reference image with raw github links

lectures/1.2_data_cube/1.2_data_cube.md

@@ -18,7 +18,7 @@ When you think about data, most likely tables come to your mind, with features o
 - Feature: Revenue
 - Value: $
 
-![Datacube Economy](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/1.2_data_cube/assets/datacubes_economy.png "Example Data Cube Economy")
+![Datacube Economy](https://raw.githubusercontent.com/EO-College/cubes-and-clouds/main/lectures/1.2_data_cube/assets/datacubes_economy.png "Example Data Cube Economy")
 
 This concept can be applied to many fields such as economics, medicine, biology, and also very well to EO data!
 
@@ -32,12 +32,12 @@ The concept of representing multidimensional data as data cubes fits ideally to
 
 Data can be represented as datacubes in EO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands [`blue`, `green`, `red`, `near-infrared`] and 3 timesteps [`2020-10-01`, `2020-10-13`, `2020-10-25`], displayed here in an orderly, timeseries-like manner:
 
-![Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions)](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/1.2_data_cube/assets/dc_timeseries.png "An examplary raster datacube with 4 dimensions: x, y, bands and time")
+![Raster datacube timeseries: 12 imagery tiles are depicted, grouped by 3 dates along a timeline (time dimension). Each date has a blue, green, red and near-infrared band (bands dimension). Each single tile has the dimensions x and y (spatial dimensions)](https://raw.githubusercontent.com/EO-College/cubes-and-clouds/main/lectures/1.2_data_cube/assets/dc_timeseries.png "An examplary raster datacube with 4 dimensions: x, y, bands and time")
 > Figure: An examplary raster datacube with 4 dimensions: x, y, bands and time. Reference: [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
 
 It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:
 
-![Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates)](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/1.2_data_cube/assets/dc_flat.png "This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.")
+![Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates)](https://raw.githubusercontent.com/EO-College/cubes-and-clouds/main/lectures/1.2_data_cube/assets/dc_flat.png "This is the 'raw' data collection that is our example datacube. The grayscale images are colored for understandability, and dimension labels are displayed.")
 > Figure: Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates). Reference: [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
 
 ### Dimensions

lectures/2.3_data_access/2.3_data_access.md

@@ -1,4 +1,4 @@
-# Access EO Data from the Cloud
+# Access EO Data from the Cloud with OpenEO
 
 Using a cloud provider for accessing data, and in this specific scenario Earth Observation data, could improve your productivity a lot. To get the most out of it, we will provide you some important insights.
 

lectures/2.3_data_access/exercises/23_data_access_aggregate.ipynb

@@ -15,7 +15,7 @@
    "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
    "metadata": {},
    "source": [
-    "# Temporal aggregation\n",
+    "# Temporal aggregation with OpenEO\n",
     "\n",
     "## Aggregate Operators"
    ]
@@ -141,7 +141,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

lectures/2.3_data_access/exercises/23_data_access_apply.ipynb

@@ -15,7 +15,7 @@
    "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
    "metadata": {},
    "source": [
-    "# Apply Operator\n",
+    "# Data processing using the apply Operator with OpenEO\n",
     "\n",
     "## Apply Operator\n",
     "\n",
@@ -781,9 +781,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cubes-and-clouds-cubes-and-clouds2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-cubes-and-clouds-cubes-and-clouds2-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -796,6 +796,13 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

lectures/2.3_data_access/exercises/23_data_access_filter.ipynb

@@ -15,7 +15,7 @@
    "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
    "metadata": {},
    "source": [
-    "# Filter Operators\n",
+    "# Data processing via Filter Operators with OpenEO\n",
     "\n",
     "## Filter Operators\n",
     "\n",
@@ -203,7 +203,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

lectures/2.3_data_access/exercises/23_data_access_lazy_loading.ipynb

@@ -15,7 +15,7 @@
    "id": "be23f346-9a95-4315-b8b0-dff22a207084",
    "metadata": {},
    "source": [
-    "# OpenEO data processing\n",
+    "# OpenEO data processing hands-on\n",
     "\n",
     "The exercise will use the openEO Python Client Side Processing functionality, which allows to experiment using openEO without a connection to an openEO back-end."
    ]
@@ -123,7 +123,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

lectures/2.3_data_access/exercises/23_data_access_reduce.ipynb

@@ -15,7 +15,7 @@
    "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
    "metadata": {},
    "source": [
-    "# Reduce Operators\n",
+    "# Compute statistics using Reduce Operators with OpenEO\n",
     "\n",
     "## Reduce Operators\n",
     "\n",
@@ -331,7 +331,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

lectures/2.3_data_access/exercises/23_data_access_resample.ipynb

@@ -15,7 +15,7 @@
    "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
    "metadata": {},
    "source": [
-    "# Resample Operators\n",
+    "# Spatial resamping using Resample Operators with OpenEO\n",
     "\n",
     "## Resample Operators\n",
     "\n",
@@ -149,9 +149,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cubes-and-clouds-cubes-and-clouds2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-cubes-and-clouds-cubes-and-clouds2-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -164,6 +164,13 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.11.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,