diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index dc44201ca5b23e..b3745db8ee9e28 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,9 +1,13 @@
-!!! DELETE THIS BEFORE SUBMITTING!!! It's just a checklist to help you :)
+TODO: Describe your changes here..
 
-1. Give your pull request a **good title**
-    - e.g. "Fix typo in ansible-galaxy tutorial" or "Add new transcriptomics tutorial covering a new sequencing technology"
-2. Describe your changes in detail, list anything you still need some help with or things that are still TODO
-3. Check that your images are allowed to be re-hosted by the GTN!
-4. Not ready for review yet? Make it a **Draft** pull request
+<!-- Contributor Checklist
+
+1. Give your pull request a descriptive title
+2. Describe your changes in detail at the top of this text box
+3. List anything you still need some help with or things that are still TODO
+4. Check that your images are allowed to be re-hosted by the GTN!
+5. Not ready for review yet? Make it a **Draft** pull request
    - Once you are done making changes, choose **Ready for Review**
    - Then the automated tests will run and we will know to review and merge it
+
+-->
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 96cc207e8f5421..a626f68365b535 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -51,11 +51,12 @@ jobs:
             ${{ runner.os }}-gems-
       - name: Install dependencies
         run: |
-          sudo apt-get install -y build-essential libxi-dev libglu1-mesa-dev libglew-dev pkg-config # for node
+          sudo apt-get install -y build-essential libxi-dev libglu1-mesa-dev libglew-dev pkg-config graphviz # for node
           gem install bundler
           bundle config path vendor/bundle
           bundle install --jobs 4 --retry 3
-          npm install
+          rm -f package.json package-lock.json yarn.lock
+          npm install markdown-yaml-metadata-parser lunr
           bundle pristine ffi
       # END Dependencies
 
@@ -68,7 +69,6 @@ jobs:
           make annotate ACTIVATE_ENV=pwd
           curl -L https://docs.google.com/spreadsheets/d/1NfZhi5Jav7kl9zFCkeb7rIC2F8xW1isruv1TeO4WpNI/export\?format\=tsv | ruby bin/prepare_feedback.rb
           curl -L https://hexylena.github.io/toolshed-version-database/tool-meta.json > metadata/tool-meta.json
-          npm install
           make rebuild-search-index ACTIVATE_ENV=pwd
           cat metadata/swagger.yaml | python bin/yaml2json.py > api/swagger.json
           rdoc bin _plugins/ --output gtn_rdoc
diff --git a/.github/workflows/monthly-release-backfill.yml b/.github/workflows/monthly-release-backfill.yml
index b8171611e85e6f..af77c5010ca076 100644
--- a/.github/workflows/monthly-release-backfill.yml
+++ b/.github/workflows/monthly-release-backfill.yml
@@ -47,7 +47,8 @@ jobs:
           pip install pyyaml
           bundle config path vendor/bundle
           bundle install --jobs 4 --retry 3
-          npm install
+          rm -f package.json package-lock.json yarn.lock
+          npm install markdown-yaml-metadata-parser lunr
           bundle pristine ffi
       # END Dependencies
 
@@ -68,7 +69,6 @@ jobs:
           sed -i s"|^github_repository_branch: .*|github_repository_branch: '${SOURCE_TAG}'|g" _config.yml
           sed -i s"|^title: .*|title: 'GTN Archive ${SOURCE_TAG}'|g" _config.yml
           curl -L https://hexylena.github.io/toolshed-version-database/tool-meta.json > metadata/tool-meta.json
-          npm install
           make rebuild-search-index ACTIVATE_ENV=pwd
           cat metadata/swagger.yaml | python bin/yaml2json.py > api/swagger.json
           JEKYLL_ENV=production bundle exec jekyll build --strict_front_matter -d _site/training-material
diff --git a/.github/workflows/monthly-release.yml b/.github/workflows/monthly-release.yml
index 8af9d9e70c7280..95fec41a22a680 100644
--- a/.github/workflows/monthly-release.yml
+++ b/.github/workflows/monthly-release.yml
@@ -54,7 +54,8 @@ jobs:
           pip install pyyaml
           bundle config path vendor/bundle
           bundle install --jobs 4 --retry 3
-          npm install
+          rm -f package.json package-lock.json yarn.lock
+          npm install markdown-yaml-metadata-parser lunr
           bundle pristine ffi
       # END Dependencies
 
@@ -70,7 +71,6 @@ jobs:
           sed -i s"|^github_repository_branch: .*|github_repository_branch: '${SOURCE_TAG}'|g" _config.yml
           sed -i s"|^title: .*|title: 'GTN Archive ${SOURCE_TAG}'|g" _config.yml
           curl -L https://hexylena.github.io/toolshed-version-database/tool-meta.json > metadata/tool-meta.json
-          npm install
           make rebuild-search-index ACTIVATE_ENV=pwd
           cat metadata/swagger.yaml | ruby bin/yaml2json.rb > api/swagger.json
           JEKYLL_ENV=production bundle exec jekyll build --strict_front_matter -d _site/training-material
diff --git a/.github/workflows/social.yml b/.github/workflows/social.yml
new file mode 100644
index 00000000000000..0c371766d3c83f
--- /dev/null
+++ b/.github/workflows/social.yml
@@ -0,0 +1,74 @@
+name: "[Social] Autogenerate missing social media cards"
+
+on:
+  schedule:
+    # * is a special character in YAML so you have to quote this string
+    # We'll run this daily at noon.
+    - cron:  '0 12 * * *'
+  workflow_dispatch:
+    inputs:
+      all_old:
+        type: string
+        description: If you want to re-generate ALL images set to a non-empty value
+
+jobs:
+  runner-job:
+    if: github.repository_owner == 'galaxyproject'
+    runs-on: ubuntu-latest
+    steps:
+      # Shallow should be fine for video
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 500
+
+      # BEGIN Dependencies
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+          architecture: 'x64'
+      - name: Install dependencies
+        run: |
+          sudo apt-get install -y imagemagick
+          mkdir -p ~/.fonts/
+          wget https://galaxy-training.s3.amazonaws.com/social/recursive.ttf -O ~/.fonts/recursive.ttf
+          fc-cache -rv
+      # END Dependencies
+
+
+      # We get the previous build ID to know how many videos were changed since
+      # then.
+      #
+      # We *could* fetch the diff in the past N days but if a build fails for
+      # any reason, we'd lose those changes permanently and things would never
+      # get built.
+      #
+      # So instead we just track the last successfully built timestamp
+      - name: Previous build ID
+        id: build_id
+        run: |
+          echo "timestamp=$(curl https://galaxy-training.s3.amazonaws.com/social/timestamp.txt -f 2>/dev/null || echo none)" >> $GITHUB_ENV
+
+          # Support regenerating all
+          if [[ "${{ github.event.inputs.selected_tag }}" != "" ]]; then
+            echo "timestamp=0" >> $GITHUB_ENV
+          fi
+
+      # The actual compilation process deposits everything in the ./social/ folder.
+      # And requires fetching metadata from the live website (api/social-meta.json)
+      - name: Build Social Media Cards
+        run: |
+          mkdir -p social/
+          python bin/social-cards.py $PREVIOUS_TIMESTAMP
+        env:
+          PREVIOUS_TIMESTAMP: ${{ env.timestamp }}
+
+      # Deploy step, scary.
+      - uses: jakejarvis/s3-sync-action@v0.5.1
+        with:
+          args: --acl public-read --follow-symlinks
+        env:
+          AWS_S3_BUCKET: galaxy-training
+          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          SOURCE_DIR: './social/'
+          DEST_DIR: 'social/'
diff --git a/.github/workflows/video.yml b/.github/workflows/video.yml
index 30aa5d693d5b73..0ec03340f8a73a 100644
--- a/.github/workflows/video.yml
+++ b/.github/workflows/video.yml
@@ -10,7 +10,7 @@ on:
 jobs:
   runner-job:
     if: github.repository_owner == 'galaxyproject'
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     services:
       # Label used to access the service container
       mozillatts:
diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml
index 8dd3e8d89b4dfa..10ab150f87ef43 100644
--- a/CONTRIBUTORS.yaml
+++ b/CONTRIBUTORS.yaml
@@ -709,7 +709,7 @@ angiolini:
     joined: 2024-05
     elixir_node: uk
     orcid: 0000-0002-0814-8830
-    
+
 emmaleith:
     name: Emma Leith
     email: leith023@umn.edu
@@ -844,7 +844,7 @@ GabrielaLopez:
     orcid: 0000-0002-0814-8830
     joined: 2023-01
     elixir_node: uk
-    
+
 gallardoalba:
     name: Cristóbal Gallardo
     joined: 2020-11
@@ -914,6 +914,7 @@ hechth:
   joined: 2020-01
   email: helge.hecht@recetox.muni.cz
   orcid: 0000-0001-6744-996X
+  elixir_node: cz
 
 heylf:
     name: Florian Heyl
@@ -953,6 +954,13 @@ hexylena:
       lat: 51.91
       lon: 4.46
 
+hfraser:
+  name: Heather Fraser
+  joined: 2024-09
+  email: heather.fraser@open.ac.uk
+  elixir_node: uk
+  github: false
+
 hmenager:
     name: Hervé Ménager
     joined: 2017-09
@@ -1001,7 +1009,7 @@ hujambo-dunia:
 igcbioinformatics:
     name: IGC Bioinformatics Unit
     joined: 2018-12
-    
+
 igormakunin:
     name: Igor Makunin
     joined: 2024-09
@@ -1364,7 +1372,7 @@ LonsBio:
 loraine-gueguen:
     name: Loraine Guéguen
     joined: 2017-09
-    
+
 lybCNU:
     name: Yongbin Li
     joined: 2024-08
@@ -1554,7 +1562,7 @@ Melkeb:
 melpetera:
     name: Mélanie Petera
     joined: 2019-06
-    
+
 meltemktn:
     name: Meltem Kutnu
     joined: 2024-09
@@ -1565,6 +1573,12 @@ mgramm1:
     orcid: 0000-0002-4217-5277
     joined: 2021-03
 
+mhintze:
+    name: Mark Hintze
+    email: mark.hintze@open.ac.uk
+    joined: 2024-09
+    github: false
+
 miaomiaozhou88:
     name: Miaomiao Zhou
     email: m.zhou1@avans.nl
@@ -1677,7 +1691,7 @@ nakucher:
     name: Natalie Kucher
     email: nkucher3@jhu.edu
     joined: 2023-06
-    
+
 natalie-wa:
     name: Natalie Whitaker-Allen
     email: nwhitak5@jh.edu
@@ -1830,6 +1844,19 @@ RareSeas:
     name: Adelaide Rhodes
     joined: 2021-05
 
+paige-kulzer:
+  name: Paige Kulzer
+  joined: 2024-10
+  email: pkulzer@charlotte.edu
+  linkedin: paige-kulzer-210b97274
+  contact_for_training: true
+  bio:
+    "My current role involves bioinformatics, data management, and training for a genome browser called the Integrated Genome Browser (IGB)."
+  location:
+    country: US
+    lat: 35.227
+    lon: -80.843
+
 PatCapon39:
     name: Patrick Capon
     joined: 2024-02
@@ -1874,7 +1901,7 @@ paulzierep:
 
 pavanvidem:
     name: Pavankumar Videm
-    orcid: 0000-0002-5192-126X  
+    orcid: 0000-0002-5192-126X
     joined: 2017-09
     affiliations:
       - uni-freiburg
@@ -2140,7 +2167,7 @@ shwdey:
     name: Shweta Pandey
     email: shwphd@gmail.com
     joined: 2024-06
-    
+
 simonbray:
     name: Simon Bray
     joined: 2019-05
@@ -2183,6 +2210,11 @@ slugger70:
 
         "Have I told you about my watch?"
 
+SNG888:
+    name: Sandra Ng
+    email: s.ng@qmul.ac.uk
+    joined: 2023-07
+
 Sofokli5:
     name: Sofoklis Keisaris
     email: sofoskeisaris@gmail.com
@@ -2263,6 +2295,12 @@ tb143:
     name: Tim Beck
     joined: 2023-09
 
+tbrown91:
+    name: Tom Brown
+    email: brown@izw-berlin.de
+    orcid: 0000-0001-8293-4816
+    joined: 2024-08
+
 tcollins2011:
     name: Tyler Collins
     joined: 2024-09
@@ -2293,6 +2331,16 @@ themudo:
     name: Gonçalo Themudo
     joined: 2021-10
 
+ThibaudGlinez:
+    name: Glinez Thibaud
+    email: thibaud.glinez@mnhn.fr
+    orcid: 0009-0006-8655-7505
+    joined: 2024-02
+    elixir_node: fr
+    affiliations:
+      - pndb
+      - elixir-europe
+
 thobalose:
     name: Thoba Lose
     joined: 2019-11
@@ -2313,7 +2361,7 @@ timonschlegel:
     name: Timon Schlegel
     orcid: 0009-0001-3228-105X
     joined: 2024-05
-    
+
 timothygriffin:
     name: Timothy J. Griffin
     email: tgriffin@umn.edu
@@ -2368,7 +2416,7 @@ tsenapathi:
 
 trungN:
     name: Nguyen Vo Minh Trung
-    joined: 2024-09    
+    joined: 2024-09
 
 TKlingstrom:
     name: Tomas Klingström
@@ -2423,6 +2471,8 @@ wee-snufkin:
     name: Julia Jakiela
     email: juliajot36@gmail.com
     joined: 2022-06
+    elixir_node: uk
+    orcid: 0009-0001-2017-8805
 
 willdurand:
     name: William Durand
@@ -2452,6 +2502,12 @@ wm75:
       - uni-freiburg
       - elixir-europe
 
+wverastegui:
+    name: Wudmir Rojas
+    email: wudmir.rojas@recetox.muni.cz
+    joined: 2024-10
+    elixir_node: cz
+
 xiliu:
     name: Xi Liu
     github: false
@@ -2505,6 +2561,11 @@ yvesvdb:
     affiliations:
       - ifb
 
+zargham-ahmad:
+    name: Zargham Ahmad
+    email: zargham.ahmad@recetox.muni.cz
+    joined: 2024-10
+    elixir_node: cz
 
 SNG888:
     name: Sandra Ng
diff --git a/_includes/tutorial_list.html b/_includes/tutorial_list.html
index 5d869ba99575ee..a49f8713d4e49c 100644
--- a/_includes/tutorial_list.html
+++ b/_includes/tutorial_list.html
@@ -44,7 +44,12 @@
           {% endif %}
 
           {% if material.layout == 'custom' %} <!-- e.g. for events -->
-            <td> {{material.title | markdownify }} </td>
+            <td>
+              {{material.title | markdownify }} 
+              <div class="duplicate" style="display: none;">
+                {{material.description | markdownify }}
+              </div>
+            </td>
             <td colspan="3"> {{material.description | markdownify }}</td>
           {% else %}
           <td class="tutorial_title">
diff --git a/_layouts/base.html b/_layouts/base.html
index c1c245d072c7e5..293930649ee22f 100644
--- a/_layouts/base.html
+++ b/_layouts/base.html
@@ -46,7 +46,13 @@
         {%- elsif page.tags contains "gat" %}{% assign coverimage = "assets/images/gat.png" %}
         {%- else %}{% assign coverimage = "assets/images/GTNLogo1000.png" %}{% endif %}
         {% assign og_image = page.og_image | default: page['cover-image'] | default: topic.og_image | default: coverimage | default: "assets/images/GTNLogo1000.png" %}
+	{%- if page.layout == "tutorial_hands_on" or page.layout == "tutorial_slides" or page.layout == "event" or page.layout == "workflow" or page.layout == "faq" or page.layout == "introduction_slides" or page.layout == "learning-pathway"  -%}
+	<meta property="og:image" content="https://galaxy-training.s3.amazonaws.com/social{{ page.url | replace: ".html", ".png" }}">
+	{% elsif page.layout == "topic" or page.layout == "recordings" %}
+	<meta property="og:image" content="https://galaxy-training.s3.amazonaws.com/social{{ page.url }}index.png">
+	{%- else -%}
 	<meta property="og:image" content="{% if og_image contains 'https://' %}{{ og_image }}{% else %}{{ site.url }}{{ site.baseurl }}/{{ og_image }}{% endif %}">
+	{%- endif -%}
 
         {%- if page.layout == "tutorial_hands_on" -%}
         <script type="application/ld+json">
diff --git a/_layouts/base_slides.html b/_layouts/base_slides.html
index 73d5274023dc94..5ca78978f30902 100644
--- a/_layouts/base_slides.html
+++ b/_layouts/base_slides.html
@@ -41,7 +41,11 @@
         {%- elsif page.tags contains "gat" %}{% assign coverimage = "/assets/images/gat.png" %}
         {%- else %}{% assign coverimage = "/assets/images/GTNLogo1000.png" %}{% endif %}
         {% assign og_image = page.og_image | default: page['cover-image'] | default: topic.og_image | default: coverimage | default: "assets/images/GTNLogo1000.png" %}
+	{%- if page.layout == "tutorial_slides" or page.layout == "introduction_slides" -%}
+	<meta property="og:image" content="https://galaxy-training.s3.amazonaws.com/social{{ page.url | replace: ".html", ".png" }}">
+	{%- else -%}
 	<meta property="og:image" content="{% if og_image contains 'https://' %}{{ og_image }}{% else %}{{ site.url }}{{ site.baseurl }}/{{ og_image }}{% endif %}">
+	{% endif %}
 
         <script type="application/ld+json">
             {% include _includes/material.jsonld material=page topic=topic site=site %}
diff --git a/_layouts/contributor_index.html b/_layouts/contributor_index.html
index 4d9e844fde9e82..38a24a80a4542b 100644
--- a/_layouts/contributor_index.html
+++ b/_layouts/contributor_index.html
@@ -23,9 +23,9 @@
 					<h1>
 						{{ entity.name | default: page.contributor }}
 					</h1>
-				{% if entity.funder_name %}
-					<p>Funded by {{ entity.funder_name }}</p>
-				{% endif %}
+					{% if entity.funder%}
+					<p>The activities listed below were partially funded by this project.</p>
+					{% endif %}
 				</hgroup>
 
 				{% if entity.bio %}
@@ -34,9 +34,6 @@ <h1>
 				</p>
 				{% endif %}
 
-				{% if entity.funder %}
-				<div class="label label-default label-large" style="{{ 'GTN Funder' | colour_tag }}">GTN Funder</div>
-				{% endif %}
 				{% if entity.in_memoriam %}
 				<div class="alert alert-dark" role="alert">
 					<h2 class="alert-heading">In Memoriam</h2>
diff --git a/_layouts/event.html b/_layouts/event.html
index 8e325857253495..d9e10e16f8b023 100644
--- a/_layouts/event.html
+++ b/_layouts/event.html
@@ -38,9 +38,11 @@ <h1>{{ event.title }}</h1>
   <li class="nav-item" role="presentation">
     <button class="nav-link active" id="home-tab" data-toggle="tab" data-target="#overview" type="button" role="tab" aria-controls="overview" aria-selected="true">Course Overview</button>
   </li>
+  {% if event.program %}
   <li class="nav-item" role="presentation">
     <button class="nav-link" id="program-tab" data-toggle="tab" data-target="#program" type="button" role="tab" aria-controls="program" aria-selected="false">Program</button>
   </li>
+  {% endif %}
   {% if event.infrastructure %}
   <li class="nav-item" role="presentation">
      <button class="nav-link" id="setup-tab" data-toggle="tab" data-target="#setup" type="button" role="tab" aria-controls="setup" aria-selected="false" >Setup</button>
@@ -61,8 +63,10 @@ <h1>{{ event.title }}</h1>
     <p>{{ event.description | markdownify }}</p>
     <p>{{ content | markdownify }}</p>
 
+    {% if event.audience %}
     <h3>Who is this course for?</h3>
     {{ event.audience }}
+    {% endif %}
 
     {% if event.requirements %}
     <h3 id="requirements">Requirements</h3>
@@ -424,10 +428,12 @@ <h3>Promote the Event</h3>
           {% elsif event.registration.open == false %}
           <a href="#" class="btn btn-primary btn-lg btn-block">Registration opens soon!</a>
           {% else %}
-		  <a href="{{ event.registration.link }}" class="btn btn-primary btn-lg btn-block">Register Now!</a>
-            {% if event.registration.deadline %}
-            <p style="margin-top:1em"><b>Deadline:</b> {{event.registration.deadline | date: "%-d %B %Y" }}</p>
-            {% endif %}
+            {% if event.registration.link %}
+		    <a href="{{ event.registration.link }}" class="btn btn-primary btn-lg btn-block">Register Now!</a>
+              {% if event.registration.deadline %}
+              <p style="margin-top:1em"><b>Deadline:</b> {{event.registration.deadline | date: "%-d %B %Y" }}</p>
+              {% endif %}
+            {%endif %}
           {% endif %}
 		{% endif %}
         {% endunless %}
@@ -436,8 +442,10 @@ <h3>Promote the Event</h3>
 		<b> Event details </b>
 		<table>
 			<tr><td style="min-width: 2rem">{% icon event-date %}</td><td>{{ event | collapse_date_pretty }}</td></tr>
-			<tr><td>{% icon event-location %}</td><td>{% if event.location %}{{ event.location | format_location }}{% else %}Please set a location{% endif %}</td></tr>
+			<tr><td>{% icon event-location %}</td><td>{% if event.location %}{{ event.location | format_location }}{% elsif event.mode == "online" %}Online{% else %}Please set a location{% endif %}</td></tr>
+            {% if event.cost %}
 			<tr><td>{% icon event-cost %}</td><td>{{ event.cost }}</td></tr>
+            {% endif %}
 			{% if event.contact_email %}
 			<tr><td>{% icon email %}</td><td><a href="mailto:{{ event.contact_email }}?subject={{ event.title | uri_escape }}">{{event.contact_email}}</a></td></tr>
 			{% endif %}
diff --git a/_layouts/topic-maintainer.html b/_layouts/topic-maintainer.html
index 6b0c31047f4e7e..19bb848e8a6110 100644
--- a/_layouts/topic-maintainer.html
+++ b/_layouts/topic-maintainer.html
@@ -61,13 +61,48 @@ <h2>Action Items</h2>
 	</tbody>
 </table>
 
+<h2>Topic Materials</h2>
+<table  class="table table-striped scroll-header">
+	<thead>
+		<tr>
+			<th scope="col">Material</th>
+			<th scope="col">Contributions v2 <a href="{% link topics/contributing/faqs/new-contributions.md %}">{% icon help %}</a></th>
+			<th scope="col">Pre-requisites <a href="{% link topics/contributing/faqs/why-prerequisites.md  %}">{% icon help %}</a></th>
+			<th scope="col">Follow up trainings</th>
+			<th scope="col">Data on Zenodo</th>
+			<th scope="col">Notebook</th>
+			<th scope="col">Server Compatibility</th>
+		</tr>
+	</thead>
+	<tbody>
+	{% for material in topic_material_flat %}
+		<tr>
+			<th scope="row"><a href="{{ site.baseurl }}{{ material.url }}">{{ material.title }}</a></th>
+			<td>{% if material.contributions %} ✅ {% else %} ❌ {% endif %}</td>
+			<td>{% if material.requirements %} ✅ {% else %} ❌ {% endif %}</td>
+			<td>{% if material.follow_up_training %} ✅ {% else %} ❌ {% endif %}</td>
+			<td>{% if material.zenodo_link != ""%} ✅ {% else %} ❌ {% endif %}</td>
+			<td>{% if material.notebook %} ✅ {% else %} ❌ {% endif %}</td>
+			<td>
+				<button type="button" class="btn btn-primary" data-toggle="modal" data-target="#{{material.id | replace:'/', '-'}}-compat">
+					View
+				</button>
+			</td>
+		</tr>
+	{% endfor %}
+	</tbody>
+</table>
+
 <h2>Topic Workflows</h2>
-<table  class="table table-striped">
+<table  class="table table-striped scroll-header">
 	<thead>
 		<tr>
 			<th>Material</th>
 			<th>Workflow</th>
 
+			<th>Updated</th>
+			<th>Version</th>
+
 			<th>Tests</th>
 			<th>Reports</th>
 			<th>Comments</th>
@@ -79,6 +114,8 @@ <h2>Topic Workflows</h2>
 		<tr>
 			<td><a href="{{ site.baseurl }}{{ material.url }}">{{ material.title }}</a></td>
 			<td><a href="{{ workflow.url_html }}">{{ workflow.name }}</a></td>
+			<td>{{ workflow.modified | date: "%b %-d, %Y"}} </td>
+			<td>{{ workflow.version }} </td>
 			<td>{% if workflow.tests %} ✅ {% else %} ❌ {% endif %}</td>
 			<td>{% if workflow.features.report %} ✅ {% else %} ❌ {% endif %}</td>
 			<td>{% if workflow.features.comments %} ✅ {% else %} ❌ {% endif %}</td>
@@ -89,6 +126,97 @@ <h2>Topic Workflows</h2>
 </table>
 
 
+{% for material in topic_material_flat %}
+<div id="{{material.id | replace:'/', '-'}}-compat" class="modal" tabindex="-1" aria-labelledby="{{material.id | replace:'/', '-'}}-label" aria-hidden="true">
+	<div class="modal-dialog modal-lg modal-dialog-scrollable">
+		<div class="modal-content">
+			<div class="modal-header">
+				<h5 class="modal-title" id="{{material.id | replace:'/', '-'}}-label">Current Server Compatibility table for "{{ material.title }}"</h5>
+				<button type="button" class="close" data-dismiss="modal" aria-label="Close">
+					<span aria-hidden="true">&times;</span>
+				</button>
+			</div>
+			<div class="modal-body" style="overflow-x: auto">
+				<p>Data as of {{ site.data.build.now }}</p>
+	<table class="table table-striped compat scroll-header scroll-row">
+		<thead>
+		<tr>
+			<th scope="col">Tool</th>
+			{% for server in material.supported_servers_matrix.servers %}
+			<th scope="col">
+				<a href="{{ server.url }}">
+				{{ server.name }}
+				</a>
+			</th>
+			{% endfor %}
+		</tr>
+		</thead>
+		<tbody>
+
+		{% for tool in material.supported_servers_matrix.tools %}
+		<tr>
+			<th scope="row">{{ tool.id }}</th>
+			{% for res in tool.servers %}
+				{% if res.state == "exact" %}
+					<td class="exact">
+						{{ res.version }}
+					</td>
+				{% elsif res.state == "local" %}
+					<td class="local">
+						built-in
+					</td>
+				{% elsif res.state == "inexact" %}
+					<td class="inexact">
+						{% for x in res.versions %}
+						{{ x }}{% if forloop.last == false %}, {% endif %}
+						{% endfor %}
+					</td>
+				{% else %}
+					<td class="missing">Missing</td>
+				{% endif %}
+			{% endfor %}
+		</tr>
+		{% endfor %}
+		</tbody>
+	</table>
+			</div>
+		</div>
+	</div>
+</div>
+{% endfor %}
+<style>
+td.exact, td.local {
+	background-color: var(--feedback-color);
+	color: black;
+}
+td.inexact {
+	background-color: var(--solution-color);
+	color: black;
+}
+
+/* https://adrianroselli.com/2020/01/fixed-table-headers.html */
+
+table.compat th[scope=row] {
+  background-color: var(--color-background);
+}
+
+table.scroll-header th:not([scope=row]) {
+  position: -webkit-sticky;
+  position: sticky;
+  top: 0;
+  z-index: 2;
+  background-color: var(--color-background);
+}
+table.scroll-row th[scope=row] {
+  position: -webkit-sticky;
+  position: sticky;
+  left: 0;
+  z-index: 1;
+  text-align: end;
+}
+</style>
+
+
 <h2>Your topic's Workflow Testing Results</h2>
 
