diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb
index 8fc5d018..6b98ee39 100644
--- a/fundamentals/02.1_indexing_Basic.ipynb
+++ b/fundamentals/02.1_indexing_Basic.ipynb
@@ -53,7 +53,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import xarray as xr\n",
@@ -71,7 +73,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
@@ -81,7 +85,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da = ds[\"air\"]"
@@ -111,7 +117,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "np_array = ds[\"air\"].data  # numpy array\n",
@@ -128,7 +136,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "np_array[1, 0, 0]"
@@ -144,7 +154,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# extract a time-series for one spatial location\n",
@@ -154,7 +166,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -185,7 +196,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da[:, 20, 40]"
@@ -194,41 +207,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "```{caution}\n",
-    "Positional indexing deviates from the NumPy behavior when indexing with multiple arrays. \n",
-    "```\n",
-    "We can show this with an example: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np_array[:, [0, 1], [0, 1]].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "da[:, [0, 1], [0, 1]].shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Please note how the dimension of the `DataArray()` object is different from the `numpy.ndarray`.\n",
-    "\n",
-    "```{tip}\n",
-    "However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n",
-    "```"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -253,7 +232,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.isel(lat=20, lon=40).plot();"
@@ -269,7 +250,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.isel(time=slice(0, 20), lat=20, lon=40).plot();"
@@ -286,95 +269,113 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "But what if I wanted to select data only for 2014, how would I know the indices for it? Xarray reduce this complexity by introducing label-based indexing. \n",
+    "Indexing a DataArray directly works (mostly) just like it does for numpy arrays, except that the returned object is always another DataArray; however,when indexing with multiple arrays, positional indexing in Xarray behaves differently compared to NumPy.\n",
     "\n",
-    "## Label-based Indexing\n",
-    "\n",
-    "To select data by coordinate labels instead of integer indices we can use the same syntax, using `sel` instead of `isel`:"
+    "```{caution}\n",
+    "Positional indexing deviates from the NumPy behavior when indexing with multiple arrays. \n",
+    "```\n",
+    "We can show this with an example: "
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
-    "For example, let's select the data for one day 2014-01-01 at Lat 25 N and Lon 210 E using `sel` :"
+    "np_array[:, [0, 1], [0, 1]].shape"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "tags": [
-     "hide-output"
-    ]
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();"
+    "da[:, [0, 1], [0, 1]].shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "Now, let's select data for year 2014 at this coordinate:\n"
+    "Please note how the dimension of the `DataArray()` object is different from the `numpy.ndarray`.\n",
+    "\n",
+    "```{tip}\n",
+    "However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n",
+    "```"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "da.sel(lat=50.0, lon=200.0, time=\"2014\")"
+    "So far, we have explored positional indexing, which relies on knowing the exact indices. But, what if you wanted to select data specifically for a particular latitude? It becomes challenging to determine the corresponding indices in such cases. Xarray reduce this complexity by introducing label-based indexing. \n",
+    "\n",
+    "## Label-based Indexing\n",
+    "\n",
+    "To select data by coordinate labels instead of integer indices we can use the same syntax, using `sel` instead of `isel`:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similarly we can do slicing or filter a date range using the `.slice` function: "
+    "For example, let's select all data for Lat 25 °N and Lon 210 °E using `sel` :"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [],
    "source": [
-    "# demonstrate slicing\n",
-    "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))"
+    "da.sel(lat=25, lon=210).plot();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Xarray also supports label-based indexing, just like pandas using `.loc`. To do label based indexing, use the `loc` attribute:"
+    "Similarly we can do slicing or filter a range using the `.slice` function: "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "da.loc[\"2014-02-14\":\"2014-12-13\"]"
+    "# demonstrate slicing\n",
+    "da.sel(lon=slice(210, 215))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "da.time"
+    "# demonstrate slicing\n",
+    "da.sel(lat=slice(50, 25), lon=slice(210, 215))"
    ]
   },
   {
@@ -389,10 +390,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")"
+    "da.drop_sel(lat=50.0, lon=200.0)"
    ]
   },
   {
@@ -403,17 +406,14 @@
     "\n",
     "## Nearest Neighbor Lookups\n",
     "\n",
-    "The label based selection methods `sel()` support `method` and `tolerance` keyword argument. The `method` parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `pad`, `backfill` or `nearest`:\n",
-    "\n"
+    "The label based selection methods `sel()` support `method` and `tolerance` keyword argument. The `method` parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `pad`, `backfill` or `nearest`:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "tags": [
-     "hide-output"
-    ]
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -422,7 +422,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "`tolerance` argument limits the maximum distance for valid matches with an inexact lookup:"
    ]
@@ -430,7 +432,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)"
@@ -450,75 +454,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercises\n",
-    "\n",
