⚡ Update, general update.

imarranz · Dec 23, 2024 · 3be78eb · 3be78eb
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diff --git a/srcsite/03_workflow/034_workflow_management_concepts.md b/srcsite/03_workflow/034_workflow_management_concepts.md
@@ -24,6 +24,9 @@ CRISP-DM is a widely adopted model that provides a comprehensive framework for c
 <div align='center'>
 
 ```mermaid
+---
+title: CRISP-DM (Cross-Industry Standard Process for Data Mining)
+---
 graph TD
     subgraph CRISP-DM
         style CRISP-DM fill:#EEEEEE, stroke:#333, stroke-width:2px
@@ -62,34 +65,38 @@ Developed by Microsoft, TDSP structures projects into five key phases: business
 <div align='center'>
 
 ```mermaid
+---
+title: TDSP (Team Data Science Process)
+---
 graph TD
 
+    %% Definición del subgrafo y estilo
     subgraph TDSP
         style TDSP fill:#EEEEEE, stroke:#333, stroke-width:2px
 
+        %% Definición de los nodos
         A(Start)
         B(Business Understanding)
         C(Data Accquisition & Understanding)
         D(Modeling)
         E(Deployment)
         F(End)
 
-
+        %% Conexiones entre nodos
         A --> B
         B <--> C
-
         B <--> D
-
         C <--> D
-
         D <--> E
-
         E <--> C
-
         E --> F
     end
 
+    %% Definición de estilo personalizado para los nodos
     classDef miestilo fill:#8dc5e8,stroke:#333,stroke-width:2px
+    classDef subgraphTitle fill:#ffffff,stroke:#333,stroke-width:0px,font-size:16px
+
+    %% Aplicación del estilo personalizado a los nodos
     class A,B,C,D,E,F miestilo
 
 
@@ -104,6 +111,9 @@ KDD is a non-linear, iterative process focusing on the discovery of actionable k
 <div align='center'>
 
 ```mermaid
+---
+title: KDD (Knowledge Discovery in Databases)
+---
 graph TD
     subgraph KDD
         style KDD fill:#EEEEEE, stroke:#333, stroke-width:2px
@@ -143,6 +153,9 @@ Guo's model is particularly useful for ensuring that data science projects are r
 <div align='center'>
 
 ```mermaid
+---
+title: Guo's Data Science Workflow
+---
 graph LR
 
     subgraph GUO
@@ -218,3 +231,30 @@ graph LR
 ```
 
 </div>
+
+```mermaid
+sequenceDiagram
+
+    participant Sarah
+
+    participant John
+
+    Sarah->>John: Hello John, how are you?
+
+    John-->>Sarah: Not too bad, thanks!
+
+
+```
+
+```mermaid
+gantt
+ title Example Gantt diagram
+    dateFormat  YYYY-MM-DD
+    section Team 1
+    Research & requirements :done, a1, 2020-03-08, 2020-04-10
+    Review & documentation : after a1, 20d
+    section Team 2
+    Implementation      :crit, active, 2020-03-25  , 20d
+    Testing      :crit, 20d
+
+```
diff --git a/srcsite/05_acquisition/0550_data_acquisition_and_preparation.md b/srcsite/05_acquisition/0550_data_acquisition_and_preparation.md
@@ -149,4 +149,4 @@ To ensure robust and reliable metabolomic data analysis, several techniques are
 
   * **Feature Selection**: In metabolomics, feature selection methods help identify the most relevant metabolites associated with the biological question under investigation. By reducing the dimensionality of the data, these techniques improve model interpretability and enhance the detection of meaningful metabolic patterns.
 
-Data cleaning in metabolomics is a rapidly evolving field, and several tools and algorithms have been developed to address these challenges. Notable software packages include XCMS, MetaboAnalyst, and MZmine, which offer comprehensive functionalities for data preprocessing, quality control, and data cleaning in metabolomics studies.
+Data cleaning in metabolomics is a rapidly evolving field, and several tools and algorithms have been developed to address these challenges. Notable software packages include [XCMS](https://xcmsonline.scripps.edu/), [MetaboAnalyst](https://www.metaboanalyst.ca/), and [MZmine](https://mzmine.github.io/mzmine_documentation/), which offer comprehensive functionalities for data preprocessing, quality control, and data cleaning in metabolomics studies.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -149,4 +149,4 @@ To ensure robust and reliable metabolomic data analysis, several techniques are

		* Feature Selection: In metabolomics, feature selection methods help identify the most relevant metabolites associated with the biological question under investigation. By reducing the dimensionality of the data, these techniques improve model interpretability and enhance the detection of meaningful metabolic patterns.

		Data cleaning in metabolomics is a rapidly evolving field, and several tools and algorithms have been developed to address these challenges. Notable software packages include XCMS, MetaboAnalyst, and MZmine, which offer comprehensive functionalities for data preprocessing, quality control, and data cleaning in metabolomics studies.
		Data cleaning in metabolomics is a rapidly evolving field, and several tools and algorithms have been developed to address these challenges. Notable software packages include [XCMS](https://xcmsonline.scripps.edu/), [MetaboAnalyst](https://www.metaboanalyst.ca/), and [MZmine](https://mzmine.github.io/mzmine_documentation/), which offer comprehensive functionalities for data preprocessing, quality control, and data cleaning in metabolomics studies.