 Coming Soon™
diff --git a/_layouts/tutorial_hands_on.html b/_layouts/tutorial_hands_on.html
index 7573628cd1d611..bc89cc0c770fc1 100644
--- a/_layouts/tutorial_hands_on.html
+++ b/_layouts/tutorial_hands_on.html
@@ -289,11 +289,14 @@ <h3>You've Finished the Tutorial</h3>
                         <p style="display: none" id="tutorial-finish-text">Please also consider filling out the <a href="#gtn-feedback">Feedback Form</a> as well!</p>
                         <script>
                         function tutorial_finish() {
-                          if(plausible !== undefined){
-                            plausible('TutorialComplete', {callback: tutorial_finish_finish, props: {path: document.location.pathname}})
-                          } else {
-                            tutorial_finish_finish();
+                          if(typeof plausible !== 'undefined'){
+                            // Plausible may be undefined (script blocked)
+                            // or it may be defined, but opted-out (select box/DNT), 
+                            // which means `plausible()` will work but not send data, *nor* execute the callback.
+                            plausible('TutorialComplete', {props: {path: document.location.pathname}})
                           }
+                          // since the callback is completely cosmetic, we'll just issue it optimistically.
+                          tutorial_finish_finish();
                         }
                         function tutorial_finish_finish() {
                           document.getElementById("tutorial-finish-button").innerText = 'Congrats! Thanks for letting us know! 🎉'
diff --git a/_plugins/generator-recordings.rb b/_plugins/generator-recordings.rb
index a34ed62e178c6a..1d592c760c27e6 100644
--- a/_plugins/generator-recordings.rb
+++ b/_plugins/generator-recordings.rb
@@ -28,6 +28,7 @@ def generate(site)
         page2.data['topic_name'] = material['topic_name']
         page2.data['tutorial_name'] = material['tutorial_name']
         page2.data['material'] = material
+        page2.data['title'] = 'Recordings for ' + material['title']
         site.pages << page2
       end
     end
diff --git a/_plugins/generator-workflows.rb b/_plugins/generator-workflows.rb
index eb60cd5f9118a9..3470d03b296b73 100644
--- a/_plugins/generator-workflows.rb
+++ b/_plugins/generator-workflows.rb
@@ -44,11 +44,13 @@ def generate(site)
           page2 = PageWithoutAFile.new(site, '', '', "#{workflow['path'].gsub(/.ga$/, '.html')}")
           path = File.join('/', workflow['path'].gsub(/.ga$/, '.html'))
           page2.content = nil
+          page2.data['title'] = workflow['title']
           page2.data['layout'] = 'workflow'
           page2.data['material'] = material
           page2.data['workflow'] = workflow
           page2.data['js_requirements'] = {'mathjax' => false, 'mermaid' => true}
           page2.data['short_id'] = shortlinks[path]
+          page2.data['redirect_from'] = ["/short/#{shortlinks[path]}"]
           site.pages << page2
         end
       end
diff --git a/_plugins/gtn.rb b/_plugins/gtn.rb
index ca6ed3cc904c62..826c02e6cf58ec 100644
--- a/_plugins/gtn.rb
+++ b/_plugins/gtn.rb
@@ -399,7 +399,7 @@ def convert_workflow_path_to_trs(str)
 
     def layout_to_human(layout)
       case layout
-      when /slides/
+      when /_slides/ # excludes slides-plain
         'Slides'
       when /tutorial_hands_on/
         'Hands-on'
@@ -407,6 +407,8 @@ def layout_to_human(layout)
         'FAQs'
       when 'news'
         'News'
+      when 'workflow'
+        'Workflow'
       end
     end
 
@@ -569,6 +571,10 @@ def list_usegalaxy_servers_shuffle(_site)
     def topic_name_from_page(page, site)
       if page.key? 'topic_name'
         site.data[page['topic_name']]['title']
+      elsif page['url'] =~ /^\/faqs\/gtn/
+        'GTN FAQ'
+      elsif page['url'] =~ /^\/faqs\/galaxy/
+        'Galaxy FAQ'
       else
         site.data.fetch(page['url'].split('/')[2], { 'title' => '' })['title']
       end
@@ -939,6 +945,13 @@ def find_learningpaths_including_topic(site, topic_id)
     page.data['short_id'] = shortlinks_reversed[page.url]
   end
 
+  # Annotate symlinks
+  site.pages.each do |page|
+    page.data['symlink'] = File.symlink?(page.path) 
+    # Elsewhere we checked more levels deep, maybe enable if needed.
+    # || File.symlink?(File.dirname(page.path)) || File.symlink?(File.dirname(File.dirname(page.path)))
+  end
+
   Jekyll.logger.info '[GTN] Annotating events'
   site.pages.select { |p| p.data['layout'] == 'event' || p.data['layout'] == 'event-external' }.each do |page|
 
diff --git a/_plugins/gtn/contributors.rb b/_plugins/gtn/contributors.rb
index 393c005c63d9d2..acc232ae5fd5bb 100644
--- a/_plugins/gtn/contributors.rb
+++ b/_plugins/gtn/contributors.rb
@@ -91,9 +91,26 @@ def self.get_non_authors(material)
     # +data+:: +Hash+ of the YAML frontmatter from a material
     # Returns:
     # +Array+ of contributor IDs
-    def self.get_funders(data)
+    def self.get_funders(site, data)
       if data.key?('contributions') && data['contributions'].key?('funding')
-        data['contributions']['funding']
+        # The ones specifically in the Grants table
+        data['contributions']['funding'].reject{ |f| site.data['funders'].key?(f) }
+      else
+        []
+      end
+    end
+
+    ##
+    # Get the funders of a material.
+    # Params:
+    # +site+:: +Jekyll::Site+ object
+    # +data+:: +Hash+ of the YAML frontmatter from a material
+    # Returns:
+    # +Array+ of grant IDs
+    def self.get_grants(site, data)
+      if data.key?('contributions') && data['contributions'].key?('funding')
+        # The ones specifically in the Grants table
+        data['contributions']['funding'].select{ |f| site.data['funders'].key?(f) }
       else
         []
       end
diff --git a/_plugins/gtn/ro-crate.rb b/_plugins/gtn/ro-crate.rb
index 0f6fd4b7475d8c..97bc89f9021265 100644
--- a/_plugins/gtn/ro-crate.rb
+++ b/_plugins/gtn/ro-crate.rb
@@ -82,7 +82,8 @@ def self.write(site, dir, associated_material, workflow, url, baseurl, time_base
         ['Workflow Author(s)', workflow['creators'].map { |c| c['name'] }],
         ['Tutorial Author(s)', Gtn::Contributors.get_authors(associated_material).map { |n| name2md(site, n) }],
         ['Tutorial Contributor(s)', Gtn::Contributors.get_non_authors(associated_material).map { |n| name2md(site, n) }],
-        ['Funder(s)', Gtn::Contributors.get_funders(associated_material).map { |n| name2md(site, n) }],
+        ['Funder(s)', Gtn::Contributors.get_funders(site, associated_material).map { |n| name2md(site, n) }],
+        ['Grants(s)', Gtn::Contributors.get_grants(site, associated_material).map { |n| name2md(site, n) }],
       ].reject { |_, v| v.empty? }
 
       description = %Q(
diff --git a/_plugins/gtn/supported.rb b/_plugins/gtn/supported.rb
index 444896aade0731..0617c7c189fba6 100644
--- a/_plugins/gtn/supported.rb
+++ b/_plugins/gtn/supported.rb
@@ -87,6 +87,94 @@ def self.calculate(data, tool_list)
         end
       }
     end
+
+    ##
+    # Identify the servers that support a given tool list
+    #
+    # Params:
+    # +data+:: The data from metadata/public-server-tools.json
+    # +tool_list+:: The list of tools to check (either 'upload1' or
+    #      'toolshed.g2.bx.psu.edu/repos/iuc/circos/circos/0.69.8+galaxy10' style tools)
+    # Returns:
+    # +supported+:: A hash of supported servers, with the following structure:
+    #  {
+    #    servers: [a, b, c]
+    #    tools: [
+    #      {
+    #        id: toolshed.g2.bx.psu.edu/repos/iuc/circos/circos/0.69.8+galaxy10,
+    #        version: 0.69.8+galaxy10,
+    #        servers: [
+    #          [0.69.8+galaxy10],
+    #          [0.69.8+galaxy11, 0.69.8+galaxy12],
+    #          nil
+    #        ]
+    #      }
+    #    ]
+    #  }
+    def self.calculate_matrix(data, tool_list)
+      structure = {
+        'servers' => [],
+        'tools' => [],
+      }
+      # p "Calculating supported servers for this tool list"
+      if data.nil? || data.empty? || tool_list.empty? || tool_list.nil?
+        return structure
+      end
+
+      structure['servers'] = data['servers']
+
+      tool_list.each do |tool|
+        tool_for_server = {
+          'id' => tool,
+          'servers' => []
+        }
+
+        if tool.count('/') > 4
+          # E.g. toolshed.g2.bx.psu.edu/repos/iuc/circos/circos/0.69.8+galaxy10
+          tool_id = tool.split('/')[0..4].join('/')
+          tool_version = tool.split('/')[5..].join('/')
+          tool_for_server['version'] = tool_version
+        else
+          tool_id = tool
+          tool_version = 'local'
+          tool_for_server['version'] = tool_version
+        end
+
+        data['servers'].each.with_index do |server, s_index|
+          # If we've never seen this tool anywhere
+          if ! data['tools'].key?(tool_id)
+            res = {'state' => 'missing'}
+          else
+            # If we've seen this exact version on the current server/index
+            if data['tools'][tool_id].key?(tool_version) && data['tools'][tool_id][tool_version].include?(s_index)
+              res = {'state' => 'exact', 'version' => tool_version}
+            elsif data['tools'][tool_id]
+              res = {
+                'state' => 'inexact', 
+                'versions' => data['tools'][tool_id].select{|k, v| v.include?(s_index)}.keys
+              }
+
+              if res['versions'].length == 1 && res['versions'][0] == '_'
+                res = {
+                  'state' => 'local'
+                }
+              elsif res['versions'].length == 0
+                res = {'state' => 'missing'}
+              end
+            end
+          end
+
+          res['server'] = server['url']
+          tool_for_server['servers'].push(res)
+
+        end
+
+        structure['tools'].push(tool_for_server)
+      end
+
+      # TODO: would be nice to re-sort the servers by the number of exact matches
+      structure
+    end
   end
 end
 
diff --git a/_plugins/jekyll-environment_variables.rb b/_plugins/jekyll-environment_variables.rb
index f5175dd4574b9a..d1a6431b6d3cd2 100644
--- a/_plugins/jekyll-environment_variables.rb
+++ b/_plugins/jekyll-environment_variables.rb
@@ -1,6 +1,8 @@
 # frozen_string_literal: true
 
 # Plugin to add environment variables to the `site` object in Liquid templates
+require 'date'
+require 'time'
 require './_plugins/gtn/scholar'
 require './_plugins/gtn/git'
 
@@ -19,6 +21,15 @@ def generate(site)
       # Add other environment variables to `site.config` here...
       Gtn::Scholar.load_bib(site)
       site.config.update(Gtn::Git.discover)
+
+      site.data['build'] = {
+        'today' => Date.today,
+        'now' => Time.now,
+        'jekyll' => {
+          'version' => Jekyll::VERSION,
+          'environment' => Jekyll.env,
+        }
+      }
     end
   end
 end
diff --git a/_plugins/jekyll-jsonld.rb b/_plugins/jekyll-jsonld.rb
index a8bb42036d25aa..deec0a5954d483 100644
--- a/_plugins/jekyll-jsonld.rb
+++ b/_plugins/jekyll-jsonld.rb
@@ -184,7 +184,7 @@ def generate_funding_jsonld(id, contributor, site)
         '@context': 'https://schema.org',
         '@type': 'Grant',
         identifier: contributor['funding_id'],
-        url: contributor['url'] || Gtn::Contributors.fetch_funding_url(contributor),
+        url: Gtn::Contributors.fetch_funding_url(contributor) || contributor['url'],
         funder: generate_funder_jsonld(id, contributor, site)
       }
 
@@ -274,10 +274,10 @@ def generate_event_jsonld(page, site)
       instructors = Gtn::Contributors.get_instructors(page.to_h).map do |x|
         to_pfo_jsonld(x, site, json: false)
       end
-      funders = Gtn::Contributors.get_funders(page.to_h).map do |x|
+      funders = Gtn::Contributors.get_funders(site, page.to_h).map do |x|
         to_pfo_jsonld(x, site, json: false)
       end
-      funding = Gtn::Contributors.get_funders(page.to_h).map do |x|
+      funding = Gtn::Contributors.get_grants(site, page.to_h).map do |x|
         generate_funding_jsonld(x, Gtn::Contributors.fetch_contributor(site, x), site)
       end
 
@@ -511,6 +511,18 @@ def generate_learning_pathway_jsonld(page, site)
         material.fetch('objectives', [])
       end.flatten.compact
 
+      funders = materials.map do |material|
+        Gtn::Contributors.get_funders(site, material).map do |x|
+          to_pfo_jsonld(x, site, json: false)
+        end
+      end.flatten.uniq.compact
+
+      funding = materials.map do |material|
+        Gtn::Contributors.get_grants(site, material).map do |x|
+          generate_funding_jsonld(x, Gtn::Contributors.fetch_contributor(site, x), site)
+        end
+      end.flatten.uniq.compact
+
       # TODO: add topic edam terms too? Not sure.
       parts = []
       materials.each do |material|
@@ -558,8 +570,8 @@ def generate_learning_pathway_jsonld(page, site)
         # learningResourceType
         # teaches
 
-        # funder: funders, # Org or person
-        # funding: funding, # Grant
+        funder: funders, # Org or person
+        funding: funding, # Grant
         publisher: GTN,
         provider: GTN,
         syllabusSections: syllab,
@@ -690,7 +702,8 @@ def generate_material_jsonld(material, topic, site)
         # "contentRating":,
         # "contentReferenceTime":,
         # "contributor" described below
-        copyrightHolder: GTN,
+        # copyrightHolder: GTN,
+        # copyrightNotice: m
         # "copyrightYear":,
         # "correction":,
         # "creator":,
@@ -704,7 +717,7 @@ def generate_material_jsonld(material, topic, site)
         # "encodingFormat":,
         # "exampleOfWork":,
         # "expires":,
-        # "funder":,
+        # "funder": funding,
         # "genre":,
         # "hasPart" described below
         headline: (material['title']).to_s,
@@ -780,6 +793,24 @@ def generate_material_jsonld(material, topic, site)
         end
       end
 
+      if material.key?('copyright')
+        # copyrightHolder: GTN,
+        data['copyrightNotice'] = material['copyright']
+      else
+        # I'm not sure this is accurate.
+        data['copyrightHolder'] = GTN
+      end
+
+      funders = Gtn::Contributors.get_funders(site, material).map do |x|
+        to_pfo_jsonld(x, site, json: false)
+      end
+      funding = Gtn::Contributors.get_grants(site, material).map do |x|
+        generate_funding_jsonld(x, Gtn::Contributors.fetch_contributor(site, x), site)
+      end
+
+      data['funder'] = funders
+      data['funding'] = funding
+
       data['identifier'] = "https://gxy.io/GTN:#{material['short_id']}" if material.key?('short_id')
 
       data.update(A11Y)
diff --git a/_plugins/jekyll-topic-filter.rb b/_plugins/jekyll-topic-filter.rb
index c5b502d6c42ea3..a1091a302ae745 100644
--- a/_plugins/jekyll-topic-filter.rb
+++ b/_plugins/jekyll-topic-filter.rb
@@ -730,6 +730,13 @@ def self.resolve_material(site, material)
                                       []
                                     end
 
+    page_obj['supported_servers_matrix'] = if topic['type'] == 'use' || topic['type'] == 'basics'
+      Gtn::Supported.calculate_matrix(site.data['public-server-tools'], page_obj['tools'])
+    else
+      []
+    end
+
+
     topic_name_human = site.data[page_obj['topic_name']]['title']
     page_obj['topic_name_human'] = topic_name_human # TODO: rename 'topic_name' and 'topic_name' to 'topic_id'
     admin_install = Gtn::Toolshed.format_admin_install(site.data['toolshed-revisions'], page_obj['tools'],
@@ -1134,6 +1141,7 @@ def list_materials_flat(site, topic_name)
         .list_materials_structured(site, topic_name)
         .map { |k, v| v['materials'] }
         .flatten
+        .uniq { |x| x['id'] }
     end
 
     def list_all_tags(site)
diff --git a/_plugins/social.rb b/_plugins/social.rb
new file mode 100644
index 00000000000000..270f6ce5001249
--- /dev/null
+++ b/_plugins/social.rb
@@ -0,0 +1,175 @@
+require './_plugins/gtn'
+
+Jekyll::Hooks.register :site, :post_write do |site|
+  # No need to run this except in prod.
+  if Jekyll.env != 'production'
+    # Build our Images!
+    count = 0
+    social_start = Time.now
+    templates = {}
+    meta = {}
+
+    # Pre-existing templates
+    Dir.glob('assets/branding/social/*.svg').each do |file|
+      templates[File.basename(file, '.svg')] = File.read(file)
+    end
+
+    Jekyll.logger.info "[GTN/Socials] Writing social media cards, #{templates.keys} discovered"
+    # svgs = []
+
+    (site.pages + site.posts).each do |page|
+      if page.data['layout'].nil? || page.data['layout'] == ''
+        next
+      end
+
+      if ['faq-page', 'slides-plain', 'workflow-list', 'base', 'event-track', 'embed', 'none', 'page'].include? page.data['layout']
+        next
+      end
+
+      title_override = nil
+      parts = page.path.split('/')
+      if parts.include?('topics')
+        topic_id = parts[parts.index('topics') + 1]
+        topic_name = site.data[topic_id]['title'].upcase
+      else
+        topic_id = nil
+        topic_name = ""
+      end
+      mod_time = Gtn::ModificationTimes.obtain_time(page.path)
+      # Default is usually fine
+      authors = Gtn::Contributors.get_authors(page.data).map { |c| Gtn::Contributors.fetch_name(site, c) }
+
+      # Handle specific layouts.
+      case page.data['layout']
+      when 'event'
+        tpl = templates['event'].dup
+        tpl = tpl.gsub('TOPIC_NAME', 'EVENT')
+        # Event specific
+        authors = Gtn::Contributors.get_organisers(page.data).map { |c| Gtn::Contributors.fetch_name(site, c) }
+        if page.data['mode'] == 'online'
+          tpl = tpl.gsub('In-Person', 'Online')
+        end
+        if page.data['async'] == false
+          tpl = tpl.gsub('Asynchronous', 'Synchronous')
+        end
+      when 'workflow'
+        tpl = templates['workflow'].dup
+        tpl = tpl.gsub('TOPIC_NAME', site.data[page.data['workflow']['topic_id']]['title'].upcase)
+        authors = page.data['workflow']['creators'].map { |c| c['name'] }
+
+        mod_time = page.data['workflow']['modified']
+      when 'faq'
+        tpl = templates['faq'].dup
+        if page.url =~ /faqs\/gtn/
+          tpl = tpl.gsub('TOPIC_NAME', 'GTN FAQ')
+        elsif page.url =~ /faqs\/galaxy/
+          tpl = tpl.gsub('TOPIC_NAME', 'Galaxy FAQ')
+        end
+      when 'recordings'
+        tpl = templates['recording'].dup
+        authors = page.data['material']['ref'].data.fetch('recordings', [])
+          .reverse.map { |r| r.fetch('speakers', []) + r.fetch('captioners',[]) }.flatten.uniq
+        authors = authors.map { |c| Gtn::Contributors.fetch_name(site, c) }
+
+        mod_time = [
+          Gtn::ModificationTimes.obtain_time(page.path),
+          Gtn::ModificationTimes.obtain_time(page.data['material']['ref'].path)
+        ].max
+      when 'tutorial_hands_on'
+        tpl = templates['tutorial'].dup
+      when 'tutorial_slides'
+        tpl = templates['slides'].dup
+      when 'introduction_slides'
+        tpl = templates['slides'].dup
+      when 'learning-pathway'
+        tpl = templates['learning-pathway'].dup
+        tpl = tpl.gsub('TOPIC_NAME', 'LP')
+        authors = page.data['editorial_board'].map { |c| Gtn::Contributors.fetch_name(site, c) }
+      when 'topic'
+        tpl = templates['topic'].dup
+        tpl = tpl.gsub('TOPIC_NAME', 'Topic')
+        topic = site.data[page.data['topic_name']]
+        title_override = topic['title']
+        authors = topic.fetch('editorial_board', []).map { |c| Gtn::Contributors.fetch_name(site, c) }
+
+        mod_time = [
+          Gtn::ModificationTimes.obtain_time(page.path),
+          Gtn::ModificationTimes.obtain_time(page.path.gsub('index.md', 'metadata.yaml')),
+          Gtn::ModificationTimes.obtain_time("metadata/#{page.data['topic_name']}.yaml"),
+        ].max
+      else
+        Jekyll.logger.debug "[GTN/Socials] Skipping #{page.data['layout']} => #{page.path}"
+        next
+      end
+
+      if ! topic_name.nil?
+        tpl = tpl.gsub('TOPIC_NAME', topic_name)
+      end
+
+      tpl = tpl.gsub(/gxy.io\/GTN:....../, "gxy.io/GTN:#{page.data['short_id']}")
+
+      # Title
+      tpl = tpl
+        .gsub('TITLE2TITLE2', '')
+        .gsub('TITLE3TITLE3', '')
+        .gsub('TITLE4TITLE4', '')
+        .gsub('TITLE1TITLE1', (title_override || page.data['title'].gsub('_', ' ')).gsub('&', '&amp;'))
+
+      # Authors
+      if authors.length > 0
+        tpl = tpl.gsub('AUTHOR1', authors[0])
+      else
+        tpl = tpl.gsub('AUTHOR1', '')
+      end
+      if authors.length > 1
+        tpl = tpl.gsub('AUTHOR2', authors[1])
+      else
+        tpl = tpl.gsub('AUTHOR2', '')
+      end
+      if authors.length > 2
+        tpl = tpl.gsub('AUTHOR3', authors[2])
+      else
+        tpl = tpl.gsub('AUTHOR3', '')
+      end
+      if authors.length > 4
+        tpl = tpl.gsub('AUTHOR4', 'et al.')
+      elsif authors.length > 3
+        tpl = tpl.gsub('AUTHOR4', authors[3])
+      else
+        tpl = tpl.gsub('AUTHOR4', '')
+      end
+
+      if page.url.end_with?('.html')
+        svg_path = page.url.sub('.html', '.svg')
+      else
+        svg_path = File.join(page.url, 'index.svg')
+      end
+      svg = File.join(site.dest, svg_path)
+      if ! File.directory?(File.dirname(svg))
+        FileUtils.mkdir_p(File.dirname(svg))
+      end
+      File.open(svg, 'w') do |f|
+        f.write(tpl)
+      end
+      # svgs << svg
+
+      meta[svg_path] = mod_time.to_i
+
+      # Convert to PNG
+      count += 1
+    end
+    Jekyll.logger.info "[GTN/Socials] Social media cards written in #{Time.now - social_start} seconds for #{count} pages"
+
+    File.open(File.join(site.dest, 'api', 'social-meta.json'), 'w') do |f|
+      f.write(meta.to_json)
+    end
+
+    # This takes on the order of 30 minutes.
+    # png_start = Time.now
+    # svgs.each do |svg|
+    #   # I'm not sure about this.
+    #   `magick -density 50 #{svg} #{svg.sub('.svg', '.png')}`
+    # end
+    # Jekyll.logger.info "[GTN/Socials] Social media cards convert to png in #{Time.now - png_start} seconds"
+  end
+end
diff --git a/assets/branding/social/event.svg b/assets/branding/social/event.svg
new file mode 100644
index 00000000000000..0ba7919134a013
--- /dev/null
+++ b/assets/branding/social/event.svg
@@ -0,0 +1,683 @@
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+
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+   height="165.89375mm"
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+   id="svg1"
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+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
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diff --git a/assets/branding/social/faq.svg b/assets/branding/social/faq.svg
new file mode 100644
index 00000000000000..318d35e8004b3e
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diff --git a/assets/branding/social/slides.svg b/assets/branding/social/slides.svg
new file mode 100644
index 00000000000000..63ce8f57d2697b
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diff --git a/assets/branding/social/tutorial.svg b/assets/branding/social/tutorial.svg
new file mode 100644
index 00000000000000..c1a2e2a6d39c34
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+       d="m 168.79214,153.2562 a 0.15163611,0.15163611 0 0 0 -0.21338,0 l -2.19429,2.19429 c -1.01594,1.01594 -2.73058,1.12358 -3.85227,0 -1.12358,-1.12358 -1.01595,-2.83633 0,-3.85227 l 2.19428,-2.19429 c 0.0585,-0.0585 0.0585,-0.15485 0,-0.21339 l -0.75157,-0.75157 a 0.15163611,0.15163611 0 0 0 -0.21338,0 l -2.19429,2.19429 c -1.59756,1.59756 -1.59756,4.18274 0,5.77841 1.59756,1.59567 4.18274,1.59756 5.77841,0 l 2.19429,-2.19429 c 0.0585,-0.0585 0.0585,-0.15484 0,-0.21338 z m 4.88333,-8.95087 c -1.59756,-1.59756 -4.18274,-1.59756 -5.77841,0 l -2.19618,2.19429 a 0.15163611,0.15163611 0 0 0 0,0.21338 l 0.74969,0.74968 c 0.0585,0.0585 0.15484,0.0585 0.21338,0 l 2.19429,-2.19428 c 1.01594,-1.01594 2.73058,-1.12358 3.85227,0 1.12358,1.12358 1.01594,2.83633 0,3.85227 l -2.19428,2.19429 a 0.15163611,0.15163611 0 0 0 0,0.21339 l 0.75157,0.75157 c 0.0585,0.0585 0.15484,0.0585 0.21338,0 l 2.19429,-2.19429 c 1.59567,-1.59756 1.59567,-4.18274 0,-5.7803 m -4.20162,3.41606 a 0.15163611,0.15163611 0 0 0 -0.21339,0 l -4.27716,4.27527 a 0.15163611,0.15163611 0 0 0 0,0.21339 l 0.7478,0.74779 c 0.0585,0.0585 0.15484,0.0585 0.21338,0 l 4.27527,-4.27527 c 0.0585,-0.0585 0.0585,-0.15484 0,-0.21338 z"
+       id="path12" />
+  </g>
+  <metadata
+     id="metadata1">
+    <rdf:RDF>
+      <cc:Work
+         rdf:about="">
+        <dc:title>Social Media Card</dc:title>
+        <dc:creator>
+          <cc:Agent>
+            <dc:title>@hexylena</dc:title>
+          </cc:Agent>
+        </dc:creator>
+        <cc:license
+           rdf:resource="http://creativecommons.org/licenses/by-sa/4.0/" />
+        <dc:language>EN</dc:language>
+      </cc:Work>
+      <cc:License
+         rdf:about="http://creativecommons.org/licenses/by-sa/4.0/">
+        <cc:permits
+           rdf:resource="http://creativecommons.org/ns#Reproduction" />
+        <cc:permits
+           rdf:resource="http://creativecommons.org/ns#Distribution" />
+        <cc:requires
+           rdf:resource="http://creativecommons.org/ns#Notice" />
+        <cc:requires
+           rdf:resource="http://creativecommons.org/ns#Attribution" />
+        <cc:permits
+           rdf:resource="http://creativecommons.org/ns#DerivativeWorks" />
+        <cc:requires
+           rdf:resource="http://creativecommons.org/ns#ShareAlike" />
+      </cc:License>
+    </rdf:RDF>
+  </metadata>
+</svg>
diff --git a/assets/css/main.scss b/assets/css/main.scss
index f6bed07e7caf2b..b338788a4710b8 100644
--- a/assets/css/main.scss
+++ b/assets/css/main.scss
@@ -1289,6 +1289,9 @@ div.highlight .btn{
 	transition: none;
   }
 }
+.btn {
+	cursor: pointer;
+}
 
