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diff --git a/doc/notebooks/XarrayforArviZ.ipynb b/doc/notebooks/XarrayforArviZ.ipynb
index 69b677a93a..ee77ee48b7 100644
--- a/doc/notebooks/XarrayforArviZ.ipynb
+++ b/doc/notebooks/XarrayforArviZ.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Introduction to xarray, InferenceData, and netCDF for ArviZ"
+    "# Introduction to xarray, InferenceData, and NetCDF for ArviZ"
    ]
   },
   {
@@ -42,9 +42,12 @@
     "\n",
     "\n",
     "## Why not Pandas Dataframes or Numpy Arrays?\n",
-    "Data from probabilistic programming is naturally high dimensional. To add to the complexity ArviZ must handle the data generated from multiple Bayesian Modeling libraries, such as pymc3 and pystan. This is an application that the *xarray* package handles quite well. The xarray package lets users manage high dimensional data with human readable dimensions and coordinates quite easily.\n",
+    "Data from probabilistic programming is naturally high dimensional. To add to the complexity ArviZ must handle the data generated from multiple Bayesian Modeling libraries, such as PyMC3 and PyStan. This is an application that the *xarray* package handles quite well. The xarray package lets users manage high dimensional data with human readable dimensions and coordinates quite easily.\n",
     "\n",
-    "Although seemingly more complex at a glance the Arviz devs believe that the usage of *xarray*, *InferenceData*, and *NetCDF* will simplify the handling, referencing, and serialization of data generated by MCMC runs."
+    "![InferenceData Structure](InferenceDataStructure.png)  \n",
+    "\n",
+    "Above is a visual representation of the data structures and their relationships. Although seemingly more complex at a glance the ArviZ devs believe that the usage of *xarray*, *InferenceData*, and *NetCDF* will simplify the handling, referencing, and serialization of data generated during Bayesian analysis.  \n",
+    "\n"
    ]
   },
   {
@@ -171,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It should be noted that the observed dataset contains only 8 data variables and doesn't have a chain and draw dimension or coordinates unlike posterior. This difference in sizes is the motivating reason behind *InferenceData*. Rather than force multiple different sized arrays into one array, or force users to manage multiple objects corresponding to different datasets, it is easier to hold references to each *xarray.Dataset* in an *InferenceData* object."
+    "It should be noted that the observed dataset contains only 8 data variables and doesn't have a chain and draw dimension or coordinates unlike posterior. This difference in sizes is the motivating reason behind *InferenceData*. Rather than force multiple different sized arrays into one array, or have users to manage multiple objects corresponding to different datasets, it is easier to hold references to each *xarray.Dataset* in an *InferenceData* object."
    ]
   },
   {
@@ -179,7 +182,7 @@
    "metadata": {},
    "source": [
     "## NetCDF\n",
-    "[NetCDF](https://www.unidata.ucar.edu/software/netcdf/) is a standard for referencing array oriented files. In other words while, *xarray.Dataset*s, and by extension *InferenceData*, are convenient for accessing arrays in Python memory, *NetCDF* provides a convenient mechanism for persistence of model data on disk. In fact the NetCDF dataset was the inspiration between *InferenceData* as NetCDF4 supports the concepts of groups. *InferenceData* merely wraps xarray.Dataset with the same functionality,\n",
+    "[NetCDF](https://www.unidata.ucar.edu/software/netcdf/) is a standard for referencing array oriented files. In other words while, *xarray.Dataset*s, and by extension *InferenceData*, are convenient for accessing arrays in Python memory, *NetCDF* provides a convenient mechanism for persistence of model data on disk. In fact the NetCDF dataset was the inspiration for *InferenceData* as NetCDF4 supports the concept of groups. *InferenceData* merely wraps xarray.Dataset with the same functionality,\n",
     "\n",
     "Most users will not have to concern themselves with the *NetCDF* standard but for completeness it is good to make its usage transparent. It is also worth noting that the NetCDF4 file standard is interoperable with HDF5 which may be familiar from other contexts.\n",
     "\n",
@@ -256,7 +259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.5.6"
   }
  },
  "nbformat": 4,