Merge branch 'main' of github.com:BrainhackMTL/winter2026

Author: mathdugre

data/projects/project_5.json

@@ -1,7 +1,7 @@
 {
   "Title": "<p>HyPyP Cookbook Sprint: a community-driven onboarding kit (docs, tutorials, examples, and small feature fixes)</p>\n",
-  "Leaders": "<ul>\n<li>Rémy Ramadour</li>\n<li>Patrice Fortin (waiting list)</li>\n</ul>\n",
-  "Collaborators": "<ul>\n<li>Vincent Chamberland</li>\n<li>Nadine Mohamed</li>\n<li>Mariem Hafsia</li>\n<li>Saskia Helbling (waiting list)</li>\n<li>Anne Monnier (waiting list)</li>\n<li>Anaïs Rourre (waiting list)</li>\n</ul>\n",
+  "Leaders": "<ul>\n<li>Rémy Ramadour // remy.ramadour.hsj@ssss.gouv.qc.ca // Discord: @ramdam79</li>\n<li>Patrice Fortin // patrice.fortin.hsj@ssss.gouv.qc.ca // Discord: @osokin</li>\n</ul>\n",
+  "Collaborators": "<ul>\n<li>Vincent Chamberland</li>\n<li>Nadine Mohamed</li>\n<li>Mariem Hafsia</li>\n<li>Saskia Helbling</li>\n<li>Anne Monnier</li>\n<li>Anaïs Rourre</li>\n</ul>\n",
   "Project Description": "<p>HyPyP is an open-source Python toolbox for hyperscanning and interpersonal brain/physiology synchrony analyses. The goal of this BrainHack project is to build a community-driven onboarding kit that makes HyPyP easier to learn, run, and extend—especially for newcomers.</p>\n<p>Concretely, we will produce a Cookbook: a small set of well-documented, reproducible tutorials (notebooks), improved documentation, and contributor-friendly entry points (“good first issues”). This will help students, researchers, and engineers quickly go from “I installed HyPyP” to “I can run a complete synchrony workflow and understand what I’m doing,” while also making it easier for new contributors to participate.</p>\n<p>As part of the Cookbook effort, we will also identify the main bottlenecks and roadblocks that create friction for newcomers (installation issues, unclear steps, common pitfalls) and either remove them when feasible or document clear solutions (troubleshooting/FAQ).</p>\n<p>This is a high-impact, low-friction community effort: in only a few days, we can significantly reduce the barrier to entry for hyperscanning analyses and improve reproducibility. The project is designed to be inclusive: contributors can help through writing, reviewing, coding, testing, visualization, or pedagogy, at beginner/intermediate/advanced levels.</p>\n<p>Bonus: if time allows, we’ll implement a small “quality-of-life” improvement (e.g., clearer API wrapper, tests, example dataset, or minor feature fix) so the event yields both documentation and code improvements.</p>\n<p>Participants will:</p>\n<ul>\n<li>clone the repo, install dependencies, and run a “hello world” notebook,</li>\n<li>pick a task (tutorial writing, doc improvement, tests/CI, visualization, small feature),</li>\n<li>submit contributions via PRs using templates and “good first issues” labels.</li>\n</ul>\n<p>We will provide a clear README, a beginner-friendly checklist, and a live coordination channel during the event.</p>\n",
   "Link to project repository/sources": "<p><a href=\"https://github.com/ppsp-team/HyPyPCookbook\">https://github.com/ppsp-team/HyPyPCookbook </a></p>\n",
