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+Applications
+============
+
+This directory contains a series of practicals which focus
+on using Python to accomplish specific tasks, and practicals which introduce
+a range of useful Python-based libraries.
+
+Practicals are split into the following sub-categories (and sub-folders):
+
+1. `data_visualisation` : learn to use various data visualisation packages.
+2. `fslpy`: a collection of utilities and data abstractions used within FSL and by FSLeyes.
+3. `matlab_vs_python` : a number of data analysis examples with head-to-head comparisons between matlab and python code.
+4. `modelling` : multiple examples of analysis methods in python.
+5. `pandas` : processing and analsing tabular data with the powerful Pandas package.
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+DATA VISUALISATION
+==================
+
+This directory contains a series of practicals which focus
+on using Python for data visualisation.
+
+1. `bokeh` - package for creating interactive visualisations. This folder contains two files: a notebook that collates multiple examples from the `bokeh` documentation into a single notebook, and an example Python script that uses `bokeh` to create a simple GUI to run FSL BET.
+2. `fsleyes_render.md` - learn to use FSLeyes in the command line to create a range of static and dynamic figures. This is not focused on Python per se.
+3. `matplotlib.ipynb` - the most popular generic data visualisation package in Python.
+4. `plotly.ipynb` - an alternative to `bokeh` for creating interactive plots.
+5. `nilearn.ipynb` - package for doing machine learning in neuroimaging in Python. This tutorial focuses on using the visualisation tools in `nilearn`.
diff --git a/applications/data_visualisation/nilearn/nilearn.ipynb b/applications/data_visualisation/nilearn.ipynb
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