 
 div.highlight:hover .btn,div.highlight .btn:focus{
diff --git a/bin/schema-tutorial.yaml b/bin/schema-tutorial.yaml
index d43dccad047f5a..a0340c245f87a5 100644
--- a/bin/schema-tutorial.yaml
+++ b/bin/schema-tutorial.yaml
@@ -481,6 +481,7 @@ mapping:
             history: https://humancellatlas.usegalaxy.eu/u/j.jakiela/h/generating-a-single-cell-matrix-using-alevin-3
           - label: "Older Alevin version"
             history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---answer-key
+            date: 2024-01-01
         sequence:
           - type: map
             mapping: &answer_histories
@@ -499,6 +500,7 @@ mapping:
           input_histories:
             - label: "UseGalaxy.eu"
               history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---input-1
+              date: "2021-09-01"
         sequence:
           - type: map
             mapping: *answer_histories
@@ -533,7 +535,7 @@ mapping:
               pattern: /^(?:([0-9]*)[Hh])*(?:([0-9]*)[Mm])*(?:([0-9.]*)[Ss])*$/
             youtube_id:
               type: str
-              pattern: /[A-Za-z0-9_-]{9,13}/
+              pattern: /[A-Za-z0-9_-]{11}/
             type:
               type: str
             archive-id:
diff --git a/bin/social-cards.py b/bin/social-cards.py
new file mode 100644
index 00000000000000..20cdebdb68fd18
--- /dev/null
+++ b/bin/social-cards.py
@@ -0,0 +1,43 @@
+import requests
+import json
+import os
+import sys
+import subprocess
+
+GTN = 'https://training.galaxyproject.org/training-material'
+# GTN = 'http://localhost:4000/training-material'
+
+meta = requests.get(f"{GTN}/api/social-meta.json").json()
+
+last_timestamp = int(sys.argv[1])
+
+# meta looks like:
+# {
+# "/topics/proteomics/tutorials/protein-id-sg-ps/workflows/wf_proteinID_SG_PS.svg": 1710761924,
+# "/topics/proteomics/tutorials/protein-id-sg-ps/workflows/wf_proteinID_SG_PS_multipleFiles.svg": 1710761924,
+# "/topics/proteomics/tutorials/metaquantome-function/workflows/main_workflow.svg": 1710761924,
+# }
+
+count = 0
+max_ts = 0
+for path, time in meta.items():
+    if time < last_timestamp:
+        continue
+
+    # Do something with the path
+    print(f"New social card: {path}")
+    # download the svg
+    out = f'../gtn-social/{path}'
+    os.makedirs(os.path.dirname(out), exist_ok=True)
+
+    if not os.path.exists(out):
+        subprocess.check_call(['wget', GTN + path, '-O', out])
+
+    # Convert to png
+    if not os.path.exists(out.replace('.svg', '.png')):
+        subprocess.check_call(['magick', '-density', '100', out, out.replace('.svg', '.png')])
+        subprocess.check_call(['optipng', out.replace('.svg', '.png')])
+    max_ts = max(max_ts, time)
+
+with open('../gtn-social/timestamp.txt', 'w') as f:
+    f.write(str(max_ts))
diff --git a/events/2023-10-02-mtb-ngs.html b/events/2023-10-02-mtb-ngs.md
similarity index 100%
rename from events/2023-10-02-mtb-ngs.html
rename to events/2023-10-02-mtb-ngs.md
diff --git a/events/2024-06-10-mtb-ngs.html b/events/2024-06-10-mtb-ngs.md
similarity index 100%
rename from events/2024-06-10-mtb-ngs.html
rename to events/2024-06-10-mtb-ngs.md
diff --git a/events/2024-09-16-bioinfo_bootcamp-2024.md b/events/2024-09-16-bioinfo_bootcamp-2024.md
new file mode 100644
index 00000000000000..6ec5ba575f7a9d
--- /dev/null
+++ b/events/2024-09-16-bioinfo_bootcamp-2024.md
@@ -0,0 +1,49 @@
+---
+layout: event
+title: 2024 Bioinformatics Bootcamp - The Open University
+description: |
+    The School of Life, Health, and Chemical Sciences (LHCS) at The Open University (OU) is running a free, week-long Bioinformatics Bootcamp from the 16-20th September aimed at level 2 and level 3 OU students who are studying life, health and chemical sciences modules and have already completed 120 credits of level 1 study.
+
+cover-image: events/images/BB_24_logo.png
+cover-image-alt: logo for the Bioinformatics Bootcamp event consisting of a laptop with a DNA helix in it and the letters BB and number '24'
+date_start: 2024-09-16
+date_end: 2024-09-20
+contributions:
+  organisers:
+    - nomadscientist
+    - hfraser
+    - mhintze
+  instructors:
+    - wee-snufkin
+    - nomadscientist
+    - hexhowells
+    - MarisaJL
+  funding: []
+location:
+  name:  The Open University - Online
+  city: Milton Keynes
+  country: United Kingdom
+
+tags:
+ - single-cell
+ - introductory
+
+mode: "online"
+async: false
+
+infrastructure:
+  tiaas: true    # tiaas = Training Infrastructure as a Service, and can be requested (for free) from all major Galaxies
+
+  servers:
+    - server: https://usegalaxy.eu
+      name: Galaxy EU
+---
+This will be a fantastic opportunity to gain hands-on experience of the Galaxy bioinformatics platform and analyse real biological data.  On day 2, you’ll have an option to choose a pathway: an interactive web-based graphical user interface (GUI) pathway to run the bioinformatics analysis, or a coding environment using the Python programming language to perform the analysis. So, if you ever fancied trying computer coding, the second pathway is for you.  You’ll analyse the biological data in exactly the same way in each pathway, and it will be possible to swap between GUI and coding based on your confidence.
+
+Prior knowledge of bioinformatics or computer coding is not required, and you’ll be supported through your study by a dedicated team of experienced researchers and PhD students. The Bioinformatics Bootcamp will run every day approximately from 09:15-17:30 with a break for lunch from 12-13:00 and afternoon break between 15:00-16:00.   There will be a mix of live online Zoom meetings, lectures, and self-directed Galaxy tutorials with online support available from 09:00-18:00 each day. Each day will end with a Key Points Game to summarize outcomes and answer any questions. On Friday, we’ll aim to end early before 15:00.
+
+The Bioinformatics Bootcamp will be “real-time” or synchronous compared to typical OU study, which you can do in your own time, so we request that you ensure you are available to attend all sessions each day.
+
+We will provide full information on registering for Galaxy and other necessary platforms in the last week in August and will have a drop-in session to troubleshoot any problems on these registrations and any questions you have about the Bootcamp on Monday, 2nd, September from 19:30-20:30.
+
+We strongly thank the Galaxy-Europe and Galaxy Training Network for their computational and training resources that make this course possible for the first time.
diff --git a/events/2024-10-29-spoc-write-a-thon.md b/events/2024-10-29-spoc-write-a-thon.md
new file mode 100644
index 00000000000000..2a09297042b1f3
--- /dev/null
+++ b/events/2024-10-29-spoc-write-a-thon.md
@@ -0,0 +1,18 @@
+---
+layout: event-external
+google_form_id: 1728584662
+title: "🖖🏾 SPOC Write-a-thon"
+description: Single-cell & sPatial Omics Community (SPOC) contributors in Galaxy will
+  get together online or asynchronously to draft an updates paper on all things SPOC.
+  For those unable to make the event live, please add your thoughts and authoring
+  in advance of the Write-a-thon using the shared [googledoc](https://docs.google.com/document/d/179G-VDl7NggXr2AhMPQ8UPrYwoa7KgPd29Us6G74O7U/edit?usp=sharing).
+external: https://docs.google.com/document/d/179G-VDl7NggXr2AhMPQ8UPrYwoa7KgPd29Us6G74O7U/edit?usp=sharing
+contributions:
+  organisers:
+  - nomadscientist
+location:
+  name: Online
+date_start: 2024-10-29
+tags:
+ - single-cell
+---
diff --git a/events/2024-12-06-spoc-cofest-2024.md b/events/2024-12-06-spoc-cofest-2024.md
new file mode 100644
index 00000000000000..e85ee009b3b143
--- /dev/null
+++ b/events/2024-12-06-spoc-cofest-2024.md
@@ -0,0 +1,36 @@
+---
+layout: event
+title: Single-cell and sPatial Omics | Collaboration Fest
+description: |
+    The Single-cell and sPatial Omics Community of Practice (SPOC) are hosting their first Collaboration Fest, welcoming new and experienced contributors to our training materials.
+cover-image: events/images/spoc-cofest.png
+cover-image-alt: Single-cell and sPatial Omics Community of Practice logo showing a circle of diverse people holding hands around a swirling single-cell cluster plot.
+date_start: 2024-12-06
+contributions:
+  organisers:
+    - nomadscientist
+
+tags:
+ - single-cell
+ - cofest
+
+mode: "online"
+async: false
+
+---
+"Many hands make small work, and together, we can continue offering free, comprehensive single-cell training to scientists across the globe!"
+
+Join us for a day-long event (live on Zoom, although you're very welcome to join asynchronously) to improve Galaxy single-cell training resources! Tasks range from editing typos or improving explanations, to testing a tutorial or adding automated workflow testing.
+
+## We expect four types of attendees:
+
+ - Single-cell bioinformaticians who do not use Galaxy
+ - New Galaxy single-cell users
+ - Experienced Galaxy contributors
+ - Experienced Galaxy SPOC contributors
+
+## To join
+
+See our [event doc](https://docs.google.com/document/d/1GRAY_uE3dtyaGlKR7rqCXgjrJQUkgch-eyc7AsipYMc/edit?tab=t.k5a3rg712bse) for more details, a link to add it to your calendar, and information on what (brief) training you may need to do beforehand.
+
+"First and foremost Galaxy CoFests are about growing the community of future contributors."
diff --git a/events/galaxy-academy-2024.md b/events/galaxy-academy-2024.md
index 32366901555524..a238aae31fc972 100644
--- a/events/galaxy-academy-2024.md
+++ b/events/galaxy-academy-2024.md
@@ -3,7 +3,9 @@ layout: event
 
 title: Galaxy Training Academy 2024
 description: |
-  The Galaxy Academy is a online training event for Beginners as well as learners who would like to improve their Galaxy data analysis skills. Over the course of one week, we will have a different topic and focus every day.
+  The Galaxy Academy is a self-paced online training event for beginners as well as learners who would like to improve their Galaxy data analysis skills. Over the course of one week, we will have a different topic and focus every day.
+
+  <button id="program-button" class="btn btn-info" onclick="$('#program-tab').tab('show');">Start the Course!</button>
 
 cover-image: events/images/galaxy-academy-logo.png
 cover-image-alt: logo for the Galaxy Academy event consisting of a laptop surrounded by illustrations of DNA molecules
@@ -25,18 +27,18 @@ mode: online
 
 contributions:
     organisers:
+        - teresa-m
+        - natalie-wa
+        - nakucher
         - erxleben
         - annasyme
         - nekrut
         - dannon
         - delphine-l
-        - GarethPrice-Aus 
+        - GarethPrice-Aus
         - jdavcs
         - mschatz
-        - natalie-wa
-        - nakucher
         - shiltemann
-        - teresa-m
     instructors:
         - ahmedhamidawan
         - erxleben
@@ -53,7 +55,7 @@ contributions:
         - elichad
         - EngyNasr
         - emmaustin20
-        - GarethPrice-Aus 
+        - GarethPrice-Aus
         - igormakunin
         - jdavcs
         - lldelisle
@@ -92,9 +94,11 @@ location:
   name: online
 
 infrastructure:
+  tiaas: true
   servers:
     - server: https://usegalaxy.eu
       name: Galaxy EU
+      tiaas_link: https://usegalaxy.eu/join-training/gta2024
     - server: https://usegalaxy.org
       name: Galaxy Main
     - server: https://usegalaxy.org.au/
@@ -105,10 +109,30 @@ infrastructure:
 
 
 program:
-  - section: "Monday: Galaxy introduction"
+  - section: "Monday: Introduction"
+    description: When you are ready to start, just work your way through the program below by watching videos and/or following the text-based tutorials. There are no live sessions, so you can determine your own schedule. A large team of instructors is available on Slack to answer your questions 24/7! Enjoy!
+  - section: Course introduction
+    subsection: true
+    tutorials:
+      - name: Welcome & Course logistics
+        type: custom
+        description: |
+          [<i class="fas fa-video" aria-hidden="true"></i> Video: Welcome to the course!](https://youtu.be/OyMpSNEDyEA)
+      - type: custom
+        name: Daily Icebreaker
+        description: |
+          **Please take a moment to introduce yourself and tell us one fun fact about yourself!**
+
+          Post your answer to Slack [#social](https://gtnsmrgsbord.slack.com/channels/social) channel. *(See the setup tab for instructions for joining Slack)*.
+
+
+  - section: "Galaxy introduction"
+    subsection: true
     description: |
-      Kick off the week with a hands-on introduction to Galaxy, covering everything from basic navigation and data manipulation to reproducing published analyses, quality control, and mapping sequences to a reference genome. Whether you're new to Galaxy or looking to strengthen your skills, today's sessions will equip you with the foundational knowledge needed for more advanced topics. 
-      In the morning you can take part in the Icebreaker by joining us in the [Slack introduction channel](https://gtnsmrgsbord.slack.com/archives/C07NKAJ8THA). Or you can directly start with the tutorials. If you need support contact us via the [Slack introduction channel](https://gtnsmrgsbord.slack.com/archives/C07NKAJ8THA).
+      Kick off the week with a hands-on introduction to Galaxy, covering everything from basic navigation and data manipulation to reproducing published analyses, quality control, and mapping sequences to a reference genome. Whether you're new to Galaxy or looking to strengthen your skills, today's sessions will equip you with the foundational knowledge needed for more advanced topics.
+      In the morning you can take part in the Icebreaker by joining us in the [Slack introduction channel](https://gtnsmrgsbord.slack.com/archives/C07NKAJ8THA). Or you can directly start with the tutorials.
+
+      **Need help with these tutorials?** Ask your questions via the [Slack introduction channel](https://gtnsmrgsbord.slack.com/archives/C07NKAJ8THA).
   - section: Start to get to know Galaxy
     subsection: true
     tutorials:
@@ -135,7 +159,20 @@ program:
       - name: mapping
         topic: sequence-analysis
 
-  - section: "Tuesday to Thursday: Pick a track"
+  - section: "Tuesday to Thursday: Choose your own Adventure!"
+    tutorials:
+      - type: custom
+        name: Daily Icebreakers
+        description: |
+          **Tuesday:** For today's ice breaker, we would love to know one weird fact that you know for no reason.
+          **Wednesday:** We would love to hear where you find inspiration. Maybe you find inspirtaiton through nature or maybe you have a prominant role model in your life—we'd love to learn more about you!
+          **Thursday:** if you could take any one movie prop from a movie set, what would it be?
+
+          Post your answers each day to Slack [#social](https://gtnsmrgsbord.slack.com/channels/social) channel.
+
+
+  - section: "Pick a track"
+    subsection: true
     description: |
       Over the course of these three days, you can choose your preferred track and learn how to use Galaxy for data analysis in this research field. If you find multiple topics interesting, feel free to explore more than one track. Each track will guide you through the process, from basic to more advanced analyses, to accommodate learners of all levels.
 
@@ -160,23 +197,40 @@ program:
 
   - section: "Friday: Grab bag"
     description: |
-      Can't get enough? Then please pick one of our FAIR tutorials or any of the tutorials of the GTN. Please be aware that only trainings that are part of the introduction day or a learning track have been tested on all instances for the event. The trainers present on Slack will do their best to help you if you have a problem and answer questions, but they may not be expert in the topic you selected.
-      You can directly start with you prefered tutorial. If you need support contact us via the Slack Channel [#gta_friday-grab-bag](https://gtnsmrgsbord.slack.com/archives/C07N2A4HQ15).
+      Can't get enough? Then please pick one of our FAIR tutorials below, or any of the tutorials of the [GTN](https://training.galaxyproject.org). Please be aware that only trainings that are part of the introduction day or a learning track have been tested on all instances for the event. The trainers present on Slack will do their best to help you if you have a problem and answer questions, but they may not be expert in the topic you selected.
+      You can directly start with you prefered tutorial.
+
+      **Need help with these tutorials?** Ask your questions via the Slack Channel [#gta_friday-grab-bag](https://gtnsmrgsbord.slack.com/archives/C07N2A4HQ15).
   - section: Fair training
     subsection: true
     tutorials:
+      - type: custom
+        name: Daily Icebreaker
+        description: |
+          **For the last ice breaker, we would love to know what the most interesting or exciting thing you learned this week is!!**
+
+          Post your answer on Slack [#social](https://gtnsmrgsbord.slack.com/channels/social) channel
       - type: custom
         name: "[An overview of the RO-Crate concept and its implementations](https://gallantries.github.io/video-library/videos/ro-crates/intro/slides/)"
         description: |
-          Lecture Video
+          [<i class="fas fa-video" aria-hidden="true"></i> Lecture Video](https://gallantries.github.io/video-library/videos/ro-crates/intro/slides/)
       - name: ro-crate-intro
         topic: fair
       - type: custom
         name: "[Registering Galaxy workflows in WorkflowHub](https://gallantries.github.io/video-library/videos/ro-crates/workflowhub/tutorial/)"
         description: |
-          Lecture Video
+          [<i class="fas fa-video" aria-hidden="true"></i> Lecture Video](https://gallantries.github.io/video-library/videos/ro-crates/workflowhub/tutorial/)
       - name: ro-crate-galaxy-best-practices
         topic: fair
+
+  - section: Explore the GTN!
+    subsection: true
+    description: |
+      Feel free to try any other tutorials you see on the GTN, and we will do our best to guide you!
+    tracks:
+      - title: Go to the GTN!
+        link: https://training.galaxyproject.org
+
 ---
 # Welcome to the Galaxy Training Academy
 Do you want to learn how to use Galaxy, a open source data analysis platform? Then you are at the right place. We offer here a **5-day Global Online and Asynchronous learning event**.
diff --git a/events/images/BB_24_logo.png b/events/images/BB_24_logo.png
new file mode 100644
index 00000000000000..235bec02036b9c
Binary files /dev/null and b/events/images/BB_24_logo.png differ
diff --git a/events/images/spoc-cofest.png b/events/images/spoc-cofest.png
new file mode 100644
index 00000000000000..f27163e6fcd386
Binary files /dev/null and b/events/images/spoc-cofest.png differ
diff --git a/events/tracks/gta2024-assembly.md b/events/tracks/gta2024-assembly.md
index cf18ceb327f586..7d091da4f2d0f6 100644
--- a/events/tracks/gta2024-assembly.md
+++ b/events/tracks/gta2024-assembly.md
@@ -22,22 +22,20 @@ contributions:
 
 
 program:
-  - section: "Introduction" 
+  - section: "Introduction"
     description: |
-      In this section, we will introduce what is a genome assembly, how it works, and the metrics to evaluate the quality of an assembly.  
+      In this section, we will introduce what is a genome assembly, how it works, and the metrics to evaluate the quality of an assembly.
     tutorials:
-      - name: get-started-genome-assembly
-        topic: assembly
       - type: custom
-        name: "[Introduction To Genome Assembly](https://docs.google.com/presentation/d/1TPr6yKrnNj4cUb5We-E7SXAL_1h2Cqogq0tDTkgETgQ/edit?usp=sharing)"
+        name: "[Introduction To Genome Assembly](https://youtu.be/9WZe7VGtr-k)"
         description: |
-          [Lecture Slides](https://docs.google.com/presentation/d/1TPr6yKrnNj4cUb5We-E7SXAL_1h2Cqogq0tDTkgETgQ/edit?usp=sharing); [Lecture Video](https://youtu.be/9WZe7VGtr-k)
+          [<i class="fas fa-video" aria-hidden="true"></i> Lecture Video](https://youtu.be/9WZe7VGtr-k) ([Slides](https://docs.google.com/presentation/d/1TPr6yKrnNj4cUb5We-E7SXAL_1h2Cqogq0tDTkgETgQ/edit?usp=sharing))
       - name: ERGA-post-assembly-QC
         topic: assembly
 
-  - section: "Genome Assembly" 
+  - section: "Genome Assembly"
     description: |
-      In this section, we will present genome assemblies for different types of organisms.  
+      In this section, we will present genome assemblies for different types of organisms.
     tutorials:
       - name: mrsa-illumina
         topic: assembly
@@ -46,25 +44,21 @@ program:
       - name: metagenomics-assembly
         topic: assembly
 
-  - section: "Non-nuclear genome assembly" 
+  - section: "Non-nuclear genome assembly"
     description: |
-      When sequencing organisms with non-nuclear DNA, the data contain sequencing for both nuclear and non-nuclear DNA. In this section we will learn how to assemble organelles genome.  
+      When sequencing organisms with non-nuclear DNA, the data contain sequencing for both nuclear and non-nuclear DNA. In this section we will learn how to assemble organelles genome.
     tutorials:
-      - type: custom
-        name: "Mitochondrial Genome Assembly"
-        description: |
-          Tutorial (will come soon)
+      - name: mitochondrion-assembly
+        topic: assembly
       - name: chloroplast-assembly
         topic: assembly
-        
-  - section: "Assembly decontamination" 
+
+  - section: "Assembly decontamination"
     description: |
-      In this section we will learn how remove sequences that do not belong to the organism you want to sequence.  
+      In this section we will learn how remove sequences that do not belong to the organism you want to sequence.
     tutorials:
-      - type: custom
-        name: "Assembly decontamination"
-        description: |
-          Tutorial (will come soon)
+      - name: assembly-decontamination
+        topic: assembly
 ---
 
 
diff --git a/events/tracks/gta2024-microbiome.md b/events/tracks/gta2024-microbiome.md
index 12c5380db27747..7e8179ae093e08 100644
--- a/events/tracks/gta2024-microbiome.md
+++ b/events/tracks/gta2024-microbiome.md
@@ -4,7 +4,7 @@ redirect_from:
 - /events/tracks/gta2024-bacterial-micorbiome
 
 title: Microbiome analysis
-description: This track explains the capability to analyze microbiome data using Galaxy, covering essential tasks such as amplicon analysis and its visualization. It progresses to more advanced topics, including assembly and binning - the requirements for reconstructing Metagenome-Assembled Genomes (MAGs) from microbiome samples, and finally extends to complex analyses like metatranscriptome studies and pathogen detection. Start with the tutorial at your own pace. If you need support contact us via the Slack Channel [#gta_microbiome](https://gtnsmrgsbord.slack.com/archives/C07NA7FSGR4).
+description: This track explains the capability to analyze microbiome data using Galaxy, covering essential tasks such as amplicon analysis and its visualization. It progresses to more advanced topics, including assembly and binning - the requirements for reconstructing Metagenome-Assembled Genomes (MAGs) from microbiome samples, and finally extends to complex analyses like metatranscriptome studies and pathogen detection. Start with the tutorial at your own pace. If you need support contact us via the Slack Channel [#gta_microbiome](https://gtnsmrgsbord.slack.com/archives/C07NA7FSGR4). Please note that the tutorials of this track where all tested successfully on useGalaxy.eu, therefore it is recommended to run all tools on this server !
 
 slack_channel: gta_microbiome
 
@@ -22,6 +22,7 @@ contributions:
         - paulzierep
         - plushz
         - RZ9082
+        - pratikdjagtap
 
 
 program:
@@ -38,10 +39,6 @@ program:
     tutorials:
       - name: dada-16S
         topic: microbiome
-      - type: custom
-        name: "MGnify amplicon workflow"
-        description: |
-          Tutorial (will come soon)
   
   - section: "Metagenomics" 
     description: |
diff --git a/events/tracks/gta2024-single-cell.md b/events/tracks/gta2024-single-cell.md
index 179846e68b0c55..5628d6a405962c 100644
--- a/events/tracks/gta2024-single-cell.md
+++ b/events/tracks/gta2024-single-cell.md
@@ -26,6 +26,10 @@ program:
     description: |
       If you encounter any issue please ask us in this Slack channel. 
     tutorials:
+      - name: scrna-intro
+        topic: single-cell
+      - name: scrna-data-formats
+        topic: single-cell
       - name: scrna-preprocessing-tenx
         topic: single-cell
       - name: scrna-scanpy-pbmc3k
diff --git a/events/tracks/gta2024-transcriptomics.md b/events/tracks/gta2024-transcriptomics.md
index a3a2cffd8ba2bf..0af2885efe23eb 100644
--- a/events/tracks/gta2024-transcriptomics.md
+++ b/events/tracks/gta2024-transcriptomics.md
@@ -1,7 +1,7 @@
 ---
 layout: event-track
 
-title: Transcritomics
+title: Transcriptomics
 description: Welcome to the transcriptomics track. Here you can learn how to map the raw RNA-seq sequencing data to a reference, preform an differential expression analysis, GO analysis. Further you can learn how to plot your processed data. Additionally you can also learn how to perform a differential Isofrom expression analysis, as well as how to annotate lncRNAs. If you want to learn more in the field of transciptomics please also have a look at the single cell track. Start with the tutorial at your own pace. If you need support contact us via the Slack Channel [gta_transcriptomics](https://gtnsmrgsbord.slack.com/archives/C07NGRVEG8J).
 
 slack_channel: gta_transcriptomics
@@ -30,7 +30,7 @@ contributions:
 
 
 program:
-  - section: "Transcritomics introduction" 
+  - section: "Transcriptomics introduction" 
     description: |
       If you are new to transcriptomics please start with the following tutorials. If you encounter any issue please ask us on Slack.  
     tutorials:
diff --git a/faqs/galaxy/can-i-use-a-public-galaxy-for-my-private-data.md b/faqs/galaxy/can-i-use-a-public-galaxy-for-my-private-data.md
new file mode 100644
index 00000000000000..b56498eab6f828
--- /dev/null
+++ b/faqs/galaxy/can-i-use-a-public-galaxy-for-my-private-data.md
@@ -0,0 +1,18 @@
+---
+title: Can I use a public Galaxy for my private data?
+layout: faq
+area: support
+box_type: tip
+google_form_id: 1728322033
+contributors:
+- hexylena
+---
+Of course\*!
+
+If your data is not sensitive (i.e. human patient) but just private (sequencing from other animals/bacteria/etc), then it is absolutely ok to use a public galaxy server like usegalaxy.eu or usegalaxy.org!
+
+ Data uploaded is private to your account, it isn't available to others publicly. No one will scoop your results, if you use a public galaxy server to analyse your data :)
+
+ A great benefit of this is then when your paper is being reviewed you can share that history or workflow with reviewers, and when it's published you can click a button to share those results with the world as well, such that others can reproduce your analysis!
+
+ (of course system administrators can see the files on disk but they are not interested and will not be looking at your data. If you file a bug report they may see your data but they are system administrators, not bioinformatics experts that might be interested in your results.)
diff --git a/faqs/galaxy/visualisations_igb.md b/faqs/galaxy/visualisations_igb.md
new file mode 100644
index 00000000000000..436829e87c4139
--- /dev/null
+++ b/faqs/galaxy/visualisations_igb.md
@@ -0,0 +1,25 @@
+---
+title: Open History files in Integrated Genome Browser (IGB)
+area: visualisation
+box_type: tip
+layout: faq
+contributors: [paige-kulzer]
+---
+
+You can open some file types in Integrated Genome Browser (IGB), a desktop genome browser. ([Supported File Types](https://wiki.bioviz.org/confluence/display/igbman/File+Formats))
+
+Here's how:
+
+1. Install IGB on your computer ([download page](https://bioviz.org/download.html)).
+2. Start IGB.
+3. In Galaxy, click the desired dataset's name to expand it.
+   - Check that the reference genome (dbkey) is set
+   ([instructions]({% link faqs/galaxy/datasets_change_dbkey.html %})).
+4. Click on the Charts icon {% icon galaxy-barchart %}
+5. In the central panel, next to `display in IGB`, choose `View`.
+
+When you choose "View" in Galaxy, your browser opens a new tab showing a page from BioViz.org. Check the newly opened page for next steps.
+
+Having trouble? Working with a custom genome assembly not yet available in Galaxy or IGB?
+
+Contact [the IGB team](https://bioviz.org/help.html) for help and advice!
diff --git a/metadata/git-mod-d14fd5ed9616d1957ef6f84225762d6b467e69e7.txt b/metadata/git-mod-4a250adf7376ad60a6c9f94133f0683677f05074.txt
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index 4b43764b27e425..dfe760db4aa41d 100644
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@@ -1,3 +1,160 @@
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@@ -1,3 +1,71 @@
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 GTN_GTN:1727337973
 
