Update README.md

typos and broken link

Author: haeussma

README.md

@@ -6,10 +6,10 @@
 
 ## ℹ️ Overview
 
-`chromatopy` is a Python package which aims to streamline the data processing and analysis of chromatographic reaction data. It allows to process raw or pre-processed chromatographic data, enrich it with metadata such as reaction time, temperature, pH, and initial concentrations of reaction components. Finally, the peaks of interest can be aggregated, concentrations calculated, and the time-course data for each analyte transformed to EnzymeML data.
+`chromatopy` is a Python package that aims to streamline the data processing and analysis of chromatographic reaction data. It allows processing raw or pre-processed chromatographic data, enriching it with metadata such as reaction time, temperature, pH, and initial concentrations of reaction components. Finally, the peaks of interest can be aggregated, concentrations calculated, and the time-course data for each analyte transformed to EnzymeML data.
 
-`chromatopy` is designed to work seamlessly with [OpenChrom](https://lablicate.com/platform/openchrom), enabling batch processing of proprietary chromatographic data. After processing in OpenChrom and exporting to an open file format, the data can be further analyzed in Jupyter Notebooks using `chromatopy`. This allows for the creation and application of calibration curves and the generation of EnzymeML files for subsequent data analysis.
-For some output formats, `chromatopy` provides a direct interface to read in data. For more information on the supported file formats and data preparation to use the `chromatopy` workflow, refer to the [_Data Preparation_](supported_formats.md) section.
+`chromatopy` is designed to work seamlessly with [OpenChrom](https://lablicate.com/platform/openchrom), enabling batch processing of proprietary chromatographic data. After processing in OpenChrom and exporting to an open file format, the data can be further analyzed in Jupyter Notebooks using `chromatopy`. This allows for creating and applying calibration curves and generating EnzymeML files for subsequent data analysis.
+For some output formats, `chromatopy` provides a direct interface to read in data. For more information on the supported file formats and data preparation to use the `chromatopy` workflow, refer to the [data preparation](https://fairchemistry.github.io/chromatopy/supported_formats/#supported-formats) section.
 
 ``` mermaid
 graph LR
@@ -79,7 +79,7 @@ Create calibration standard
 - **🌱 Low friction data processing**   
 Leave behind data processing in spreadsheet applications and directly start with data analysis based on raw data.
 - **🧪 Enrich reaction data with metadata**  
-Assign metadata like initial concentrations of reactants, temperature, pH, etc. to reaction data to yield modeling-ready data.
+Assign metadata like initial concentrations of reactants, temperature, pH, etc., to reaction data to yield modeling-ready data.
 - **📈 Create and apply calibration curves**  
 Create calibrators for your analytes and use them throughout your data analysis for seamless concentration calculation.
 - **📂 FAIR data**  
@@ -93,4 +93,4 @@ Install `chromatopy` using `pip`:
 pip install git+https://github.com/FAIRChemistry/chromatopy.git
 ```
 
-For more information an examples, please refer to the [Documentation](https://fairchemistry.github.io/chromatopy/) section.
+For more information and examples, please refer to the [Documentation](https://fairchemistry.github.io/chromatopy/) section.