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Final report

Your final report will consist of the README.md file in your GitHub repository. It will count for 30% of your final grade and will have to be submitted by Tuesday September 3rd. The submission procedure is to create a release of your GitHub project, see "Draft a new release" under "releases".

Expected Outline

Your README.md file should include the following sections:

  1. High-level summary of the project (about 50 words)
  2. Project definition (about 200 words) What were you trying to accomplish, what was your question. This section may include a link to the presentation done during week 3.
  3. Learning experience (about 200 words) Describe how the project actually happened. In particular, which tools, data and technologies were learned. This section should include paragraphs that you may have already written during week 2 (but it doesn't have to).
  4. Results (about 200 words) The deliverables of your project, including but not limited to notebooks, code, figures, etc.

Grading Criteria

The grading criteria will similar to the ones used for the final presentation.

Grading criteria will be evaluated using a 3-level scale:

  • 1: does not meet expectations
  • 2: partially meets expectations
  • 3: totally meets expectations

Use of open-science best practices (1-3)

Expectation: the project uses 3 open-science tools learnt during week 1, or provides a convincing reason to not use them. The 3 tools may be selected in the following list, but other relevant tools will be accepted too.

  • Git
  • GitHub
  • Containers
  • Python
  • BIDS
  • Jupyter notebooks
  • Binder

Skills and technologies learnt (1-3)

Expectation: the project uses 1 skill, method or technology learnt by the student during the school, through formal presentations or informal interactions. The skill, method or technology may be selected in the following list, but other relevant ones will be accepted too.

  • Machine learning
  • Multivariate statistics and matrix factorizations
  • Estimation of connectivity
  • High-performance computing
  • DataLad

Project relevance (1-3)

Expectation: the project is relevant to brain data analysis.

Clarity (1-3)

Expectation: the report is easy to follow, and deliverables such as code or figures are properly described.