< and what [a running instance of a container is](http://paislee.io/how-to-automate-docker-deployments/)
2. [Pull and remove a Docker image](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#29)
3. [Use a docker container](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#29)
4. [Mount a host volume into the container](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#30)
5. [Map a host port into the container](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#34)[ and use a Jupyter Lab](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#34)
6. [Modify and save a running container](http://docs.oracle.com/cd/E52668_01/E75728/html/section_c5q_n2z_fp.html)
7. [Build a container](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#31)
8. [Remove an instance of a container](http://nipy.org/workshops/2017-03-boston/lectures/lesson-container/#30)
9. [Create an image from a stopped instance of a container](http://docs.oracle.com/cd/E52668_01/E75728/html/section_c5q_n2z_fp.html)
**Git**
1. [Configure git](http://swcarpentry.github.io/git-novice/02-setup/)
2. [Fork a repository on GitHub](https://help.github.com/articles/fork-a-repo/)
3. [Clone a repository locally](https://help.github.com/articles/cloning-a-repository/)
4. [Set a pointer to upstream repository](https://help.github.com/articles/configuring-a-remote-for-a-fork/)
5. [Make modifications to my repository and save the modifications as a commit](https://git-scm.com/book/en/v2/Git-Basics-Recording-Changes-to-the-Repository)
6. [Merge changes others have made to the repository](https://help.github.com/articles/merging-an-upstream-repository-into-your-fork/)
7. [Resolve conflicts between my changes and other changes](https://help.github.com/articles/resolving-a-merge-conflict-using-the-command-line/)
8. [Understand that git uses references to manage content](https://www.atlassian.com/git/tutorials/refs-and-the-reflog)
9. [Understand the different components of the .git folder](https://medium.freecodecamp.com/understanding-git-for-real-by-exploring-the-git-directory-1e079c15b807)
**Git-annex/DataLad**
1. [Use git annex to retrieve data from a git annex repo](https://git-annex.branchable.com/git-annex-get/)
2. [Add additional remotes to git annex repo which provide data files](https://git-annex.branchable.com/walkthrough/adding_a_remote/) [[version-control/#26](http://nipy.org/workshops/2017-03-boston/lectures/version-control/#26)]
3. [Use datalad to fetch individual data elements](http://docs.datalad.org/en/latest/generated/man/datalad-get.html)
4. [Use datalad to fetch metadata of all datasets at http://datasets.datalad.org ](http://docs.datalad.org/en/latest/metadata.html#sample-datasets-with-meta-data)
5. [Use datalad to fetch individual datasets](http://docs.datalad.org/en/latest/generated/man/datalad-install.html)
6. [Understand differences between git and git-annex](http://nipy.org/workshops/2017-03-boston/lectures/version-control/#32)
7. [Understand differences between git-annex and datalad](http://nipy.org/workshops/2017-03-boston/lectures/version-control/#36)
**Numpy/Scipy/Matplotlib/Seaborn**
1. Access values in n-dimensional numpy arrays (ndarrays) by [slicing and basic indexing](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#Indexing-in-N-dimensions)
2. Apply vectorized numpy functions like sum() or mean() to ndarrays
3. Multiply one 2D numpy array by another (i.e., matrix multiplication)
4. [Read data from a CSV](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#Wrangling-data-in-pandas) or similar file into a pandas DataFrame with read_csv
5. [Access columns and rows](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#Indexing-pandas-DataFrames) in a DataFrame
6. [Use ](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#The-split-apply-combine-pattern)[groupby](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#The-split-apply-combine-pattern)[ to apply the same operation](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#The-split-apply-combine-pattern) (e.g., to each subset of a DataFrame
7. [Make basic plots](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#%22Simple%22-examples) (e.g., line or scatter plots) with matplotlib
8. [Use seaborn to create high-level plots](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/scientific-python/scientific_python.ipynb#Plotting-in-seaborn) by passing in pandas DataFrames
**Using Python for neuroimaging**
1. Nibabel
1. [Load and inspect images](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#Loading-and-inspecting-images-in-nibabel), such as .nii or .mgz files, [in nibabel](http://nipy.org/nibabel/nibabel_images.html)
2. [View summary information with nib-ls](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#nib-ls) and in the Python shell
3. [Save modified images and verify their contents](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#Creating-and-saving-images)
4. Load non-image formats, such as FreeSurfer surfaces or annotation files
2. Nilearn
5. [Plot anatomical images, overlays and statistical maps](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#Plotting-images)
6. [Plot surfaces](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#Plotting-surfaces)
