updated stage 3

docs/index.md

@@ -9,12 +9,12 @@ Current state of data analysis and implications on reproducibility and scalabili
 ## 📚 Stages
 The project is structured into three stages, dividing the task of implementing generic analysis workflows for biocatalytic data:  
 
-### 1. 🛠️ Making experimental data accessible
+### 1. Making experimental data accessible
 Development of generic Python tools, allowing reading in unprocessed data from various analytical sources such as spectrophotomety, chromatograph, or NMR. The tools facilitate the preprocessing of the data, concentration calculation, and the generation of EnzymeML files.
 
-### 2. 📈 Data Analysis
+### 2. Data Analysis
 Depending on the needs of the project members, existing Python tools will be used or extended to analyze the data. Experimental questions 
 Development of Python-based tools for the analysis of enzyme-catalyzed reactions.  
 
-### 3. 🤝 In-person Training Course
-Development of training materials for the use of the data standard and the Python-based tools.
+### 3. In-person Workshop
+During a three-day workshop, the project members will be trained in the use of the developed tools and their implementation in Jupyter notebooks to form custom analysis workflows.