Merge pull request #11 from EnzymeML/update-docs

added content

docs/index.md

@@ -1,38 +1,72 @@
-# EnzymeML
+---
+title: The specific page with hidden the header 1 title 
+template: home.html
+---
+
+![EnzymeML Logo](img/enzymeml.svg){ width="10%" }
 
 ## What is EnzymeML?
 
-- **Schema**: EnzymeML standardizes biocatalytic reaction data, providing a structured, machine-readable format for storing and transferring enzyme kinetics data. 
-- **Programmatic Data Exchange**: It enables seamless integration with software tools, databases, and electronic laboratory notebooks to ensure consistent and interoperable data handling.
+EnzymeML is a standardized data format for catalytic reaction data, designed to ensure consistency and interoperability. It enables researchers to store, share, and enrich reaction data with detailed metadata in JSON or XML formats. Tools for reading and writing EnzymeML simplify data handling and ensure reproducibility, paving the way for data-driven research in biocatalysis.
+
+
+## Why EnzymeML?
+
+🧩 **Standardization**  
+  An EnzymeML Document is a standardized data format. It allows representing all data of a biocatalytic reaction in a consistent structure, independent of the experimental setup.
+
+🔄 **Data Exchange**  
+  EnzymeML facilitates the sharing of enzyme-catalyzed reaction data across scientists, tools, and databases, enabling collaborative research and integration of diverse datasets.
+
+⚡ **Efficiency**  
+  Standardization of data formats enables automation of data processing, minimizing manual steps, reducing errors, and saving time.
+
+🌟 **FAIR Data**  
+  EnzymeML makes data interoperable, constituting an important building block for making data FAIR.
+
+🤖 **No data - no AI-party**  
+  Structured data is the foundation for making your data compatible with machine learning.
+
 
 ## What comprises an EnzymeML Document?
 
-*figure illustrating the EnzymeML document structure*
+An EnzymeML Document organizes and contextualizes data of a biocatalytic reaction. It integrates information about enzymes, substrates, products, experimental conditions, and measurement data, providing a structured representation that links these elements seamlessly. No matter what reaction is assayed, the data is always in the same consistent structure, enabling efficient data analysis, visualization, and sharing.
 
-## Why EnzymeML?
+![EnzymeML Document Structure](img/enzymeml_parts.png)
+
+## How to use EnzymeML?
+
+Several tools are available to read and write EnzymeML Documents, ranging from graphical desktop applications to Python libraries, specifically tailored to streamline the processing of data from different analytical instruments:
+
+🔬 **Photometric Data**:  
+  The [MTPHandler](https://github.com/fairchemistry/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://github.com/fairchemistry/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://github.com/fairchemistry/NMRPy) Python library streamlines the processing of NMR time-course data.
 
-- **Data Exchange**:  
-EnzymeML facilitates the sharing of enzyme kinetics data across scientists, tools, and databases, enabling collaborative research and integration of diverse datasets.
-- **Efficiency**:  
-By standardizing and automating data processing, it minimizes manual steps, reducing errors and saving time.
-- **Compatibility**:  
-EnzymeML adheres to FAIR principles and integrates with the Systems Biology Markup Language (SBML), ensuring broad usability and extensibility.
-- **No data - no AI-party**:  
-EnzymeML is a data exchange format for biocatalysis catalyzed reaction data.
+📔 **EnzymeML Suite**:  
+  The [EnzymeML Suite](https://github.com/fairchemistry/EnzymeML-Suite) is a desktop application for creating, editing, and visualizing EnzymeML Documents.
 
-## How to Use EnzymeML?
+🛠️ **EnzymeML API**:  
+  The [pyenzyme](https://pyenzyme.readthedocs.io/en/latest/index.html#) Python library enables reading and writing EnzymeML Documents programmatically.
 
-Besides the generic [EnzymeML API](https://pyenzyme.readthedocs.io/en/latest/index.html#) to read and write EnzymeML documents programmatically, there are tools enabling the processing of data from different analytical instruments:
+## Use Cases
 
-- __Photometric Data__:  
+To get an impression of the tools, have a look at the following [use cases](use_cases.md).
 
