diff --git a/open_science/exercise_0.ipynb b/open_science/exercise_0.ipynb
index 5ba23b4..8982d96 100644
--- a/open_science/exercise_0.ipynb
+++ b/open_science/exercise_0.ipynb
@@ -1,157 +1,159 @@
 {
  "cells": [
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "scrolled": false
    },
    "outputs": [],
    "source": [
     "## General\n",
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.animation as animation\n",
     "import matplotlib as mpl\n",
     "from matplotlib import colors\n",
     "import xarray as xr\n",
     "from scipy.io import netcdf \n",
     "import numpy as np\n",
     "#from ipywidgets import widgets\n",
     "from IPython.display import display, HTML\n",
     "import collections\n",
     "import operator\n",
     "import datetime\n",
     "from datetime import datetime\n",
     "from datetime import timedelta\n",
     "from datetime import date\n",
     "\n",
     "\n",
     "## Local\n",
     "from utils.data_access_api import DataAccessApi\n",
     "#import dc_au_colormaps\n",
     "import utils.dc_mosaic\n",
     "import datacube\n",
     "dc = datacube.Datacube()\n",
     "api = datacube.api.API(datacube=dc)\n",
     "from utils.dc_notebook_utilities import *\n",
     "import utils.dc_utilities as utilities\n",
     "from utils.dc_utilities import perform_timeseries_analysis\n",
     "from utils.dc_chunker import create_geographic_chunks, combine_geographic_chunks\n",
     "\n",
     "# To keep screen as clean as possble\n",
     "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "## Useful function\n",
     "def tupple_to_cond(cats):\n",
     "    for m in range(1,len(cats)+1):\n",
     "        if m == 1: cond_cats = '(dataset_in.cf_mask == %i)' % (cats[m-1])\n",
     "        else: cond_cats = '%s | (dataset_in.cf_mask == %i)' % (cond_cats, cats[m-1])\n",
     "    return cond_cats"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Define the time dimension of the cube\n",
     "year_splitting = [2001,2002]\n",
     "start = str(year_splitting[0])+'-1-1'\n",
     "end = str(year_splitting[-1])+'-12-31'\n",
     "start_date_init = datetime.strptime(start, '%Y-%m-%d')\n",
     "end_date_init = datetime.strptime(end, '%Y-%m-%d')\n",
     "plat =\"LANDSAT_7\"\n",
     "prod = \"ls7_ledaps_swiss\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Coordinates for the GVA canton\n",
     "min_lon = 5.93771 \n",
     "max_lon = 6.31743\n",
     "min_lat = 46.12089\n",
     "max_lat = 46.37531"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "tmp_dc = DataAccessApi()\n",
     "acquisitions_list_init=tmp_dc.list_acquisition_dates(product=prod, platform=plat, \n",
     "        latitude=(min_lat, max_lat), longitude=(min_lon,max_lon),  \n",
     "        time=(start_date_init,end_date_init))  "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "## Dataset creation with following measurements: red, nir and cf_mask (mask for clouds)\n",
+    "## Plotting the red band only as example\n",
     "\n",
     "for row in year_splitting:   \n",
     "    print(row)\n",
     "    acquisitions_list_year = []\n",
     "    for y in row:\n",
     "        #print(y)\n",
     "        for t in acquisitions_list_init:\n",
     "            if (t.year==y): \n",
     "                acquisitions_list_year.append(t)\n",
     "    #print(\"Acquisition list \",acquisitions_list_year)\n",
     "    #print(\"====================\")\n",
     "    dataset_in = dc.load(platform=plat,\n",
     "                     product=prod,\n",
     "                     time=(acquisitions_list_year[0],acquisitions_list_year[-1]),\n",
     "                     lon=(min_lon, max_lon), \n",
     "                     lat=(min_lat, max_lat),\n",
-    "                     measurements=['red', 'nir','cf_mask'])"
+    "                     measurements=['red', 'nir','cf_mask'])\n",
+    "    dataset_in.red.plot(cmap='OrRd',figsize=(8, 6))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
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