Week 1 will feature “pitch sessions”, where each participant and instructor give a short overview of skills or resources they can teach. Two school organizers will be available every day for the whole duration of the school. The instructors, will present pitches and be available for half-day work session or tutorials once or twice a week, based on requests from the students. International guest residents will also be present for one week each during the school. Week 1 will be mostly focused on defining and piloting the project. As a participant, you will need to decide:

• What general topic do you want to work on? e.g. group comparison using fMRI, software for analysis of MEG data
• What skills do you want to learn, working on this project? e.g. preprocess fMRI data and run a classifier with sklearn, how to use git, etc.
• What resources do you want to work on? e.g. the CORR dataset, the nipype library, the Glasser parcellation paper, etc.
• What objectives do you want to achieve with the project? e.g. find differences in connectivity between two groups, replicate a multimodal brain parcellation, etc.
• What will be the outcome(s) of your project? a short proceedings paper, a new public dataset, a new feature in a toolbox, etc.

Each project will go through several rounds of written submission and feedback. During week 1, tutorials will likely revolve around data resources, as well as overview of libraries.

Week 2 will start with a presentation of the “final” projects and teams. Students will typically lead a project and be involved in one or two others. The content of a typical day will include:

• Work on projects. Most of the time will be reserved to actually doing the work.
• Tutorials. Slots will also be reserved to organize tutorials, but these will be organized on demand, based on the needs of the students.
• Unconference. Everyone will also have to present either a progress that they have made or a problem they need help during an “unconference” session held at 3pm each day.
• Collaborate. There will finally be a one hour break in the afternoon where students have to help someone else with their project

Tutorials organized during week 2 are expected to touch on technical issues, such as high-performance computing, running processing pipelines and applying machine learning tools.

Week 3 will concentrate on wrapping up deliverables. There should be at least one communication deliverable for each project, such as a short brainhack proceeding paper or a blog post. The deliverables will go through rounds of submission and review similar to the project pitches of week 1. The days will remain structured as week 2. Tutorials during week 3 are expected to touch more on scientific communication, visualizations and techniques for code and data sharing. The end of week 3 will feature a public “wrap up” event, with a series of presentations of the different projects completed at the brainhack school.