   "Goals for Brainhack Montreal": "<p><strong>Milestone 1 — “Onboarding Kit” baseline (Day 1)</strong></p>\n<p><strong><em>Deliverables</em></strong></p>\n<ul>\n<li>A refreshed Quickstart in README: install, minimal example, expected output.</li>\n<li>A Cookbook skeleton: folder structure + table of contents.</li>\n<li>At least 5 “good first issues” ready to pick.</li>\n<li>A first pass “Friction log” (list of newcomer bottlenecks/roadblocks + proposed fixes or documentation links).</li>\n</ul>\n<p><strong><em>Beginner-friendly tasks</em></strong></p>\n<ul>\n<li>Rewrite/install troubleshooting (macOS/Windows/Linux, conda/poetry/pip).</li>\n<li>Add a glossary page: key concepts + links.</li>\n<li>Report friction points while onboarding (what broke / what was unclear) and propose wording fixes.</li>\n</ul>\n<p><strong><em>Intermediate tasks</em></strong></p>\n<ul>\n<li>Add a minimal simulated dataset and a script/notebook that runs end-to-end.</li>\n<li>Add CI smoke test: “import + run minimal pipeline”.</li>\n</ul>\n<p><strong>Milestone 2 — Tutorials that run end-to-end (Day 2)</strong></p>\n<p><strong><em>Deliverables</em></strong></p>\n<ul>\n<li>2–3 runnable notebooks with expected outputs saved (figures/tables):</li>\n<li>Hello synchrony (simulated signals → compute synchrony metrics)</li>\n<li>EEG hyperscanning minimal (MNE preprocessing → HyPyP metrics)</li>\n<li>Stats &amp; surrogates (permutation/surrogate testing + interpretation)</li>\n<li>Optional: a short notebook section or mini-notebook for common group stats (t-test / ANOVA) on synchrony outputs.</li>\n</ul>\n<p><strong><em>Beginner tasks</em></strong></p>\n<ul>\n<li>Improve narrative: explanations, figures, “what to check if results look wrong”.</li>\n<li>Add docstrings / examples to 2–3 public functions.</li>\n</ul>\n<p><strong><em>Intermediate tasks</em></strong></p>\n<ul>\n<li>Add unit tests for one tutorial pipeline (golden outputs / shape checks).</li>\n<li>Add a “common pitfalls” page (filtering, alignment, edge effects, leakage).</li>\n<li>Turn top friction items into fixes or documented solutions (FAQ / troubleshooting entries).</li>\n</ul>\n<p><strong><em>Advanced tasks</em></strong></p>\n<ul>\n<li>Identify and fix one small “paper cut” in the API (naming, defaults, error messages).</li>\n<li>Optional: add a small feature/wrapper improving usability, with tests + docs.</li>\n</ul>\n<p><strong>Milestone 3 — Integration &amp; demo-ready output (Day 3)</strong></p>\n<p><strong><em>Deliverables</em></strong></p>\n<ul>\n<li>A single BrainHack Demo Notebook that runs in &lt;10 minutes.</li>\n<li>A short “How to contribute” page + contributor credit instructions.</li>\n<li>A short report: what was improved, what remains, next steps.</li>\n<li>A consolidated “Friction log” summary (top bottlenecks + resolutions / workarounds).</li>\n</ul>\n<p><strong><em>Quality bar</em></strong></p>\n<ul>\n<li>Everything runs from a clean environment (fresh install) using the Quickstart.</li>\n<li>PRs merged, issues triaged, next milestones listed for after the event.</li>\n</ul>\n",

