diff --git a/tutorial/data/Italy.geojson b/tutorial/data/Italy.geojson
new file mode 100644
index 0000000..08e5f12
--- /dev/null
+++ b/tutorial/data/Italy.geojson
@@ -0,0 +1,26 @@
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+]
+}
diff --git a/tutorial/figures/daskgatewayclick.png b/tutorial/figures/daskgatewayclick.png
new file mode 100644
index 0000000..96f6bb6
Binary files /dev/null and b/tutorial/figures/daskgatewayclick.png differ
diff --git a/tutorial/part3/chunking_introduction.ipynb b/tutorial/part3/chunking_introduction.ipynb
index 5e283d1..96a1256 100755
--- a/tutorial/part3/chunking_introduction.ipynb
+++ b/tutorial/part3/chunking_introduction.ipynb
@@ -5,7 +5,7 @@
    "id": "1bfbae7a-12f1-4787-a520-c3de7529168d",
    "metadata": {},
    "source": [
-    "# Data chunking with zarr and kerchunk."
+    "# Data chunking and zarr."
    ]
   },
   {
@@ -42,7 +42,7 @@
     "    <ul>\n",
     "        <li>Learn about chunking</li>\n",
     "        <li>Learn about zarr </li>\n",
-    "        <li>Use kerchunk to consolidate chunk metadata and prepare single ensemble datasets for parallel computing</li>\n",
+    "        \n",
     "    </ul>\n",
     "</div>"
    ]
@@ -60,13 +60,12 @@
     "\n",
     "When we process our data piece by piece, it's easier to have our input or ouput data also saved in __chunks__. [Zarr](https://zarr.readthedocs.io/en/stable/) is the reference library in the Pangeo ecosystem to save our Xarray multidimentional datasets in __chunks__.\n",
     "\n",
-    "[Zarr](https://zarr.readthedocs.io/en/stable/) is not the only file format which uses __chunk__. We will also be using [kerchunk library](https://fsspec.github.io/kerchunk/) in this notebook to build a virtual __chunked__ dataset based on NetCDF files, and show how it optimizes the access and analysis of large datasets.\n",
     "\n",
     "The analysis is very similar to what we have done in previous episodes, however we will use data on a global coverage and not only on a small geographical area (e.g. Lombardia).\n",
     "\n",
     "### Data\n",
     "\n",
-    "In this episode, we will be using Global Long Term Statistics (1999-2019) products provided by the [Copernicus Global Land Service](https://land.copernicus.eu/global/index.html) and access them through [S3-comptabile storage](https://en.wikipedia.org/wiki/Amazon_S3) ([OpenStack Object Storage \"Swift\"](https://wiki.openstack.org/wiki/Swift)) with a data catalog we have created and made publicly available."
+    "In this episode, we will be using "
    ]
   },
   {
@@ -78,11 +77,9 @@
     "\n",
     "This episode uses the following main Python packages:\n",
     "\n",
-    "- fsspec {cite:ps}`d-fsspec-2018`\n",
-    "- s3fs {cite:ps}`d-s3fs-2016`\n",
+    "\n",
     "- xarray {cite:ps}`d-xarray-hoyer2017` with [`netCDF4`](https://pypi.org/project/h5netcdf/) and [`h5netcdf`](https://pypi.org/project/h5netcdf/) engines\n",
     "- dask {cite:ps}`d-dask-2016`\n",
-    "- kerchunk {cite:ps}`d-kerchunk-2021`\n",
     "- geopandas {cite:ps}`d-geopandas-jordahl2020`\n",
     "- matplotlib {cite:ps}`d-matplotlib-Hunter2007`\n",
     "\n",
@@ -724,476 +721,6 @@
     "Zarr can be considered as an Analysis Ready, cloud optimized data (ARCO) file format, discussed in [data_discovery](./data_discovery.ipynb) section."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d9103092",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Opening multiple NetCDF files and Kerchunk\n",
-    "\n",
-    "As shown in the [Data discovery](./data_discovery.ipynb) chapter, when we have several files to read at once, we need to use Xarray `open_mfdataset`. When using `open_mfdataset` with NetCDF files, each NetCDF file is considerd as 'one chunk' by default as seen above.\n",
-    "\n",
-    "When calling `open_mfdataset`, Xarray also needs to analyse each NetCDF file to get metadatas and tried to build a coherent dataset from them. Thus, it performs multiple operations, like concartenate the coordinate, checking compatibility, etc. This can be time consuming ,especially when dealing with object storage or you have more than thousands of files. And this has to be repeated every time, even if we use exactly the same set of input files for different analysis.\n",
