[ ] What is the broad challenge we are trying to solve? (pattern for Europe as a whole)
[ ] What is the specific research question? How does it translate to a data science problem? (how do you define health? how do you define social inequalities?)
[ ] How does the output of the project look like and how is it going to be used?
[ ] What is the budget?
The data
[ ] Is data available? Can I legally use it? Is it appropriate for the research question?
[ ] Can the technical team speak to domain experts / visit / connect with them to help with overcoming knowledge gaps about the data? (we can probably rely on the postdoc for domain knowledge)
[ ] What variables are you interested in?
[ ] Talk about the missing data that we can't answer questions on (unemployed, missing data)
[ ] Data sensitivity and privacy
The expectations
[ ] Feasibility plan for putting together the proposal
[ ] What are the stakeholders’ expectations? (managing expectations + what's the minimum viable product that they want / can expect?)
[ ] What are their reservations about data science approaches?
[ ] What is in-scope and out-of-scope? (how long do we have to produce minimum viable)
[ ] How does the output of the project look like and how is it going to be used?
[ ] What are the expectations from using deep learning specifically? (recommend using a simpler approach)
a) can we use ML over DL
b) will there be domain expertise to help us extract the features?
c) what compute facilities are needed
Success
[ ] What metrics do we use to measure the success of the project?
[ ] What is the expected impact? Is it realistic?
Other questions
[ ] What about computational resources and timelines?
[ ] Clarify total number of people involved, their roles and experience
[ ] Depending on outcome, what specific skills are needed on the technical team? (eg: software engineering)
[ ] How will we communicate with the whole team and how often? Do they require a timeline?
The goal
The data
The expectations
Success
Other questions