data/projects/project_7.json

@@ -0,0 +1,25 @@
+{
+  "Title": "<p>Analysing Dynamic Data for MoCA Solo</p>\n",
+  "Leaders": "<p>Murray Gilles: murray.gillies@mocacognition.com\nSaber Naderi: saber.naderi@mocacognition.com\nYijae Kim: yijae.kim@mocacognition.com / Discord handle: yijae1_42148_49872</p>\n",
+  "Collaborators": "<p>Émile Dusablon Dion: emile.dusablondion@mocacognition.com / Discord handle: emile_98830\nJack Tacchi: jack.tacchi@osedea.com\nWalid Masoudimansour: walid.masoudimansour@osedea.com</p>\n",
+  "Project Description": "<p>MoCA Cognition has developed a new digital tool called MoCA Solo to quantify a patient’s cognitive performance. It is based on the very well-established paper test “MoCA 8.1”, used to assess people with Mild Cognitive Impairment and Alzheimer’s disease. It requires the patient to perform an array of tasks, such as clock drawing, naming animals or recalling 5 words. For the paper version, a point is awarded for each task, but how the task is completed can only be observed live by a human. In the MoCA Solo application, all data is recorded in raw format, i.e. all audio files and every click on the iPad are collected for post data collection analysis.</p>\n<p>MoCA Cognition has collected data with the MoCA Solo application from 500 English-speaking participants, including patients and healthy individuals. All the current outcome measures have been annotated by three human raters. MoCA Cognition has used this data to develop AI scoring algorithms to automatically score the current outcome measures without the need for the presence of a human. These scores characterize the patient and the goal of this activity was to reproduce the paper MoCA score.</p>\n<p>While the current outcome measures are available from both the algorithms and the ground truths for the 500 people, we haven’t explored the dynamics of how the MoCA Solo tests were completed. We are interested in whether there is information in the dynamics of how the tests are completed which correlates with the existing outcome measures. An example of this could be the time it takes for a patient to name an animal shown in an image and the ability to recall the 5 words in delayed recall. The goal of the project is to create algorithms that take the dynamic data and find features that correlate with the existing measures such as the MoCA Score.</p>\n<p>Demo video of MoCA Solo: https://youtu.be/sl_4nr-n8SM\nMoCA Paper 8.1 &amp; Instructions : https://captiva.neurosurgery.ufl.edu/resources/moca/</p>\n",
+  "Link to project repository/sources": null,
+  "Goals for Brainhack Montreal": "<p>The main goal of this project is to create algorithms that take the dynamic data from MoCA Solo and find features that correlate with the existing measures such as the MoCA Score.</p>\n<p>Detailed sub-goals will be defined and added closer to the hackathon date.</p>\n",
+  "Skills": "<ul>\n<li>Data science &amp; analysis</li>\n<li>Programming (Python or R)</li>\n<li>Machine learning / algorithm development</li>\n<li>Neuropsychology / Cognitive science (not mandatory, but nice to have)</li>\n</ul>\n",
+  "Tech stack": "<ul>\n<li>Python Programming language</li>\n<li>Python data libraries</li>\n<li>Machine learning libraries (scikit-learn, etc.)</li>\n</ul>\n",
+  "Onboarding documentation / Expectations": null,
+  "What will participants learn?": "<ul>\n<li>Feature engineering from dynamic cognitive task data</li>\n<li>Correlation analysis</li>\n<li>Algorithm development</li>\n<li>Interdiciplinary thinking</li>\n<li>Collaboration in a real research workflow</li>\n</ul>\n",
+  "Data to use": "<p>Some aspects of the dataset like access method are still being finalized. These details will be added here prior to the event.</p>\n",
+  "Credit to collaborators": null,
+  "Image": null,
+  "Type": "<p>coding_methods</p>\n",
+  "Project Maturity Status": "<p>2 - releases existing</p>\n",
+  "Topic": "<p>statistical_modelling</p>\n",
+  "Tools": "<p>Jupyter</p>\n",
+  "Programming language": "<p>Python</p>\n",
+  "Modalities": "<p>not_applicable</p>\n",
+  "Git skills": "<p>1_commit_push</p>\n",
+  "Anything else?": null,
+  "Things to do after the project is submitted and ready to review.": "<ul>\n<li>[x] Short summary of your project pitch to present during the hackathon.</li>\n</ul>\n",
+  "issue_number": "7"
+}

