fix json ld failures

cc #5362

topics/community/tutorials/sig_define/tutorial.md

@@ -37,6 +37,8 @@ follow_up_training:
         - sig_create
 ---
 
+In Galaxy, the term *[Special Interest Group](https://galaxyproject.org/community/sig)* (**SIG**) refers to a dedicated scientific community that crosses individual lab boundaries and wants to collaborate, share resources, support each other, and/or collectively advocate on a given theme. We have **SIGs** based on [**region**](https://galaxyproject.org/community/sig/#regional-communities), [**domain of science**](https://galaxyproject.org/community/sig/#communities-of-practice), and more. You might consider that a **SIG** covers any group of like-minded Galaxy enthusiasts not currently combined into a [**Working Group**](https://galaxyproject.org/community/wg/).
+
 > <agenda-title></agenda-title>
 >
 > In this tutorial, we will cover:
@@ -46,10 +48,6 @@ follow_up_training:
 >
 {: .agenda}
 
-# Special Interest Groups
-
-In Galaxy, the term *[Special Interest Group](https://galaxyproject.org/community/sig)* (**SIG**) refers to a dedicated scientific community that crosses individual lab boundaries and wants to collaborate, share resources, support each other, and/or collectively advocate on a given theme. We have **SIGs** based on [**region**](https://galaxyproject.org/community/sig/#regional-communities), [**domain of science**](https://galaxyproject.org/community/sig/#communities-of-practice), and more. You might consider that a **SIG** covers any group of like-minded Galaxy enthusiasts not currently combined into a [**Working Group**](https://galaxyproject.org/community/wg/).
-
 <div class='right'><img src="../../images/mind_map.svg" alt="Person looking at a diagram with a central rectangle connected to many other nodes representing people and connections" width="25" /></div>
 
 You can find a directory of current [**SIGs** below](https://galaxyproject.org/community/sig/).

topics/dev/tutorials/tool-generators-advanced/tutorial.md

@@ -51,19 +51,17 @@ recordings:
 
 ---
 
+Galaxy users who write and share scripts useful for scientific analyses are likely to be reading this material, perhaps after seeing the "Hello Galaxy"
+demonstration. It was written to help you find out about the capabilities and limits of the ToolFactory by experimenting with it yourself.
+It is hoped that this advanced tutorial will introduce some features that potentially make the ToolFactory useful in your work.
+
 > <agenda-title></agenda-title>
 >
 > 1. TOC
 > {:toc}
 >
 {: .agenda}
 
----
-
-Galaxy users who write and share scripts useful for scientific analyses are likely to be reading this material, perhaps after seeing the "Hello Galaxy"
-demonstration. It was written to help you find out about the capabilities and limits of the ToolFactory by experimenting with it yourself.
-It is hoped that this advanced tutorial will introduce some features that potentially make the ToolFactory useful in your work.
-
 # Background and a user's guide to this training material
 
 This training material is unlike most other GTN tutorials. There is no specific tool building curriculum on offer because it is hard to know how

topics/single-cell/tutorials/GO-enrichment/tutorial.md

@@ -37,6 +37,11 @@ contributors:
 - MennaGamal
 ---
 
+In the tutorial [Filter, plot and explore single-cell RNA-seq data with Scanpy]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}), we took an important step in our single-cell RNA sequencing analysis by identifying marker genes for each of the clusters in our dataset. These marker genes are crucial, as they help us distinguish between different cell types and states, giving us a clearer picture of the cellular diversity within our samples.
+However, simply identifying marker genes is just the beginning. To truly understand what makes each cluster unique, we need to dive deeper into the biological functions these genes are involved in. This is where Gene Ontology (GO) enrichment analysis comes into play.
+We will perform GO enrichment analysis as a type of over-representation analysis (ORA), ORA is a statistical method that determines whether genes from pre-defined sets (e.g. genes belonging to a specific GO term) are expressed more than would be expected in a subset of your data. The most commonly used statistical tests are Fischer's exact test and hypergeometric test, more details about them are explained in the tutorial slides.
+
+
 > <agenda-title></agenda-title>
 >
 > In this tutorial, we will cover:
@@ -46,13 +51,6 @@ contributors:
 >
 {: .agenda}
 
-# Introduction
-
-In the tutorial [Filter, plot and explore single-cell RNA-seq data with Scanpy]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}), we took an important step in our single-cell RNA sequencing analysis by identifying marker genes for each of the clusters in our dataset. These marker genes are crucial, as they help us distinguish between different cell types and states, giving us a clearer picture of the cellular diversity within our samples.
-However, simply identifying marker genes is just the beginning. To truly understand what makes each cluster unique, we need to dive deeper into the biological functions these genes are involved in. This is where Gene Ontology (GO) enrichment analysis comes into play.
-We will perform GO enrichment analysis as a type of over-representation analysis (ORA), ORA is a statistical method that determines whether genes from pre-defined sets (e.g. genes belonging to a specific GO term) are expressed more than would be expected in a subset of your data. The most commonly used statistical tests are Fischer's exact test and hypergeometric test, more details about them are explained in the tutorial slides.
-
-
 # Data description
 
 In this tutorial will use the following datasets: