{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "
\n", " \n", "
\n", "
\n", "

Doing reproducible research

\n", " \n", "

Welcome to the Reproducible Imaging Tutorial!\n", " It covers various concepts enabling reproducible research. We recommend that you start with\n", " the introductory section to familiarize yourself with core\n", " tools and then move on to more advanced concepts. The notebooks will use a combination\n", " of Python and Shell commands to introduce you to various tools.\n", "

\n", " All of the notebooks used in this tutorial can be found on https://github.com/satra/reproducible-imaging.\n", "

\n", " For the tutorial, we will use a Docker container. You need to install a Docker and download a docker image that provides you an \n", " environment.\n", "

\n", "
\n", "\n", " \n", "\n", " \n", " \n", "

Introductory

\n", "

This section is meant as an introduction to core elements of reproducible research.

\n", "
\n", " Versioned data\n", " Reusable environments\n", " Re-usable dataflows\n", " \n", "
\n", "\n", "

Intermediate and advanced notebooks forthcoming

\n", "

If you would like to help, join the discussion here .\n", " \n", "\n", "

External Resources

\n", "

This section will give you helpful links and resources, so that you always know where to go to learn more.

\n", "
\n", " Repronim\n", " Simple workflow\n", " Datalad\n", " Neurodocker\n", " Nipype\n", " BIDS\n", " OpenNeuro\n", "
\n", "\n", "
\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "To inspect the html code of this page, click:
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\n", " \n", "
\n", "
\n", "

Doing reproducible research

\n", " \n", "

Welcome to the Reproducible Imaging Tutorial!\n", " It covers various concepts enabling reproducible research. We recommend that you start with\n", " the introductory section to familiarize yourself with core\n", " tools and then move on to more advanced concepts. The notebooks will use a combination\n", " of Python and Shell commands to introduce you to various tools.\n", "

\n", " All of the notebooks used in this tutorial can be found on https://github.com/satra/reproducible-imaging.\n", "

\n", " For the tutorial, we will use a Docker container. You need to install a Docker and download a docker image that provides you an \n", " environment.\n", "

\n", "
\n", "\n", " \n", "\n", " \n", " \n", "

Introductory

\n", "

This section is meant as an introduction to core elements of reproducible research.

\n", "
\n", " Versioned data\n", " Reusable environments\n", " Re-usable dataflows\n", " \n", "
\n", "\n", "

Intermediate and advanced notebooks forthcoming

\n", "

If you would like to help, join the discussion here .\n", " \n", "\n", "

External Resources

\n", "

This section will give you helpful links and resources, so that you always know where to go to learn more.

\n", "
\n", " Repronim\n", " Simple workflow\n", " Datalad\n", " Neurodocker\n", " Nipype\n", " BIDS\n", " OpenNeuro\n", "
\n", "\n", "
\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "To inspect the html code of this page, click:
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