 A	assets/js/tutorial-mode.js
diff --git a/metadata/shortlinks.yaml b/metadata/shortlinks.yaml
index b8d30ad1e2cdad..7c383acdc14b25 100644
--- a/metadata/shortlinks.yaml
+++ b/metadata/shortlinks.yaml
@@ -582,6 +582,10 @@ name:
   ecology/Ecoregionalization_tutorial: "/topics/ecology/tutorials/Ecoregionalization_tutorial/tutorial.html"
   single-cell/GO-enrichment: "/topics/single-cell/tutorials/GO-enrichment/tutorial.html"
   single-cell/GO-enrichment/slides: "/topics/single-cell/tutorials/GO-enrichment/slides.html"
+  climate/earth_system: "/topics/climate/tutorials/earth_system/tutorial.html"
+  metabolomics/qcxms-predictions: "/topics/metabolomics/tutorials/qcxms-predictions/tutorial.html"
+  microbiome/lotus2-identifying-fungi: "/topics/microbiome/tutorials/lotus2-identifying-fungi/tutorial.html"
+  metabolomics/qcxms-predictions/slides: "/topics/metabolomics/tutorials/qcxms-predictions/slides.html"
 id:
   T00000: "/topics/admin/tutorials/ansible/tutorial.html"
   T00001: "/topics/admin/tutorials/ansible-galaxy/tutorial.html"
@@ -2009,6 +2013,18 @@ id:
   F00422: "/topics/single-cell/faqs/gtn-in-galaxy_mode-cs.html"
   W00286: "/topics/ecology/tutorials/Ecoregionalization_tutorial/workflows/Ecoregionalization_workflow.html"
   W00287: "/topics/single-cell/tutorials/GO-enrichment/workflows/main_workflow.html"
+  T00457: "/topics/climate/tutorials/earth_system/tutorial.html"
+  T00458: "/topics/metabolomics/tutorials/qcxms-predictions/tutorial.html"
+  T00459: "/topics/microbiome/tutorials/lotus2-identifying-fungi/tutorial.html"
+  S00125: "/topics/metabolomics/tutorials/qcxms-predictions/slides.html"
+  F00423: "/faqs/galaxy/visualisations_igb.html"
+  F00424: "/topics/contributing/faqs/input-and-answer-keys.html"
+  F00425: "/topics/contributing/faqs/new-contributions.html"
+  F00426: "/topics/contributing/faqs/why-prerequisites.html"
+  W00288: "/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS.html"
+  W00289: "/topics/microbiome/tutorials/lotus2-identifying-fungi/workflows/lotus2-fungi-workflow.html"
+  E00014: "/events/2023-10-02-mtb-ngs.html"
+  E00015: "/events/2024-06-10-mtb-ngs.html"
 misc:
   ansible-galaxy: "/topics/admin/tutorials/ansible-galaxy/tutorial.html"
   feeds: "/feeds/index.html"
diff --git a/metadata/workflowhub.yml b/metadata/workflowhub.yml
index de649890711a40..06769810c294c2 100644
--- a/metadata/workflowhub.yml
+++ b/metadata/workflowhub.yml
@@ -130,11 +130,13 @@ metabolomics/gc_ms_with_xcms/main-workflow: '1119'
 metabolomics/lcms/main-workflow: '1065'
 metabolomics/msi-analyte-distribution/wf-msi-distribution: '1061'
 metabolomics/msi-finding-nglycans/msi-finding-diff-analytes: '1123'
+metabolomics/qcxms-predictions/end-to-end-ei--mass-spectra-prediction-workflow-using-qcxms: '1367'
 microbiome/beer-data-analysis/main-workflow: '1097'
 microbiome/dada-16S/workflow: '1105'
 microbiome/diversity/workflow: '1349'
 microbiome/general-tutorial/amplicon: '1070'
 microbiome/general-tutorial/wgs-worklow: '1067'
+microbiome/lotus2-identifying-fungi/lotus2-fungi-workflow: '1366'
 microbiome/metagenomics-assembly/metagenomics-assembly-tutorial-workflow: '1103'
 microbiome/metagenomics-assembly/workflow-generate-dataset-for-assembly-tutorial: '1099'
 microbiome/metatranscriptomics-short/workflow1-preprocessing: '1092'
diff --git a/search2.html b/search2.html
index 9c40201536083e..5124c6b6f06c4e 100644
--- a/search2.html
+++ b/search2.html
@@ -27,6 +27,7 @@
 		<option value="Slides">Slides</option>
 		<option value="FAQs">FAQs</option>
 		<option value="News">News</option>
+		<option value="Workflow">Workflows</option>
 	</select>
 	</div>
 	</div>
@@ -74,10 +75,12 @@
 			</tr>
 		{%- endfor -%}
 		{%- for page in site.pages -%}
-			{%- if page.short_id -%}
+			{%- assign layout = page.layout | layout_to_human -%}
+			{%- unless page.symlink %}
+			{%- if layout -%}
 			{%- if page.short_id != 'F00001' -%}
 			<tr id="{{ page.short_id }}">
-				<td><a class="badge badge-info" href="https://gxy.io/GTN:{{ page.short_id }}">{%- icon purl -%} GTN:{{ page.short_id }}</a></td>
+				<td>{% if page.short_id %}<a class="badge badge-info" href="https://gxy.io/GTN:{{ page.short_id }}">{%- icon purl -%} GTN:{{ page.short_id }}</a>{% else %}<abbr title="To Be Assigned">TBA</a>{% endif %}</td>
 				<td>{{ page.layout | layout_to_human }}</td>
 				<td><a href="{{ site.baseurl }}{{ page.url }}">{{ page.title }}</a><br/>
 					{%- for tag in page.tags -%}
@@ -90,6 +93,7 @@
 			</tr>
 			{%- endif -%}
 			{%- endif -%}
+			{%- endunless -%}
 		{%- endfor -%}
 	</tbody>
 </table>
diff --git a/topics/assembly/tutorials/ERGA-post-assembly-QC/tutorial.md b/topics/assembly/tutorials/ERGA-post-assembly-QC/tutorial.md
index c1eb9cb8a9aa85..642874a5cde0d2 100644
--- a/topics/assembly/tutorials/ERGA-post-assembly-QC/tutorial.md
+++ b/topics/assembly/tutorials/ERGA-post-assembly-QC/tutorial.md
@@ -1,30 +1,46 @@
 ---
 layout: tutorial_hands_on
-
 title: ERGA post-assembly QC
 questions:
 - "What combination of tools can assess the quality of a genome assembly?"
-- "What metrics can help to analyse the quality of an asembly?"
+- "What metrics can help to analyse the quality of an assembly?"
 - "How do we evaluate the outputs?"
 objectives:
-- "Apply the post-assembly-QC-workflow using the necessary tools"
-- "Analyse and evaluate the results of the workflow"
+- Apply the post-assembly-QC-workflow using the necessary tools
+- Analyse and evaluate the results of the workflow
 time_estimation: 3H
 key_points:
-- "The ERGA post-assembly pipeline allows users to assess and improve the quality of genome assemblies"
-- "The ERGA post-assembly pipeline consists of three main steps: Genome assembly decontamination and overview with BlobToolKit, providing analysis information and statistics, and Hi-C scaffolding."
+- The ERGA post-assembly pipeline allows users to assess and improve the quality of
+  genome assemblies
+- 'The ERGA post-assembly pipeline consists of three main steps: Genome assembly decontamination
+  and overview with BlobToolKit, providing analysis information and statistics, and
+  Hi-C scaffolding.'
 contributors:
 - GitFab93
 - gallardoalba
 - tbrown91
+- delphine-l
 tags:
-  - plants
-  - animals
-  - genome
-  - assembly
-  - QC
+- plants
+- animals
+- genome
+- assembly
+- QC
+recordings:
+- youtube_id: n4PNTTa2d6U
+  length: 44M
+  galaxy_version: 24.1.3.dev0
+  date: '2024-10-06'
+  speakers:
+  - delphine-l
+  captioners:
+  - delphine-l
+  bot-timestamp: 1728229662
+
+
 ---
 
+
 ![ERGA logo, 4 colorful letters on white background, with the European star circle](../../images/post-assembly-QC/ERGA.logo.jpg){: width="20px"}
 
 The European Reference Genome Atlas ([ERGA](https://www.erga-biodiversity.eu/), {% cite Mazzoni2023 %}) is a large-scale network of researchers aiming to generate high-quality reference genomes for all eukaryotic life in Europe and build capacity to allow researchers anywhere to generate reference genomes and use them to answer questions regarding species conservation and biodiversity. ERGA uses state-of-the-art sequencing technologies and advanced bioinformatics tools to produce high-quality genome assemblies.
@@ -52,7 +68,7 @@ In this tutorial we will evaluate three genome assemblies, belonging to three di
 
 *Chondrosia reniformis* is a slow-growing marine sponge cosmopolitan species which can be found in the Mediterranean Sea and the eastern Atlantic Ocean in shallow waters; it is considered to playing an important ecological role in the marine ecosystem by filtering large volumes of water and providing habitat for other species ({% cite Voultsiadou2005 %}). The members of this species are gonochoristic and oviparous, whose physiology and behavious seems to highly influence the presence of endosymbiosis heterotrophic bacteria ({% cite sara1997 %}). 
 
-![Figure 1: Chrondosia reniformis](../../images/post-assembly-QC/chondrosia.jpg "Example *Chrondosia reniformis* sponge."
+![Figure 1: Chrondosia reniformis](../../images/post-assembly-QC/chondrosia.jpg "Example <em>Chrondosia reniformis</em> sponge.")
 
 The assembly is based on 70x PacBio HiFi and Arima2 Hi-C data generated by the [Aquatic Symbiosis Genomics Project](https://www.aquaticsymbiosisgenomics.org/). The assembly process included the following sequence of steps: initial PacBio assembly generation with Hifiasm, retained haplotig separation with purge_dups, and Hi-C based scaffolding with YaHS. The mitochondrial genome was assembled using MitoHiFi. Finally, the primary assembly was analysed and manually improved using gEVAL.
 
@@ -60,7 +76,7 @@ The assembly is based on 70x PacBio HiFi and Arima2 Hi-C data generated by the [
 
 *Erythrolamprus reginae* is a species of colubrid snake found in South America. This species has been reported to include triploid individuals with parthenogenic reproduction; this type of seems to be associated with higher mutation rates and more tandem duplications in the genome ({% cite Bogart1980 %}). 
 
-![Figure 2: Erythrolamprus reginae](../../images/post-assembly-QC/erytrocampus.jpg "Example *Erythrolamprus reginae* snake.")
+![Figure 2: Erythrolamprus reginae](../../images/post-assembly-QC/erytrocampus.jpg "Example <em>Erythrolamprus reginae</em> snake.")
 
 The assembly used in this tutorial correspond to the curated primary assembly generated by the [VGP project](https://vertebrategenomesproject.org/), based on 34x Pacbio HiFi and Arima2 Hi-C data, by using the [VGP assembly pipeline]({% link topics/assembly/tutorials/vgp_genome_assembly/tutorial.md %}).
 
@@ -68,7 +84,7 @@ The assembly used in this tutorial correspond to the curated primary assembly ge
 
 *Eschrichtius robustus*, commonly known as the gray whale, is a species of whale found primarily in the North Pacific Ocean. Adult gray whales can reach lengths of up to 14.9 meters and weights of up to 36,000 kilograms. It is a diploid species, genetically characterized by high homozygosity ({% cite BrnicheOlsen2018 %}).
 
-![Figure 3: Eschrichtius robustus](../../images/post-assembly-QC/eschrichtius.jpg "Example *Eschrichtius robustus* whale.")
+![Figure 3: Eschrichtius robustus](../../images/post-assembly-QC/eschrichtius.jpg "Example <em>Eschrichtius robustus</em> whale.")
 
 The assembly used in this tutorial correspond to the curated primary assembly generated by the [VGP project](https://vertebrategenomesproject.org/), based on 29x Pacbio HiFi data, by using the [VGP assembly pipeline]({% link topics/assembly/tutorials/vgp_genome_assembly/tutorial.md %}).
 
@@ -90,15 +106,15 @@ As a first step we will get the data from Zenodo.
 >    - Copy the tabular data, paste it into the textbox and press <kbd>Build</kbd>
 >
 >       ```
->   dataset_01   https://zenodo.org/record/7786773/files/hifi.fastq.gz   fastq.gz    HiFi    CReniformis_Pacbio
->   dataset_01   https://zenodo.org/record/7784764/files/m64055_210602_211608.hifi_reads.fastq.gz   fastq.gz    HiFi    EReginae_PacBio
->   dataset_02   https://zenodo.org/record/7784764/files/m54306Ue_211020_191957.hifi_reads.fastq.gz   fastq.gz    HiFi    EReginae_PacBio
->   dataset_03   https://zenodo.org/record/7784764/files/m54306Ue_211016_070813.hifi_reads.fastq.gz   fastq.gz    HiFi    EReginae_PacBio
->   dataset_04   https://zenodo.org/record/7786773/files/m54306U_210529_225553.hifi_reads.fastq.gz   fastq.gz    HiFi    EReginae_PacBio
->   dataset_01   https://zenodo.org/record/7781236/files/m54306Ue_220411_220734.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastq.gz    HiFi    ERobustus_PacBio
->   dataset_02   https://zenodo.org/record/7781236/files/m54306Ue_220520_051743.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastq.gz    HiFi    ERobustus_PacBio
->   dataset_03   https://zenodo.org/record/7781236/files/m64055e_220615_033108.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastq.gz    HiFi    ERobustus_PacBio
->   dataset_04   https://zenodo.org/record/7786773/files/m64055e_220603_182128.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastq.gz    HiFi    ERobustus_PacBio
+>   dataset_01   https://zenodo.org/record/7786773/files/hifi.fastq.gz   fastqsanger.gz    HiFi    CReniformis_Pacbio
+>   dataset_01   https://zenodo.org/record/7784764/files/m64055_210602_211608.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    EReginae_PacBio
+>   dataset_02   https://zenodo.org/record/7784764/files/m54306Ue_211020_191957.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    EReginae_PacBio
+>   dataset_03   https://zenodo.org/record/7784764/files/m54306Ue_211016_070813.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    EReginae_PacBio
+>   dataset_04   https://zenodo.org/record/7786773/files/m54306U_210529_225553.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    EReginae_PacBio
+>   dataset_01   https://zenodo.org/record/7781236/files/m54306Ue_220411_220734.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    ERobustus_PacBio
+>   dataset_02   https://zenodo.org/record/7781236/files/m54306Ue_220520_051743.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    ERobustus_PacBio
+>   dataset_03   https://zenodo.org/record/7781236/files/m64055e_220615_033108.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    ERobustus_PacBio
+>   dataset_04   https://zenodo.org/record/7786773/files/m64055e_220603_182128.demultiplex.bc1010--bc1010.hifi_reads.fastq.gz   fastqsanger.gz    HiFi    ERobustus_PacBio
 >       ```
 >    - Click on `Build`
 >    - From **Rules** menu select `Add / Modify Column Definitions`
@@ -119,16 +135,16 @@ As a first step we will get the data from Zenodo.
 >       ```
 >   CReformitis_assembly    https://zenodo.org/record/7831298/files/assembly_sponge.fasta.gz    fasta.gz    assembly
 >   CReformitis_metadata    https://zenodo.org/record/7781236/files/metadata_chon.yaml  yaml    metadata
->   CReniformis_Hi-C_F   https://zenodo.org/record/7786773/files/hiC_1.fastq.gz   fastq.gz    Hi-C
->   CReniformis_Hi-C_R   https://zenodo.org/record/7786773/files/hiC_2.fastq.gz   fastq.gz    Hi-C
->   EReginae_Hi-C_F_01   https://zenodo.org/record/7831762/files/rEryReg1_Royal_Ground_Snake_R1.fastq.gz.gz.001   fastq.gz    Hi-C
->   EReginae_Hi-C_F_02   https://zenodo.org/record/7831762/files/rEryReg1_Royal_Ground_Snake_R1.fastq.gz.gz.002   fastq.gz    Hi-C
->   EReginae_Hi-C_F_03   https://zenodo.org/record/7831763/files/rEryReg1_Royal_Ground_Snake_R1.fastq.gz.gz.003   fastq.gz    Hi-C
->   EReginae_Hi-C_R_01   https://zenodo.org/record/7833514/files/rEryReg1_Royal_Ground_Snake_R2.fastq.gz.gz.001   fastq.gz    Hi-C
->   EReginae_Hi-C_R_02   https://zenodo.org/record/7833514/files/rEryReg1_Royal_Ground_Snake_R2.fastq.gz.gz.002   fastq.gz    Hi-C
->   EReginae_Hi-C_R_03   https://zenodo.org/record/7831763/files/rEryReg1_Royal_Ground_Snake_R2.fastq.gz.gz.003   fastq.gz    Hi-C
->   EReginata_assembly    https://zenodo.org/record/7788734/files/rEryReg1.pri.cur.20230105.fasta.gz    fasta.gz    assembly
->   ERegina_metadata    https://zenodo.org/record/7781236/files/metadata_eryth.yaml  yaml    metadata
+>   CReniformis_Hi-C_F   https://zenodo.org/record/7786773/files/hiC_1.fastq.gz   fastqsanger.gz    Hi-C
+>   CReniformis_Hi-C_R   https://zenodo.org/record/7786773/files/hiC_2.fastq.gz   fastqsanger.gz    Hi-C
+>   EReginae_Hi-C_F_01   https://zenodo.org/record/7831762/files/rEryReg1_Royal_Ground_Snake_R1.fastq.gz.gz.001   fastqsanger.gz    Hi-C
+>   EReginae_Hi-C_F_02   https://zenodo.org/record/7831762/files/rEryReg1_Royal_Ground_Snake_R1.fastq.gz.gz.002   fastqsanger.gz    Hi-C
+>   EReginae_Hi-C_F_03   https://zenodo.org/record/7831763/files/rEryReg1_Royal_Ground_Snake_R1.fastq.gz.gz.003   fastqsanger.gz    Hi-C
+>   EReginae_Hi-C_R_01   https://zenodo.org/record/7833514/files/rEryReg1_Royal_Ground_Snake_R2.fastq.gz.gz.001   fastqsanger.gz    Hi-C
+>   EReginae_Hi-C_R_02   https://zenodo.org/record/7833514/files/rEryReg1_Royal_Ground_Snake_R2.fastq.gz.gz.002   fastqsanger.gz    Hi-C
+>   EReginae_Hi-C_R_03   https://zenodo.org/record/7831763/files/rEryReg1_Royal_Ground_Snake_R2.fastq.gz.gz.003   fastqsanger.gz    Hi-C
+>   EReginae_assembly    https://zenodo.org/record/7788734/files/rEryReg1.pri.cur.20230105.fasta.gz    fasta.gz    assembly
+>   EReginae_metadata    https://zenodo.org/record/7781236/files/metadata_eryth.yaml  yaml    metadata
 >   ERobustus_assembly  https://zenodo.org/record/7781236/files/mEscRob2.pri.cur.20221201.fasta.gz  fasta.gz    assembly
 >   ERobustus_metadata  https://zenodo.org/record/7781236/files/metadata_esch.yaml  yaml    metadata
 >   Taxonomy_data   https://zenodo.org/record/7781236/files/new_taxdump.tar.gz  gz  taxonomy
@@ -156,7 +172,7 @@ Once we have imported all the datasets, we will move each one to its corresponde
 > 2. Rename the histories as  `Case 1: *Chondrosia reniformis*`, `Case 2: *Erythrolamprus reginae*` and  `Case 3: *Eschrichtius robustus*`.
 > 3. Click in **History options** and select `Show Histories Side-by-Side`
 > 4. Click in `Select histories`, and include the histories corresponding to the three species.
-> 5. Move the datasets to its correspondent history.
+> 5. Drag and drop to move the datasets to their correspondent histories.
 >
 >    > <comment-title>Non-unique datasets</comment-title>
 >    > Both the **Taxonomic_data** and the **Diamond_database** should be included in all of them.
@@ -219,7 +235,7 @@ In this tutorial we will use **Minimap2** for generation the coverage data. Mini
 {: .hands_on}
 
 
-## Generate sequence similarity data with DIAMOND
+## Generate sequence similarity data with **DIAMOND**
 
 **DIAMOND** is a sequence alignment tool that utilizes a more efficient algorithm compared to BLAST, allowing for much faster searches of large sequence databases. Specifically, DIAMOND uses a sensitive seed-extension approach that compares a set of small segments (seeds) from the query sequence to a database, and then extends the alignments based on the highest-scoring hits. This approach allows DIAMOND to perform up to 20,000 times faster than BLAST, with comparable or improved sensitivity and accuracy ({% cite Buchfink2014 %}).
 
@@ -259,11 +275,14 @@ In this tutorial we will use **Minimap2** for generation the coverage data. Mini
 
 > <hands-on-title> Estimate single copy gene representation completeness </hands-on-title>
 >
-> 1. {% tool [Busco](toolshed.g2.bx.psu.edu/repos/iuc/busco/busco/5.4.4+galaxy0) %} with the following parameters:
->    - {% icon param-file %} *"Sequences to analyse"*: `outfile` (output of **Replace** {% icon tool %})
+> 1. {% tool [Busco](toolshed.g2.bx.psu.edu/repos/iuc/busco/busco/5.7.1+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Sequences to analyse"*: `CReformitis_assembly`
+>    - *"Lineage data source"*: `Use cached lineage Data`
+>    - *"Cached database with lineage"*: `Busco v5 Lineage Datasets`
 >    - *"Mode"*: `Genome assemblies (DNA)`
->        - *"Use Augustus instead of Metaeuk"*: `Use Metaeuk`
->    - *"Auto-detect or select lineage?"*: `Auto-detect`
+>        - *"Select a gene predictor"*: `Metaeuk`
+>    - *"Auto-detect or select lineage?"*: `Auto-detect` (For *Erythrolamprus reginae*, select `Select lineage` then `Vertebrata` in *"Lineage"*)
+>    - *"auto-lineage group"*: `All taxonomic groups (--auto-lineage)`
 >    - *"Which outputs should be generated"*: `Short summary text`
 >
 >
@@ -315,6 +334,7 @@ To get a more meaningful analysis and therefore more useful information about ou
 >        - {% icon param-file %} *"BUSCO full table file"*: `Full table` (output of **Busco** {% icon tool %})
 >        - *"BLAST/Diamond hits"*: `Enabled`
 >           - *"BLAST/Diamond hits dataset"*: output of **Diamond** {% icon tool %}
+>           - *"BLAST/Diamond file column order"*: `1=qseqid,7=staxids,6=bitscore,2=sseqid,3=qstart,4=qend,5=evalue` 
 >        - {% icon param-file %} *"BAM/SAM/CRAM read alignment file"*: output of **Minimap2** {% icon tool %}
 >
 > 3. {% tool [Interactive BlobToolKit](interactive_tool_blobtoolkit) %} with the following parameters:
@@ -419,7 +439,7 @@ Meryl is a powerful tool for counting k-mers in large-scale genomic datasets. It
 >
 > 1. {% tool [Meryl](toolshed.g2.bx.psu.edu/repos/iuc/meryl/meryl/1.3+galaxy6) %} with the following parameters:
 >    - *"Operation type selector"*: `Count operations`
->        - {% icon param-collection %} *"Input sequences"*: `output` (Input dataset collection)
+>        - {% icon param-collection %} *"Input sequences"*: `CReniformis_Pacbio` 
 >        - *"K-mer size selector"*: `Set a k-mer size`
 >            - *"K-mer size"*: `21`
 >
@@ -494,8 +514,7 @@ Merqury works by comparing k-mers of an assembly to those from unassembled high-
 >    - *"Evaluation mode"*: `Default mode`
 >        - {% icon param-file %} *"K-mer counts database"*: `read_db` (output of **Meryl** {% icon tool %})
 >        - *"Number of assemblies"*: `One assembly (pseudo-haplotype or mixed-haplotype)`
->            - {% icon param-file %} *"Genome assembly"*: `output` (Input dataset)
->
+>            - {% icon param-file %} *"Genome assembly"*: `CReformitis_assembly` 
 >
 >    > <comment-title> Output </comment-title>
 >    >
@@ -538,7 +557,7 @@ gfastats is a tool for providing summary statistics and genome file manipulation
 > <hands-on-title> Generate summary statistics </hands-on-title>
 >
 > 1. {% tool [gfastats](toolshed.g2.bx.psu.edu/repos/bgruening/gfastats/gfastats/1.2.0+galaxy0) %} with the following parameters:
->    - {% icon param-file %} *"Input file"*: `output` (Input dataset)
+>    - {% icon param-file %} *"Input file"*: `CReformitis_assembly` 
 >    - *"Specify target sequences"*: `Disabled`
 >    - *"Tool mode"*: `Summary statistics generation`
 >        - *"Report mode"*: `Genome assembly statistics (--nstar-report)`
@@ -585,29 +604,62 @@ It is based on cross-linking the DNA in the nucleus and its histones (proteins a
 
 ## Pre-processing Hi-C data
 
-> <hands-on-title> Mapping Hi-C reads against a reference genome </hands-on-title>
+The following analysis use Hi-C reads in single datasets: one for the forward reads and one for the reverse reads. If you have more than one fastq datasets, as we do for *Erythrolamprus reginae* you will need to merge them into single datasets. 
+If you have only one pair of datasets, skip this step. 
+
+> <hands-on-title> Merge  <em>Erythrolamprus reginae</em> Hi-C data into single datasets </hands-on-title>
+> 1. Create a collection with the Hi-C forward reads name `EReginae Hi-C forward reads`.
+>
+> 2. Do the same with the reverse reads (R) and name it `EReginae Hi-C reverse reads`.
+>
+> 3. For species with more than one set of Hi-C reads, verify that the datasets are ordered the same way in both collections. If not, sort both collections with the tool  {% tool Sort collection %} 
+>
+> 4. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters:
+>    - {% icon param-collection %} *"Collection of files to collapse into single dataset"*: `EReginae Hi-C forward Reads`
 >
+> 5. Rename the dataset `EReginae Hi-C forward Reads`
+> 
+> 6. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters:
+>    - {% icon param-collection %} *"Collection of files to collapse into single dataset"*: `EReginae Hi-C reverse reads`
+>
+> 7. Rename the dataset `EReginae Hi-C reverse Reads`
+>
+> {% snippet faqs/galaxy/collections_build_list.md name="EReginae Hi-C forward Reads" datasets_description="the fastq.gz containing the Hi-C forward reads (F)" n="1" %}
+>
+> {% snippet faqs/galaxy/datasets_rename.md name="Hi-C forward Reads" %}
+> 
+{: .hands_on}
+
+> <hands-on-title> Mapping Hi-C reads against a reference genome </hands-on-title>
+> 
 > 1. {% tool [BWA-MEM2](toolshed.g2.bx.psu.edu/repos/iuc/bwa_mem2/bwa_mem2/2.2.1+galaxy0) %} with the following parameters:
 >    - *"Will you select a reference genome from your history or use a built-in index?"*: `Use a genome from history and build index`
->        - {% icon param-file %} *"Use the following dataset as the reference sequence"*: `output` (Input dataset)
+>        - {% icon param-file %} *"Use the following dataset as the reference sequence"*: `CReformitis_assembly` 
+> 
 >    - *"Single or Paired-end reads"*: `Single`
->        - {% icon param-file %} *"Select fastq dataset"*: `output` (output of **Collapse Collection** {% icon tool %})
+>        - {% icon param-file %} *"Select fastq dataset"*: `Hi-C forward Reads` (output of **Collapse Collection** {% icon tool %})
+> 
 >    - *"Set read groups information?"*: `Do not set`
 >    - *"Select analysis mode"*: `1.Simple Illumina mode`
 >    - *"BAM sorting mode"*: `Sort by read names  (i.e., the QNAME field) `
 >
-> 2. Run {% tool [BWA-MEM2](toolshed.g2.bx.psu.edu/repos/iuc/bwa_mem2/bwa_mem2/2.2.1+galaxy0) %} again with the following parameters:
+> 2. Rename the bam dataset `Mapped Hi-C forward Reads`
+>
+>
+> 3. {% tool [BWA-MEM2](toolshed.g2.bx.psu.edu/repos/iuc/bwa_mem2/bwa_mem2/2.2.1+galaxy0) %} with the following parameters:
 >    - *"Will you select a reference genome from your history or use a built-in index?"*: `Use a genome from history and build index`
->        - {% icon param-file %} *"Use the following dataset as the reference sequence"*: `output` (Input dataset)
+>        - {% icon param-file %} *"Use the following dataset as the reference sequence"*: `CReformitis_assembly`
 >    - *"Single or Paired-end reads"*: `Single`
->        - {% icon param-file %} *"Select fastq dataset"*: `output` (output of **Collapse Collection** {% icon tool %})
+>        - {% icon param-file %} *"Select fastq dataset"*: `Hi-C reverse Reads` (output of **Collapse Collection** {% icon tool %})
 >    - *"Set read groups information?"*: `Do not set`
 >    - *"Select analysis mode"*: `1.Simple Illumina mode`
 >    - *"BAM sorting mode"*: `Sort by read names  (i.e., the QNAME field) `
 >
-> 3. {% tool [Filter and merge](toolshed.g2.bx.psu.edu/repos/iuc/bellerophon/bellerophon/1.0+galaxy0) %} with the following parameters:
->    - {% icon param-file %} *"First set of reads"*: `bam_output` (output of **BWA-MEM2** {% icon tool %})
->    - {% icon param-file %} *"Second set of reads"*: `bam_output` (output of **BWA-MEM2** {% icon tool %})
+> 4. Rename the bam dataset `Mapped Hi-C reverse Reads`
+>
+> 5. {% tool [Filter and merge](toolshed.g2.bx.psu.edu/repos/iuc/bellerophon/bellerophon/1.0+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"First set of reads"*: `Mapped Hi-C forward Reads` (output of **BWA-MEM2** {% icon tool %})
+>    - {% icon param-file %} *"Second set of reads"*: `Mapped Hi-C reverse Reads` (output of **BWA-MEM2** {% icon tool %})
 >
 {: .hands_on}
 
