GithubHelp home page GithubHelp logo

healthbioscienceideas / sdc-bids-intromri Goto Github PK

View Code? Open in Web Editor NEW

This project forked from carpentries-incubator/sdc-bids-intromri

0.0 0.0 0.0 23.67 MB

Introduction to MRI and BIDS

Home Page: https://carpentries-incubator.github.io/SDC-BIDS-IntroMRI

License: Other

Shell 0.21% Ruby 0.62% Python 43.15% R 5.05% Makefile 4.56% Jupyter Notebook 46.41%

sdc-bids-intromri's Introduction

Introduction to Working with MRI Data in Python

Create a Slack Account with us Slack Status Binder

An introduction to working with magnetic resonance imaging (MRI) data in Python.

About the Lesson

This lesson teaches:

  • a (re?) introduction to MR nomenclature - with BIDS
  • how neuroimaging data is stored
  • "converting" your data to BIDS
  • BIDS apps
  • queueing up neuroimaging pipelines

Episodes

# Episode Time Question(s)
1 Neuroimaging Fundamentals 30 What are the common neuroimaging modalities?
2 Anatomy of a NIfTI 30 How is MRI data organized in a NIfTI file?
3 Brain Imaging Data Structure 30 How can I organize my study?
4 Open MRI Datasets 30 How can I download and query an MRI dataset?

Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag good_first_issue. This indicates that the maintainers will welcome a pull request fixing this issue.

Maintainer(s)

Current maintainers of this lesson are

Authors

A list of contributors to the lesson can be found in AUTHORS

License

Instructional material from this lesson is made available under the Creative Commons Attribution (CC BY 4.0) license. Except where otherwise noted, example programs and software included as part of this lesson are made available under the MIT license. For more information, see LICENSE.

Citation

To cite this lesson, please consult with CITATION

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.