This review page covers concepts and topics discussed during the workshop. Each topic also provides links to workshop materials or external resources to find information on how to carry out the task. For self-assessment: On a scale of 1-5, with 5 being I’m confident I can use this tool to perform this action, where would you rate yourself. **Docker** 1. [Understand what a container is]( and what [a running instance of a container is]( 2. [Pull and remove a Docker image]( 3. [Use a docker container]( 4. [Mount a host volume into the container]( 5. [Map a host port into the container]([ and use a Jupyter Lab]( 6. [Modify and save a running container]( 7. [Build a container]( 8. [Remove an instance of a container]( 9. [Create an image from a stopped instance of a container]( **Git** 1. [Configure git]( 2. [Fork a repository on GitHub]( 3. [Clone a repository locally]( 4. [Set a pointer to upstream repository]( 5. [Make modifications to my repository and save the modifications as a commit]( 6. [Merge changes others have made to the repository]( 7. [Resolve conflicts between my changes and other changes]( 8. [Understand that git uses references to manage content]( 9. [Understand the different components of the .git folder]( **Git-annex/DataLad** 1. [Use git annex to retrieve data from a git annex repo]( 2. [Add additional remotes to git annex repo which provide data files]( [[version-control/#26](] 3. [Use datalad to fetch individual data elements]( 4. [Use datalad to fetch metadata of all datasets at ]( 5. [Use datalad to fetch individual datasets]( 6. [Understand differences between git and git-annex]( 7. [Understand differences between git-annex and datalad]( **Numpy/Scipy/Matplotlib/Seaborn** 1. Access values in n-dimensional numpy arrays (ndarrays) by [slicing and basic indexing]( 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]( or similar file into a pandas DataFrame with read_csv 5. [Access columns and rows]( in a DataFrame 6. [Use ]([groupby]([ to apply the same operation]( (e.g., to each subset of a DataFrame 7. [Make basic plots]( (e.g., line or scatter plots) with matplotlib 8. [Use seaborn to create high-level plots]( by passing in pandas DataFrames **Using Python for neuroimaging** 1. Nibabel 1. [Load and inspect images](, such as .nii or .mgz files, [in nibabel]( 2. [View summary information with nib-ls]( and in the Python shell 3. [Save modified images and verify their contents]( 4. Load non-image formats, such as FreeSurfer surfaces or annotation files 2. Nilearn 5. [Plot anatomical images, overlays and statistical maps]( 6. [Plot surfaces]( 7. [Mask/unmask functional datasets]( 8. [Apply scikit-learn algorithms to functional data]( **Reprozip** 1. [What kind of operating systems can reprozip be used on?]( 2. [What kind of environments can reprounzip be used on?]( 3. [Install reprozip and reprounzip]( 4. [Use reprozip to capture the trace of a command]( 5. [Use reprozip to pack the files required to replay the command and share with a colleague]( 6. [Provide a colleague with instructions to replay the command from the pack file.]( 7. [Use reprounzip to replay the command in a virtual machine]( **Nipype intro (Interfaces, Workflows)** 1. Understand Nipype’s design elements: [Interfaces, Workflows and Plugins]( 2. Use the [nipype.interfaces.fsl.BET]( interface directly 3. Understand the three main components of a Nipype interface: [inputs, outputs and core.](!-My-tool-is-not-(yet)-supported-by-Nipype) 4. [Get help when using a Nipype interface]( 5. Able to write a [simple Nipype workflow importing interfaces from nipype]( 6. Able to [find data in a data folder using the DataGrabber]( 7. Able to find data [using bids on a BIDS dataset]( 8. Able to use [iterables]( 9. Able to use [Mapnodes]( 10. [Able to use a Joinnode]( 11. Able to use the [Function interface]( 12. Able to [save data using a DataSink]( 13. Able to [use a Plugin]( **Mindboggle** 1. Explain what is the [purpose of the Mindboggle software]( 2. Explain what data Mindboggle takes as its [inputs]( 3. Explain what kind of data Mindboggle generates as its [output]( 4. [Install Mindboggle]( 5. Run the [mindboggle command]( 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]( on own dicoms 2. Look at generated [dicominfo and understand each column]( 3. Create [heuristic keys]( for target scans 4. Use dicominfo to set [unique criteria per scan]( 5. Verify BIDS structure with [validator](