-    "\n",
-    "[Kerchunk library](https://fsspec.github.io/kerchunk/) can build virtual Zarr Dataset over NetCDF files which enables efficient access to the data from traditional file systems or cloud object storage.\n",
-    "\n",
-    "And that is not the only optimisation kerchunk brings to pangeo ecosystem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc2feb8d",
-   "metadata": {},
-   "source": [
-    "### Exploiting native file chunks for reading datasets\n",
-    "\n",
-    "As already mentioned, many data formats (for instance [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format), [netCDF4](https://unidata.github.io/netcdf4-python/) with HDF5 backend, [geoTIFF](https://en.wikipedia.org/wiki/GeoTIFF)) have chunk capabilities. Chunks are defined at the creation of each file.  Let's call them '__native file chunks__' to distinguish that from '__Dask chunks__'. These native file chunks can be retrieved and used when opening and accessing the files. This will allow to significantly reduce the amount of IOs, bandwith, and memory usage when analyzing Data Variables.\n",
-    "\n",
-    "[kerchunk library](https://fsspec.github.io/kerchunk/) can extract native file chunks layout and metadata from each file and combine them into one virtual Zarr dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b2c231f",
-   "metadata": {},
-   "source": [
-    "### Extract chunk information\n",
-    "\n",
-    "We extract native file chunk information from each NetCDF file using `kerchunk.hdf`.\n",
-    "Let's start with a single file.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f2798bd8",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import kerchunk.hdf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46a2de91",
-   "metadata": {},
-   "source": [
-    "We use `kerchunk.hdf` because our files are written in `netCDF4`  format which is based on HDF5 and `SingleHdf5ToZarr` to translate the metadata of one HDF5 file into Zarr metadata format. The parameter `inline_threshold` is an *optimization* and tells `SingleHdf5ToZarr` to include chunks smaller than this value directly in the output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "335e1fab",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "remote_filename = 'https://object-store.cloud.muni.cz/swift/v1/foss4g-data/CGLS_LTS_1999_2019/c_gls_NDVI-LTS_1999-2019-1221_GLOBE_VGT-PROBAV_V3.0.1.nc'\n",
-    "with fsspec.open(remote_filename) as inf:\n",
-    "    h5chunks = kerchunk.hdf.SingleHdf5ToZarr(inf, remote_filename, inline_threshold=100)\n",
-    "    chunk_info = h5chunks.translate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0bfa43e",
-   "metadata": {},
-   "source": [
-    "Let's have a look at `chunk_info`. It is a Python dictionary so we can use `pprint` to print it nicely.\n",
-    "\n",
-    "Content is a bit complicated, but it's only metadata in Zarr format indicating what's in the original file, and where the chunks of the file are located (bytes offset)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33c5ce25",
-   "metadata": {
-    "tags": [
-     "hide-output"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from pprint import pprint\n",
-    "pprint(chunk_info)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4f4ed77",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-warning\">\n",
-    "    <i class=\"fa-check-circle fa\" style=\"font-size: 22px;color:#666;\"></i> <b>Exercise</b>\n",
-    "    <br>\n",
-    "    <ul>\n",
-    "        <li>Did you recognise the similarities with test.zarr's zarr metadata file? </li>\n",
-    "    </ul>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "804dffbd",
-   "metadata": {},
-   "source": [
-    "After we have collected information on the native file chunks in the original data file and consolidated our Zarr metadata, we can open the files using `zarr` and pass this chunk information into a storage option. We also need to pass `\"consolidated\": False` because the original dataset does not contain any `zarr` consolidating metadata."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b35a2a88",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "LTS = xr.open_dataset(\n",
-    "    \"reference://\", \n",
-    "    engine=\"zarr\",\n",
-    "    backend_kwargs={\n",
-    "        \"storage_options\": {\n",
-    "            \"fo\": chunk_info,\n",
-    "        },\n",
-    "        \"consolidated\": False\n",
-    "    }\n",
-    ")\n",
-    "LTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6eb894f3",
-   "metadata": {},
-   "source": [
-    "As you can notice above, all the Data Variables are already chunked according to the native file chunks of the NetCDF file."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13813577",
-   "metadata": {},
-   "source": [
-    "### Combine all LTS files into one kerchunked single ensemble dataset\n",
-    "\n",
-    "Now we will combine all the files into one kerchunked consolidated dataset, and try to open it as a xarray dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1bb8ac14",
-   "metadata": {},
-   "source": [
-    "Let us first collect the chunk information for each file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b31397e5",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "fs.ls('foss4g-data/CGLS_LTS_1999_2019/')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25c5dfdd",
-   "metadata": {},
-   "source": [
-    "We have 36 files to process, but for this chunking_introduction example, we'll just use 6 file so that it take less time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b3bf519",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from datetime import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ada26891",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "s3path = 's3://foss4g-data/CGLS_LTS_1999_2019/c_gls_NDVI-LTS_1999-2019-0[7-8]*.nc'\n",
-    "chunk_info_list = []\n",
-    "time_list = []\n",
-    "\n",
-    "for file in fs.glob(s3path):\n",
-    "    url = 'https://object-store.cloud.muni.cz/swift/v1/' + file\n",
-    "    t = datetime.strptime(file.split('/')[-1].split('_')[3].replace('1999-', ''), \"%Y-%m%d\")\n",
-    "    time_list.append(t)\n",
-    "    print('working on ', file)\n",
-    "    with fsspec.open(url) as inf:\n",
-    "        h5chunks = kerchunk.hdf.SingleHdf5ToZarr(inf, url, inline_threshold=100)\n",
-    "        chunk_info_list.append(h5chunks.translate())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5545adc5",
-   "metadata": {},
-   "source": [
-    "This time we use `MultiZarrToZarr` to combine multiple kerchunked datasets into a single logical aggregated dataset. Like when opening multiple files with Xarray `open_mfdataset`, we need to tell `MultiZarrToZarr` how to concatenate all the files. There is no time dimension in the original dataset, but one file corresponds to one date (average over the period 1999-2019 for a given 10-day period e.g. January 01, January 11, January 21, etc.)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7095a0b1",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "from kerchunk.combine import MultiZarrToZarr\n",
-    "mzz = MultiZarrToZarr(\n",
-    "    chunk_info_list,\n",
-    "    coo_map={'INDEX': 'INDEX'},\n",
-    "    identical_dims=['crs'],\n",
-    "    concat_dims=[\"INDEX\"],\n",
-    ")\n",
-    "\n",
-    "out = mzz.translate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8db85d9b",
-   "metadata": {},
-   "source": [
-    "Then, we can open the complete dataset using our consolidated Zarr metadata."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c70f8160",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "LTS = xr.open_dataset(\n",
-    "    \"reference://\", \n",
-    "    engine=\"zarr\",\n",
-    "    backend_kwargs={\n",
-    "        \"storage_options\": {\n",
-    "            \"fo\": out,\n",
-    "        },\n",
-    "        \"consolidated\": False\n",
-    "    }\n",
-    ")\n",
-    "LTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eee93dae",
-   "metadata": {},
-   "source": [
-    "We can save the consolidated metadata for our dataset in a file, and reuse it later to access the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3a0b9096",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4da8a98a",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "jsonfile='test.json'\n",
