The Institut de Neurosciences de la Timone (INT) is organizing a hackaton, the first Brainhack Marseille, as part of the Brainhack Global 2019, with the support of NeuroSchool / nEURo*AMU and ANR Lives. The event will take place at INT on 13/14 November 2019. Please register using this form (registrations limited to 50 people, deadline: November 5th). All participants can propose projects, new or already under development. The program includes tutorials & hacking on the first day, and a second day entirely dedicated to hacking on projects.
Organizers: Bastien Cagna, David Meunier, Dipankar Bachar, Etienne Combrisson, Guillaume Auzias, Martin Szinte, Sylvain Takerkart, Régis Trapeau.
Everybody who studies the brain! Brain imaging, bio informatics, behavioral studies...etc. This event welcomes every academic who would like to share his experience or participate in neuroscience-related projects.
To initiate collborations and sharing, submit your project! It is not mandatory but will be the starting point of the hackaton.
|Wednesday 13th||Thursday 14th|
Opening & projects pitches
Unconference: Standardized fMRI preprocessing using BIDS and FMRIPREP
Martin Szinte- Resources
Tutorial: How to use Git?
David Meunier - Slides
Unconference on open science
Guillaume Auzias - Slides
Tutorial: Stats & Python
Guillaume Auzias - Python examples
To submit a project, add an issue to the gitlab project using the "Project" template (you need to have a framagit account for this).
Here is a subset of the submitted projects:
Led by: Fred Barthelemy, Sylvain Takerkart, Kevin Blaize
The goal of this project is to define standards for data management and application of the FAIR principles to our data in the context of open science. We propose to organize the discussions / workgroups around 4 points:
Prerequisites: No prerequisities
Led by: David Meunier, Bastien Cagna
The aim of this project is to create a python package that provide all the tools needed to preprocess anatomical data of non humain primate. It also aim to provide a standard pipeline for different species, starting with macaques.
Prerequisites: MRI preprocessing, Python
Links: Git repository
Led by: Dipankar Bachar
Single cell SplitSeq is a recent and cheap method to produce single cell data. But, as it is a new method, there exists not many tools and pipeline to de-multiplex and analyse the splitseq data. So, the aim of this brain hack project is to better understand the method of SpliSeq , to test the existing tools, to propose/create new algorithm and if possible to develop tools/pipeline in order to demultiplex/analyse Spliseq data.
Prerequisites: Understanding basic Single Cell transcription method.
Led by: Étienne Combrisson
Visbrain is an open-source python package for plotting brain data. It relies on VisPy, a package that provide efficient GPU based plotting. VisPy recently added the support of embedded plot inside jupyter notebook / lab. The aim of the project is to also brings the Jupyter support to Visbrain and be able to plot brain data inside it and allow efficient real-time interactions.
In addition, the support for custom colormaps / colorbars is also required (see #50).
Prerequisites: Object oriented Python programming, Jupyter notebooks / lab enthousiame.
Led by: Régis Trapeau, Bastien Cagna
The goal would be to further develop this project created during the PRIME-DE meeting in London.
PRIMate-Ressource Exchange aims to provide an overview of the main difficulties and curate a collection of solutions that currently exist within the broader NHP-MRI community for specific processing steps that are commonly performed on NHP MRI data.
Prerequisites: Experience, feedback or questions on PNH MRI processing.
Led by: Bjørg Kilavik
Discuss adequate existing analysis methods and tools, and if necessary work towards establishing new tools (in matlab or python) to quantify the dynamics of individual burst/spindle events of LFP beta oscillations across macaque motor cortical layers.
Example scientific questions that can be addressed once such tools are identified/developed:
Prerequisites: Experience in analysis of electrophysiological data, or in analysis techniques used in other fields that can be translated to the electrophysiological domain.