data/projects/project_8.json

@@ -0,0 +1,25 @@
+{
+  "Title": "<p>BIDSbook</p>\n",
+  "Leaders": "<p>Basile Pinsard</p>\n",
+  "Collaborators": null,
+  "Project Description": "<ul>\n<li>Useful for many data management of multiple data acquisition and sharing projects\nhttps://jupyterbook.org</li>\n</ul>\n",
+  "Link to project repository/sources": null,
+  "Goals for Brainhack Montreal": "<ul>\n<li>Meet people</li>\n<li>Brainstorm the ideas</li>\n<li>Create a project stub</li>\n<li>Write notebooks to generate generic figures from standard datasets for the jupyterbook</li>\n<li>Create pages for BIDS (and maybe standard BIDS-apps derivatives).</li>\n</ul>\n",
+  "Skills": "<ul>\n<li>Python:basics</li>\n<li>Markdown:basics</li>\n<li>Git: basics</li>\n<li>Datalad:basics</li>\n</ul>\n",
+  "Tech stack": "<ul>\n<li>JupyterBook</li>\n<li>Datalad for crawling datasets metadata</li>\n<li>PyBIDS to query datasets content</li>\n<li>Github action setup to automate build.</li>\n</ul>\n",
+  "Onboarding documentation / Expectations": null,
+  "What will participants learn?": null,
+  "Data to use": "<p>https://github.com/OpenNeuroStudies/OpenNeuroStudies</p>\n",
+  "Credit to collaborators": null,
+  "Image": null,
+  "Type": "<p>data_management</p>\n",
+  "Project Maturity Status": "<p>0 - concept but no content</p>\n",
+  "Topic": "<p>data_visualisation</p>\n",
+  "Tools": "<p>BIDS</p>\n",
+  "Programming language": "<p>Python</p>\n",
+  "Modalities": "<p>other</p>\n",
+  "Git skills": "<p>2_branches_PRs</p>\n",
+  "Anything else?": null,
+  "Things to do after the project is submitted and ready to review.": "<ul>\n<li>[ ] Short summary of your project pitch to present during the hackathon.</li>\n</ul>\n",
+  "issue_number": "8"
+}

data/projects/project_9.json

@@ -0,0 +1,25 @@
+{
+  "Title": "<p>Wonkyconn - quality metrics and insights into your fMRI connectomes!</p>\n",
+  "Leaders": "<p>Hao-Ting Wang @htwangtw\nClara El Khantour @claraElk\nPierre Bergeret @pbergeret12</p>\n",
+  "Collaborators": null,
+  "Project Description": "<p>Evaluating the residual motion and analytic insights on the fMRI connectome and visualising reports.\nThe project is based on the code from <a href=\"https://github.com/SIMEXP/fmriprep-denoise-benchmark\">SIMEXP/fmriprep-denoise-benchmark</a> and the publication by <a href=\"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011942\">Wang et al. (2024)</a>.</p>\n",
+  "Link to project repository/sources": "<p>https://github.com/HALFpipe/wonkyconn</p>\n",
+  "Goals for Brainhack Montreal": "<ul>\n<li>Clean up documentation</li>\n<li>Improve figures and reports</li>\n<li>Code quality</li>\n</ul>\n",
+  "Skills": "<ul>\n<li>Python: advanced</li>\n<li>BIDS: basic</li>\n<li>fMRI and connectome</li>\n</ul>\n",
+  "Tech stack": "<ul>\n<li>Python</li>\n<li>Docker</li>\n<li>GitHub Actions</li>\n</ul>\n",
+  "Onboarding documentation / Expectations": null,
+  "What will participants learn?": null,
+  "Data to use": null,
+  "Credit to collaborators": null,
+  "Image": null,
+  "Type": "<p>pipeline_development</p>\n",
+  "Project Maturity Status": "<p>2 - releases existing</p>\n",
+  "Topic": "<p>connectome</p>\n",
+  "Tools": "<p>BIDS, fMRIPrep</p>\n",
+  "Programming language": "<p>containerization, documentation, Python</p>\n",
+  "Modalities": "<p>fMRI</p>\n",
+  "Git skills": "<p>3_continuous_integration</p>\n",
+  "Anything else?": null,
+  "Things to do after the project is submitted and ready to review.": "<ul>\n<li>[ ] Short summary of your project pitch to present during the hackathon.</li>\n</ul>\n",
+  "issue_number": "9"
+}