Update README and fix typos

Author: r-ford

README.md

@@ -30,13 +30,13 @@ the necessary skills to support scalable and reproducible research.
 
 ## Authors
 
-[Wolfgang Wagner](@wagner-wolfgang), [Martin Schobben](@martinschobben),
-[Nikolas Pikall](@npikall), [Joseph Wagner](@wagnerjoseph), [Davide Festa](@maybedave),
-[Felix David Reuß](@FelixReuss), [Luka Jovic](@lukojovic)
+[Wolfgang Wagner](https://github.com/wagner-wolfgang), [Martin Schobben](https://github.com/martinschobben),
+[Nikolas Pikall](https://github.com/npikall), [Joseph Wagner](https://github.com/wagnerjoseph), [Davide Festa](https://github.com/maybedave),
+[Felix David Reuß](https://github.com/FelixReuss), [Luka Jovic](https://github.com/lukojovic)
 
 ### Contributors
 
-<a href="https://github.com/TUW-GEO/eo-datascience-cookbook/graphs/contributors">
+<a href="https://github.com/ProjectPythia/eo-datascience-cookbook/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=ProjectPythia/eo-datascience-cookbook" />
 </a>
 
@@ -92,7 +92,7 @@ Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyt
 If you are interested in running this material locally on your computer, you will
 need to follow this workflow:
 
-1. Clone the `https://github.com/TUW-GEO/eo-datascience-cookbook` repository:
+1. Clone the `https://github.com/ProjectPythia/eo-datascience-cookbook` repository:
 
    ```bash
     git clone https://github.com/TUW-GEO/eo-datascience-cookbook

notebooks/courses/microwave-remote-sensing/01_in_class_exercise.ipynb

@@ -97,7 +97,7 @@
    "id": "8",
    "metadata": {},
    "source": [
-    "Each collection has multiple items. An item is one spatio-temporal instance the collection, for instance a satellite image. If items are needed for a specific timeframe or for a specific region of interest, we can define this as a query.\n"
+    "Each collection has multiple items. An item is one spatio-temporal instance in the collection, for instance a satellite image. If items are needed for a specific timeframe or for a specific region of interest, we can define this as a query."
    ]
   },
   {
@@ -173,7 +173,7 @@
    "id": "14",
    "metadata": {},
    "source": [
-    "Using our previously loaded STAC catalog, we can now search for items full-filling our query. In this example we are using the bounding box. If we want to use an area of interest specified in the geojson format - one hast to use the intersects parameter as documented in the comment below.\n"
+    "Using our previously loaded STAC catalog, we can now search for items fulfilling our query. In this example we are using the bounding box. If we want to use an area of interest specified in the geojson format - one has to use the intersects parameter as documented in the comment below."
    ]
   },
   {
@@ -483,9 +483,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/courses/microwave-remote-sensing/02_in_class_exercise.ipynb

@@ -139,11 +139,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "aoi = subset.isel(time=0, x=slice(0, 500), y=slice(0, 500))\n",
@@ -201,7 +197,7 @@
    "id": "9",
    "metadata": {},
    "source": [
-    "*Figure 1: Visually comparing $\\sigma^0$ on a logarithmic and linear scale (left column). In addition, the benefit of using the robust plotting method is shown (middle column). The robust argument uses the 2^nd^ and 98^th^ percentiles of the data to compute the color limits to eliminate washing out the plot due to data outliers.*\n",
+    "*Figure 1: Visually comparing $\\sigma^0$ on a logarithmic and linear scale (left column). In addition, the benefit of using the robust plotting method is shown (middle column). The robust argument uses the 2<sup>nd</sup> and 98<sup>th</sup> percentiles of the data to compute the color limits to eliminate washing out the plot due to data outliers.*\n",
     "\n",
     "In the plot above you can see the difference between the two scales. The values in dB are more evenly distributed and are therefore easier to plot. The values in linear scale are more spread out and are therefore harder to interpret.\n",
     "This is why we use the dB scale for plotting/visualization.\n",
@@ -431,9 +427,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/courses/microwave-remote-sensing/03_in_class_exercise.ipynb

@@ -265,9 +265,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/courses/microwave-remote-sensing/04_in_class_exercise.ipynb

@@ -484,9 +484,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/courses/microwave-remote-sensing/05_in_class_exercise.ipynb

@@ -234,9 +234,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/courses/microwave-remote-sensing/06_in_class_exercise.ipynb

@@ -345,9 +345,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/courses/microwave-remote-sensing/08_in_class_exercise.ipynb

@@ -180,7 +180,7 @@
     "\n",
     "$$γ = γ_{proc}*γ_{geom}*γ_{vol}*γ_{SNR}*γ_{temp}$$\n",
     "\n",
-    "where $$γ_{proc}$$ refers to inaccuracies in the processing (e.g., coregistration errors), $$γ_{geom}$$ refers to the baseline decorrelation (different position of satellites during the two acquisitions), $$γ_{vol}$$ refers to volume decorrelation (vegetation related), $$γ_{SNR}$$ refers to the radar instrument thermal noise and $$γ_{temp}$$ refers to the decorrelation caused by change of position of the objects in the scene during the time interval of the images acquisitions (e.g., plant growth, wind-induced movements or ground deformation due to earthquakes, landslides).\n",
+    "where $γ_{proc}$ refers to inaccuracies in the processing (e.g., coregistration errors), $γ_{geom}$ refers to the baseline decorrelation (different position of satellites during the two acquisitions), $γ_{vol}$ refers to volume decorrelation (vegetation related), $γ_{SNR}$ refers to the radar instrument thermal noise and $γ_{temp}$ refers to the decorrelation caused by change of position of the objects in the scene during the time interval of the images acquisitions (e.g., plant growth, wind-induced movements or ground deformation due to earthquakes, landslides).\n",
     "\n",
     "Therefore, we can conclude that interferometric accuracy is sensitive to many processes, hence isolating the ground deformation signal involves several operations. On the other hand, interferometric coherence sensitivity could be exploited to track and map phenomena that cause its degradation (e.g., vegetation features, and water content).\n"
    ]
@@ -381,9 +381,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "microwave-remote-sensing",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "microwave-remote-sensing"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/templates/classification.ipynb

@@ -811,9 +811,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "classification",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "classification"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -825,7 +825,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,

notebooks/tutorials/floodmapping.ipynb

@@ -610,9 +610,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "floodmapping",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "floodmapping"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -624,7 +624,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,