Risks and Mitigation

Use this section to think ahead about potential project failures—technical, biological, or logistical—and how you’ll respond if they emerge.

Known Risks

• What are the top 2–3 foreseeable risks?
  • Example: Public dataset may lack full phenotype metadata.
  • Example: Method may not scale to full cohort due to runtime or memory issues.

Mitigation Strategies

• What are the specific backup strategies if each risk occurs?
  • Example: Switch to a subset of phenotypes; use synthetic phenotypes for benchmarking.
  • Example: Profile code early and substitute lighter-weight methods.

Dependencies

• What tools, datasets, collaborators, or timelines is your project reliant on?
  • Example: Access to controlled dbGaP dataset
  • Example: Pipeline component under active development by another lab

Risk Evaluation Frequency

• How often will you re-assess risks and revise plans?
  • Example: Monthly during major phases; after each code sprint; before abstract deadlines.