added imperial slides, features photo, and made links work on the training page

Author: rasmaormane

assets/media/imperial.jpg

@@ -19,17 +19,17 @@ For any events where public registration is available, we will post the details
 
 ---
 
-# 1. Imperial College London
+## 1. Imperial College London
 
-🗓 Thursday 19th March
+🗓 **When:** Thursday 19th March
 
-📍 Imperial College London (London, UK)
+📍 **Where:** Imperial College London (London, UK)
 
-![imperial](assets/media/imperial.jpg)
+![imperial](assets/media/imperial.png)
 
 ---
 
-# 2. Wavelength Conference 2026
+## 2. Wavelength Conference 2026
 
 🗓 **When:** Thursday 9th April
 
@@ -41,7 +41,7 @@ For any events where public registration is available, we will post the details
 
 ---
 
-# 3. ARIA
+## 3. ARIA
 
 🗓 **When:** Wednesday 15th April
 
@@ -53,7 +53,7 @@ For any events where public registration is available, we will post the details
 
 ---
 
-# 4. Pangeos
+## 4. Pangeos
 
 🗓 **When:** Wednesday 23rd April
 
@@ -65,7 +65,7 @@ For any events where public registration is available, we will post the details
 
 ---
 
-# 5. Met4EO
+## 5. Met4EO
 
 🗓 **When:** 20th-22nd May
 
@@ -77,7 +77,7 @@ For any events where public registration is available, we will post the details
 
 ---
 
-### 🗸 In these tutorial, we will:
+🗸 **In these tutorial, we will:**
 
 - Introduce the key concepts behind metrological uncertainty propagation 
 - Walk through the core components of the toolkit:
@@ -86,9 +86,9 @@ For any events where public registration is available, we will post the details
 - Work through a sensor calibration example using Google Colab-based Jupyter notebooks
 
 
-We’ll also provide support to get you started using CoMet with your own example use-case if time permits — so feel free to bring a Python example from your work involving measurement uncertainty.
+We’ll also provide support to get you started using CoMet with your own example use-case if time permits - so feel free to bring a Python example from your work involving measurement uncertainty.
 
 Whether you're working in satellite Cal/Val, EO data processing, or any other measurement-driven field, this session will help you implement rigorous, traceable uncertainty handling in your processing chains.
 
-### 🗸 No installation is required beforehand – all examples will run in Google Colab!
+🗸 **No installation is required beforehand – all examples will run in Google Colab!**
 

assets/media/imperial.png

@@ -4,7 +4,6 @@ date: 2026-03-19
 type: landing
 
 design:
-  # Default section spacing
   spacing: "6rem"
 
 sections:
@@ -15,48 +14,40 @@ sections:
         - title: 🙌 Hands-on training session
           text: >
             Welcome to the CoMet tutorial held at Imperial College London!
-            
+
             On this page you'll find links to the training materials.
-            
-            This session covers key concepts around uncertainties, with guided exercises using the CoMet toolkit. 
 
-            💡 You will:
-            - Gain a conceptual overview of uncertainties in Earth Observation data processing.
-            - Learn how to use the CoMet tools in practical workflows.
-            - Apply methods through interactive notebooks hosted on Google Colab.
+            This session covers key concepts around uncertainties, with guided exercises using the CoMet toolkit.
+
+            <br><br>
 
           feature_icon: check
-#          features:
-#            - "**Uncertainties 101**: A short introduction to key metrological concepts and why they matter."
-#            - "[**Exercise 1**](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise1.ipynb)"
-#            - "[**Exercise 2**](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise2.ipynb)"
-#            - "[**Exercise 3**](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise3.ipynb)"
+
           features:
             - "📃 **Uncertainties 101**: A short introduction to key concepts and why they matter.  
-              &nbsp;&nbsp;• Why uncertainty matters? 
-              &nbsp;&nbsp;• What is the CoMet Toolkit? 
+              &nbsp;&nbsp;• Why uncertainty matters  
+              &nbsp;&nbsp;• What is the CoMet Toolkit  
               &nbsp;&nbsp;• How CoMet helps with uncertainty handling"
 
             - "[🔗 **Exercise 1**: Introduction to Punpy Capabilities](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise1.ipynb)  
-              &nbsp;&nbsp;• Get familiar with the punpy tool 
-              &nbsp;&nbsp;• Propagate uncertainties on manually provided input data through a simple measurement functions using punpy 
-              &nbsp;&nbsp;• Explore the various ways uncertainties with different error correlations can be propagated"
+              &nbsp;&nbsp;• Get familiar with the punpy tool  
+              &nbsp;&nbsp;• Propagate uncertainties on manually provided input data  
+              &nbsp;&nbsp;• Explore different error‑correlation propagation options"
 
-            - "[🔗 **Exercise 2**: Multi-Dimension Datasets](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise2.ipynb)  
-              &nbsp;&nbsp;• Store error-correlation information for multi-dimensional measurement datasets using obsarray
-              &nbsp;&nbsp;• Practice on a multi-dimensional Earth Observation dataset example
-              &nbsp;&nbsp;• Propagate uncertainties from these datasets through measurement functions using punpy"
+            - "[🔗 **Exercise 2**: Multi‑Dimension Datasets](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise2.ipynb)  
+              &nbsp;&nbsp;• Store error‑correlation information with obsarray  
+              &nbsp;&nbsp;• Work with a multi‑dimensional EO dataset  
+              &nbsp;&nbsp;• Propagate uncertainties using punpy"
 
-            - "[🔗 **Exercise 3**: HYPERNETS Use Case](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise3.ipynb)
-              &nbsp;&nbsp;• Get familiar with a sample EO data (HYPERNETS)
-              &nbsp;&nbsp;• Use the previous exercises to add uncertainties to the HYPERNETS data processing chain
-              &nbsp;&nbsp;• Generate and interpret uncertainty-aware outputs"
-
-          # Upload image to `assets/media/` and reference the filename here
-          image: imperial.jpg
-          caption: 'Image credit: Imperial'
+            - "[🔗 **Exercise 3**: HYPERNETS Use Case](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise3.ipynb)  
+              &nbsp;&nbsp;• Explore a sample HYPERNETS dataset  
+              &nbsp;&nbsp;• Add uncertainties into the processing chain  
+              &nbsp;&nbsp;• Generate and interpret uncertainty‑aware outputs"
+
+          image: imperial.png
+          caption: "Image credit: Imperial"
           button:
             text: Download Slides
-            url: LPS_introduction_CoMet_toolkit.pdf
+            url: Imperial_CoMet_toolkit.pdf
             icon: hero/download
 ---