-- __Chromatographic Data__:  
+<div class="grid cards" markdown>
 
-- __NMR Data__:  
+- 🔬 **[MTPHandler](https://github.com/fairchemistry/MTPHandler)**
+- 🌈 **[Chromatopy](https://github.com/fairchemistry/Chromatopy)**
+- 🧲 **[NMRPy](https://github.com/fairchemistry/NMRPy)**
+- 🛠️ **[pyenzyme](https://pyenzyme.readthedocs.io/en/latest/index.html#)**
 
+</div>
 
-Chromatopy A versatile library used for processing chromatographic data.
+## Cite EnzymeML
 
-MTPHandler A library for handling data from microtiter plate (MTP) readers. It supports various file formats and provides tools for data processing.
+If you use EnzymeML in your work, please cite the following paper:
 
-NMRPy A library that facilitates the analysis of Nuclear Magnetic Resonance (NMR) data. It helps in spectral processing, visualization, and interpretation of (time-course) NMR experiments.

docs/model.md

@@ -7,7 +7,7 @@ hide:
 
 This page provides comprehensive information about the structure and components of the data model, including detailed descriptions of the types and their properties, information on enumerations, and an overview of the ontologies used and their associated prefixes. Below, you will find a graph that visually represents the overall structure of the data model.
 
-??? quote "Graph"
+!!! quote "EnzymeML Data Model"
     ``` mermaid
     flowchart TB
         enzymemldocument(EnzymeMLDocument)

docs/publications.md

@@ -1,13 +1,13 @@
 # Publications
 
 ## 2024
-- [Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies (_Processes_)](https://www.nature.com/articles/s41592-022-01763-1)
+[Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies (_Processes_)](https://www.nature.com/articles/s41592-022-01763-1)
 
 ## 2023
-- [EnzymeML: seamless data flow and modeling of enzymatic data (_Nature Methods_)](https://www.nature.com/articles/s41592-022-01763-1)
+[EnzymeML: seamless data flow and modeling of enzymatic data (_Nature Methods_)](https://www.nature.com/articles/s41592-022-01763-1)
 
 ## 2022
-- [EnzymeML—a data exchange format for biocatalysis and enzymology (_FEBS_)](https://febs.onlinelibrary.wiley.com/doi/full/10.1111/febs.16318)
+[EnzymeML—a data exchange format for biocatalysis and enzymology (_FEBS_)](https://febs.onlinelibrary.wiley.com/doi/full/10.1111/febs.16318)
 
 ## 2021
-- [Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis (_ChemCatChem_)](https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cctc.202100822)
+[Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis (_ChemCatChem_)](https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cctc.202100822)

docs/stylesheets/extra.css

@@ -1,6 +1,10 @@
 :root {
-    --md-primary-fg-color: #40707D;
+    --md-primary-fg-color: #1D2E5B;
     --md-primary-fg-color--light: #ECB7B7;
-    --md-accent-fg-color: #90030C;
-    --md-primary-fg-color--dark: #90030C;
+    --md-accent-fg-color: #E71A4B;
+    --md-primary-fg-color--dark: #E71A4B;
+}
+
+.md-typeset h1:first-child {
+    display: none;
 }

docs/use_cases.md

@@ -1,4 +1,6 @@
-# Motivation
+# Use Cases
+
+## coming soon 🚧
 
 ## Photometric Plate Reader Data
 *ipynb example*

mkdocs.yml

@@ -1,17 +1,18 @@
-site_name: EnzymeML
 repo_url: https://github.com/EnzymeML/enzymeml-specifications
+site_name: EnzymeML
 edit_uri: edit/main/docs/
 
 theme:
   logo: img/enzml_logo.png
+  name: material
+  custom_dir: overrides
 
   font:
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   icon:
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     edit: material/pencil
     view: material/eye
-  name: material
   features:
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     - navigation.sections
@@ -46,6 +47,7 @@ nav:
 
 markdown_extensions:
   - attr_list
+  - md_in_html
   - admonition
   - pymdownx.details
   - pymdownx.superfences
@@ -65,6 +67,8 @@ markdown_extensions:
       permalink: true
 plugins:
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+
 
 extra_css:
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overrides/home.html

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overrides/partials/mycontent.html

@@ -0,0 +1,14 @@
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+This file was generated by shenweiyan - for custom home page and hidding h1 header
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