-    "Practice the syntax you’ve learned so far:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{exercise}\n",
-    ":label: indexing-1\n",
-    "\n",
-    "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n",
-    "```\n",
-    "\n",
-    "````{solution} indexing-1\n",
-    ":class: dropdown\n",
-    "```python\n",
-    "ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n",
-    "```\n",
-    "\n",
-    "````"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{exercise}\n",
-    ":label: indexing-2\n",
-    "\n",
-    "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n",
-    "```\n",
-    "````{solution} indexing-2\n",
-    ":class: dropdown\n",
-    "```python\n",
-    "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n",
-    "```\n",
-    "````"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{exercise}\n",
-    ":label: indexing-3\n",
-    "\n",
-    "Remove all entries at 260 and 270 degrees :\n",
-    "\n",
-    "```\n",
-    "````{solution} indexing-3\n",
-    ":class: dropdown\n",
-    "```python\n",
-    "ds.drop_sel(lon=[260, 270])\n",
-    "```\n",
-    "````"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -538,7 +481,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds.sel(time='2013-01-01 06:00')"
@@ -554,7 +499,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds.sel(time='2013-01-01')"
@@ -562,9 +509,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "We can use this feature to select all points in a month"
+    "We can use this feature to select all points in a year:"
    ]
   },
   {
@@ -575,14 +524,14 @@
    },
    "outputs": [],
    "source": [
-    "ds.sel(time=\"2014-May\")"
+    "ds.sel(time=\"2014\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "or a year"
+    "or a month:"
    ]
   },
   {
@@ -593,12 +542,14 @@
    },
    "outputs": [],
    "source": [
-    "ds.sel(time=\"2014\")"
+    "ds.sel(time=\"2014-May\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### Selecting data for a range of dates\n",
     "\n",
@@ -608,7 +559,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# This will return a subset of the dataset corresponding to the entire year of 2013.\n",
@@ -617,7 +570,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "```{note}\n",
     "\n",
@@ -638,7 +593,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "dates = ['2013-07-09', '2013-10-11', '2013-12-24']\n",
@@ -657,7 +614,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds.sel(time=ds.time.dt.month == 7)"
@@ -665,7 +624,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "Or, if you wanted to select data from a specific day of each month, you could use:"
    ]
@@ -673,12 +634,85 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds.sel(time=ds.time.dt.day == 15)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Practice the syntax you’ve learned so far:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{exercise}\n",
+    ":label: indexing-1\n",
+    "\n",
+    "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n",
+    "```\n",
+    "\n",
+    "````{solution} indexing-1\n",
+    ":class: dropdown\n",
+    "```python\n",
+    "ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n",
+    "```\n",
+    "\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{exercise}\n",
+    ":label: indexing-2\n",
+    "\n",
+    "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n",
+    "```\n",
+    "````{solution} indexing-2\n",
+    ":class: dropdown\n",
+    "```python\n",
+    "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n",
+    "```\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{exercise}\n",
+    ":label: indexing-3\n",
+    "\n",
+    "Remove all entries at 260 and 270 degrees :\n",
+    "\n",
+    "```\n",
+    "````{solution} indexing-3\n",
+    ":class: dropdown\n",
+    "```python\n",
+    "ds.drop_sel(lon=[260, 270])\n",
+    "```\n",
+    "````"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -703,11 +737,6 @@
     "\n",
     "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb
index 0f57cae1..804af21c 100644
--- a/intermediate/indexing/advanced-indexing.ipynb
+++ b/intermediate/indexing/advanced-indexing.ipynb
@@ -24,7 +24,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -46,7 +48,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
@@ -72,7 +76,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.isel(time=0, lat=[2, 4, 10, 13], lon=[1, 6, 7]).plot();  # -- orthogonal indexing"
@@ -90,7 +96,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n",
@@ -108,7 +116,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "\n",
     "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n",
@@ -130,7 +140,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Define target latitude and longitude (where weather stations might be)\n",
@@ -142,7 +154,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "lon_points"
@@ -158,7 +172,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")"
@@ -174,7 +190,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims"
@@ -194,7 +212,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")"
@@ -219,7 +239,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "da = xr.DataArray(\n",
diff --git a/workshops/scipy2023/index.ipynb b/workshops/scipy2023/index.ipynb
index c984c0ee..bd6bed23 100644
--- a/workshops/scipy2023/index.ipynb
+++ b/workshops/scipy2023/index.ipynb
@@ -26,7 +26,7 @@
     "| Topic | Time | Notebook Links | \n",
     "| :- | - | - | \n",
     "| Introduction and Setup | 1:30 (20 min) | --- | \n",
-    "| Indexing | 1:50 (30 min) | [Advanced and Vectorized Indexing](../../intermediate/indexing/advanced-indexing.ipynb) | \n",
+    "| Indexing | 1:50 (30 min) | [Quick Introduction to Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) <br> [Advanced and Vectorized Indexing](../../intermediate/indexing/advanced-indexing.ipynb) <br> [Boolean Indexing & Masking](../../intermediate/indexing/boolean-masking-indexing.ipynb) | \n",
     "| *10 minute Break*  \n",
     "| Computational Patterns | 2:30 (50 min) | [Computation Patterns](../../intermediate/01-high-level-computation-patterns.ipynb) |  \n",
     "| *10 minute Break*  | \n",