’s emphasis on deep integration across disciplines — not just collaboration, but disciplines reshaping each other. This connects to the team GCR proposal assignment in Lecture 7.

📚 Key References

• 📄 NSF PAPPG — Proposal preparation requirements
• 📄 NSF Merit Review — Intellectual Merit + Broader Impacts criteria
• 📄 NSF Award Search — Search funded awards in your target program
• 📄 NIH Application Guide — Comprehensive NIH guide
• 📄 NIH Review Criteria (Scoring Guidance) — The 1–9 scoring system explained
• 📄 NIH RePORTER — Search funded NIH projects
• 📄 NSF GCR Program — Growing Convergence Research
• 📄 NSF GRFP — Graduate Research Fellowship

✍️ Practice Session: Draft Your Challenge & Objectives (~60 min)

Individual writing exercise — students work on their own research, not hypothetical examples:

Step 1 — Draft a Research Challenge (20 min):

Using the “While…However” template:

While [broad area] is critical for [benefit/goal], a major challenge is [specific knowledge gap], which limits our ability to [achieve something important]. This gap exists because [current state] fails to [explain/account for phenomenon].

Write one paragraph (4–6 sentences) framing the core challenge of your dissertation research. If you don’t have a dissertation topic yet, frame a challenge from your lab’s recent work.

Step 2 — Draft 3 Research Objectives (15 min):

Following the three-objective framework:

1. Foundational: Establish the core tool, method, or framework
2. Mechanistic: Elucidate underlying mechanisms or test core hypotheses
3. Application/Validation: Apply findings to demonstrate utility

Write one sentence each. Use strong action verbs (test, measure, establish, elucidate, characterize).

Step 3 — Peer Review in Pairs (15 min):

Swap with a partner from a different field. Each reviewer answers:

• Can I understand the challenge even though I’m not in this field?
• Do the objectives directly address the stated challenge?
• Is the framing fundamental or applied? Does it need to be shifted?
• Would I fund this? What’s missing?

Step 4 — Revise and Submit (10 min):

Revise based on partner feedback. Submit on Blackboard for instructor review.

Why this matters: Your Mentimeter survey showed 10/14 students need a written proposal for qualifying exams. This exercise produces a draft they can actually use. It’s the most directly career-relevant activity in the module.

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Lecture 3: Writing the Research Narrative — From Challenge to Objectives

Goal: Deepen narrative writing skills with field-specific best practices, the NIH specific aims page structure, and hands-on revision of the challenge statements drafted in Lecture 2.

📊 Presentation (~25 min)

• ✍️ Writing Compelling Research Challenges & Objectives

  Field-specific best practices from funded NSF proposals (BIO, PHY, CMMI, CBET, CHE). Covers the three-objective framework in depth with real examples, quantifying ambition, the “if-then” logic connecting challenge to objectives, and common mistakes to avoid.

📋 NIH Specific Aims Page (~15 min)

The 1-page specific aims document is arguably the most important page in biomedical research:

Paragraph 1 — The Hook: Open with the problem’s significance. Why does this matter?

Paragraph 2 — The Gap: What is unknown? What has been tried and why did it fail?

Paragraph 3 — Your Solution: Long-term goal, objective of this application, central hypothesis, and its basis.

The Aims (numbered): 2–3 specific, measurable aims with brief rationale for each.

Paragraph 4 — The Payoff: Expected outcomes and significance.

NIH Content — Placeholder for Development: This section will be expanded with:
• 2–3 real NIH specific aims pages (strong and weak) for in-class analysis
• Before/after comparison showing a weak aims page revised to be competitive
• NIH study section simulation exercise (scoring with 1–9 scale)
• Guest lecturer from an NIH-funded lab or study section member (TBD)
• Key differences in how NIH vs. NSF reviewers evaluate “significance” vs. “intellectual merit”
These materials will be developed in consultation with NIH-experienced faculty and may include a dedicated guest lecture session.

✍️ Revision Workshop: Strengthen Your Lecture 2 Drafts (~40 min)

Students receive written feedback on their Lecture 2 challenge/objectives submissions. Working in pairs:

Round 1 — Strengthen the Challenge (15 min):

• Does your challenge use deficit-based language? (“It remains poorly understood…,” “Current methods fail to…”)
• Is the knowledge gap specific enough? (Not “Cancer is a major problem” but “The lack of real-time biomarkers for X limits early intervention in Y”)
• Apply the “While…However” structure if you haven’t already

Round 2 — Sharpen the Objectives (15 min):

• Do your objectives follow the Foundational → Mechanistic → Application sequence?
• Does each objective start with a strong action verb?
• Can you articulate the “if-then” logic? (“If Obj 1 succeeds, then Obj 2 becomes possible, which enables Obj 3”)
• Are success criteria clear?