7. [Mask/unmask functional datasets](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#Masking-and-Un-masking-data)
8. [Apply scikit-learn algorithms to functional data](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nibabel_nilearn/nibabel_nilearn.ipynb#Simple-MVPA-Example)
**Reprozip**
1. [What kind of operating systems can reprozip be used on?](http://nipy.org/workshops/2017-03-boston/lectures/repeatable-research/index.html#9)
2. [What kind of environments can reprounzip be used on?](https://reprozip.readthedocs.io/en/1.0.x/unpacking.html#unpackers)
3. [Install reprozip and reprounzip](https://reprozip.readthedocs.io/en/1.0.x/install.html)
4. [Use reprozip to capture the trace of a command](http://nipy.org/workshops/2017-03-boston/lectures/repeatable-research/index.html#18)
5. [Use reprozip to pack the files required to replay the command and share with a colleague](http://nipy.org/workshops/2017-03-boston/lectures/repeatable-research/index.html#26)
6. [Provide a colleague with instructions to replay the command from the pack file.](http://nipy.org/workshops/2017-03-boston/lectures/repeatable-research/index.html#28)
7. [Use reprounzip to replay the command in a virtual machine](https://reprozip.readthedocs.io/en/1.0.x/unpacking.html#the-vagrant-unpacker-building-a-virtual-machine)
**Nipype intro (Interfaces, Workflows)**
1. Understand Nipype’s design elements: [Interfaces, Workflows and Plugins](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nipype_101_tasks/Day4.ipynb)
2. Use the [nipype.interfaces.fsl.BET](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/new-workflows-and-interfaces/advanced_interfaces.ipynb#Example-of-interface:-FSL%27s-BET) interface directly
3. Understand the three main components of a Nipype interface: [inputs, outputs and core.](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/new-workflows-and-interfaces/advanced_interfaces.ipynb#But-wait!-My-tool-is-not-(yet)-supported-by-Nipype)
4. [Get help when using a Nipype interface](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/new-workflows-and-interfaces/advanced_interfaces.ipynb#Specifying-the-outputs)
5. Able to write a [simple Nipype workflow importing interfaces from nipype](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/new-workflows-and-interfaces/advanced_workflows.ipynb#The-smoothing-workflow)
6. Able to [find data in a data folder using the DataGrabber](https://miykael.github.io/nipype_tutorial/notebooks/basic_data_input.html)
7. Able to find data [using bids on a BIDS dataset](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/basic-bids/basic_data_input_bids.ipynb)
8. Able to use [iterables](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nipype_101_tasks/Day4.ipynb#Task-2:-Run-both-subjects%27-functional-through-MCFLIRT)
9. Able to use [Mapnodes](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nipype_101_tasks/Day4.ipynb#Task-1:-Run-both-T1s-through-a-single-BET-interface)
10. [Able to use a Joinnode](https://gist.github.com/satra/ed3760e9f849d9ca82a9)
11. Able to use the [Function interface](http://nbviewer.jupyter.org/github/nipy/workshops/blob/master/170327-nipype/notebooks/nipype_101_tasks/Day4.ipynb#Task-4:-Use-a-function-node-to-get-T1w-+-EPIs-using-bids)
12. Able to [save data using a DataSink](https://miykael.github.io/nipype_tutorial/notebooks/basic_data_output.html)
13. Able to [use a Plugin](https://miykael.github.io/nipype_tutorial/notebooks/basic_plugins.html)
**Mindboggle**
1. Explain what is the [purpose of the Mindboggle software](http://mindboggle.readthedocs.io/en/latest/faq/why_mindboggle.html).
2. Explain what data Mindboggle takes as its [inputs](http://mindboggle.readthedocs.io/en/latest/#before-running-mindboggle).
3. Explain what kind of data Mindboggle generates as its [output](http://mindboggle.readthedocs.io/en/latest/#mindboggle-output).
4. [Install Mindboggle](http://mindboggle.readthedocs.io/en/latest/#installing-mindboggle).
5. Run the [mindboggle command](http://mindboggle.readthedocs.io/en/latest/#running-mindboggle) from the command line.
6. Run the roygbiv command from the command line to visualize mindboggle output on the browser.
7. Use a function directly from Mindboggle’s Python library in a Python console or Jupyter notebook.
**CPAC**
1. Understand CPAC’s purpose and why you might want to use it
2. Start the CPAC gui
3. Using the GUI configure and test a pipeline
4. Run a CPAC from command line to generate a data configuration file
5. Execute CPAC pipeline from command line
6. View a CPAC crash file to debug errors
**Heudiconv/BIDS**
1. Run heudiconv [docker image](http://nipy.org/workshops/2017-03-boston/lectures/bids-heudiconv/#14) on own dicoms
2. Look at generated [dicominfo and understand each column](https://github.com/nipy/workshops/blob/gh-pages/2017-03-boston/lectures/bids-heudiconv/visualizer.ipynb)
3. Create [heuristic keys](http://nipy.org/workshops/2017-03-boston/lectures/bids-heudiconv/#18) for target scans
4. Use dicominfo to set [unique criteria per scan](http://nipy.org/workshops/2017-03-boston/lectures/bids-heudiconv/#22)
5. Verify BIDS structure with [validator](http://incf.github.io/bids-validator/)
]]>