Introduction to version control using git and github.
We cover common scenarios where git can be useful. We start with cloning a repository and keeping it up to date. We then show how to create and use your own repository. Finally we will show how to contribute to other projects via forking and pull requests.
Introduction to version control using git and github.
We cover common scenarios where git can be useful. We start with cloning a repository and keeping it up to date. We then show how to create and use your own repository. Finally we will show how to contribute to other projects via forking and pull requests.
We will take a spaghetti script written in MATLAB and turn it into an understandable and reusable code living happily in a powerful GitHub repository.
Presentation on coding tips, example of fixing a bad code, practical part, where people try to write their own functions and create documentation on github.
The session is tailored to a person, who starts their first project using matlab. First, it will include a brief introduction to good coding practices highlighting that matlab can also be used to share code. Then I’ll give some general matlab coding tips with some examples and will go line-by-line through some old 'inherited' code demonstrating what’s wrong with it and showing an example of how it can be re-written in a more usable way. After, I’ll outline how to share the code on github, what should be included in the documentation, so the code can be easily used by others. At the end, there will be a task to create a couple of functions (to plot provided data), make a github repository and give documentation on github.
Organize your data according to the Brain Imaging Data Structure (BIDS) — your future self will thank you!
The Brain Imaging Data Structure (BIDS) is seeing widespread adoption as a standard for data organization, data sharing, and automated processing. This TrainTrack session provides an introduction to the organizational principles of BIDS with examples from the fMRI literature. We’ll touch on the expanding BIDS ecosystem (BIDS derivatives and extensions), as well as tools for facilitating BIDS conversion and data sharing. Finally, we’ll discuss automated processing workflows that capitalize on BIDS—i.e. BIDS Apps—for fMRI quality control (MRIQC) and preprocessing (fMRIPrep).
A beautiful visualization is not just a good-looking colored brain. It is an informative and efficient way to represent our data and results.
A beautiful visualization is not just a good-looking piece of graph, chart, or colored brain. It is an informative and efficient way to represent our data and results, which follows the scientific principles (e.g. honesty truth, consistency etc.). A compelling visualization is essential to organize the data, display the results, communicate the information, as well as change the way we perceive the world. The Brain Art special interest group will walk you through the basics, share the skills of brain visualization, and make the brain visualization novel, creative, aesthetic and fun to the community.
We are covering what containers are and how they can be built and used in different scenarios.
We cover how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers. We will show how to build docker containers from scratch and using Neurodocker. Finally we will cover how to use containers on an HPC with singularity.
Mix of presentation and hands-on to get acquainted with the main machine learning concepts and apply them to neuroimaging datasets.
This traintrack session aims at providing an overview of the main machine learning concepts and apply them “live” in a dedicated Jupyter notebook. The main prerequisite is familiarity with Python and git, but no previous machine learning knowledge is required (this is an introduction to machine learning!). In addition to a toy dataset we will also explore real neuroimaging datasets.
Version control applied to your data... and much more.
Datalad is a versatile data management and data publication multitool. In this session, you can learn the basic concepts and commands for version control and reproducible data analysis. You’ll get to see, create, and install DataLad datasets of many shapes and sizes, master local version workflows and provenance-captured analysis-execution, and you will get ideas for your next data analysis project. Don’t forget to bring your computer and code along!
Learn the basics to programming for neuroimaging data analyses.
Chao-Gan Yan
An introduction and demo of the Canadian Open Neuroscience Platform.
The Canadian Open Neuroscience Platform is a decentralized web platform for the open sharing of data and pipelines. We will present the main features of the Canadian Open Neuroscience Platform, including: (1) finding and downloading a dataset, (2) finding and running a pipeline, (3) uploading a dataset, and (4) uploading a tool. The demo will be recorded and could also serve as a tutorial. Only a web browser will be required to follow this tutorial, although advanced features would require Docker and DataLad.
Collaborations to eliminate open science silos
INCF is a standards organization for open and FAIR neuroscience. INCF supports open science working groups and groups that provide training on open science practices. Since 2013, INCF has supported OHBM’s open science efforts; yet there has been little crossover between OHBM’s open science community and INCF. The purpose of this session is to present INCF’s open science efforts and discuss how the OHBM community can support the open science efforts of INCF, what resources/services INCF should offer in order to maximize the efforts of the community, and why an open science practitioner or advocate would choose or not choose to become a member and support such an organization such as INCF.