@@ -618,9 +670,9 @@ PretextMap converts BAM/SAM files into genome contact maps. With those contact m
 
 > <hands-on-title> Generate a contact map with PretextMap and PretextSnapshot </hands-on-title>
 >
-> 1. {% tool [PretextMap](toolshed.g2.bx.psu.edu/repos/iuc/pretext_map/pretext_map/0.1.9+galaxy0) %} with the following parameters:
+> 1. {% tool [PretextMap](toolshed.g2.bx.psu.edu/repos/iuc/pretext_map/pretext_map/0.1.9+galaxy1) %} with the following parameters:
 >    - {% icon param-file %} *"Input dataset in SAM or BAM format"*: `outfile` (output of **Filter and merge** {% icon tool %})
->    - *"Sort by"*: `Don't sort`
+>    - *"Sort by"*: `Ascending`
 >
 > 2. {% tool [Pretext Snapshot](toolshed.g2.bx.psu.edu/repos/iuc/pretext_snapshot/pretext_snapshot/0.0.3+galaxy1) %} with the following parameters:
 >    - {% icon param-file %} *"Input Pretext map file"*: `pretext_map_out` (output of **PretextMap** {% icon tool %})
@@ -656,7 +708,7 @@ Bandage is a tool to visualise *de novo* assembly graphs with connections. ({% c
 > <hands-on-title> Generate assembly graph </hands-on-title>
 >
 > 1. {% tool [gfastats](toolshed.g2.bx.psu.edu/repos/bgruening/gfastats/gfastats/1.3.6+galaxy0) %} with the following parameters:
->    - {% icon param-file %} *"Input file"*: `output` (Input dataset)
+>    - {% icon param-file %} *"Input file"*: `CReformitis_assembly` (Input dataset)
 >    - *"Specify target sequences"*: `Disabled`
 >    - *"Tool mode"*: `Genome assembly manipulation`
 >        - *"Output format"*: `GFA`
diff --git a/topics/assembly/tutorials/vgp_workflow_training/tutorial.md b/topics/assembly/tutorials/vgp_workflow_training/tutorial.md
index 58f1d743e0b22a..74ccf114388a3e 100644
--- a/topics/assembly/tutorials/vgp_workflow_training/tutorial.md
+++ b/topics/assembly/tutorials/vgp_workflow_training/tutorial.md
@@ -49,7 +49,7 @@ abbreviations:
   VGP: Vertebrate Genome Project
   G10K: Genome 10K
 recordings:
-- youtube_id: LO-migvwcM
+- youtube_id: _LO-migvwcM
   length: 1H56M
   galaxy_version: 24.1.2.dev0
   date: '2024-09-12'
diff --git a/topics/climate/images/earth_system/earth_system_galaxy.png b/topics/climate/images/earth_system/earth_system_galaxy.png
new file mode 100644
index 00000000000000..7ee792eddab1c5
Binary files /dev/null and b/topics/climate/images/earth_system/earth_system_galaxy.png differ
diff --git a/topics/climate/tutorials/earth_system/faqs/index.md b/topics/climate/tutorials/earth_system/faqs/index.md
new file mode 100644
index 00000000000000..9ce3fe4fce824b
--- /dev/null
+++ b/topics/climate/tutorials/earth_system/faqs/index.md
@@ -0,0 +1,3 @@
+---
+layout: faq-page
+---
diff --git a/topics/climate/tutorials/earth_system/tutorial.md b/topics/climate/tutorials/earth_system/tutorial.md
new file mode 100644
index 00000000000000..aec515f2f6693b
--- /dev/null
+++ b/topics/climate/tutorials/earth_system/tutorial.md
@@ -0,0 +1,205 @@
+---
+layout: tutorial_hands_on
+title: Getting your hands-on earth data
+questions:
+- What is the Earth System?
+- What type of data is available?
+objectives:
+- Learn about the Earth System terminology
+- Learn about the different source of earth data
+- Learn about earth observations, reanalysis, and visualisation
+time_estimation: 1H
+key_points:
+- Earth System 
+- Essential variables
+- Observations, reanalysis, visualisation
+tags:
+  - earth-system
+  - ocean
+  - land
+  - atmosphere
+  - biodiversity
+contributions:
+    authorship:
+        - Marie59
+    funding:
+        - gallantries
+        - fairease
+        - eurosciencegateway
+---
+
+
+This tutotrial aims at familiarzing you with Earth Science and discovering the earth data available on Galaxy. The target audience is not a scientist but
+anyone interested in learning about Earth system.
+
+> <agenda-title></agenda-title>
+>
+> In this tutorial, we will cover:
+>
+> 1. TOC
+> {:toc}
+>
+{: .agenda}
+
+🌍🌏🌎
+
+The Earth System is a complex and dynamic system that encompasses the interactions between the atmosphere, hydrosphere, geoshpere, and biosphere. Understanding and analyzing data from the Earth System Model is essential, for example to predict and mitigate the impacts of climate change. Currently, [Galaxy Earth System](https://earth-system.usegalaxy.eu/) uses 4 usecases to have an overview of an earth System: ocean, land, atmosphere, and biodiversity.
+Of course, these components cross over each other thus, we have data that can fit two or more compartiments.
+
+![Graphic visualization of an Earth System](../../images/earth_system/earth_system_galaxy.png)
+
+Next we will see for each compartiment which kind of data (and how to get them) are availabe on Galaxy Earth System
+
+
+# 🌊 Ocean 🌊
+
+The ocean is a key component of the Earth’s climate system. Thus, it needs continuous real-time monitoring to help scientists better understand its dynamics and predict its evolution. 
+
+## The Argo program
+All around the world, oceanographers have managed to join their efforts and set up a Global Ocean Observing System among which Argo is a key component. Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level.
+
+The data available here are from the Argo gliders network. They contain the folowing variables: 
+- latitude, longitude, and time
+- physcical parameters: water temperature, pressure, and salinity
+- biogeochemical parameters: chlorophyll, oxygen, and nitrate
+
+To access those data you can use the ({% tool [Argo data access](toolshed.g2.bx.psu.edu/repos/ecology/argo_getdata/argo_getdata/0.1.15+galaxy0) %})tool. 
+
+For more information follow the tutorial related to it called [Analyse Argo data]({% link topics/climate/tutorials/argo_pangeo/tutorial.md %}) or use the workflow Full Analyse Argo data `https://earth-system.usegalaxy.eu/u/marie.josse/w/full-analyse-argo-data`
+
+
+## Copernicus Data Space Ecosystem
+
+The Copernicus Data Space Ecosystem provides access to Earth observation data.
+The goal of the **Sentinel-3 mission** is to accurately and reliably measure sea surface topography, sea and land surface temperature, and ocean and land surface color. This data will support ocean forecasting systems, environmental monitoring, and climate monitoring.
+
+To access those data you can use the [Copernicus Data Space Ecosystem](https://usegalaxy.eu/root?tool_id=interactive_tool_copernicus_notebook) tool. 
+
+For more information follow the tutorial related to it called [Sentinel 5P data visualisation]({% link topics/climate/tutorials/sentinel5_data/tutorial.md %}). 
+
+Once you get into the Jupyterlab go in the left pannel in `samples > openeo > Sentinel_3.ipynb `. Yhis way you have an example on how to get Sentinel 3 data.
+
+## EMODnet Chemistry
+
+[EMODnet Chemistry](https://emodnet.ec.europa.eu/en/chemistry) provides easy access to marine chemical data, standardised, harmonised and validated data collections and data products for visualisation. These are relevant for the European marine environmental policies to assess the status and trends of ecosystems. 
+Numerous substances are considered, most of which are invisible to the naked eye and are monitored using specialised sensors or laboratory analyses. This evidence-based information supports action against environmental changes that pose a threat to marine ecosystems and human health.
+
+### A focus on ODV (Ocean Data View) collections
+
+This [webODV](https://emodnet-chemistry.webodv.awi.de/) service facilitates to explore, subset, visualize, and extract data sets in multiple formats from the harmonized, standardized, validated data collections that EMODnet Chemistry is regularly producing and publishing for all European sea basins for eutrophication and contaminants. 
+You can also find these collections on the [Sextant catalogue](https://sextant.ifremer.fr/eng/Data/Catalogue#/search?isTemplate=n&from=1&to=30&sortBy=dateStamp&sortOrder=desc&languageStrategy=searchInDetectedLanguage&any=EMODnet%20Chemistry)
+
+You can analize thes kind of data through the [Ocean Data View (ODV)](https://usegalaxy.eu/root?tool_id=interactive_tool_odv) tool. 
+
+For more information follow the related tutorials and workflows:
+ - [Ocean's variables study]({% link topics/climate/tutorials/ocean-variables/tutorial.md %}) and the corresponding workflow  `https://usegalaxy.eu/u/marie.josse/w/oceans-var-v2`
+ - [Ocean Data View (ODV)]({% link topics/climate/tutorials/ocean-data-view/tutorial.md %})
+
+
+## Copernicus Marine Data Store
+
+The [Copernicus Marine Data Store](https://marine.copernicus.eu/access-data) offers a ocean product catalogue, to download or visualize data accross nearly 15variables, including hindcast, current and forecast data.
+
+A tool is currently being develop to enable easy access to this data. More information to come.
+
+
+
+# 🏜️ Land 🏞️
+
+This component comprehend all from the solid part of earth land, rock, volcano activities to the deragation of the land surface. This compartiment amis at assessing land and soil degradation, such as erosion, loss of organic matter and soil biodiversity, compaction, salinization, landslides, contamination, sealing, desertification and climate change.
+
+## Copernicus Data Space Ecosystem
+
+The Copernicus Data Space Ecosystem provides access to Earth observation data.
+
+The Copernicus Sentinel-2 mission consists of two polar-orbiting satellites that are positioned in the same sun-synchronous orbit, with a phase difference of 180°.
+
+It aims to monitor changes in land surface conditions. The satellites have a wide swath width (290 km) and a high revisit time. This capability will support monitoring of changes on the Earth's surface.
+
+The Sentinel-2 twin satellites provide image data that largely contribute to existing and ongoing multispectral observations.
+
+These satellites support a variety of services and applications offered by Copernicus, including:
+- Land monitoring
+- Agriculture
+- Emergency management
+- Risk mapping
+-  Security
+- Forestry
+- Climate change
+- Disaster control
+- Marine
+- Humanitarian relief operations
+
+The Scene Classification algorithm uses the reflective properties of scene features to establish the presence or absence of clouds in a scene. It is based on a series of threshold tests that use as input: TOA reflectance of several Sentinel-2 spectral bands, band ratios and indexes like Normalised Difference Vegetation Index (NDVI) and Normalised Difference Snow and Ice Index (NDSI). For each of these threshold tests, a level of confidence is associated. It produces at the end of the processing chain a probabilistic cloud mask quality indicator and a snow mask quality indicator. The most recent version of the SCL algorithm includes also morphological operations, usage of auxiliary data like Digital Elevation Model and Land Cover information and exploit the parallax characteristics of Sentinel-2 MSI instrument to improve its overall classification accuracy.
+
+The NDVI ratio can be determined from the contribution of visible wavelength and near-infra-red wavelengths. Strong and well-nourished vegetation will absorb most of the visible wavelengths that it receives and will reflect back a large proportion of the near-infra-red light, whereas poor condition vegetation or thin areas, will reflect more visible wavelength light and less near-infra-red light.
+
+To access those data you can use the [Copernicus Data Space Ecosystem](https://usegalaxy.eu/root?tool_id=interactive_tool_copernicus_notebook) tool. 
+
+For more information follow the tutorial related to it called [From NDVI data with OpenEO to time series visualisation with Holoviews]({% link topics/ecology/tutorials/ndvi_openeo/tutorial.md %}). 
+
+
+## QGIS (Geographical Information System)
+
+Based on a [QGIS official tutorial](https://docs.qgis.org/2.18/en/docs/training_manual/online_resources/wfs.html), you can learn how to access, filter and import GIS data through WFS web service using [QGIS Galaxy interactive tool](https://usegalaxy.eu/root?tool_id=interactive_tool_qgis)
+
+In the Geographical Information System landscape, there is existing standards to help users deal with remote data. The most common web services are Web Map Services (WMS) and Web Feature Services (WFS). If WMS allows users only to access and display maps stored remotely, WFS is giving access to the features of data so you can modify it and create your own data and maps.
+
+To learn more on how to do that folow [QGIS Web Feature Services]({% link topics/ecology/tutorials/QGIS_Web_Feature_Services/tutorial.md %})
+
+# ☁️ Atmosphere 🌫️
+
+This component represents the layer of gas around the Earth.
+
+## Copernicus Data Space Ecosystem
+
+The Copernicus Data Space Ecosystem provides access to Earth observation data. 
+The Copernicus **Sentinel-5 Precursor** mission is the first Copernicus mission that focuses on monitoring our atmosphere. It is the result of a close partnership between ESA, the European Commission, the Netherlands Space Office, industry, data users and scientists.
+
+To access those data you can use the [Copernicus Data Space Ecosystem](https://usegalaxy.eu/root?tool_id=interactive_tool_copernicus_notebook) tool. 
+
+### Volcano 🌋
+
+You can get atmospheric data on volcanic activity by taking a subset of Sentinel 5P L2 data  for instance,  from the 1st of April to the 30th of may 2021 of the Antilles. Especially of the La Soufriere Saint Vincent (Antilles) where a volcaninc erruption occured 9th of April. This dataset is focused on the dioxide sulfur (SO2) and Aerosol index (AI) spread out. Indeed, the knowledge of volcanic activity at a high temporal resolution is crucial for robustly determining large-scale impacts of volcanoes on atmosphere (air quality, air traffic) and climate. As such, this platform will be also of interest for scientists involved in the field of volcanic impacts at large, including institutions in charge of the monitoring of air quality and aviation safety.
+ 
+For more information follow the tutorial related to it called [Sentinel 5P data visualisation]({% link topics/climate/tutorials/sentinel5_data/tutorial.md %}). 
+
+
+## Climate Data Store
+
+Dive into this wealth of information about the Earth's past, present and future climate with the ({% tool [Climate Data Store](toolshed.g2.bx.psu.edu/repos/climate/c3s/c3s/0.2.0) %}). With this tool you can have acces to atmospheric data such as ERA5 data. ERA5 provides a snapshot of the atmosphere, land surface and ocean waves for each hour from 1959 onwards (for 1950 to 1958 a preliminary version is available). It includes an uncertainty estimate which highlights the considerable evolution of the observing system, on which reanalysis products rely.
+
+
+# 🐙 Biodiversity 🐿️
+
+This englobes the part of the land, ocean, and athmosphere in which organisms are able to live.
+
+## Marine biodiversity 🐳
+
+European marine microbial biodiversity is a poorly understood and under-exploited component of continental biodiversity. This component aims to provide a set of tools for the analysis
+of spatial- and time-comparable marine microbial metagenomics data sets for the exploration of biodiversity and its correlations with environmental quality.
+
+### OBIS Ocean Biodiversity Information System
+
+OBIS is a global open-access data and information clearing-house on marine biodiversity for science, conservation and sustainable development. In order to get occurences of marine biodiversity from OBIS you can use the ({% tool [OBIS occurences](toolshed.g2.bx.psu.edu/repos/ecology/obis_data/obis_data/0.0.2) %}) tool.
+
+For more information follow the tutorial related to it called [OBIS marine indicators]({% link topics/ecology/tutorials/obisindicators/tutorial.md %}) or use the workflow Marine omics visualisation `https://usegalaxy.eu/u/marie.josse/w/marine-omics-visualisation`
+
+## Land biodiversity 🦤
+
+GBIF (Global Biodiversity Information Facility, www.gbif.org) is for sure THE most remarkable biodiversity data aggregator worldwide giving access to more than 1 billion records across all taxonomic groups. The data provided via these sources are highly valuable for research. However, some issues exist concerning data heterogeneity, as they are obtained from various collection methods and sources (OBIS is part of GBIF).
+
+
+
+To access those data you can use the ({% tool [Get species occurrences data](toolshed.g2.bx.psu.edu/repos/ecology/spocc_occ/spocc_occ/0.9.0) %}) tool. 
+
+For more information follow the tutorial related to it called [Cleaning GBIF data for the use in Ecology]({% link topics/ecology/tutorials/gbif_cleaning/tutorial.md %}).
+
+
+
+# Conclusion 
+Here you have an overview of what's is currently available in terms of data accessibility in Galaxy Earth System. This an on going work more is to come !
+
+# Extra information
+
+Coming up soon even more tutorials on and other Earth-System related trainings. Keep an {% icon galaxy-eye %} open if you are interested!
diff --git a/topics/contributing/faqs/input-and-answer-keys.md b/topics/contributing/faqs/input-and-answer-keys.md
new file mode 100644
index 00000000000000..d7f46db52a7f72
--- /dev/null
+++ b/topics/contributing/faqs/input-and-answer-keys.md
@@ -0,0 +1,27 @@
+---
+title: Input Histories & Answer Keys
+area: gtn
+box_type: tip
+layout: faq
+contributors: [hexylena, nomadscientist]
+---
+
+Tutorials sometimes require significant amounts of data or data prepared in a very specific manner which often is shown to cause errors for learners that significantly affect downstream results. Input histories are an answer to that:
+
+```yaml
+input_histories:
+  - label: "UseGalaxy.eu"
+    history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---input-1
+    date: "2021-09-01"
+```
+
+Additionally once the learner has gotten started, tutorials sometimes feature tools which produce stochastic outputs, or have very long-running steps. In these cases, the tutorial authors may provide answer histories to help learners verify that they are on the right track, or to enable them to catch up if they fall behind or something goes wrong.
+
+```yaml
+answer_histories:
+    - label: "UseGalaxy.eu"
+    history: https://humancellatlas.usegalaxy.eu/u/j.jakiela/h/generating-a-single-cell-matrix-using-alevin-3
+    - label: "Older Alevin version"
+    history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---answer-key
+    date: 2024-01-01
+```
diff --git a/topics/contributing/faqs/new-contributions.md b/topics/contributing/faqs/new-contributions.md
new file mode 100644
index 00000000000000..10f1bcbc5ea4f2
--- /dev/null
+++ b/topics/contributing/faqs/new-contributions.md
@@ -0,0 +1,38 @@
+---
+title: Using the new Contributions Annotation framework
+area: gtn
+box_type: tip
+layout: faq
+contributors: [hexylena]
+---
+
+If you are writing a tutorial or slides, there are two ways to annotate contributions:
+
+The old way, which doesn't accurately track roles
+
+```
+contributors: [hexylena, shiltemann]
+```
+
+And the new way which lets you annotate who has helped build a tutorial in a much richer way:
+
+```yaml
+contributions:
+    authorship:
+        - shiltemann
+        - bebatut
+    editing:
+        - hexylena
+        - bebatut
+        - natefoo
+    testing:
+        - bebatut
+    infrastructure:
+        - natefoo
+    translation:
+        - shiltemann
+    funding:
+        - gallantries
+```
+
+This is especially important if you want to track funding or infrastructure contributions. The old way doesn't allow for this, and thus we would *strongly* recommend you use the new format!
diff --git a/topics/contributing/faqs/why-prerequisites.md b/topics/contributing/faqs/why-prerequisites.md
new file mode 100644
index 00000000000000..645ee848ae33d0
--- /dev/null
+++ b/topics/contributing/faqs/why-prerequisites.md
@@ -0,0 +1,41 @@
+---
+title: Annotating Pre-requisites
+area: gtn
+box_type: tip
+layout: faq
+contributors: [hexylena]
+---
+
+If you are adding a tutorial, annotating the pre-requisites is an important task! It will help ensure learners know what they need to know before starting the tutorial. They also let instructors plan a schedule optimally.
+
+Internal requirements often include specific features of Galaxy you plan to use in your training material, and let learners know which tutorials to follow first, before starting your tutorial. 
+
+```yaml
+requirements:
+  - type: "internal"
+    topic_name: galaxy-interface
+    tutorials:
+      - collections
+      - upload-rules
+```
+
+Or you can have external requirements, which link to another site.
+
+```yaml
+requirements:
+  -
+    type: "external"
+    title: "Trackster"
+    link: "https://wiki.galaxyproject.org/Learn/Visualization"
+```
+
+Least commonly needed are software requirements. These are usually used in e.g. Galaxy Admin Training tutorials, but if you have specific software requirements, you can list them here:
+
+```yaml
+requirements:
+- type: none
+  title: "Web browser"
+- type: none
+  title: "A linux-based machine or linux emulator"
+- type: none
+```
diff --git a/topics/dev/tutorials/tool-from-scratch/tutorial.md b/topics/dev/tutorials/tool-from-scratch/tutorial.md
index 694c0c8932e2ad..53313f5a73202f 100644
--- a/topics/dev/tutorials/tool-from-scratch/tutorial.md
+++ b/topics/dev/tutorials/tool-from-scratch/tutorial.md
@@ -433,8 +433,7 @@ anyone wrapping a tool, a more complete list is available in [the Galaxy tool do
 
 Planemo is an important tool within the Galaxy development workflow. Although it will be covered in greater detail in the
 next section of this tutorial, one of its functions is necessary for the tool wrapper development. `planemo` is available
-via the python package manager `pip`. To that end, before beginning this section, please install `planemo` locally by running
-`planemo`. To that end, before beginning this section, please install planemo locally by running
+via the python package manager `pip`. To that end, before beginning this section, please install planemo locally by running
 
 > <hands-on-title>Installing `planemo` via pip</hands-on-title>
 >
diff --git a/topics/epigenetics/tutorials/ewas-suite/tutorial.bib b/topics/epigenetics/tutorials/ewas-suite/tutorial.bib
index 9f35ee16d2c289..eec1da45bb16cc 100644
--- a/topics/epigenetics/tutorials/ewas-suite/tutorial.bib
+++ b/topics/epigenetics/tutorials/ewas-suite/tutorial.bib
@@ -14,7 +14,8 @@ @article{Hugo2015
 
 @misc{Illumina,
   title = {{Illumina} Infinium Methylation Assay Overview},
-  howpublished = {\url{https://emea.illumina.com/science/technology/beadarray-technology/infinium-methylation-assay.html?langsel=/gb/}},
+  howpublished  = {\url{https://emea.illumina.com/science/technology/beadarray-technology/infinium-methylation-assay.html?langsel=/gb/}},
+  url= {https://emea.illumina.com/science/technology/microarray/infinium-methylation-assay.html},
   note = {Accessed: 2020-02-22}
 }
 
@@ -34,4 +35,16 @@ @article{Hansen
     eprint = {https://academic.oup.com/bioinformatics/article-pdf/30/10/1363/17344721/btu049.pdf},
 }
 
-
+@article{Murat2020,
+  title = {Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy},
+  volume = {9},
+  ISSN = {2047-217X},
+  url = {http://dx.doi.org/10.1093/gigascience/giaa049},
+  DOI = {10.1093/gigascience/giaa049},
+  number = {5},
+  journal = {GigaScience},
+  publisher = {Oxford University Press (OUP)},
+  author = {Murat,  Katarzyna and Gr\"{u}ning,  Bj\"{o}rn and Poterlowicz,  Paulina Wiktoria and Westgate,  Gillian and Tobin,  Desmond J and Poterlowicz,  Krzysztof},
+  year = {2020},
+  month = may 
+}
diff --git a/topics/epigenetics/tutorials/ewas-suite/tutorial.md b/topics/epigenetics/tutorials/ewas-suite/tutorial.md
index 0896265bf4e9f8..86e908ecb836f8 100644
--- a/topics/epigenetics/tutorials/ewas-suite/tutorial.md
+++ b/topics/epigenetics/tutorials/ewas-suite/tutorial.md
@@ -215,4 +215,4 @@ GO:0048545 | response to steroid hormone | 1.38EE-54 | 2.11E-51 | HDAC9/ RAB10/
 # Conclusion
 
