Project planning

This guide can serve as a standalone reference for project planning and fellowship writing. Each section builds on conceptual, strategic, and practical aspects separately, but they should be taken into consideration together for best results.

Background and concepts

Ideal vs reality of designing a project for public funding

• Basic vs applied science: Funding often requires navigating the balance between fundamental discovery and real-world application.
• Funding agencies as...
  • Investors (ideal): They take on risk to enable novel, groundbreaking ideas.
  • Consumers (reality): They seek 'safe bets' by prioritizing projects with a track record of feasibility and evidence.

Triangle of knowledge

• Hierarchy: Data → Information → Knowledge → Wisdom
• Flow between layers:
  • Data is raw input (e.g., measurements, numbers).
  • Information is data contextualized with meaning.
  • Knowledge is synthesized information with clear insight.
  • Wisdom incorporates experience, values, and context beyond natural science (includes personal experience, philosophy, and metaphysics).

Strategies

Project design

Balancing feasibility and novelty

• Aim 1: Incremental research (low risk, highly feasible).
• Aim 2: Novel, higher-risk research (potential for greater impact but riskier).
• Aim 3: Hybrid approach, combining incremental and novel aspects (not always necessary).

Balancing profiling and perturbing

• Profiling aim: Descriptive/observational, e.g., understanding how mutation X is linked to pathway A, or observing population-level traits.
• Perturbing aim: Functional/causal, e.g., testing if gene X causes phenotype Y, or clinical trials.

The two strategies can be linked. For instance: Aim 1 is slightly incremental with profiling only; Aim 2: more ambitious/novel based on perturbations.

Shallow-pass strategy for project execution

The Shallow-pass strategy is a systematic approach to project execution that emphasizes early, rapid, and minimally viable progress through a project’s full timeline, followed by iterative deepening of depth (both conceptually and technically) as needed. It draws on established principles from Agile development, Rapid prototyping, Incremental research, and Lean startup approaches.

Core concept

The strategy envisions a 2D space where:

• Horizontal axis: Timeline from start to finish (milestones, objectives, deliverables).
• Vertical axis: Depth of conceptual understanding, technical precision, or experimental thoroughness.

Instead of moving deeply through each individual task in sequence (e.g., perfecting each aim before moving to the next), the Shallow-pass strategy encourages a shallow, complete pass over all key components of the project first.

This approach creates a “minimum viable version” of the entire project, akin to a minimum viable product (MVP), and allows for early identification of bottlenecks, feasibility issues, and unknowns.

Once this shallow layer is complete, depth is added step-by-step to specific areas where gains are most needed or most valuable. This "depth-first" progression occurs only when justified by clear metrics or emerging insights.

Execution

1. Initial pass (shallow path): The goal is to complete a simple, end-to-end version of the project with minimal depth. This quick, rough pass identifies critical barriers, risks, and time-sinks. For example, instead of training a deep learning model on a large dataset, you might start with a simple logistic regression on a small subset, or run a pilot experiment to validate feasibility.
2. Iterative deepening (vertical progression): Revisit earlier steps and selectively add depth to areas that yield the most benefit. If an aspect works well at shallow depth, further refinement may be unnecessary. For example, after a successful logistic regression, you might increase depth by training a neural network, scaling up the dataset, or adding multi-modal inputs.
3. When to stop (completion): Avoid the "infinite perfection" trap by setting success metrics (e.g., 90% model accuracy) at the outset. Once these criteria are met, stop. If your classifier achieves 95% accuracy when the goal was 90%, additional work may have little value. Completion is defined by sufficiency, not perfection.

Common pitfalls and how to avoid them

Writing process

1. Planning and supervisor coordination

• Agree on timeline: Clarify deadlines for draft and final versions.
• Clarify expectations: What level of polish should the draft have?
• Define the review process: Involve other stakeholders if needed (e.g., senior lab members).
• Co-design the project: Discuss aims and approaches early with the supervisor.
• Accept constraints: Some projects are pre-defined due to funding calls, and flexibility is limited.
• Maintain perspective: The fellowship is often an "academic exercise" — flexibility in practice is often possible once funding is secured.

2. Content development

Start with the big picture

• Identify the central question or goal.
• "Walk back" from the goal to define specific aims, objectives, and the path to achieving them.

Clarity is everything

• Be simple, clear, and precise. Over-complication weakens the proposal.
• Make abstract concepts tangible by showing concrete outputs, metrics, and practical outcomes.

Is a hypothesis necessary?

• Hypotheses are not always required, but without one, the proposal may require stronger justification for the work.

3. Proposal structure

Top-down approach

1. Hypothesis/Objectives/Goals
2. Impact:
   • Why should this be addressed?
   • What impact will success have (on science, society, policy, etc.)?
   • What will we learn even if the project fails?
3. Aims: Typically 2-3 aims.
4. Tasks for each aim (1-2 per aim):
   • Goal of the task
   • Why it matters
   • Required resources, data, or inputs
   • Methods, experiments, and tools required
5. Introduction (3-4 points):
   1. The problem being addressed.
   2. What has been tried previously.
   3. Key technologies, datasets, or resources now available to address the problem.
   4. A hint of the specific approach you will take.
6. Challenges and mitigation
   • Think of the top challenges in the proposal design.
   • Separate them by conceptual and technical
   • Try to preemptively address them at design stage

4. Proposal writing process

Gradual, hierarchical writing

• Expand each bullet point into 1-2 sentences in the order that you designed them:
  • Hypothesis/objectives/goals
  • Impact
  • Aims/tasks (very shallowly)
  • Introduction
• Build each section gradually.

Figures & visual aids

• Sketch first: Draw rough concepts on paper or whiteboard.
• Collaborate: Co-sketch ideas with a supervisor or team member.
• Repurpose existing visuals: Use (but adapt) figures from previous grants/papers.
• Preliminary data: Include early evidence or preliminary results where relevant.

5. Use of AI/LLMs (Large Language Models)

Dos and Don'ts

• Don’t outsource the thinking: AI works best after you've defined a clear structure with bullet points and key ideas.
• Provide context: Add as much detail as possible for the AI (including PDFs of previous work).
• Emphasize priorities: Explicitly state which points are most important.
• LLMs are tools, not authors: You are in control of the ideas, while the AI is a writing assistant.

Why use LLMs?

• Overcome writer's block: It’s easier to edit text than to create it from scratch.
• Improve language quality: English polish can match the top-tier writing of competitive grant applications.
• Use parallel sessions: Write in one session and criticize/review in another.

6. Feedback process

• Be open to feedback: Supervisors may have different perspectives, and their feedback often reflects reviewers' likely reactions.
• Ask for clarification: If something feels unfair or unclear, discuss it with the supervisor.
• Learn from changes: Understand why edits were made — this is where the learning happens.

Summary of key takeaways

1. Designing a project: Balance feasibility and novelty. Balance profiling (description) and perturbation (causation).
2. Executing a project: Use the "square triangle" approach — shallow, fast progress first; deeper, slower progress later.
3. Writing a proposal: Work step-by-step, define key objectives first, and address the big picture.
4. Clarity and structure are essential for success.
5. Involve supervisors early in project design, figure development, and editing.
6. Leverage LLMs for support, but control the process.
7. Learn from feedback — it's one of the most valuable aspects of the process.

Additional Resources

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