Round 3 — The “Elevator Test” (10 min):

Each student reads their challenge + objectives aloud in 90 seconds. Partner answers: “What will you learn?” and “Why should I care?” If the partner can’t answer both, revise.

💡 Key Takeaways

• The NIH specific aims page is a complete argument in one page — master its structure even if you target NSF
• The “While…However” template works across agencies and fields
• “If-then” logic between objectives tells reviewers your plan is coherent
• The elevator test reveals whether your narrative communicates to non-specialists

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Lecture 4: Proposal Components — Intellectual Merit, Broader Impacts & Case Studies

Goal: Understand what makes Intellectual Merit and Broader Impacts compelling through real case studies, then practice evaluating and writing these sections.

📊 Presentations (~30 min)

• 📋 Writing Excellent Intellectual Merit & Broader Impacts

  NSF’s two review criteria in depth. Side-by-side comparisons of weak vs. strong approaches. Key insight: the strongest proposals weave IM and BI together, rather than treating them as separate sections.

• 🏆 NSF CAREER: A Case Study in Proposal Revision

  Two versions of the same CAREER proposal (2018 unfunded → 2019 funded). The “irregular lattice = FEM” proof was the killer preliminary result. Key lesson: scientific substance beats structural polish.

🎯 Team Activity: “Review Panel Simulation” (~40 min)

Teams role-play as an NSF review panel evaluating two short (1-page) proposal excerpts provided by the instructor:

Step 1 — Individual Review (10 min): Each student reads both excerpts and assigns ratings (Excellent / Very Good / Good / Fair / Poor) for Intellectual Merit and Broader Impacts separately. Write 2–3 sentences of justification for each rating.

Step 2 — Panel Discussion (15 min): Teams discuss as a panel. Appoint a “panel chair” who must synthesize the group’s views. Where do you agree? Where do you disagree? What would you tell the PI to improve?

Step 3 — Panel Summary and Share-Out (15 min): Each panel presents their consensus rating and the single most important strength and weakness they identified. Class compares how different panels rated the same proposals.

Why simulate a panel, not just review? Because the panel discussion is where proposals live or die. Students need to experience how individual ratings get negotiated into a group consensus — and how a champion or detractor can swing the outcome.

📚 References

• 📄 NSF CAREER Program — Current solicitation
• 🔍 Writing a Winning NSF CAREER Proposal — Detailed lessons from proposal versions
• 📄 Science of Science Funding — Research on what makes proposals successful

💡 Key Takeaways

• Preliminary data is the single strongest signal to reviewers
• NSF CAREER rewards transformative vision over conservative scope — “improvements” can weaken proposals
• Broader impacts that emerge from the research are stronger than bolted-on outreach
• Panel dynamics matter — one strong advocate can fund a proposal, one strong critic can sink it

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Lecture 5: AI in the Research Enterprise

Goal: Examine AI’s role in science through its history, capabilities, limitations, and ethical implications — connecting the proposal module to the paper writing module that follows.

🎮 AI Quiz & History (~25 min)

Interactive Poll Everywhere quiz on AI history and facts (competitive, with leaderboard):

• Who is the most famous figure in AI history? (Students said Sam Altman and Alan Turing — but 0/16 named Donald Hebb)
• First formal model of an artificial neuron? (13/16 correctly identified McCulloch & Pitts, 1943)
• How many citations does ResNet have? (Answer: ~300K — students’ guesses ranged from 10K to 1M)
• “1960s or 2020s?” exercise — students try to date AI claims:
  • “Machines will be capable, within twenty years, of doing any work a man can do” → 1960s (11 correct)
  • “We are at the beginning of a new era… a new reasoning engine” → 2020s (10 correct)
  • “Within a generation… the problem of creating ‘artificial intelligence’ will be substantially solved” → 1960s (7 correct — many students thought this was modern!)
  • “The ultimate goal is to make computers that can learn to solve problems on their own” → 1960s (10 correct)
  • “AI is probably the most important thing humanity has ever worked on” → 2020s (13 correct)
  • “We are building ‘electronic brains’ that will one day be our partners in thought” → 1960s (13 correct)

The lesson: AI hype cycles repeat. The rhetoric of the 1960s is nearly indistinguishable from 2020s discourse. This is a science communication case study in real time.

🧩 AI Winter Jigsaw Discussion (~20 min)

Using the Toosi et al. (2021) paper “A brief history of AI: how to prevent another winter”:

• Groups 1 & 2 (The Analysts): Diagnose the causes. What specific factors led to the loss of faith and funding?
• Groups 3, 4 & 5 (The Strategists): Extract the lessons. What concrete practices should researchers adopt to build sustainable, ethical, and well-communicated progress?
• 15 min in groups → 5 min share-out of key insights

🤖 Mini-Debate: Can AI Generate Research Hypotheses? (~20 min)

Prompt with data: “GPT-4 generated 100 hypotheses in 3 hours; experts rated 40% as ‘plausible.’ A PhD student generates 5–10 hypotheses over 3 years.”