-    "with open(jsonfile, mode='w') as f :\n",
-    "    json.dump(out, f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b7baa9e",
-   "metadata": {},
-   "source": [
-    "We can then load data from this catalog."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c8e1c1a2",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "LTS = xr.open_dataset(\n",
-    "    \"reference://\", engine=\"zarr\",\n",
-    "    backend_kwargs={\n",
-    "        \"storage_options\": {\n",
-    "            \"fo\":'./test.json',\n",
-    "        },\n",
-    "        \"consolidated\": False\n",
-    "    }\n",
-    ")\n",
-    "LTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e1a8397",
-   "metadata": {},
-   "source": [
-    "The catalog (json file we created) can be shared on the cloud (or GitHub, etc.) and anyone can load it from there too.\n",
-    "This approach allows anyone to easily access LTS data and select the Area of Interest for their own study."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "baa71c11",
-   "metadata": {},
-   "source": [
-    "We have prepared json file based on 36 netcdf file, and published it online as catalogue=\"https://object-store.cloud.muni.cz/swift/v1/foss4g-catalogue/c_gls_NDVI-LTS_1999-2019.json\"\n",
-    "We can try to load it.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "19dd3626-528e-44dc-aeb0-47f1560f525d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "catalogue=\"https://object-store.cloud.muni.cz/swift/v1/foss4g-catalogue/c_gls_NDVI-LTS_1999-2019.json\"\n",
-    "LTS = xr.open_dataset(\n",
-    "    \"reference://\", engine=\"zarr\",\n",
-    "    backend_kwargs={\n",
-    "        \"storage_options\": {\n",
-    "            \"fo\":catalogue\n",
-    "                    },\n",
-    "        \"consolidated\": False\n",
-    "    }\n",
-    ")\n",
-    "LTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca50a427-f7ca-497d-b4bf-359b68c56f07",
-   "metadata": {},
-   "source": [
-    "We will use this catalogue in [dask_introduction](./dask_introduction.ipynb) chapter. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d36efa13-b1e5-429a-9b2c-537b07d9e49a",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Operations on a chunked dataset\n",
-    "\n",
-    "Let's have a look of our chunked test dataset backend representation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1302b99f",
-   "metadata": {},
-   "source": [
-    "` test.data` is the backend array Python representation of Xarray's Data Array, [__Dask Array__](https://docs.dask.org/en/stable/array.html) when using chunking, Numpy by default.\n",
-    "\n",
-    "We will introduce Dask arrays and Dask graphs visualization in the next section [dask_introduction](./dask_introduction.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5da222df-6f45-434c-b741-34ef27352199",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
-   "source": [
-    "Anyway, when applying `chunk` function you may have the impression that the chunks sizes just changes and everything will be fine.\n",
-    "\n",
-    "However, as you can see in the graph visualization above, Xarray will actually have to fetch at least one entire initial chunk that was defined when opening the Dataset at first before rechunking at a smaller size or even selecting one value. This is true when applying any funtions on any values: Xarray will work by loading entire chunks.\n",
-    "\n",
-    "You can imagine that it will not be very optimal if you load one file as an entire chunk, or if your initial chunks are too big (your Python Jupyter kernel may crash!), especially with large numbers of files and large files. Choosing an appropriate chunk size is really important and depends on your analysis.\n",
-    "\n",
-    "You can find a really nice article by Dask team on how to chose the right chunk size [here](https://blog.dask.org/2021/11/02/choosing-dask-chunk-sizes)."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "1665722c-4c65-4334-987e-1f5bc4841036",
diff --git a/tutorial/part3/scaling_dask.ipynb b/tutorial/part3/scaling_dask.ipynb
index 0ffbfb2..c7cef20 100755
--- a/tutorial/part3/scaling_dask.ipynb