 
-Epigenetic aberrations which involve DNA modifications give researchers an interest in identifying novel non-genetic factors responsible for complex human phenotypes such as height, weight, and disease. To identify methylation changes researchers need to perform complicated and time consuming computational analysis. Here, the EWAS suite becomes a solution for this inconvenience and provides a simplified downstream analysis available as a ready to run pipline in supplementary materials.
+Epigenetic aberrations which involve DNA modifications give researchers an interest in identifying novel non-genetic factors responsible for complex human phenotypes such as height, weight, and disease. To identify methylation changes researchers need to perform complicated and time consuming computational analysis. Here, the EWAS suite becomes a solution for this inconvenience and provides a simplified downstream analysis available as a ready to run pipline in supplementary materials. For more details, see the associated publication {% cite Murat2020 %}
diff --git a/topics/genome-annotation/tutorials/amr-gene-detection/data/trackList.json b/topics/genome-annotation/tutorials/amr-gene-detection/data/trackList.json
index 6448e4987a4c09..56ea77d466b8d3 100644
--- a/topics/genome-annotation/tutorials/amr-gene-detection/data/trackList.json
+++ b/topics/genome-annotation/tutorials/amr-gene-detection/data/trackList.json
@@ -1,10 +1,6 @@
 {
    "formatVersion" : 1,
    "hideGenomeOptions" : false,
-   "names" : {
-      "type" : "Hash",
-      "url" : "names/"
-   },
    "plugins" : [
       {
          "location" : "https://cdn.jsdelivr.net/gh/TAMU-CPT/blastview@97572a21b7f011c2b4d9a0b5af40e292d694cbef/",
diff --git a/topics/genome-annotation/tutorials/bacterial-genome-annotation/data/trackList.json b/topics/genome-annotation/tutorials/bacterial-genome-annotation/data/trackList.json
index bcfe6552152946..f75e19b22fb33b 100644
--- a/topics/genome-annotation/tutorials/bacterial-genome-annotation/data/trackList.json
+++ b/topics/genome-annotation/tutorials/bacterial-genome-annotation/data/trackList.json
@@ -2,10 +2,6 @@
    "defaultTracks" : "e1c36f2c4382e60836c6c4b4d8026fa5_0",
    "formatVersion" : 1,
    "hideGenomeOptions" : false,
-   "names" : {
-      "type" : "Hash",
-      "url" : "names/"
-   },
    "plugins" : [
       {
          "location" : "https://cdn.jsdelivr.net/gh/TAMU-CPT/blastview@97572a21b7f011c2b4d9a0b5af40e292d694cbef/",
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diff --git a/topics/metabolomics/images/qcxms_motivation.png b/topics/metabolomics/images/qcxms_motivation.png
new file mode 100644
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diff --git a/topics/metabolomics/images/qcxms_predictions_conformers.svg b/topics/metabolomics/images/qcxms_predictions_conformers.svg
new file mode 100644
index 00000000000000..92f951f6ac14de
--- /dev/null
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diff --git a/topics/metabolomics/images/qcxms_predictions_ms_exp_pred.png b/topics/metabolomics/images/qcxms_predictions_ms_exp_pred.png
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diff --git a/topics/metabolomics/tutorials/qcxms-predictions/data-library.yaml b/topics/metabolomics/tutorials/qcxms-predictions/data-library.yaml
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index 00000000000000..9c9c25c7feabef
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/data-library.yaml
@@ -0,0 +1,19 @@
+---
+destination:
+  type: library
+  name: GTN - Material
+  description: Galaxy Training Network Material
+  synopsis: Galaxy Training Network Material. See https://training.galaxyproject.org
+items:
+- name: Title of the topic
+  description: Summary of the topic
+  items:
+  - name: Predicting EI+ mass spectra with QCxMS
+    items:
+    - name: 'DOI: 10.5281/zenodo.11501975'
+      description: latest
+      items:
+      - url: https://zenodo.org/api/records/11501975/files/molecules_input.sdf/content
+        src: url
+        ext: auto
+        info: https://zenodo.org/records/11501975
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/faqs/index.md b/topics/metabolomics/tutorials/qcxms-predictions/faqs/index.md
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index 00000000000000..9ce3fe4fce824b
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/faqs/index.md
@@ -0,0 +1,3 @@
+---
+layout: faq-page
+---
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/slides.html b/topics/metabolomics/tutorials/qcxms-predictions/slides.html
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index 00000000000000..de506ade1b91e0
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/slides.html
@@ -0,0 +1,82 @@
+---
+layout: tutorial_slides
+logo: "GTN"
+
+title: "Predicting EI+ mass spectra with QCxMS"
+contributors:
+  - hechth
+  - wverastegui
+  - zargham-ahmad
+  - wee-snufkin
+---
+name:qcxms-spectra-predictions
+
+### Motivation
+
+.pull-left[
+- MS data annotation poses a universal bottleneck in research.
+- *In silico* spectra prediction using machine learning or quantum chemistry is a promising technique for annotation of unknown compounds.
+- QCxMS offers reasonably accurate in silico annotation, especially for organic molecules.
+- The complexity of quantum chemistry predictions presents challenges for non-HPC experts.
+- Integrating QCxMS into Galaxy provides valuable molecular insights.
+
+Our Goal: Make semi-empirical Quantum Chemistry (QC)-based predictions accessible without advanced computational skills.
+]
+
+.pull-right[
+![Proposed schema for assigning five levels of confidence in compound annotation using common evidence provided by GC-HRMS.](../../images/qcxms_motivation.png)
+]
+
+---
+
+### Method
+![Overview of the QCxMS methodology. The tool starts with an equilibration and sampling step to create the base coordinates of the individual trajectories.](../../images/qcxms_method.png)
+
+- Calculations can be performed using multiple quantum chemistry frameworks.
+- This includes full *ab initio* molecular dynamics or parameterized *semi-empirical quantum mechanical* methods.
+- Semi-empirical methods are significantly faster (hours vs. weeks) while yielding reasonable accuracy.
+
+---
+
+### HPC Workflow
+![Illustration of the QCxMS workflow executed on a high-performance computing (HPC) cluster](../../images/qcxms_hpc_workflow.jpeg)
+
+- The HPC-based workflow is configured to work with a PBS job scheduler and is therefore somwhat limited in flexibility and user friendliness.
+- Due to the difficulties of working with HPC clusters, we ported this workflow to Galaxy.
+
+---
+
+### Galaxy Workflow
+![Illustration of the QCxMS workflow executed on a high-performance computing (HPC) cluster](../../images/qcxms_galaxy_workflow.png)
+
+- We use xTB to optimize the input geometry.
+- The QCxMS neutral run tool performs the equilibration and sampling to create the trajectories.
+- The QCxMS production run tool performs the simulation of each trajectory.
+- The QCxMS getres tool collects the results into an MSP file.
+
+---
+
+### Galaxy Tool Structure
+![Architecture of the QCxMS Galaxy tools.](../../images/qcxms_galaxy_tool_architecture.png)
+
+- Each trajectory is computed in a single job, using data-level parallelism.
+- All QCxMS tools utilize the same docker container with an executable which was compiled with an optimized compiler.
+
+---
+
+### Runtime Performance Metrics
+![Performance of the QCxMS workflow for Ethylene (6 atoms), Mirex (22 atoms), Benzophenone (24 atoms) and Enilconazole (33 atoms). Benzophenone, despite being larger than Mirex, requires less time to compute due to its planar geometry and overall smaller complexity.](../../images/qcxms_runtime_statistics.png)
+
+- Runtime depends primarily on size of the molecule.
+- Structure also plays a crucial role - the more complex the geometry and structure, the longer the computations take.
+- Memory requirements scale linear with size of the molecule.
+
+---
+
+### References
+
+- Grimme, S. (2013). Towards First Principles Calculation of Electron Impact Mass Spectra of Molecules. Angewandte Chemie International Edition, 52(24), 6306–6312. https://doi.org/10.1002/anie.201300158
+- Bauer, C. A., & Grimme, S. (2016). How to Compute Electron Ionization Mass Spectra from First Principles. The Journal of Physical Chemistry A, 120(21), 3755–3766. https://doi.org/10.1021/acs.jpca.6b02907
+- Bannwarth, C., Ehlert, S., & Grimme, S. (2019). GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions. Journal of Chemical Theory and Computation, 15(3), 1652–1671. https://doi.org/10.1021/acs.jctc.8b01176
+- Koopman, J., & Grimme, S. (2021). From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics. Journal of the American Society for Mass Spectrometry, 32(7), 1735–1751. https://doi.org/10.1021/jasms.1c00098
+- Hecht, H., Rojas, W. Y., Ahmad, Z., Křenek, A., Klánová, J., & Price, E. J. (2024). Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals. Analytical Chemistry, 96(33), 13652–13662. https://doi.org/10.1021/acs.analchem.4c02589
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/tutorial.bib b/topics/metabolomics/tutorials/qcxms-predictions/tutorial.bib
new file mode 100644
index 00000000000000..792d26c2f921b2
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/tutorial.bib
@@ -0,0 +1,246 @@
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+doi={10.1021/acs.analchem.6b01622}
+}
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+@article{Zhu2023,
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+
+@article{Vetter1994,
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+
+
+@article{Wang2020,
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+  volume = {12},
+  ISSN = {1758-2946},
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+
+
+@article{Koopman2019,
+  title = {Calculation of Electron Ionization Mass Spectra with Semiempirical GFNn-xTB Methods},
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+
+
+@inproceedings{Lindsay1980ApplicationsOA,
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+@article{Allen2014,
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+@article{Allen2014metabolomics,
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+
+
+@article{DjoumbouFeunang2019,
+  title = {CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification},
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+  author = {Djoumbou-Feunang,  Yannick and Pon,  Allison and Karu,  Naama and Zheng,  Jiamin and Li,  Carin and Arndt,  David and Gautam,  Maheswor and Allen,  Felicity and Wishart,  David S.},
+  year = {2019},
+  month = apr,
+  pages = {72}
+}
+
+@article{Wang2021,
+  title = {CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification},
+  volume = {93},
+  ISSN = {1520-6882},
+  url = {http://dx.doi.org/10.1021/acs.analchem.1c01465},
+  DOI = {10.1021/acs.analchem.1c01465},
+  number = {34},
+  journal = {Analytical Chemistry},
+  publisher = {American Chemical Society (ACS)},
+  author = {Wang,  Fei and Liigand,  Jaanus and Tian,  Siyang and Arndt,  David and Greiner,  Russell and Wishart,  David S.},
+  year = {2021},
+  month = aug,
+  pages = {11692–11700}
+}
+
+
+@article{Wei2019,
+  title = {Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks},
+  volume = {5},
+  ISSN = {2374-7951},
+  url = {http://dx.doi.org/10.1021/acscentsci.9b00085},
+  DOI = {10.1021/acscentsci.9b00085},
+  number = {4},
+  journal = {ACS Central Science},
+  publisher = {American Chemical Society (ACS)},
+  author = {Wei,  Jennifer N. and Belanger,  David and Adams,  Ryan P. and Sculley,  D.},
+  year = {2019},
+  month = mar,
+  pages = {700–708}
+}
+
+@article{Koopman2021,
+  title = {From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics},
+  volume = {32},
+  ISSN = {1879-1123},
+  url = {http://dx.doi.org/10.1021/jasms.1c00098},
+  DOI = {10.1021/jasms.1c00098},
+  number = {7},
+  journal = {Journal of the American Society for Mass Spectrometry},
+  publisher = {American Chemical Society (ACS)},
+  author = {Koopman,  Jeroen and Grimme,  Stefan},
+  year = {2021},
+  month = jun,
+  pages = {1735–1751}
+}
+
+
+@article{Weininger1988,
+  title = {SMILES,  a chemical language and information system. 1. Introduction to methodology and encoding rules},
+  volume = {28},
+  ISSN = {1520-5142},
+  url = {http://dx.doi.org/10.1021/ci00057a005},
+  DOI = {10.1021/ci00057a005},
+  number = {1},
+  journal = {Journal of Chemical Information and Computer Sciences},
+  publisher = {American Chemical Society (ACS)},
+  author = {Weininger,  David},
+  year = {1988},
+  month = feb,
+  pages = {31–36}
+}
+
+
+@article{Grimme2013,
+  title = {Towards First Principles Calculation of Electron Impact Mass Spectra of Molecules},
+  volume = {52},
+  ISSN = {1521-3773},
+  url = {http://dx.doi.org/10.1002/anie.201300158},
+  DOI = {10.1002/anie.201300158},
+  number = {24},
+  journal = {Angewandte Chemie International Edition},
+  publisher = {Wiley},
+  author = {Grimme,  Stefan},
+  year = {2013},
+  month = apr,
+  pages = {6306–6312}
+}
+
+@article{Bauer2014,
+  title = {First principles calculation of electron ionization mass spectra for selected organic drug molecules},
+  volume = {12},
+  ISSN = {1477-0539},
+  url = {http://dx.doi.org/10.1039/c4ob01668h},
+  DOI = {10.1039/c4ob01668h},
+  number = {43},
+  journal = {Org. Biomol. Chem.},
+  publisher = {Royal Society of Chemistry (RSC)},
+  author = {Bauer,  Christoph Alexander and Grimme,  Stefan},
+  year = {2014},
+  pages = {8737–8744}
+}
+
+@article{Bauer2016,
+  title = {How to Compute Electron Ionization Mass Spectra from First Principles},
+  volume = {120},
+  ISSN = {1520-5215},
+  url = {http://dx.doi.org/10.1021/acs.jpca.6b02907},
+  DOI = {10.1021/acs.jpca.6b02907},
+  number = {21},
+  journal = {The Journal of Physical Chemistry A},
+  publisher = {American Chemical Society (ACS)},
+  author = {Bauer,  Christoph Alexander and Grimme,  Stefan},
+  year = {2016},
+  month = may,
+  pages = {3755–3766}
+}
+
+@article{Bannwarth2020,
+  title = {Extended <scp>tight‐binding</scp> quantum chemistry methods},
+  volume = {11},
+  ISSN = {1759-0884},
+  url = {http://dx.doi.org/10.1002/wcms.1493},
+  DOI = {10.1002/wcms.1493},
+  number = {2},
+  journal = {WIREs Computational Molecular Science},
+  publisher = {Wiley},
+  author = {Bannwarth,  Christoph and Caldeweyher,  Eike and Ehlert,  Sebastian and Hansen,  Andreas and Pracht,  Philipp and Seibert,  Jakob and Spicher,  Sebastian and Grimme,  Stefan},
+  year = {2020},
+  month = aug 
+}
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/tutorial.md b/topics/metabolomics/tutorials/qcxms-predictions/tutorial.md
new file mode 100644
index 00000000000000..50b751b0c6e370
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/tutorial.md
@@ -0,0 +1,596 @@
+---
+layout: tutorial_hands_on
+title: Predicting EI+ mass spectra with QCxMS
+zenodo_link: 'https://zenodo.org/record/13327051'
+level: Introductory
+
+questions:
+- Can I predict QC-based mass spectra starting from SMILES?
+- How can I run those computationally heavy predictions? 
+- Can I take into account different conformers?
+
+objectives: 
+- To prepare structure-data format (SDF) files for further operations analysis, starting from chemical structure descriptors in simplified molecular-input line-entry system (SMILES) format. 
+- To generate 3D conformers and optimise them using semi-empirical quantum mechanical (SQM) methods.
+- To produce simulated mass spectra for a given molecule in MSP (text based) format.  
+
+time_estimation: 1H
+
+key_points:
+- Galaxy provides access to high-performance computing (HPC) resources and hence allows the use of semi-empirical quantum mechanical (SQM) methods and *in silico* prediction of QC-based mass spectra.
+- The shown workflow allows to simulate mass spectra of a molecule starting from its SMILES. 
+- The xTB package is based on a SQM method to optimise the geometry of the generated conformers. 
+- The QCxMS tool suite performs quantum chemistry calculations to simulate mass spectra. 
+
+contributions:
+  authorship:
+    - wee-snufkin
+    - hechth
+
+requirements :
+  - type: "internal"
+    topic_name: metabolomics
+    tutorials: 
+      - lcms-preprocessing
+      - gc_ms_with_xcms
+
+---
+
+Mass spectrometry (MS) is a powerful analytical technique used in many fields, including proteomics, metabolomics, drug discovery and many more areas relying on compound identifications. Even though nowadays MS is a standard and popular method, there are many compounds which lack experimental spectra. In those cases, predicting mass spectra from the chemical structure can reveal useful information, help in compound identification and expand the spectral databases, improving the accuracy and efficiency of database search. [{% cite Zhu2023 %}, {% cite Allen2016Computational %}]. 
+There have been several methods developed to predict mass spectra, which can be classified as either first-principles physical-based simulation or data-driven statistical methods [{% cite Zhu2023 %}]. To the first category we can assign purely statistical theories (quasi-equilibrium theory (QET) or Rice–Ramsperger–Kassel–Marcus (RRKM) theories) [{% cite Vetter1994 %}], as well as QCxMS [{% cite Koopman2021 %}] and semiempirical GFNn-xTB [{% cite Koopman2019 %}] which use Born–Oppenheimer molecular dynamics (MD) combined with fragmentation pathways. Data-driven statistical methods - forming the second category - reach back to 1960s when the DENDRAL project (using rule-based heuristic programming) was started by early artificial intelligence (AI) scientists [{% cite Lindsay1980ApplicationsOA %}]. More recently, CFM-ID has been introduced [{% cite Allen2014 %}, {% cite Allen2014metabolomics %}, {% cite Allen2016Computational %}, {% cite DjoumbouFeunang2019 %}, {% cite Wang2021 %}], which uses rule-based fragmentation and employs machine learning methods. Current advancements in machine learning led to recent work using deep neural networks that allow predicting spectra from molecular graphs or fingerprints [{% cite Wei2019 %}].
+
+You will be able to check out how QCxMS works in practice since we are going to use Galaxy tool suite based on this method [{% cite Grimme2013 %}, {% cite Bauer2014 %}, {% cite Bauer2016 %}, {% cite Koopman2021 %}]. Beforehand, we will generate conformers of the query molecule with [RDKit](http://www.rdkit.org) and we will use xTB for molecular optimisation [{% cite Bannwarth2020 %}].
+But first things first, let’s get some toy data to play with and crack on! 
+
+> <question-title></question-title>
+>
+> What does QCEIMS stand for?
+>
+>   > <tip-title>Hint</tip-title>
+>   > With a little knowledge of chemistry, you’ll be able to work it out yourself!
+>   >
+>   > Look at the acronym in the following way: **QC-EI-MS**.
+>   {: .tip}
+> > <solution-title></solution-title>
+> >
+> > QC = quantum chemical 
+> >
+> > EI = electron ionisation
+> >
+> > MS = mass spectrometry
+> >
+> > Hence QCEIMS = quantum-chemical electron ionisation mass spectrometry
+> >
+> {: .solution}
+>
+{: .question}
+
+> <details-title>What is QCxMS?</details-title>
+> QCxMS is a successor of QCEIMS, where the *EI* part is replaced by *x* to take into account other ionisation methods and improve the applicability of the program. In QCEIMS, *EI* stands for *electron ionisation*, while in QCxMS, *x* refers to *EI* or *CID (collision-induced dissociation)* [{% cite Koopman2021 %}]. Currently, only *EI* simulations are supported - using *CID* with the Galaxy tool wrappers is still under development.
+{: .details}
+
+ 
+
+
+# Importing data and pre-processing
+
+In this tutorial, you can choose whether you want to predict the mass spectrum for one molecule only, or if you want to do it for multiple molecules at once. The pre-processing steps will slightly differ depending on your choice. If you are completing this tutorial just to see how the QCxMS tools work, feel free to follow the instructions for one molecule to skip some pre-processing steps.  
+
+In both cases, we start from molecule’s SMILES, and then we convert it to SDF, so if you already have SDF files to work with, simply jump in the relevant place in the workflow and carry on from there.
+
+> <details-title>What is SMILES?</details-title>
+>
+> SMILES (.smi) - the simplified molecular-input line-entry system (SMILES) is a specification in the form of a line notation for describing the structure of a chemical species using short ASCII strings. It is a linear text format which can describe the connectivity and chirality of a molecule [{% cite Weininger1988 %}]. Even though many different forms of SMILES exist, the differences are not relevant for us in this application.
+> ![Depiction of a molecular structure and the corresponding SMILES string. Starts of rings are denoted with a number after the corresponding element (i.e. C1). Non-organic elements need to be written in brackets and isotopic forms need to be denoted before the atom and in brackets as well. Atoms with charges need to be in brackets as well and the charge needs to be denoted behind the atom or the functional group. Hydrogens inside brackets need to be stated explicitly. Double bonds are denoted with a `=` and triple bonds with a `#`.](../../images/qcxms_smiles_explanation.png "A quick explanation of the SMILES system.")
+> Image credit: Helge Hecht, License: MIT
+>
+{: .details}
+
+<!---
+consider the figure with SMILES and the corresponding structure
+-->
+
+
+{% include _includes/cyoa-choices.html option1="Predict MS for a single molecule" option2="Predict MS for multiple molecules at once" default="Predict MS for a single molecule"
+       text="Choose below if you just want to follow the pipeline for prediting the spectrum for only one molecule or multiple molecules at once!" disambiguation="single_vs_multiple" %}
+
+<div class="Predict-MS-for-a-single-molecule" markdown="1">
+
+## Upload data onto Galaxy
+
+Working on a single molecule means that we will work on a dataset and not on a collection of datasets. To simulate the spectrum, we will use ethanol (C<sub>2</sub>H<sub>5</sub> OH) as an example, but you can choose any other molecule that you want, but be aware that the more complex structure you choose, the more time it will take to complete the analysis since the workflow involves generating conformers, semiempirical methods and molecular optimisation.
+We will simply start with molecule’s SMILES. 
+You have three options for uploading the data. The first two - importing via history and Zenodo link will give a file specific to this tutorial, while the last one – “Paste data uploader" gives you more flexibility in terms of the compounds you would like to test with this workflow. 
+
+> <hands-on-title>Option 1: Data upload - Import history</hands-on-title>
+>
+> 1. You can simply import [this history](https://usegalaxy.eu/u/j.jakiela/h/input-file-ei-mass-spectra-prediction-using-qcxms-1) with the input file. 
+>
+>    {% snippet faqs/galaxy/histories_import.md %}
+>
+> 2. **Rename** {% icon galaxy-pencil %} the history to your name of choice.
+>
+{: .hands_on}
+
+><hands-on-title>Option 2: Data upload - Add to history via Zenodo</hands-on-title>
+>
+> 1. Create a new history for this tutorial
+> 2. Import the input table from [Zenodo]({{ page.zenodo_link }})
+>
+>    ```
+>    https://zenodo.org/records/13327051/files/ethanol_SMILES.smi
+>    ```
+>
+>    {% snippet faqs/galaxy/datasets_import_via_link.md %}
+>
+{: .hands_on}
+
+> <hands-on-title> Option 3: Data Upload  - paste data </hands-on-title>
+> 
+> 1. Create a new history for this tutorial
+> 2. 
+>    - Click {% icon galaxy-upload %} **Upload Data** at the top of the tool panel
+>    - Select {% icon galaxy-wf-edit %} **Paste/Fetch Data** at the bottom
+>    - Paste the SMILES into the text field:  
+> ```
+> CCO
+> ```
+>    - Change **Type** from "Auto-detect" to `smi`
+>    - Press **Start** and **Close** the window
+> 3. You can then rename the dataset as you wish (here we use `ethanol_SMILES`)
+> 4. Check that the datatype is `smi`.
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="smi" %}
+>
+{: .hands_on}
+
+## SMILES to SDF
+
+> <hands-on-title> Convert SMILES to SDF </hands-on-title>
+>
+> {% tool [Compound conversion](toolshed.g2.bx.psu.edu/repos/bgruening/openbabel_compound_convert/openbabel_compound_convert/3.1.1+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Molecular input file"*: `ethanol_SMILES` (your SMILES dataset from your history)
+>    - *"Output format"*: `MDL MOL format (sdf, mol)`
+>    - *"Append the specified text after each molecule title"*: `ethanol`
+>
+{: .hands_on}
+
+We now have an SDF file, containing the atoms' coordinates and the investigated molecule's name. 
+
+# 3D Conformer generation & optimization
+
+## Generate conformers
+The next step involves generating three-dimensional (3D) conformers for our molecule. It crteaes the actual 3D topology of the molecule based on electromagnetic forces. This process might seem trivial for very small and simplistic (meaning no complex structure) molecules, but this can be more challenging for larger molecules with a more flexible geometry. This concerns for example P containing biomolecules, where P often forms a rotational center of the molecule. The number of conformers to generate can be specified as an input parameter, with a default value of 1 if not provided. This process is crucial for exploring the possible shapes and energies that a molecule can adopt. The output of this step is a file containing the generated 3D conformers.
+
+> <details-title> What are conformers? </details-title>
+>
+> Conformers are different spatial arrangements of a molecule that result from rotations around single bonds. They have different potential energies and hence some are more favourable (local minima on the potential energy surface) than others. 
+> ![Newman projections of butane conformations & their relative energy differences (not total energies). Conformations form when butane rotates about one of its single covalent bond. Torsional/dihedral angle is shown on x-axis.](../../images/qcxms_predictions_conformers.svg "Conformers of butane and their relative energy differences.")
+> Image credit: [Keministi](https://commons.wikimedia.org/wiki/File:Butane_conformations_and_relative_energies.svg), License: Creative Commons CC0 1.0. 
+>
+{: .details}
+
+
+> <hands-on-title> Generate conformers </hands-on-title>
+>
+>  {% tool [Generate conformers](toolshed.g2.bx.psu.edu/repos/bgruening/ctb_im_conformers/ctb_im_conformers/1.1.4+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Input file"*: output of **Convert to sdf** {% icon tool %}
+>    - *"Number of conformers to generate"*: `1`
+>
+{: .hands_on}
+
+Now - once again format conversion! This time we will convert the generated conformers from the SDF format to Cartesian coordinate (XYZ) format. The XYZ format lists the atoms in a molecule and their respective 3D coordinates, which is a common format used in computational chemistry for further processing and analysis.
+
+> <hands-on-title> Molecular Format Conversion </hands-on-title>
+>
+> 1. {% tool [Compound conversion](toolshed.g2.bx.psu.edu/repos/bgruening/openbabel_compound_convert/openbabel_compound_convert/3.1.1+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Molecular input file"*: output (sdf) of **Generate conformers** {% icon tool %}
+>    - *"Output format"*: `XYZ cartesian coordinates format`
+>    - *"Add hydrogens appropriate for pH"*: `7.0`
+>
+> 2. Check that the datatype is `xyz`. If it's not, just change it – below is the tip how to do it.
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="xyz" %}
+>
+{: .hands_on}
+
+
+## Molecular optimization
+
+As shown in the image in the {% icon details %} *Details box* above, different conformers have different energies. Therefore, our next step will optimize the geometry of the molecules to find the lowest energy conformation. This is crucial to achieve convergence in the next steps. If the input geometry for the QCxMS method is too crude, the ground state neutral run will not converge and we won't be able to sample the geometry to calculate individual trajectories. We will perform semi-empirical optimization on the molecules using the [Extended Tight-Binding (xTB)](https://github.com/grimme-lab/xtb) method. The level of optimization accuracy to be used can be specified as an input parameter, *"Optimization Levels"*. The default quantum chemical method is GFN2-xTB.
+
+> <hands-on-title> Molecular optimisation with xTB </hands-on-title>
+>
+> {% tool [xtb molecular optimization](toolshed.g2.bx.psu.edu/repos/recetox/xtb_molecular_optimization/xtb_molecular_optimization/6.6.1+galaxy1) %} with the following parameters:
+>    - {% icon param-file %} *"Atomic coordinates file"*: output of **Convert to xyz** {% icon tool %})
+>    - *"Optimization Levels"*: `tight`
+>    - *"Keep molecule name"*: {% icon galaxy-toggle %}  `Yes`
+>
+{: .hands_on}
+
+
+# QCxMS Spectra Prediction 
+
+## Neutral and production runs
+
+Finally, let’s predict the spectra for our molecule. As mentioned, we will use [QCxMS](https://github.com/qcxms/QCxMS) for this purpose. First, we need to prepare the necessary input files for the QCxMS production runs. These files are required for running the QCxMS simulations, which will predict the mass spectrum of the molecule. This step typically formats the optimized molecular data into a format that can be used for the production simulations. This step performs the ground state calculations. The resulting geometry trajectory is then sampled and one representation is used for each trajectory.
+
+
+> <hands-on-title> QCxMS neutral run </hands-on-title>
+>
+>  {% tool [QCxMS neutral run](toolshed.g2.bx.psu.edu/repos/recetox/qcxms_neutral_run/qcxms_neutral_run/5.2.1+galaxy3) %} with the following parameters:
+>    - {% icon param-file %} *"Molecule 3D structure [.xyz]"*: output of **xtb molecular optimization** {% icon tool %}
+>    - *"QC Method"*: `GFN2-xTB`
+>
+{: .hands_on}
+
+The outputs of the above step are as follows:
+-	.in output: Input file for the QCxMS production run 
+-	.start output: Start file for the QCxMS production run 
+-	.xyz output: Cartesian coordinate file for the QCxMS production run
+
+We can now use those files as input for the next tool which calculates the mass spectra using QCxMS. This simulation generates .res files, which contain the raw results of the mass spectra calculations. These results are essential for predicting how the molecules will appear in mass spectrometry experiments.
+
+> <hands-on-title> QCxMS production run </hands-on-title>
+>
+> 1. {% tool [QCxMS production run](toolshed.g2.bx.psu.edu/repos/recetox/qcxms_production_run/qcxms_production_run/5.2.1+galaxy3) %} with the following parameters:
+>    - {% icon param-collection %} *Dataset collection* *"in files [.in]"*: `input in files` generated by **QCxMS neutral run** {% icon tool %}
+>    - {% icon param-collection %} *Dataset collection* *"start files [.start]"*: `input start files` generated by **QCxMS neutral run** {% icon tool %}
+>    - {% icon param-collection %} *Dataset collection* *"xyz files [.xyz]"*: `input xyz files` generated by **QCxMS neutral run** {% icon tool %}
+>
+{: .hands_on}
+
+
+## Filter failed datasets
+
+It might be the case that some runs might have failed, therefore it is crucial to filter out any failed runs from the dataset to ensure only successful results are processed further. This step is important to maintain the integrity and quality of the data being analyzed in subsequent steps. The output is a file containing only the successful mass spectra results.
+
+> <hands-on-title> Filter failed datasets </hands-on-title>
+>
+> 1. {% tool [Filter failed datasets](__FILTER_FAILED_DATASETS__) %} with the following parameters:
+>    - {% icon param-collection %} *"Input Collection"*: `res files generated by QCxMS` (output of **QCxMS production run** {% icon tool %} )
+>
+{: .hands_on}
+
+
+## Get MSP spectra
+
+The filtered collection contains .res files from the QCxMS production run. This final step converts the .res files into simulated mass spectra in MSP (Mass Spectrum Peak) file format. The MSP format is widely used for storing and sharing mass spectrometry data, enabling easy comparison and analysis of the results, for example by comparing the spectra using the [matchms](https://github.com/matchms/matchms) package.
+
+> <hands-on-title> QCxMS get MSP results </hands-on-title>
+>
+> 1. {% tool [QCxMS get results](toolshed.g2.bx.psu.edu/repos/recetox/qcxms_getres/qcxms_getres/5.2.1+galaxy2) %} with the following parameters:
+>    - {% icon param-file %} *"Molecule 3D structure [.xyz]"*: `Convert to xyz` (output of **Compound conversion** {% icon tool %})
+>    - *"res files [.res]"*: output of **Filter failed datasets** {% icon tool %} (if the collection doesn't appear in the drop-down list, simply drag and drop it from the history panel to the input box)
+>
+{: .hands_on}
+
+> <details-title>MSP files </details-title>
+> MSP (Mass Spectrum Peak) file is a text file structured according to the NIST MSSearch spectra format. MSP is one of the generally accepted formats for mass spectral libraries (or collections of unidentified spectra, so called spectral archives), and it is compatible with lots of spectra processing programmes (MS-DIAL, NIST MS Search, AMDIS, matchms, etc.). It can contain one or more mass spectra, these are split by an empty line. The individual spectra essentially consist of two sections: metadata (such as name, spectrum type, ion mode, retention time, and the number of m/z peaks) and peaks, consisting of m/z and intensity tuples.
+{: .details}
+
+You can now {% icon dataset-save %} download the MSP file and open it in your spectra processing software for further investigation! 
+
+To give you some insight into how well QCxMS can perform, below is the mass spectrum of ethanol resulting from our workflow compared with an [experimental spectrum](https://hmdb.ca/spectra/c_ms/28442). Both spectra were compiled using an [online mass spectrum generator](https://www.sisweb.com/mstools/spectrum.htm) which requires only m/z values and intensities – so the values that you can get from our MSP file! As you can see, the predicted peaks nicely correspond to experimental ones. But be careful - there might be slight deviations for molecules with more structural complexity! 
+
+![Upper panel shows the experimental spectrum of ethanol, while the lower panel shows analogical spectrum but predicted with the current workflow. The predicted peaks correspond well to the experimental ones. Intensities of simulated peaks have not been predicted perfectly, but the most important trends are preserved.](../../images/qcxms_predictions_ms_exp_pred.png "Comparison between experimental (upper panel) and predicted (lower panel) mass spectra of ethanol.")
+
+
+# Conclusion
+{% icon trophy %} Well done, you’ve simulated the mass spectrum! You might want to consult your results with the [key history](https://usegalaxy.eu/u/j.jakiela/h/ms-prediction-qcxms-single-molecule). If you would like to process multiple molecules at once, you can simply use [the workflow](https://usegalaxy.eu/u/hechth/h/end-to-end-ei-mass-spectra-prediction-workflow-using-qcxms-1) or move to “Predict MS for multiple molecules at once” tab of this tutorial to learn how the pipeline differs from the one that we’ve just covered. 
+
+
+</div>
+
+<div class="Predict-MS-for-multiple-molecules-at-once" markdown="1">
+
+## Upload data onto Galaxy
+
+We will work on two simple molecules – ethanol (C<sub>2</sub>H<sub>5</sub> OH) and ethylene (C<sub>2</sub>H<sub>4</sub>). Of course, you might add more and choose any other molecules that you want, but be aware that the more complex structure you choose, the more time it will take to complete the analysis since the workflow involves generating conformers, semiempirical methods and molecular optimisation.
+We will start with a table with the first column being molecule names and the second one – corresponding SMILES. 
+You have three options for uploading the data. The first two - importing via history and Zenodo link will give a file specific to this tutorial, while the last one – “Paste data uploader" gives you more flexibility in terms of the compounds you would like to test with this workflow. 
+
+> <hands-on-title>Option 1: Data upload - Import history</hands-on-title>
+>
+> 1. You can simply import [this history](https://usegalaxy.eu/u/j.jakiela/h/input-file-end-to-end-ei-mass-spectra-prediction-workflow-using-qcxms) with the input table. 
+>
+>    {% snippet faqs/galaxy/histories_import.md %}
+>
+> 2. **Rename** {% icon galaxy-pencil %} the history to your name of choice.
+>
+{: .hands_on}
+
+><hands-on-title>Option 2: Data upload - Add to history via Zenodo</hands-on-title>
+>
+> 1. Create a new history for this tutorial
+> 2. Import the input table from [Zenodo]({{ page.zenodo_link }})
+>
+>    ```
+>    https://zenodo.org/records/13327051/files/qcxms_prediction_input.tabular
+>    ```
+>
+>    {% snippet faqs/galaxy/datasets_import_via_link.md %}
+>
+{: .hands_on}
+
+> <hands-on-title> Option 3: Data Upload  - paste data </hands-on-title>
+> 
+> 1. Create a new history for this tutorial
+> 2. 
+>    - Click {% icon galaxy-upload %} **Upload Data** at the top of the tool panel
+>    - Select {% icon galaxy-wf-edit %} **Paste/Fetch Data** at the bottom
+>    - Paste the contents into the text field, separated by space. First, enter the name of the molecule, then its SMILES. Please note that we are not using headers here. For this tutorial, we’ll use the example of ethanol and ethylene, but feel free to use your own examples.  
+> ```
+> ethanol CCO
+> ethylene C=C
+> ```
+>    - Change **Type** from "Auto-detect" to `tabular`
+>    - Find the gear symbol ({% icon galaxy-gear %}), deselect any ticked options and select only ({% icon galaxy-gear %}) **Convert spaces to tabs**
+>    - Press **Start** and **Close** the window
+> 3. You can then rename the dataset as you wish.
+> 4. Check that the datatype is `tabular`.
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="tabular" %}
+>
+{: .hands_on}
+
+## Input pre-processing
+
+Once your dataset is uploaded, we can do some simple pre-processing to prepare the file for downstream analysis. Let’s start with ‘cutting’ the table into two columns – one with SMILES, the other with the molecule name – and then separating each entry to create a dataset collection, followed by parsing out the text information. 
+
+> <hands-on-title> Cutting out name column </hands-on-title>
+>
+> 1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy1) %} with the following parameters:
+>    - {% icon param-file %} *"File to cut"*: the tabular file in your history with the compound name and SMILES
+>    - *"Operation"*: `Keep`
+>    - *"Cut by"*: `fields`
+>    - *"Delimited by"*: `Tab`
+>    - *"Is there a header for the data’s columns"*: `No`
+>    - *"List of Fields"*: `Column: 1`
+>
+> 2. You can now rename the resulting dataset or just add a tag in order not to confuse it with subsequent outputs:
+>    - {% icon galaxy-tags %} Add tag: #names
+>
+>    {% snippet faqs/galaxy/datasets_add_tag.md %}
+>
+{: .hands_on}
+
+> <hands-on-title> Creating dataset collection (molecule name) </hands-on-title>
+>
+> {% tool [Split file to dataset collection](toolshed.g2.bx.psu.edu/repos/bgruening/split_file_to_collection/split_file_to_collection/0.5.2) %} with the following parameters:
+>    - {% icon param-file %} *"Select the file type to split"*: `Tabular`
+>    - *"Tabular file to split"*: output of **Advanced Cut** {% icon tool %} (with #names tag)
+>    - *"Number of header lines to transfer to new files"*: `0`
+>    - *"Split by row or by a column?"*: `By row`
+>    - *"Specify number of output files or number of records per file?"*: `Number of records per file (‘chunk mode’)`
+>    - *"Chunk size"*: `1`
+>    - *"Base name for new files in collection"*: `split_file`
+>    - *"Method to allocate records to new files"*: `Maintain record order`
+>
+{: .hands_on}
+
+> <hands-on-title> Parsing out name info </hands-on-title>
+>
+>  {% tool [Parse parameter value](param_value_from_file) %} with the following parameters:
+>    - *"Input file containing parameter to parse out of"*: * click on {% icon param-collection %} *Dataset collection* and select output of **Split file** {% icon tool %}
+>    - *"Select type of parameter to parse"*: `Text`
+>    - *"Remove newlines ?"*: {% icon galaxy-toggle %}  `Yes`
+>
+>   > <tip-title>Can't find "Parse parameter value" tool? </tip-title>
+>   > If you have any problems with accessing **Parse parameter value** tool, you can open [the tool](https://usegalaxy.eu/root?tool_id=param_value_from_file) directly using a given link.
+>   {: .tip}
+>
+{: .hands_on}
+
+
+
+We will repeat the first two steps, but processing SMILES this time. 
+
+
+> <hands-on-title> Cutting out SMILES column </hands-on-title>
+>
+>  1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy1) %} with the following parameters:
+>    - {% icon param-file %} *"File to cut"*: the tabular file in your history with the compound name and SMILES
+>    - *"Operation"*: `Keep`
+>    - *"Cut by"*: `fields`
+>    - *"Delimited by"*: `Tab`
+>    - *"Is there a header for the data’s columns"*: `No`
+>    - *"List of Fields"*: `Column: 2`
+>
+> 2. You can now rename the resulting dataset or just add a tag in order not to confuse it with subsequent outputs:
+>    - {% icon galaxy-tags %} Add tag: #SMILES
+>
+>    {% snippet faqs/galaxy/datasets_add_tag.md %}
+>
+{: .hands_on}
+
+
+> <hands-on-title>  Creating dataset collection (SMILES) </hands-on-title>
+>
+>  1. {% tool [Split file to dataset collection](toolshed.g2.bx.psu.edu/repos/bgruening/split_file_to_collection/split_file_to_collection/0.5.2) %} with the following parameters:
+>    - {% icon param-file %} *"Select the file type to split"*: `Tabular`
+>    - *"Tabular file to split"*: output of the latest **Advanced Cut** {% icon tool %} (higher number in your history, with #SMILES tag)
+>    - *"Number of header lines to transfer to new files"*: `0`
+>    - *"Split by row or by a column?"*: `By row`
+>    - *"Specify number of output files or number of records per file?"*: `Number of records per file (‘chunk mode’)`
+>    - *"Chunk size"*: `1`
+>    - *"Base name for new files in collection"*: `split_file`
+>    - *"Method to allocate records to new files"*: `Maintain record order`
+>
+> 2. Check that the datatype is `smi`. If it's not, just change it! 
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="smi" %}
+>
+{: .hands_on}
+
+Now, onto format conversion. Let’s convert our SMILES to SDF (Structure Data File) and append the molecule’s name that we have already extracted. 
+
+> <hands-on-title> Convert SMILES to SDF </hands-on-title>
+>
+> {% tool [Compound conversion](toolshed.g2.bx.psu.edu/repos/bgruening/openbabel_compound_convert/openbabel_compound_convert/3.1.1+galaxy0) %} with the following parameters:
+>    - *"Molecular input file"*: click on {% icon param-collection %} *Dataset collection* and select output of **Split file** {% icon tool %} on SMILES
+>    - *"Output format"*: `MDL MOL format (sdf, mol)`
+>
+>   > <tip-title>Add name of the molecule</tip-title>
+>   > Another parameter of this tool, *"Append the specified text after each molecule title"*, allows you to add name of the molecule to the title of the generated file. 
+>   > If you are working on a single molecule file, you can simply type the name of that compound into the parameter box.
+>   > However, if your input is a collection of SMILES (like in this case), then you have to add the names (output of **Parse parameter value** {% icon tool %}) at the level of the workflow editor. 
+>   {: .tip}
+>
+{: .hands_on}
+
+<!---
+- *"Append the specified text after each molecule title"*: click on {% icon param-collection %} *Dataset collection* and select output of **Parse parameter value** {% icon tool %} which contains the extracted molecules' names
+-->
+
+
+We now have two SDF files, each containing the coordinates of the atoms and the name of the investigated molecule. Let’s combine them to make life easier and work on just one file. 
+
+> <hands-on-title> Concatenating the files </hands-on-title>
+>
