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03_workflow/034_workflow_management_concepts.html

@@ -233,7 +233,10 @@ <h3 id="models">Models<a class="headerlink" href="#models" title="Permanent link
 <h4 id="crisp-dm_cross-industry_standard_process_for_data_mining">CRISP-DM (Cross-Industry Standard Process for Data Mining)<a class="headerlink" href="#crisp-dm_cross-industry_standard_process_for_data_mining" title="Permanent link">#</a></h4>
 <p>CRISP-DM is a widely adopted model that provides a comprehensive framework for carrying out data mining projects. It consists of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. This model emphasizes a cyclical process allowing for continuous improvements based on insights gained from previous iterations.</p>
 <div align="center">
-<div class="mermaid">graph TD
+<div class="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
 
@@ -265,34 +268,38 @@ <h4 id="crisp-dm_cross-industry_standard_process_for_data_mining">CRISP-DM (Cros
 <h4 id="tdsp_team_data_science_process">TDSP (Team Data Science Process)<a class="headerlink" href="#tdsp_team_data_science_process" title="Permanent link">#</a></h4>
 <p>Developed by Microsoft, TDSP structures projects into five key phases: business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance. It stresses the importance of iterative learning and effective communication within data science teams.</p>
 <div align="center">
-<div class="mermaid">graph TD
+<div class="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 &amp; Understanding)
         D(Modeling)
         E(Deployment)
         F(End)
 
-
+        %% Conexiones entre nodos
         A --&gt; B
         B &lt;--&gt; C
-
         B &lt;--&gt; D
-
         C &lt;--&gt; D
-
         D &lt;--&gt; E
-
         E &lt;--&gt; C
-
         E --&gt; 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
 
 
@@ -301,7 +308,10 @@ <h4 id="tdsp_team_data_science_process">TDSP (Team Data Science Process)<a class
 <h4 id="kdd_knowledge_discovery_in_databases">KDD (Knowledge Discovery in Databases)<a class="headerlink" href="#kdd_knowledge_discovery_in_databases" title="Permanent link">#</a></h4>
 <p>KDD is a non-linear, iterative process focusing on the discovery of actionable knowledge from large volumes of data. This process involves selection, preprocessing, transformation, data mining, and the interpretation of the discovered patterns.</p>
 <div align="center">
-<div class="mermaid">graph TD
+<div class="mermaid">---
+title: KDD (Knowledge Discovery in Databases)
+---
+graph TD
     subgraph KDD
         style KDD fill:#EEEEEE, stroke:#333, stroke-width:2px
 
@@ -334,7 +344,10 @@ <h4 id="kdd_knowledge_discovery_in_databases">KDD (Knowledge Discovery in Databa
 <h4 id="guos_data_science_workflow">Guo's Data Science Workflow<a class="headerlink" href="#guos_data_science_workflow" title="Permanent link">#</a></h4>
 <p>Guo's model is particularly useful for ensuring that data science projects are reproducible and transparent. It suggests a workflow where programming and exploratory data analysis are carried out in tandem, allowing for a deeper understanding and more robust analysis.</p>
 <div align="center">
-<div class="mermaid">graph LR
+<div class="mermaid">---
+title: Guo's Data Science Workflow
+---
+graph LR
 
     subgraph GUO
         style GUO fill:#FFFFFF, stroke:#333, stroke-width:2px
@@ -407,6 +420,29 @@ <h4 id="guos_data_science_workflow">Guo's Data Science Workflow<a class="headerl
     class A,B,C,D,E,F,G,H,I,J,K,L,M,N,O miestilo
 
 </div>
+</div>
+<div class="mermaid">sequenceDiagram
+
+    participant Sarah
+
+    participant John
+
+    Sarah-&gt;&gt;John: Hello John, how are you?
+
+    John--&gt;&gt;Sarah: Not too bad, thanks!
+
+
+</div>
+<div class="mermaid">gantt
+ title Example Gantt diagram
+    dateFormat  YYYY-MM-DD
+    section Team 1
+    Research &amp; requirements :done, a1, 2020-03-08, 2020-04-10
+    Review &amp; documentation : after a1, 20d
+    section Team 2
+    Implementation      :crit, active, 2020-03-25  , 20d
+    Testing      :crit, 20d
+
 </div>
 </div>
 </div><footer>

05_acquisition/0550_data_acquisition_and_preparation.html

@@ -371,7 +371,7 @@ <h3 id="the_importance_of_data_cleaning_in_omics_sciences_focus_on_metabolomics"
 <p><strong>Feature Selection</strong>: 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.</p>
 </li>
 </ul>
-<p>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.</p>
+<p>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 <a href="https://xcmsonline.scripps.edu/">XCMS</a>, <a href="https://www.metaboanalyst.ca/">MetaboAnalyst</a>, and <a href="https://mzmine.github.io/mzmine_documentation/">MZmine</a>, which offer comprehensive functionalities for data preprocessing, quality control, and data cleaning in metabolomics studies.</p>
 
             </div>
           </div><footer>

index.html

@@ -354,5 +354,5 @@ <h3>Model Implementation and Maintenance</h3>
 
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