diff --git a/Jupyter/Geocube-Client-DataAccess.ipynb b/Jupyter/Geocube-Client-DataAccess.ipynb
index 42cbcd5..c5d380a 100644
--- a/Jupyter/Geocube-Client-DataAccess.ipynb
+++ b/Jupyter/Geocube-Client-DataAccess.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "**Short description**\n",
     "\n",
-    "This notebook introduces you to the Geocube Python Client. You will learn how to list available layers, construct a query over an aoi and a time interval and retrieve cubes of data.\n",
+    "This notebook introduces you to the Geocube Python Client. You will learn how to list available layers, construct a query over an AOI (area of interest) and a time interval and retrieve cubes of data.\n",
     "\n",
     "-------\n",
     "\n",
@@ -59,8 +59,7 @@
     "import os\n",
     "from datetime import datetime\n",
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "%matplotlib inline"
+    "from matplotlib import pyplot as plt"
    ]
   },
   {
@@ -117,7 +116,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2 - Records\n",
+    "## 3 - Records\n",
     "A record defines a data-take by its geometry, its sensing time and user-defined tags that describe the context in more detail.\n",
     "\n",
     "More details in the [Indexation Tutorial # Records](./Geocube-Client-DataIndexation.ipynb#3---Records).\n",
@@ -198,7 +197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3 - Variables\n",
+    "## 4 - Variables\n",
     "A variable describes the kind of data stored in a product, for example _a spectral band, NDVI, RGB, backscatter, classification_...\n",
     "\n",
     "It stores the information needed to **describe**, **process** and **visualize** the product.\n",
@@ -257,7 +256,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4 - Raster data\n",
+    "## 5 - Raster data\n",
     "\n",
     "As we saw in introduction of this notebook, an image is defined by a **record** and an **instance** of a variable. The data is retrieved as 2D array, that is defined by a **rectangle extent in a given CRS** and a **resolution**.\n",
     "If necessary, the datasets that composed an image are reprojected or scaled on the fly.\n",
@@ -427,7 +426,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5 - Get data covering a large AOI with tiling\n",
+    "## 6 - Get data covering a large AOI with tiling\n",
     "\n",
     "If the AOI is too large to be retrieve in one time, it must be tiled.\n",
     "\n",
@@ -481,9 +480,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 6 - Some useful functions\n",
+    "## 7 - Some useful functions\n",
     "\n",
-    "### Get a tile, a dataset or a cube from the aoi of a record\n",
+    "### Get a tile, a dataset or a cube from the AOI of a record\n",
     "The function `Tile.from_record` creates a tile covering the aoi of the record.\n",
     "Then, a dataset can be easily downloaded using `CubeParam.from_tile`.\n",
     "\n",
@@ -576,7 +575,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 7 - Conclusion\n",
+    "## 8 - Conclusion\n",
     "In this notebook, you have learnt to list records, load variable and retrieve cubes of data over a large AOI."
    ]
   }