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docs/index.md

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 ## Why EnzymeML?
 
-EnzymeML is a data model for catalyzed reaction data.
+## Unlock the Full Potential of Your Biocatalytical Data
 
-It sets information on small molecules, proteins, and their reaction in context with reaction conditions and the measured data.
+This training course is designed to empower researchers, scientists, and data analysts in biocatalysis by equipping them with the skills to manage and analyze experimental data beyond traditional Excel workflows. By leveraging Python and AI-driven tools, participants will enhance their ability to structure, process, and interpret complex datasets while ensuring adherence to FAIR data principles.
 
-EnzymeML is a standardized data model allowing for exchange of data among colleagues, database providers, and data science tools.
+## Goals
 
+### 1. Move Beyond Excel: Smarter Data Management
 
-## How to use EnzymeML?
+Excel is a widely used tool in biocatalysis, but it has limitations when handling large-scale, multidimensional datasets. This course provides participants with alternative approaches that allow for:
 
-Besides the data model, different tools are available to accelerate your processing and analysis of catalyzed reaction data.
+- Efficient data structuring and processing.
+- Automated workflows that reduce errors and improve reproducibility.
+- Scalable solutions for large datasets that exceed Excel's capabilities.
 
-- EnzymeML Suite: A desktop application for creating, editing, simulating, and visualizing EnzymeML documents.
-- Chromatopy: A Python tool for processing chromatographic data.
-- MTPHandler: A Python tool for processing plate reader data.
-- NMRpy: A Python tool for processing NMR data.
+### 2. Apply Python Directly to Your Research Data
 
+Unlike generic coding courses that rely on theoretical examples, this training is focused on your own experimental data. Participants will:
+
+- Work with their real-world datasets from their research projects.
+- Learn how to manipulate and analyze biocatalytical data using Python.
+- Gain hands-on experience in integrating computational tools into their workflows.
+
+### 3. Simplified Python Learning with AI Assistance
+
+For participants new to Python, the learning curve can be steep. This course integrates AI-driven tools to facilitate:
+
+- Code generation and debugging support.
+- Step-by-step guidance in writing and optimizing Python scripts.
+- Automated solutions for routine data processing tasks.
+
+### 4. Ensure FAIR Compliance with EnzymeML
+
+The course emphasizes FAIR-compliant data management, ensuring that experimental results are:
+
+- **Findable** – Easily searchable and indexed for future reference.
+- **Accessible** – Structured in a way that allows seamless data sharing.
+- **Interoperable** – Compatible with other datasets and computational tools.
+- **Reusable** – Properly documented and standardized to support further research.
+
+Through hands-on training, participants will learn how to generate EnzymeML documents, a standardized format for enzymatic reaction data that enhances data exchange and reproducibility.

docs/sessions/01-overview.md

@@ -1,6 +1,72 @@
-# Overview
+# Availabe tools for processing of experimental data
 
+## From Raw Experimental Data to EnzymeML-Driven Analysis
 
+Processing experimental data for analysis is often a complex and error-prone task. Typically, raw data from lab instruments such as plate readers, chromatographs, and NMR devices must be manually extracted, cleaned, and reformatted before analysis can begin. This process is time-consuming and not scalable.
 
+To streamline this workflow, Python tools such as chromatopy, MTPHandler, and NMRpy have been developed. These tools enable direct reading of raw data files from experimental instruments, automating the transformation into a structured format that is immediately usable for analysis.
 
+## Data Processing Workflow
 
+Raw data is read directly from files generated by lab instruments and transformed into EnzymeML documents. EnzymeML provides a standardized structure for storing key reaction data, including reaction conditions, catalysts, and substrate properties. This ensures that data is well-organized, FAIR-compliant, and ready for computational analysis.
+
+Within a Jupyter Notebook environment, these tools allow seamless integration of data processing, analysis, and visualization. The entire workflow—from raw data ingestion to structured analysis—is transparent, reproducible, and easy to share with others.
+
+## From Raw Data to Analyzable Data
+
+Once experimental data has been transformed into EnzymeML format, it becomes the foundation for further data science applications:
+
+- Yield, conversion, and selectivity calculations
+- Kinetic modeling and reaction simulations
+- Comprehensive visualization of experimental results
+
+The following diagram shows the workflow from raw data to analyzable data in form of an EnzymeML Document:
+
+```mermaid
+graph LR
+    A[🌈 Chromatographic Instrument] -->|output| A1[📄 Files]
+    B[🔬 Plate Reader] -->|output| B1[📄 Files]
+    C[🧲 NMR] -->|output| C1[📄 Files]
+
+    A1 -->|read| D
+    B1 -->|read| E
+    C1 -->|read| F
+
+    subgraph in Jupyter Notebook:
+        subgraph Experimental Data Processing
+            D{chromatopy}
+            E{MTPHandler}
+            F{NMRpy}
+        end
+        D -->|transform| DataObject[EnzymeML Object]
+        E -->|transform| DataObject
+        F -->|transform| DataObject
+        DataObject -.-> DS1
+        DataObject <-.-> DS2
+        DataObject -.-> DS3
+        subgraph with Data Science Python Tools:
+            DS1[Determine e.g., yield, conversion, selectivity]
+            DS2[Kinetic Modeling]
+            DS3[Visualization]
+        end
+    end
+    DataObject -->|transform| ExperimentalDocument
+    ExperimentalDocument["<b>📄 EnzymeML Document</b><br><br>
+    <i>Small Molecules</i><br>
+    <i>Proteins</i><br>
+    <i>Measurements</i><br>
+    <i>Reactions</i>"]
+```
+
+
+## 🔬 Photometric Data
+
+The [MTPHandler](https://fairchemistry.github.io/MTPHandler/) Python library streamlines the processing of photometric data from plate readers. It enables reading, processing, and exporting data from a variety of plate reader formats, blank correction, and concentration calculation in a scalable way.
+
+## 🌈 Chromatographic Data
+
+The [Chromatopy](https://fairchemistry.github.io/Chromatopy) Python library streamlines the processing of chromatographic time-course data. It enables reading, processing, and exporting data from a variety of chromatographic instruments, assignment of retention times to molecules, and concentration calculation in a scalable way.
+
+## 🧲 NMR Data
+
+The [NMRPy](https://nmrpy.readthedocs.io/en/latest/) Python library streamlines the processing of NMR time-course data.

mkdocs.yml

@@ -6,10 +6,6 @@ nav:
       - Session 1 - Overview: sessions/01-overview.md
       - Session 2 - Programming Setup: sessions/02-programming-setup.md
       - Session 3 - Read Experimental Data: sessions/03-read-experimental-data.md
-  - Use cases:
-      - Motivation: usecases/motivation.md
-      - Hackathon: usecases/hackathon.md
-
 
 theme:
   name: material