Ressources disponibles:

• Omega: the open magneto-encephalography archive.
• INDI: the international data sharing initiative.
• CORR: Consortium for Reliability and Reproducibility.
• ABIDE: Autism Brain Imaging Data Exchange.
• ADHD200: Imaging data sharing in Attention Deficit and Hyperactivity Disorder.
• NKI enhanced: community-based, lifespan imaging sample.
• OpenNeuro: various open imaging datasets.
• Neurovault: open brain maps (mostly activation maps).
• Neurosynth: published activation coordinates and meta-analysis.

Outils pour le partage de données:

• NIFTI format for brain images
• MINC format for brain images
• BIDS: Brain Imaging Data Structure
• Figshare: generic platform for data sharing
• zenodo: generic platform for data sharing
• Databases and REST API, e.g. Figshare's API
• The LORIS database.

Développement logiciel:

• Github: version control and social coding.
• Docsify: Building software documentation.
• Nose: Building automated tests for software.
• Circle CI: Continuous integration testing

Partage de code:

• Singularity: containers.
• Docker containers.
• Jupyter notebooks.
• Binder notebooks.

• Compute Canada resources.
• Industrial cloud computing solutions ( AWS, Azure ).
• CPAC: the configurable pipeline for the analysis of connectomes.
• Nipype: Building neuroimaging pipelines in python.
• CBRAIN: neuroimaging science gateway.
• Open Neuro: neuroimaging science gateway.
• BIDS apps: collection of neuroimaging pipelines.
• Boutiques: automated tool validation and deployment

• Plotly: interactive web-based figures.
• Visbrain: brain visualization in python.
• Nilearn: Brain visualization in python.
• Interactive visualization for jupyter notebooks.
• Altair Interactive visualization in python.

• Pandas: data analysis in python.
• Regression, cross validation and generalization error.
• Support vector machine, naive Bayes, random forest.
• Cluster analysis, component analysis.
• Deep neural networks, convolutional networks and gradient descent.
• Scikit-learn: machine learning with python.
• Pytorch: deep learing library.

• MNE: MEG and EEG analysis and visualization in python.
• qMRLab: open-source Matlab software for quantitative MR image analysis.
• nilearn machine learning for neuroimaging, in python.
• fMRI preprocessing and feature extraction.
• M/EEG preprocessing and feature extraction.
• structural MRI preprocessing and feature extraction.