+> {% tool [Concatenate datasets tail-to-head (cat)](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cat/9.3+galaxy1) %} with the following parameters:
+>    - {% icon param-file %} *"Datasets to concatenate"*: 
+>    * if nothing pops up when you click on {% icon param-collection %} *Dataset collection*, then click on "switch to column select" 
+>    * from the datasets list, find the output of **Compound conversion** {% icon tool %} (should have the highest number in your history) and select it
+>    * {% icon workflow-run %} Run tool! 
+>
+{: .hands_on}
+
+# 3D Conformer generation & optimization
+
+## Generate conformers
+The next step involves generating three-dimensional (3D) conformers for each molecule from the generated SDF. It crteaes the actual 3D topology of the molecules based on electromagnetic forces. This process might seem trivial for very small and simplistic (meaning no complex structure) molecules, but this can be more challenging for larger molecules with a more flexible geometry. This concerns for example P containing biomolecules, where P often forms a rotational center of the molecule. The number of conformers to generate can be specified as an input parameter, with a default value of 1 if not provided. This process is crucial for exploring the possible shapes and energies that a molecule can adopt. The output of this step is a file containing the generated 3D conformers.
+
+> <details-title> What are conformers? </details-title>
+>
+> Conformers are different spatial arrangements of a molecule that result from rotations around single bonds. They have different potential energies and hence some are more favourable (local minima on the potential energy surface) than others. 
+> ![Newman projections of butane conformations & their relative energy differences (not total energies). Conformations form when butane rotates about one of its single covalent bond. Torsional/dihedral angle is shown on x-axis.](../../images/qcxms_predictions_conformers.svg "Conformers of butane and their relative energy differences.")
+> Image credit: [Keministi](https://commons.wikimedia.org/wiki/File:Butane_conformations_and_relative_energies.svg), License: Creative Commons CC0 1.0. 
+>
+{: .details}
+
+
+> <hands-on-title> Generate conformers </hands-on-title>
+>
+>  {% tool [Generate conformers](toolshed.g2.bx.psu.edu/repos/bgruening/ctb_im_conformers/ctb_im_conformers/1.1.4+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Input file"*: output of **Concatenate datasets** {% icon tool %}
+>    - *"Number of conformers to generate"*: `1`
+>
+{: .hands_on}
+
+Now - once again format conversion! This time we will convert the generated conformers from the SDF format to Cartesian coordinate (XYZ) format. The XYZ format lists the atoms in a molecule and their respective 3D coordinates, which is a common format used in computational chemistry for further processing and analysis.
+
+> <hands-on-title> Molecular Format Conversion </hands-on-title>
+>
+> 1. {% tool [Compound conversion](toolshed.g2.bx.psu.edu/repos/bgruening/openbabel_compound_convert/openbabel_compound_convert/3.1.1+galaxy0) %} with the following parameters:
+>    - {% icon param-file %} *"Molecular input file"*: output of **Generate conformers** {% icon tool %}
+>    - *"Output format"*: `XYZ cartesian coordinates format`
+>    - *"Split multi-molecule files into a collection"*: {% icon galaxy-toggle %} `Yes`
+>    - *"Add hydrogens appropriate for pH"*: `7.0`
+>
+> 2. Check that the datatype is `xyz`. If it's not, just change it! 
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="xyz" %}
+>
+{: .hands_on}
+
+
+## Molecular optimization
+
+As shown in the image in the {% icon details %} *Details box* above, different conformers have different energies. Therefore, our next step will optimize the geometry of the molecules to find the lowest energy conformation. This is crucial to achieve convergence in the next steps. If the input geometry for the QCxMS method is too crude, the ground state neutral run will not converge and we won't be able to sample the geometry to calculate individual trajectories. We will perform semi-empirical optimization on the molecules using the [Extended Tight-Binding (xTB)](https://github.com/grimme-lab/xtb) method. The level of optimization accuracy to be used can be specified as an input parameter, *"Optimization Levels"*. The default quantum chemical method is GFN2-xTB.
+
+> <hands-on-title> Molecular optimisation with xTB </hands-on-title>
+>
+> {% tool [xtb molecular optimization](toolshed.g2.bx.psu.edu/repos/recetox/xtb_molecular_optimization/xtb_molecular_optimization/6.6.1+galaxy1) %} with the following parameters:
+>    - *"Atomic coordinates file"*: click on {% icon param-collection %} *Dataset collection* and select `Prepared ligands` (output of **Compound conversion** {% icon tool %})
+>    - *"Optimization Levels"*: `tight`
+>    - *"Keep molecule name"*: {% icon galaxy-toggle %}  `Yes`
+>
+{: .hands_on}
+
+
+# QCxMS Spectra Prediction 
+
+## Neutral and production runs
+
+Finally, let’s predict the spectra for our molecules. As mentioned, we will use [QCxMS](https://github.com/qcxms/QCxMS) for this purpose. First, we need to prepare the necessary input files for the QCxMS production runs. These files are required for running the QCxMS simulations, which will predict the mass spectra of the molecules. This step typically formats the optimized molecular data into a format that can be used for the production simulations. This step performs the ground state calculations. The resulting geometry trajectory is then sampled and one representation is used for each trajectory.
+
+
+> <hands-on-title> QCxMS neutral run </hands-on-title>
+>
+>  {% tool [QCxMS neutral run](toolshed.g2.bx.psu.edu/repos/recetox/qcxms_neutral_run/qcxms_neutral_run/5.2.1+galaxy3) %} with the following parameters:
+>    - *"Molecule 3D structure [.xyz]"*: click on {% icon param-collection %} *Dataset collection* and select output of **xtb molecular optimization** {% icon tool %}
+>    - *"QC Method"*: `GFN2-xTB`
+>
+{: .hands_on}
+
+The outputs of the above step are as follows:
+-	.in output: Input file for the QCxMS production run 
+-	.start output: Start file for the QCxMS production run 
+-	.xyz output: Cartesian coordinate file for the QCxMS production run
+
+We can now use those files as input for the next tool which calculates the mass spectra for each molecule using QCxMS (Quantum Chemistry and Mass Spectrometry). This simulation generates .res files, which contain the raw results of the mass spectra calculations. These results are essential for predicting how the molecules will appear in mass spectrometry experiments.
+
+> <hands-on-title> QCxMS production run </hands-on-title>
+>
+> 1. {% tool [QCxMS production run](toolshed.g2.bx.psu.edu/repos/recetox/qcxms_production_run/qcxms_production_run/5.2.1+galaxy3) %} with the following parameters:
+>    - {% icon param-collection %} *Dataset collection* *"in files [.in]"*: `input in files` generated by **QCxMS neutral run** {% icon tool %}
+>    - {% icon param-collection %} *Dataset collection* *"start files [.start]"*: `input start files` generated by **QCxMS neutral run** {% icon tool %}
+>    - {% icon param-collection %} *Dataset collection* *"xyz files [.xyz]"*: `input xyz files` generated by **QCxMS neutral run** {% icon tool %}
+>
+{: .hands_on}
+
+
+## Filter failed datasets
+
+It might be the case that some runs might have failed, therefore it is crucial to filter out any failed runs from the dataset to ensure only successful results are processed further. This step is important to maintain the integrity and quality of the data being analyzed in subsequent steps. The output is a file containing only the successful mass spectra results.
+
+> <hands-on-title> Filter failed datasets </hands-on-title>
+>
+> 1. {% tool [Filter failed datasets](__FILTER_FAILED_DATASETS__) %} with the following parameters:
+>    - {% icon param-collection %} *"Input Collection"*: output of **QCxMS production run** {% icon tool %}
+>
+{: .hands_on}
+
+
+## Get MSP spectra
+
+The filtered collection contains .res files from the QCxMS production run. This final step converts the .res files into simulated mass spectra in MSP (Mass Spectrum Peak) file format. The MSP format is widely used for storing and sharing mass spectrometry data, enabling easy comparison and analysis of the results, for example by comparing the spectra using the [matchms](https://github.com/matchms/matchms) package.
+
+> <hands-on-title> QCxMS get MSP results </hands-on-title>
+>
+> 1. {% tool [QCxMS get results](toolshed.g2.bx.psu.edu/repos/recetox/qcxms_getres/qcxms_getres/5.2.1+galaxy2) %} with the following parameters:
+>    - *"Molecule 3D structure [.xyz]"*: click on {% icon param-collection %} *Dataset collection* and select `Prepared ligands` (output of **Compound conversion** {% icon tool %})
+>    - {% icon param-file %} *"res files [.res]"*: output of **Filter failed datasets** {% icon tool %} (if the collection doesn't appear in the drop-down list, simply drag and drop it from the history panel to the input box)
+>
+{: .hands_on}
+
+The output of this step is a collection of two MSP files - one per each molecule. However, if you want, you can combine those two into one MSP file. 
+
+> <hands-on-title> Combine MSP files into one </hands-on-title>
+>
+> 1. {% tool [Concatenate datasets tail-to-head (cat)](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cat/9.3+galaxy1) %} with the following parameters:
+>    - {% icon param-file %} *"Datasets to concatenate"*: 
+>    * if nothing pops up when you click on {% icon param-collection %} *Dataset collection*, then click on "switch to column select" 
+>    * from the datasets list, find the output of **QCxMS get results** {% icon tool %} (should be the latest output dataset) and select it
+>    * {% icon workflow-run %} Run tool! 
+>
+{: .hands_on}
+
+> <details-title>MSP files </details-title>
+> MSP (Mass Spectrum Peak) file is a text file structured according to the NIST MSSearch spectra format. MSP is one of the generally accepted formats for mass spectral libraries (or collections of unidentified spectra, so called spectral archives), and it is compatible with lots of spectra processing programmes (MS-DIAL, NIST MS Search, AMDIS, matchms, etc.). It can contain one or more mass spectra, these are split by an empty line. The individual spectra essentially consist of two sections: metadata (such as name, spectrum type, ion mode, retention time, and the number of m/z peaks) and peaks, consisting of m/z and intensity tuples.
+{: .details}
+
+You can now {% icon dataset-save %} download the MSP file and open it in your spectra processing software for further investigation! 
+
+To give you some insight into how well QCxMS can perform, below is the mass spectrum of ethanol resulting from our workflow compared with [experimental spectrum](https://hmdb.ca/spectra/c_ms/28442). Both spectra were compiled using an [online mass spectrum generator](https://www.sisweb.com/mstools/spectrum.htm) which requires only m/z values and intensities – so the values that you can get from our MSP file! As you can see, the predicted peaks nicely correspond to experimental ones. But be careful - there might be slight deviations for molecules with more structural complexity! 
+
+![Upper panel shows the experimental spectrum of ethanol, while the lower panel shows analogical spectrum but predicted with the current workflow. The predicted peaks correspond well to the experimental ones. Intensities of simulated peaks have not been predicted perfectly, but the most important trends are preserved.](../../images/qcxms_predictions_ms_exp_pred.png "Comparison between experimental (upper panel) and predicted (lower panel) mass spectra of ethanol.")
+
+
+# Conclusion
+{% icon trophy %} Well done, you’ve simulated mass spectra! You might want to consult your results with the [key history](https://usegalaxy.eu/u/hechth/h/end-to-end-ei-mass-spectra-prediction-workflow-using-qcxms-1) or use [the workflow](https://usegalaxy.eu/u/hechth/h/end-to-end-ei-mass-spectra-prediction-workflow-using-qcxms-1) associated with this tutorial. 
+
+</div>
+
+The prediction of mass spectra might be very useful, particularly for compounds that lack experimental data. Simulating the spectra can also save time and resources. This field has been developing quite rapidly, and recent advancements in new algorithms and packages have led to more and more accurate results. However, one cannot forget that this kind of software should be used to deepen our chemical understanding of the structures of studied compounds and not as a replacement for practical experiments.
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS-tests.yml b/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS-tests.yml
new file mode 100644
index 00000000000000..5ffcc37c4888ab
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS-tests.yml
@@ -0,0 +1,71 @@
+- doc: Test outline for End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS
+  job:
+    Input Molecules with SMILES and NAME without a header.:
+      class: File
+      path: test-data/Input Molecules with SMILES and NAME without a header..tabular
+      filetype: tabular
+    Number of conformers to generate: '1'
+    Optimization Levels: crude
+    QC Method: xtb2
+  outputs:
+    conformer_output:
+      path: test-data/conformer_output.sdf
+    optimized output:
+      element_tests:
+        molecule1:
+          path: test-data/optimized output_molecule1.xyz
+        molecule2:
+          path: test-data/optimized output_molecule2.xyz
+    MSP output:
+      element_tests:
+        molecule1:
+          path: test-data/MSP output_molecule1.msp
+        molecule2:
+          path: test-data/MSP output_molecule2.msp
+    XYZ output:
+      element_tests:
+        molecule1:
+          path: test-data/XYZ output_molecule1.xyz
+        molecule2:
+          path: test-data/XYZ output_molecule2.xyz
+    '[.in] output':
+      element_tests:
+        molecule1:
+          elements:
+            TMP.1_qcxms:
+              path: test-data/[.in] output_TMP.1_qcxms.txt
+        molecule2:
+          elements:
+            TMP.2_qcxms:
+              path: test-data/[.in] output_TMP.2_qcxms.txt
+    '[.start] output':
+      element_tests:
+        molecule1:
+          elements:
+            TMP.1_qcxms:
+              path: test-data/[.start] output_TMP.1_qcxms.txt
+        molecule2:
+          elements:
+            TMP.2_qcxms:
+              path: test-data/[.start] output_TMP.2_qcxms.txt
+    '[.xyz] output':
+      element_tests:
+        molecule1:
+          elements:
+            TMP.1_start:
+              path: test-data/[.xyz] output_TMP.1_start.txt
+        molecule2:
+          elements:
+            TMP.2_start:
+              path: test-data/[.xyz] output_TMP.2_start.txt
+    res output:
+      element_tests:
+        molecule1:
+          elements:
+            TMP.1_qcxms:
+              path: test-data/res output_TMP.1_qcxms.txt
+        molecule2:
+          elements:
+            TMP.2_qcxms:
+              path: test-data/res output_TMP.2_qcxms.txt
+            
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS.ga b/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS.ga
new file mode 100644
index 00000000000000..0f9b45f49338ea
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/End-to-end-EI+-mass-spectra-prediction-workflow-using-QCxMS.ga
@@ -0,0 +1,891 @@
+{
+    "a_galaxy_workflow": "true",
+    "annotation": "This workflow predict in-silico mass spectra using semi-empirical quantum physics method.",
+    "comments": [
+        {
+            "color": "none",
+            "data": {
+                "size": 2,
+                "text": "Example:<br><br>Ethanol&nbsp;&nbsp;&nbsp; CCO<br>Ethylene&nbsp;&nbsp; C=C<br><br>"
+            },
+            "id": 1,
+            "position": [
+                10,
+                30
+            ],
+            "size": [
+                280,
+                140
+            ],
+            "type": "text"
+        },
+        {
+            "child_steps": [
+                11,
+                12,
+                13
+            ],
+            "color": "yellow",
+            "data": {
+                "title": "3D Conformer Generation &amp; Optimization"
+            },
+            "id": 3,
+            "position": [
+                1000,
+                340
+            ],
+            "size": [
+                341,
+                728
+            ],
+            "type": "frame"
+        },
+        {
+            "child_steps": [
+                14,
+                15,
+                17,
+                16
+            ],
+            "color": "green",
+            "data": {
+                "title": "QCxMS Spectra Prediction"
+            },
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+            ],
+            "position": {
+                "left": 1052.8999933509037,
+                "top": 883.3999791720917
+            },
+            "post_job_actions": {},
+            "tool_id": "toolshed.g2.bx.psu.edu/repos/recetox/xtb_molecular_optimization/xtb_molecular_optimization/6.6.1+galaxy3",
+            "tool_shed_repository": {
+                "changeset_revision": "6e1ef071fffc",
+                "name": "xtb_molecular_optimization",
+                "owner": "recetox",
+                "tool_shed": "toolshed.g2.bx.psu.edu"
+            },
+            "tool_state": "{\"coordinates\": {\"__class__\": \"ConnectedValue\"}, \"opt_level\": {\"__class__\": \"ConnectedValue\"}, \"replace_second_line\": true, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+            "tool_version": "6.6.1+galaxy3",
+            "type": "tool",
+            "uuid": "4c4921af-438e-41f4-9621-bb42043bbe6f",
+            "when": null,
+            "workflow_outputs": [
+                {
+                    "label": "optimized output",
+                    "output_name": "output",
+                    "uuid": "01a9a1af-e225-4613-94f9-dfdad37c93a2"
+                }
+            ]
+        },
+        "14": {
+            "annotation": "Produce preparation input files for production runs",
+            "content_id": "toolshed.g2.bx.psu.edu/repos/recetox/qcxms_neutral_run/qcxms_neutral_run/5.2.1+galaxy4",
+            "errors": null,
+            "id": 14,
+            "input_connections": {
+                "QC_Level": {
+                    "id": 3,
+                    "output_name": "output"
+                },
+                "mol": {
+                    "id": 13,
+                    "output_name": "output"
+                }
+            },
+            "inputs": [],
+            "label": null,
+            "name": "QCxMS neutral run",
+            "outputs": [
+                {
+                    "name": "coords1",
+                    "type": "input"
+                },
+                {
+                    "name": "coords2",
+                    "type": "input"
+                },
+                {
+                    "name": "coords3",
+                    "type": "input"
+                }
+            ],
+            "position": {
+                "left": 1440,
+                "top": 390
+            },
+            "post_job_actions": {},
+            "tool_id": "toolshed.g2.bx.psu.edu/repos/recetox/qcxms_neutral_run/qcxms_neutral_run/5.2.1+galaxy4",
+            "tool_shed_repository": {
+                "changeset_revision": "8f465c0c3b6c",
+                "name": "qcxms_neutral_run",
+                "owner": "recetox",
+                "tool_shed": "toolshed.g2.bx.psu.edu"
+            },
+            "tool_state": "{\"QC_Level\": {\"__class__\": \"ConnectedValue\"}, \"keywords\": {\"tmax\": \"20.0\", \"tinit\": \"500.0\", \"ieeatm\": \"0.6\", \"ntraj\": null}, \"mol\": {\"__class__\": \"ConnectedValue\"}, \"store_extended_output\": false, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+            "tool_version": "5.2.1+galaxy4",
+            "type": "tool",
+            "uuid": "6aa75cd8-8d14-40b1-8192-f28a6b7ea6d4",
+            "when": null,
+            "workflow_outputs": [
+                {
+                    "label": "[.in] output",
+                    "output_name": "coords1",
+                    "uuid": "e6e6a07b-d478-4a87-b491-42abee9094b1"
+                },
+                {
+                    "label": "[.xyz] output",
+                    "output_name": "coords3",
+                    "uuid": "969b3e2f-fbee-48f9-829a-c0032580e6f8"
+                },
+                {
+                    "label": "[.start] output",
+                    "output_name": "coords2",
+                    "uuid": "dc4811ad-6cff-42cc-a2f8-52ab585f47fe"
+                }
+            ]
+        },
+        "15": {
+            "annotation": "Calculate the mass spectra for a given molecule using QCxMS. It generates .res files, which are collected and converted into MSP format in the last step",
+            "content_id": "toolshed.g2.bx.psu.edu/repos/recetox/qcxms_production_run/qcxms_production_run/5.2.1+galaxy3",
+            "errors": null,
+            "id": 15,
+            "input_connections": {
+                "in_file": {
+                    "id": 14,
+                    "output_name": "coords1"
+                },
+                "start_file": {
+                    "id": 14,
+                    "output_name": "coords2"
+                },
+                "xyz_file": {
+                    "id": 14,
+                    "output_name": "coords3"
+                }
+            },
+            "inputs": [],
+            "label": null,
+            "name": "QCxMS production run",
+            "outputs": [
+                {
+                    "name": "res_files",
+                    "type": "txt"
+                }
+            ],
+            "position": {
+                "left": 1740,
+                "top": 390
+            },
+            "post_job_actions": {},
+            "tool_id": "toolshed.g2.bx.psu.edu/repos/recetox/qcxms_production_run/qcxms_production_run/5.2.1+galaxy3",
+            "tool_shed_repository": {
+                "changeset_revision": "e97bfe7ac906",
+                "name": "qcxms_production_run",
+                "owner": "recetox",
+                "tool_shed": "toolshed.g2.bx.psu.edu"
+            },
+            "tool_state": "{\"__input_ext\": \"input\", \"chromInfo\": \"/opt/galaxy/tool-data/shared/ucsc/chrom/?.len\", \"in_file\": {\"__class__\": \"ConnectedValue\"}, \"in_file|__identifier__\": \"TMP.100_qcxms\", \"start_file\": {\"__class__\": \"ConnectedValue\"}, \"start_file|__identifier__\": \"TMP.100_qcxms\", \"store_extended_output\": false, \"xyz_file\": {\"__class__\": \"ConnectedValue\"}, \"xyz_file|__identifier__\": \"TMP.100_start\", \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+            "tool_version": "5.2.1+galaxy3",
+            "type": "tool",
+            "uuid": "ad8af72d-c4e6-400f-bcd4-87b5e3ed79ac",
+            "when": null,
+            "workflow_outputs": [
+                {
+                    "label": "res output",
+                    "output_name": "res_files",
+                    "uuid": "fc53168a-99f8-441c-9325-2d812bf03100"
+                }
+            ]
+        },
+        "16": {
+            "annotation": "Remove failed runs",
+            "content_id": "__FILTER_FAILED_DATASETS__",
+            "errors": null,
+            "id": 16,
+            "input_connections": {
+                "input": {
+                    "id": 15,
+                    "output_name": "res_files"
+                }
+            },
+            "inputs": [],
+            "label": null,
+            "name": "Filter failed datasets",
+            "outputs": [
+                {
+                    "name": "output",
+                    "type": "input"
+                }
+            ],
+            "position": {
+                "left": 1440,
+                "top": 680
+            },
+            "post_job_actions": {},
+            "tool_id": "__FILTER_FAILED_DATASETS__",
+            "tool_state": "{\"input\": {\"__class__\": \"ConnectedValue\"}, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+            "tool_version": "1.0.0",
+            "type": "tool",
+            "uuid": "900b7dd1-329e-4223-bc33-ccadde91db53",
+            "when": null,
+            "workflow_outputs": []
+        },
+        "17": {
+            "annotation": "Produce simulated mass spectra into MSP file format.",
+            "content_id": "toolshed.g2.bx.psu.edu/repos/recetox/qcxms_getres/qcxms_getres/5.2.1+galaxy2",
+            "errors": null,
+            "id": 17,
+            "input_connections": {
+                "mol": {
+                    "id": 12,
+                    "output_name": "file_outputs"
+                },
+                "res_files": {
+                    "id": 16,
+                    "output_name": "output"
+                }
+            },
+            "inputs": [],
+            "label": null,
+            "name": "QCxMS get results",
+            "outputs": [
+                {
+                    "name": "msp_output",
+                    "type": "msp"
+                }
+            ],
+            "position": {
+                "left": 1740,
+                "top": 680
+            },
+            "post_job_actions": {},
+            "tool_id": "toolshed.g2.bx.psu.edu/repos/recetox/qcxms_getres/qcxms_getres/5.2.1+galaxy2",
+            "tool_shed_repository": {
+                "changeset_revision": "53dce0e49af7",
+                "name": "qcxms_getres",
+                "owner": "recetox",
+                "tool_shed": "toolshed.g2.bx.psu.edu"
+            },
+            "tool_state": "{\"__input_ext\": \"input\", \"chromInfo\": \"/opt/galaxy/tool-data/shared/ucsc/chrom/?.len\", \"mol\": {\"__class__\": \"ConnectedValue\"}, \"mol|__identifier__\": \"molecule1\", \"res_files\": {\"__class__\": \"ConnectedValue\"}, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+            "tool_version": "5.2.1+galaxy2",
+            "type": "tool",
+            "uuid": "3b4e1353-4d54-4e6f-b1f2-ba8e7329b641",
+            "when": null,
+            "workflow_outputs": [
+                {
+                    "label": "MSP output",
+                    "output_name": "msp_output",
+                    "uuid": "d81bf92a-604c-46b9-86a9-bf3c2281ebcb"
+                }
+            ]
+        }
+    },
+    "tags": [
+        "Exposomics",
+        "QCxMS",
+        "GC-MS",
+        "Metabolomics"
+    ],
+    "uuid": "9589a124-ecd8-455c-b67d-d9cc651e5f64",
+    "version": 2
+}
\ No newline at end of file
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/index.md b/topics/metabolomics/tutorials/qcxms-predictions/workflows/index.md
new file mode 100644
index 00000000000000..e092e0ae66ddd4
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/index.md
@@ -0,0 +1,3 @@
+---
+layout: workflow-list
+---
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/Input Molecules with SMILES and NAME without a header..tabular b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/Input Molecules with SMILES and NAME without a header..tabular
new file mode 100644
index 00000000000000..14154a6fb15792
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/Input Molecules with SMILES and NAME without a header..tabular	
@@ -0,0 +1,2 @@
+Ethanol	CCO
+Ethylene	C=C
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/MSP output_molecule1.msp b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/MSP output_molecule1.msp
new file mode 100644
index 00000000000000..ff78a5f7013d4f
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/MSP output_molecule1.msp	
@@ -0,0 +1,87 @@
+NAME: Ethanol
+Num Peaks: 84
+15.023475 0.809911869756469227
+16.026829 0.008994651568429985
+16.029751 0.000278522912611313
+16.031300 0.089623163906493747
+17.034654 0.000980845481650101
+17.037576 0.000047138598101878
+18.010564 0.000992171170175064
+19.014781 0.000000338258258465
+19.016840 0.000000298463184563
+20.014809 0.000002069344653646
+27.023475 1.617095512809796798
+28.026829 0.035716454989958679
+28.029751 0.000562203441420878
+28.031300 5.662797999133474924
+29.002739 1.572834097848604618
+29.030183 0.000165353949820528
+29.034654 0.119532271176382682
+29.037576 0.002777129183546995
+30.006093 0.017120974555307276
+30.006956 0.000542004787905876
+30.009015 0.000318826339004142
+30.010564 4.044698392660825093
+30.038008 0.000347141147943374
+30.040930 0.000231427431962250
+31.006984 0.003156380744542170
+31.013918 0.043528401377014268
+31.014781 0.001639487793961680
+31.016840 0.001229615934937590
+31.018389 44.035939101400863649
+32.010338 0.000159413169502071
+32.014809 0.007541644210089048
+32.021743 0.487611250393266304
+32.022606 0.017861217082459184
+32.024665 0.015182034844983958
+33.018163 0.000081974389698084
+33.022634 0.093771388708229778
+34.025988 0.002679182562368878
+41.002739 0.512505209550937813
+42.006093 0.011044873099773683
+42.006956 0.000209978570522673
+42.009015 0.000104989285261336
+42.010564 1.552864147512257098
+43.006984 0.000955402522614578
+43.009447 0.000104989285261336
+43.013918 0.033834030435363052
+43.014781 0.000604747484305829
+43.016840 0.000477432231765184
+43.018389 4.032790507599846030
+44.010338 0.000020997857052267
+44.014809 0.003278367972927454
+44.017272 0.000190972878810967
+44.018135 0.000031828813135161
+44.021743 0.088930656578227588
+44.022606 0.001239739698013728
+44.024665 0.001322388914998183
+44.026214 1.592730938497586513
+45.018163 0.000159144065675806
+45.022634 0.007355788730556762
+45.025097 0.000413246535936932
+45.025960 0.000082649307187386
+45.029568 0.033602607954067838
+45.030431 0.000489358382090505
+45.032490 0.000782973354375997
+45.034039 16.133551915282957623
+46.025988 0.000247947921562159
+46.028910 0.000082649307187386
+46.030459 0.003327636732360984
+46.032922 0.000097871669297000
+46.035844 0.000065247779531333
+46.037393 0.347903992426753184
+46.038256 0.005291315335104925
+46.040315 0.010582630670209850
+46.041864 100.000000000000000000
+47.033813 0.000097871669297000
+47.038284 0.035054966260660646
+47.040747 0.002314950579418989
+47.043669 0.000661414416888116
+47.045218 2.226778783776781179
+47.046081 0.026655731847011969
+47.048140 0.077916756924945929
+48.046109 0.180438801113245395
+48.048572 0.002050440853928147
+49.049463 0.006151322561784441
+49.052385 0.002050440853928147
+ 
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/MSP output_molecule2.msp b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/MSP output_molecule2.msp
new file mode 100644
index 00000000000000..240806395cf670
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/MSP output_molecule2.msp	
@@ -0,0 +1,19 @@
+NAME: Ethylene
+Num Peaks: 16
+25.007825 1.718727764569165428
+26.011179 0.035265849911891224
+26.014101 0.000245632799555513
+26.015650 5.444254039882509666
+27.014533 0.000280723184902515
+27.019004 0.117838106797961265
+27.021926 0.001224002998436550
+27.023475 6.439392220897993901
+28.022358 0.000333818988533364
+28.026829 0.143018583199167548
+28.029751 0.002107087766411133
+28.031300 100.000000000000000000
+29.030183 0.000131692985400696
+29.034654 2.213403184734461515
+29.037576 0.051141478775524156
+30.038008 0.018410931763823551
+ 
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/XYZ output_molecule1.xyz b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/XYZ output_molecule1.xyz
new file mode 100644
index 00000000000000..0d168ed1095eb3
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/XYZ output_molecule1.xyz	
@@ -0,0 +1,11 @@
+9
+Ethanol
+C         -0.95350        0.04780        0.04250
+C          0.48910       -0.32040       -0.18860
+O          1.27700        0.32480        0.73770
+H         -1.56874       -0.45221       -0.67609
+H         -1.07247        1.10612       -0.06096
+H         -1.24379       -0.24902        1.02867
+H          0.78033       -0.02344       -1.17445
+H          0.60977       -1.37871       -0.08701
+H          1.16641        1.28393        0.64422
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/XYZ output_molecule2.xyz b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/XYZ output_molecule2.xyz
new file mode 100644
index 00000000000000..a46f96f6208145
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/XYZ output_molecule2.xyz	
@@ -0,0 +1,8 @@
+6
+Ethylene
+C          0.64570       -0.06250        0.02180
+C         -0.67390        0.07550       -0.02300
+H          1.06315       -1.00041        0.12703
+H          1.25407        0.76814       -0.04861
+H         -1.28227       -0.75514        0.04741
+H         -1.09135        1.01341       -0.12823
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.in] output_TMP.1_qcxms.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.in] output_TMP.1_qcxms.txt
new file mode 100644
index 00000000000000..43ad8db453d554
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.in] output_TMP.1_qcxms.txt	
@@ -0,0 +1,6 @@
+
+xtb2
+tmax  20.0 
+tinit  500.0 
+ieeatm  0.6
+        
\ No newline at end of file
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.in] output_TMP.2_qcxms.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.in] output_TMP.2_qcxms.txt
new file mode 100644
index 00000000000000..43ad8db453d554
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.in] output_TMP.2_qcxms.txt	
@@ -0,0 +1,6 @@
+
+xtb2
+tmax  20.0 
+tinit  500.0 
+ieeatm  0.6
+        
\ No newline at end of file
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.start] output_TMP.1_qcxms.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.start] output_TMP.1_qcxms.txt
new file mode 100644
index 00000000000000..fbb85e6742d9e6
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.start] output_TMP.1_qcxms.txt	
@@ -0,0 +1,9 @@
+   1
+  0.62567052368471D-01
+  0.59468733880845D+05
+ -0.86407036000000D-04 -0.74139754000000D-04 -0.25136141000000D-04  0.99999999835915D+00
+ -0.75030280000000D-05  0.13795015000000D-03 -0.48822799000000D-04  0.10000000000000D+01
+  0.27471169000000D-04  0.14271537000000D-03  0.73193344000000D-03  0.32587175635032D+00
+  0.25383738000000D-03  0.19611205000000D-03  0.35523382000000D-03  0.32587211977798D+00
+  0.75676396000000D-03 -0.65143144000000D-03 -0.31461747000000D-03  0.32587212005187D+00
+  0.80970430000000D-04 -0.49839118000000D-03  0.58130008000000D-04  0.32587175702122D+00
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.start] output_TMP.2_qcxms.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.start] output_TMP.2_qcxms.txt
new file mode 100644
index 00000000000000..a6009107abd691
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.start] output_TMP.2_qcxms.txt	
@@ -0,0 +1,9 @@
+   2
+  0.16395642476199D+00
+  0.82682746673123D+04
+ -0.10914854000000D-03 -0.38953325000000D-04 -0.70226730000000D-04  0.99999999688672D+00
+  0.45012522000000D-04  0.95232177000000D-04 -0.74980134000000D-04  0.10000000000000D+01
+ -0.22968655000000D-03  0.22958900000000D-03  0.98683867000000D-03  0.60000317902482D-09
+  0.42888281000000D-03  0.66254169000000D-04  0.77068953000000D-04  0.60000317902649D-09
+  0.30862630000000D-03 -0.57204735000000D-03  0.22748508000000D-03  0.60000317902115D-09
+  0.25642952000000D-03 -0.44504428000000D-03  0.38828542000000D-03  0.60000317902418D-09
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.xyz] output_TMP.1_start.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.xyz] output_TMP.1_start.txt
new file mode 100644
index 00000000000000..159da981dd12b4
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.xyz] output_TMP.1_start.txt	
@@ -0,0 +1,8 @@
+6
+Ethylene
+C            0.41477649580435       -0.63030255256657       -1.06486647656638
+C           -0.44176868760158       -0.50104388020257       -0.06912918665727
+H            1.42056722666052       -1.02410035760537       -1.01438672780428
+H            0.10519430165571       -0.29739688343086       -2.05540165286128
+H           -0.14373125632401       -0.80600425646649        0.89783486500764
+H           -1.45282264145628       -0.12533852229384       -0.24692685036236
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.xyz] output_TMP.2_start.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.xyz] output_TMP.2_start.txt
new file mode 100644
index 00000000000000..ef9a89faa20eed
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/[.xyz] output_TMP.2_start.txt	
@@ -0,0 +1,8 @@
+6
+Ethylene
+C            0.40793601250278       -0.63374283945010       -1.06869449159234
+C           -0.44091587619203       -0.49306767147919       -0.07421567505089
+H            1.41184802667417       -1.01105042424269       -0.95498589794988
+H            0.13007776460167       -0.29106403453594       -2.03678720945506
+H           -0.10800061836526       -0.84902297697888        0.90543088636385
+H           -1.43336775880370       -0.15907579203329       -0.22963449271632
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/conformer_output.sdf b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/conformer_output.sdf
new file mode 100644
index 00000000000000..3c6b222ac3579e
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/conformer_output.sdf
@@ -0,0 +1,44 @@
+Ethanol
+     RDKit          3D
+
+  3  2  0  0  0  0  0  0  0  0999 V2000
+   -0.9535    0.0478    0.0425 C   0  0  0  0  0  0  0  0  0  0  0  0
+    0.4891   -0.3204   -0.1886 C   0  0  0  0  0  0  0  0  0  0  0  0
+    1.2770    0.3248    0.7377 O   0  0  0  0  0  0  0  0  0  0  0  0
+  1  2  1  0
+  2  3  1  0
+M  END
+>  <StructureNum>  (1) 
+1
+
+>  <ConformerNum>  (1) 
+1
+
+>  <EnergyAbs>  (1) 
+5.7229002889186056
+
+>  <EnergyDelta>  (1) 
+0
+
+$$$$
+Ethylene
+     RDKit          3D
+
+  2  1  0  0  0  0  0  0  0  0999 V2000
+    0.6457   -0.0625    0.0218 C   0  0  0  0  0  0  0  0  0  0  0  0
+   -0.6739    0.0755   -0.0230 C   0  0  0  0  0  0  0  0  0  0  0  0
+  1  2  2  0
+M  END
+>  <StructureNum>  (2) 
+2
+
+>  <ConformerNum>  (2) 
+1
+
+>  <EnergyAbs>  (2) 
+8.5000684308651895
+
+>  <EnergyDelta>  (2) 
+0
+
+$$$$
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/optimized output_molecule1.xyz b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/optimized output_molecule1.xyz
new file mode 100644
index 00000000000000..ab851efb56e46b
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/optimized output_molecule1.xyz	
@@ -0,0 +1,11 @@
+9
+Ethanol
+C           -0.98033571372536        0.04806197489010        0.03446055219892
+C            0.49091890548663       -0.29973355625565       -0.19034566316747
+O            1.34154547196055        0.31566997011057        0.74413748316563
+H           -1.60372888368724       -0.47451874475788       -0.68533986681808
+H           -1.13838235763736        1.12014177132302       -0.08016394334379
+H           -1.28015076689988       -0.24129986990165        1.03875898121561
+H            0.78336921157908       -0.02677244149020       -1.21562454053584
+H            0.64681206185554       -1.37369218488720       -0.06534025670065
+H            1.22406207106805        1.27101308096889        0.68543725398568
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/optimized output_molecule2.xyz b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/optimized output_molecule2.xyz
new file mode 100644
index 00000000000000..3e6ed29691177a
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/optimized output_molecule2.xyz	
@@ -0,0 +1,8 @@
+6
+Ethylene
+C            0.64050394264034       -0.06195626374559        0.02162210289734
+C           -0.66870394338320        0.07495626442421       -0.02282210298372
+H            1.10630539081012       -1.02923825600580        0.13097139036430
+H            1.30240727798108        0.78739374254428       -0.04944397971892
+H           -1.33060727717449       -0.77439374475875        0.04824397996319
+H           -1.13450539087384        1.04223825754165       -0.13217139052219
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/res output_TMP.1_qcxms.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/res output_TMP.1_qcxms.txt
new file mode 100644
index 00000000000000..157a9a513b4717
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/res output_TMP.1_qcxms.txt	
@@ -0,0 +1 @@
+ 1.0000000  1    1    1 1 2    1     4  6   2
diff --git a/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/res output_TMP.2_qcxms.txt b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/res output_TMP.2_qcxms.txt
new file mode 100644
index 00000000000000..041cba9d91c263
--- /dev/null
+++ b/topics/metabolomics/tutorials/qcxms-predictions/workflows/test-data/res output_TMP.2_qcxms.txt	
@@ -0,0 +1 @@
+ 1.0000000  1    2    1 1 2    1     4  6   2
diff --git a/topics/microbiome/metadata.yaml b/topics/microbiome/metadata.yaml
index 16a4f5639f788f..32def78281eebb 100644
--- a/topics/microbiome/metadata.yaml
+++ b/topics/microbiome/metadata.yaml
@@ -44,3 +44,6 @@ subtopics:
   - id: metatranscriptomics
     title: "Metatranscriptomics"
     description: "Taxonomic and functional characterisation of mixed samples using transcriptome data."
+  - id: clinical-metaproteomics
+    title: "Metaproteomics"
+    description: "These tutorials are step by step analysis from database generation to the discovery of peptides to verification, quantitation, and interpretation of the results."
diff --git a/topics/microbiome/tutorials/clinical-mp-1-database-generation b/topics/microbiome/tutorials/clinical-mp-1-database-generation
new file mode 120000
index 00000000000000..421f5cfba07136
--- /dev/null
+++ b/topics/microbiome/tutorials/clinical-mp-1-database-generation
@@ -0,0 +1 @@
+../../proteomics/tutorials/clinical-mp-1-database-generation
\ No newline at end of file
diff --git a/topics/microbiome/tutorials/clinical-mp-2-discovery b/topics/microbiome/tutorials/clinical-mp-2-discovery
new file mode 120000
index 00000000000000..830b4e684ac1fd
--- /dev/null
+++ b/topics/microbiome/tutorials/clinical-mp-2-discovery
@@ -0,0 +1 @@
+../../proteomics/tutorials/clinical-mp-2-discovery/
\ No newline at end of file
diff --git a/topics/microbiome/tutorials/clinical-mp-3-verification b/topics/microbiome/tutorials/clinical-mp-3-verification
new file mode 120000
index 00000000000000..2d061df6efe0a5
--- /dev/null
+++ b/topics/microbiome/tutorials/clinical-mp-3-verification
@@ -0,0 +1 @@
+../../proteomics/tutorials/clinical-mp-3-verification
\ No newline at end of file
diff --git a/topics/microbiome/tutorials/clinical-mp-4-quantitation b/topics/microbiome/tutorials/clinical-mp-4-quantitation
new file mode 120000
index 00000000000000..6c0c735ea0893f
--- /dev/null
+++ b/topics/microbiome/tutorials/clinical-mp-4-quantitation
@@ -0,0 +1 @@
+../../proteomics/tutorials/clinical-mp-4-quantitation
\ No newline at end of file
diff --git a/topics/microbiome/tutorials/clinical-mp-5-data-interpretation b/topics/microbiome/tutorials/clinical-mp-5-data-interpretation
new file mode 120000
index 00000000000000..ecb968f34ada7a
--- /dev/null
+++ b/topics/microbiome/tutorials/clinical-mp-5-data-interpretation
@@ -0,0 +1 @@
+../../proteomics/tutorials/clinical-mp-5-data-interpretation
\ No newline at end of file
diff --git a/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-build-list-of-dataset-pairs.png b/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-build-list-of-dataset-pairs.png
index c1a59b8c6eb1bb..238f39f9fb32d4 100644
Binary files a/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-build-list-of-dataset-pairs.png and b/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-build-list-of-dataset-pairs.png differ
diff --git a/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-download-zip.png b/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-download-zip.png
index b6b878bee95aa5..3fae65e55cf9b0 100644
Binary files a/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-download-zip.png and b/topics/microbiome/tutorials/lotus2-identifying-fungi/images/history-download-zip.png differ
diff --git a/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md b/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md
index 5370eb96c1e146..2fe9e717113a8b 100644
--- a/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md
+++ b/topics/proteomics/tutorials/clinical-mp-1-database-generation/tutorial.md
@@ -52,13 +52,13 @@ Metaproteomics is the large-scale characterization of the entire complement of p
 
