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diff --git a/_images/d9bf8c63459d4bd3ecdad9acd88eba3265c43fba0bc8c58162700d3c56609a63.png b/_images/d9bf8c63459d4bd3ecdad9acd88eba3265c43fba0bc8c58162700d3c56609a63.png
diff --git a/_sources/notebooks/01-intro/02-data-structures.ipynb b/_sources/notebooks/01-intro/02-data-structures.ipynb
@@ -146,11 +146,11 @@
    "source": [
     "## `UxDataset` & `UxDataArray` Data Structures\n",
     "\n",
-    "UXarray inherits from Xarray's two core data structures `Dataset` & `DataArary` to provide a grid-informed implementation through the `UxDataset` & `UxDataArray` data structures. \n",
+    "UXarray inherits from Xarray's two core data structures `Dataset` & `DataArray` to provide a grid-informed implementation through the `UxDataset` & `UxDataArray` data structures. \n",
     "\n",
     "The major difference between them is that UXarray's implementation is paired with a `Grid` object, accessed through the `.uxgrid` property.\n",
     "\n",
-    "UXarray also provides a overloaded `ux.open_dataset` method, which takes in both a Grid and Data file path to construct a `UxDataset`\n"
+    "UXarray also provides an overloaded `ux.open_dataset` method, which takes in both a Grid and Data file path to construct a `UxDataset`\n"
    ]
   },
   {

diff --git a/_sources/notebooks/01-intro/03-data-mapping.ipynb b/_sources/notebooks/01-intro/03-data-mapping.ipynb
@@ -10,28 +10,41 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "When attempting to visualize a data variable that resides on an unstructured grid, it's important to identify what element it is mapped to, since that will dictate what visualization to choose.\n",
     "\n",
-    "This notebook provides a quick over of how data is commonly mapped to unstructured grid elements."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+    "This notebook provides a quick overview of how data is commonly mapped to unstructured grid elements."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "import uxarray as ux"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Sample Mesh\n",
     "\n",
@@ -40,121 +53,144 @@
     "Below is a basic example of an Unstructured Grid, containing 13 Nodes, 15 Edges, and 3 Faces.\n",
     "\n",
     "![Sample Mesh](../images/sample/sample_mesh.png)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "is_executing": true
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Face-Centered Data\n",
     "\n",
     "Face-Centered data is mapped to the area that each face covers. \n",
     "\n",
     "![Faces](../images/sample/faces.png)\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Node-Centered Data\n",
     "\n",
     "Node-Centered data is assigned to the corners of each face.\n",
     "\n",
     "\n",
     "![Faces](../images/sample/nodes.png)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Edge-Centered Data\n",
     "\n",
     "Edge-Centered data is assigned to the edge that connects each pair of modes.\n",
     "\n",
     "![Edges](../images/sample/edges.jpg)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "## Identifying Data Mappings with UXarray\n",
     "\n",
     "We can identify what element a data variable is mapped to by looking at the final dimensions of a `UxDataArray` or `UxDataset`"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "file_dir = \"../../meshfiles/\""
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "grid_filename_mpas = file_dir + \"oQU480.grid.nc\"\n",
     "data_filename_mpas = file_dir + \"oQU480.data.nc\"\n",
     "uxds_mpas = ux.open_dataset(grid_filename_mpas, data_filename_mpas)\n",
     "\n",
     "uxds_mpas[\"bottomDepth\"].dims"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "The variable ``bottomDepth`` has a dimension of ``n_face``, which means that it is mapped to faces."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "grid_filename_geoflow = file_dir + \"geoflow.grid.nc\"\n",
     "data_filename_geoflow = file_dir + \"geoflow.data.nc\"\n",
     "uxds_geoflow = ux.open_dataset(grid_filename_geoflow, data_filename_geoflow)\n",
     "\n",
     "uxds_geoflow[\"v1\"].dims"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "The variable ``v1`` has a final dimension of ``n_node``, which means that it is mapped to the corner nodes of each face. However, it also has additional dimensions, ``time`` and ``meshLayers``. These additional dimensions describe the dimensionality of the data outside the unstructured grid, representing the temporal and vertical dimensions."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   }
  ],
  "metadata": {
@@ -173,7 +209,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.11.6"
   },
   "nbdime-conflicts": {
    "local_diff": [

diff --git a/_static/__pycache__/__init__.cpython-311.pyc b/_static/__pycache__/__init__.cpython-311.pyc
diff --git a/notebooks/01-intro/01-unstructured-grid-overview.html b/notebooks/01-intro/01-unstructured-grid-overview.html
@@ -507,7 +507,7 @@ <h2>Structured Grids<a class="headerlink" href="#structured-grids" title="Permal
   warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
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