• Last year’s survey: 11/16 students said AI cannot generate research hypotheses
• Teams take assigned positions (pro/con/nuanced) and debate for 15 min
• Close with: What does “generate” mean? Is pattern-matching the same as understanding?

🔎 AI Detection Callback (~5 min)

Brief callback to the AI detection data you examined in Module 1 (Lecture 3):

You saw in Module 1 that AI detection fails reliably — three models achieved 0–20% accuracy, and the “angry reviewer” persona fooled all detectors. Today we ask what AI can and cannot do in the research enterprise, given that detection is off the table.

Connect to students’ survey data: trust in AI for funding decisions was 2.7/5; 13/18 preferred human-centric AI role.

🌉 Bridge to Module 4

Close with: “You’ve now seen AI generate hypotheses, write proposal reviews, evaluate proposals, and create research timelines. In the Ethics module, we ask: what are the ethical responsibilities when using these tools? The same tensions between capability and judgment apply, but the stakes are different.”

📚 Reading

• 📄 A brief history of AI: how to prevent another winter — Toosi et al. (2021), arXiv
• Geoffrey Hinton’s and Daniel Kahneman’s perspectives on AI communication (discussed in class)

💡 Key Takeaways

• AI hype cycles repeat — the rhetoric of the 1960s is nearly indistinguishable from 2020s discourse
• AI can generate plausible-sounding hypotheses but cannot evaluate their scientific merit
• AI detection is unreliable — focus on demonstrating understanding, not catching cheaters
• Understanding AI’s history helps you communicate about AI more responsibly in your own proposals

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Lecture 6: Peer Review, Ethics & Responding to Critique

Goal: Experience the reviewer’s perspective, develop constructive review skills, and engage with ethical questions about AI, framing, and responsible communication in science.

📊 Case Study: AI Over-Values Structure (~15 min)

• 🤖 NSF CAREER: AI Analysis Case Study

  AI models attempted to evaluate which CAREER proposal version was stronger — and got it wrong. AI over-valued structural polish (“modular, reviewer-friendly”) and under-valued the scientific substance (preliminary data, demonstrated feasibility).

Discussion prompt: “If AI can’t reliably evaluate proposals, what CAN it usefully do in the review process?” Connect to the student survey showing 13/18 prefer human-centric AI roles.

✍️ Mock Panel Review (~45 min)

Teams conduct a formal mock panel review of a provided proposal excerpt (different from Lecture 4):

Step 1 — Individual Written Review (15 min): Using simplified NSF criteria, each student writes a structured review:

• Summary of proposed research (2–3 sentences)
• Intellectual Merit: Key strength and key weakness
• Broader Impacts: Key strength and key weakness
• Overall rating (E/VG/G/F/P) with justification
• One specific, constructive suggestion for improvement

Step 2 — Panel Deliberation (15 min): Teams discuss as a panel. The panel chair must:

• Identify where reviewers agree and disagree
• Facilitate discussion of the most contentious points
• Reach a consensus recommendation (fund / fund with revisions / do not fund)

Step 3 — Writing the Panel Summary (15 min): Each team writes a 1-paragraph panel summary that captures their consensus and the key reasons. Submit on Blackboard.

💬 Ethics Discussion (~20 min)

Structured around the Caltech case study (José Andrade’s mechanics course redesign: “When knowledge is instantly available, judgment becomes the differentiator”):

• Is using AI to draft a proposal section ethical? Where is the line between tool and ghost-writer?
• If you can’t detect AI-generated text, should we stop trying? What replaces detection?
• How does framing your research differently for different agencies relate to honesty? Is strategic framing the same as spin?
• Your students will write proposals that AI could write faster. What makes your proposal worth funding?

📚 References

• 📄 Conflict in Research Groups: Four Strategies — Relevant for collaborative review

💡 Key Takeaways

• Constructive peer review is a professional skill — “this is weak” without specifics doesn’t help anyone
• Panel dynamics (champion vs. detractor) matter as much as individual review quality
• Ethical framing isn’t about avoiding strategy — it’s about ensuring your framing honestly represents your research
• AI is a tool for drafting, not a substitute for scientific judgment

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Lecture 7: GCR Team Proposal Workshop

Goal: Apply everything from the module by developing a Growing Convergence Research (GCR) proposal as a team, then conducting cross-team peer review.