+++ b/tutorial/part3/scaling_dask.ipynb
@@ -264,19 +264,23 @@
    "source": [
     "### Dask Dashboard\n",
     "\n",
-    "Dask comes with a really handy interface: the Dask Dashboard. It is a web interface that you can open in a separated tab of your browser (but not with he link above, you've got to use Jupyterlabs proxy: /user/todaka/proxy/8787/status).\n",
+    "Dask comes with a really handy interface: the Dask Dashboard. It is a web interface that you can open in a separated tab of your browser (but not with he link above, you've got to use Jupyterlabs proxy: **/user/todaka/proxy/8787/status**).\n",
     "\n",
     "We will learn here how to use it through [dask jupyterlab extension](https://github.com/dask/dask-labextension).  \n",
     "\n",
     "To use Dask Dashboard through jupyterlab extension on Pangeo EOSC infrastructure,\n",
     "you will just need too look at the html link you have for your jupyterlab, and Dask dashboard port number, as highlighted in the figure below.\n",
     "\n",
+    "![Dask.array](../figures/dashboardlink.png)\n",
+    "\n",
+    "\n",
+    "![Dask.array](../figures/dasklab.png)\n",
     "\n",
-    "<img src=\"https://github.com/pangeo-data/pangeo-openeo-BiDS-2023/tree/main/tutorial/figures/dashboardlink.png\" width=\"50%\">\n",
-    "<img src=\"https://github.com/pangeo-data/pangeo-openeo-BiDS-2023/tree/main/tutorial/figures/dasklab.png\" width=\"80%\">\n",
     "\n",
     "Then click the orange icon indicated in the above figure, and type 'your' dashboard link (normally, you just need to replace 'todaka' to 'your username').  \n",
-    "<img src=\"https://github.com/pangeo-data/pangeo-openeo-BiDS-2023/tree/main/tutorial/figures/daskclick.png\" width=\"30%\">\n",
+    "\n",
+    "![Dask.array](../figures/daskclick.png)\n",
+    "\n",
     "\n",
     "\n",
     "\n",
@@ -284,8 +288,8 @@
     "\n",
     "You can click several buttons indicated with blue arrows in above figures, then drag and drop to place them as your convenience.  \n",
     "\n",
+    "![Dask.array](../figures/exampledasklab.png)\n",
     "\n",
-    "<img src=\"https://github.com/pangeo-data/pangeo-openeo-BiDS-2023/tree/main/tutorial/figuresexampledasklab.png\" width=\"50%\">\n",
     "\n",
     "\n",
     "It's really helpfull to understand your computation and how it is distributed."
@@ -557,7 +561,9 @@
    "source": [
     "Let's setup the Dask Dashboard with your new cluster.\n",
     "\n",
-    "This time, copy and past the link above indicated in Dashboard to the dask lab-extension or just click on the link to open the dashboard into another tab."
+    "***This time, just click on the link to open the dashboard into another tab.  Then copy and past the link of web site appearing to the dask lab-extension***\n",
+    "\n",
+    "![daskgateway click](../figures/daskgatewayclick.png)"
    ]
   },
   {
@@ -569,13 +575,13 @@
    "source": [
     "### Get a client from the Dask Gateway Cluster\n",
     "\n",
-    "As stated above, creating a Dask `Client` is mandatory in order to perform following Daks computations on your Dask Cluster."
+    "As stated above, creating a Dask `Client` is mandatory in order to perform following Daks computations on your Dask Cluster.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b79047f2-044a-41c3-b8f0-4acbd121fa03",
+   "id": "3f035e50-744e-4ba3-addd-cfe0f3fc9616",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -667,7 +673,7 @@
    "source": [
     "import geopandas as gpd\n",
     "try:\n",
-    "    GAUL = gpd.read_file('Italy.geojson')\n",
+    "    GAUL = gpd.read_file('../data/Italy.geojson')\n",
     "except:\n",
     "    GAUL = gpd.read_file('zip+https://mars.jrc.ec.europa.eu/asap/files/gaul1_asap.zip') \n"
    ]
@@ -763,7 +769,7 @@
    "source": [
     "import pooch\n",
     "try:\n",
-    "    cgls_ds = xr.open_dataset('C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc')\n",
+    "    cgls_ds = xr.open_dataset('../data/C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc')\n",
     "except:\n",
     "    cgls_file = pooch.retrieve(\n",
     "        url=\"https://zenodo.org/record/6969999/files/C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc\",\n",