📋 GCR Proposal Assignment Overview (~10 min)

Each team develops a Growing Convergence Research proposal that integrates skills from the entire module:

Requirements:

• A convergent research question where disciplines genuinely reshape each other (not just collaborate)
• A challenge statement using the “While…However” structure
• 3 specific objectives following the Foundational → Mechanistic → Application framework
• Intellectual Merit section (1 paragraph)
• Broader Impacts section (1 paragraph)
• Framing appropriate for a target NSF directorate (specify which one)

What “convergence” means (vs. multidisciplinary or interdisciplinary):

	Multidisciplinary	Interdisciplinary	Convergent
Structure	Disciplines work side by side	Disciplines integrate methods	Disciplines reshape each other
Example	A biologist and engineer share data	An engineer uses biological models	Biology and engineering co-create a new framework neither could conceive alone
GCR standard	✗	Partial	✓

✍️ Team Working Session (~40 min)

Teams draft their GCR proposals. Instructor circulates to provide feedback. Key checkpoints:

• Minute 10: Each team should have their convergent research question. Instructor spot-checks: “Is this truly convergent, or just interdisciplinary?”
• Minute 25: Challenge statement and objectives drafted. Quick pair-check with another team.
• Minute 40: Full draft ready for cross-team review.

🔄 Cross-Team Peer Review (~30 min)

Each team reviews another team’s draft using a simplified rubric:

1. Convergence Quality: Do the disciplines genuinely reshape each other, or is this just collaboration? (1–5)
2. Challenge Clarity: Can I understand the knowledge gap without being in this field? (1–5)
3. Objective Logic: Do the three objectives follow the “if-then” chain? (1–5)
4. Framing Match: Is the language appropriate for the target directorate? (1–5)
5. One specific suggestion for strengthening the proposal

Teams return reviews; original teams have 10 minutes to discuss and plan revisions.

Final submissions (revised based on peer review) are due on Blackboard by the following week.

💡 Key Takeaways

• True convergence means disciplines reshape each other — not just contribute side by side
• The “While…However” structure and three-objective framework apply to team proposals just as they do to individual ones
• Cross-team peer review reveals whether your proposal communicates to non-specialists
• Everything in this module — framing, narrative, review criteria, ethical reasoning — comes together in this exercise

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🏥 NIH Content Development Plan

Current status: The module’s interactive materials are primarily NSF-focused. NIH content exists as structural guidance (specific aims page format, comparison table) but lacks the depth of interactive materials available for NSF.

Planned development (for future iterations):

Priority 1 — Real NIH Specific Aims Pages: Collect 2–3 publicly shared specific aims pages (strong and weak) for in-class analysis. Many funded PIs share these on lab websites or through institutional resources. These would anchor Lecture 3’s NIH section, replacing the current text-only template.

Priority 2 — Before/After Aims Page Revision: Develop a case study showing a weak specific aims page revised to be competitive — parallel to the NSF CAREER 2018→2019 case study. Ideally from a real resubmission, anonymized with permission.

Priority 3 — NIH Study Section Simulation: Design a mock study section exercise where students score a proposal excerpt using NIH’s 1–9 scale across all five criteria (Significance, Investigator, Innovation, Approach, Environment). This would parallel the NSF panel simulation in Lectures 5 and 7.

Priority 4 — Guest Lecturer: Invite an NIH-funded faculty member or current/former study section member to discuss how NIH review actually works in practice — especially the differences from NSF that can’t be captured in a comparison table (e.g., the role of the Scientific Review Officer, triage processes, payline politics).

Priority 5 — NIH-Specific Framing Guide: Develop an interactive presentation on NIH framing conventions — how “significance” differs from NSF’s “intellectual merit,” how to frame translational work, and how the innovation criterion is evaluated.

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📚 Module Activities & Learning Objectives

Through this module, students develop skills in:

• Research Landscape Awareness: Understanding how breakthroughs happen across fields and how funding agencies evaluate research
• Strategic Framing: Matching proposal language and structure to agency culture and directorate expectations
• Research Narrative Craft: Writing compelling challenges, objectives, and significance statements — including practice with their own dissertation research
• Proposal Evaluation: Conducting mock panel reviews using official NSF criteria
• Peer Review: Providing constructive written feedback and participating in panel deliberation
• AI Literacy: Understanding AI’s capabilities and limitations in research — from hypothesis generation to proposal review to detection
• Ethical Reasoning: Engaging with questions about framing, AI use, and responsible communication

» Detailed assignment instructions, rubrics, and submission portals are available on the course Blackboard site.

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📚 Additional Resources

• 📄 How to Write a Grant Proposal (Nature) — Step-by-step guide
• 📄 NSF Broader Impacts Network — Practical examples and templates
• 📄 Grant Application Writer’s Workbook — Comprehensive proposal writing guide
• 📄 NIH Funding Mechanisms — R01, R21, R03, F31, K99/R00 explained

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