 To address this, we used tandem mass spectrometry (MS/MS) and bioinformatics tools on the Galaxy platform to develop a metaproteomics workflow to characterize the metaproteomes of clinical samples. This clinical metaproteomics workflow holds potential for general clinical applications such as potential secondary infections during COVID-19 infection, microbiome changes during cystic fibrosis as well as broad research questions regarding host-microbe interactions.
 
-![Clinical Metaproteomics workflow](../../images/clinical-mp/clinical-mp-overview.JPG)
+![Clinical Metaproteomics workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-overview.JPG %})
 
 
 The first workflow for the clinical metaproteomics data analysis is the Database generation workflow. The Galaxy-P team has developed a workflow wherein a large database is generated by downloading protein sequences of known disease-causing microorganisms and then generating a compact database from the comprehensive database using the Metanovo tool.
 
 
-![Database Generation Workflow](../../images/clinical-mp/clinical-mp-database-generation.JPG)
+![Database Generation Workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-database-generation.JPG %})
 
 
 
diff --git a/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md b/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md
index 6c12c62c79b1cc..2c9ade8468656d 100644
--- a/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md
+++ b/topics/proteomics/tutorials/clinical-mp-2-discovery/tutorial.md
@@ -58,7 +58,7 @@ This tutorial can be followed with any user-defined database but would work best
 The MSMS data will be searched against the compact database `Human UniProt Microbial Proteins (from MetaNovo) and cRAP` to identify peptide and protein sequences via sequence database searching. For this tutorial, two peptide identification programs will be used: SearchGUI/PeptideShaker and MaxQuant. However, you could use other software too, such as Fragpipe or Scribe. For the purpose of this tutorial, a dataset of the 4 RAW/MGF files will be used as the MS/MS input.
 
 
-![Discovery Workflow](../../images/clinical-mp/clinical-mp-discovery.JPG)
+![Discovery Workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-discovery.JPG %})
 
 
 > <agenda-title></agenda-title>
diff --git a/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md b/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md
index e8f53edee6b3f4..a75ecd0060cc01 100644
--- a/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md
+++ b/topics/proteomics/tutorials/clinical-mp-3-verification/tutorial.md
@@ -56,9 +56,9 @@ The PepQuery tool is used to validate the identified microbial peptides from Sea
 
 Interestingly, the PepQuery tool does not rely on searching peptides against a reference protein sequence database as “traditional” shotgun proteomics does, which enables it to identify novel, disease-specific sequences with sensitivity and specificity in its protein validation (Figure A). Then we extract microbial protein sequences that are assigned to the PepQuery verified peptides. To this, we again add the Human UniProt Reference proteome (with Isoforms) and cRAP databases for creating a database for quantitation purposes (Figure B).
 
-![Peptide Verification](../../images/clinical-mp/clinical-mp-verification-1.JPG)
+![Peptide Verification]({% link topics/proteomics/images/clinical-mp/clinical-mp-verification-1.JPG %})
 
-![Database generation from verified peptides](../../images/clinical-mp/clinical-mp-verification-2.JPG)
+![Database generation from verified peptides]({% link topics/proteomics/images/clinical-mp/clinical-mp-verification-2.JPG %})
 
 
 > <agenda-title></agenda-title>
diff --git a/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md b/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md
index f5719c65e29a73..2f9cdaab2cf8df 100644
--- a/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md
+++ b/topics/proteomics/tutorials/clinical-mp-4-quantitation/tutorial.md
@@ -51,7 +51,7 @@ The next step of the clinical metaproteomics workflow is the quantification work
 
 In this current workflow, we perform Quantification using the MaxQuant tool and the output will be interpreted in our next module.
 
-![Quantitation workflow](../../images/clinical-mp/clinical-mp-quantification.JPG)
+![Quantitation workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-quantification.JPG %})
 
 
 
diff --git a/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md b/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md
index 9f635be84ccc3e..7be1e16f311331 100644
--- a/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md
+++ b/topics/proteomics/tutorials/clinical-mp-5-data-interpretation/tutorial.md
@@ -50,7 +50,8 @@ recordings:
 
 The final workflow in the array of clinical metaproteomics tutorials is the data interpretation workflow. Interpreting MaxQuant data using MSstats involves applying a rigorous statistical framework to glean meaningful insights from quantitative proteomic datasets. The MaxQuant output is explored to understand data distribution and variability. Subsequent normalization helps account for systematic variations. MSstats allows the user to define the experimental design, including sample groups and conditions, to perform statistical analysis. The output provides valuable information about differential protein expression across conditions, estimates of fold changes, and associated p-values, aiding in the identification of biologically significant proteins. Furthermore, MSstats enables quality control and data visualization, ultimately enhancing our ability to draw meaningful conclusions from complex proteomic datasets. Additional tutorial material for using MaxQuant and MSstatTMT for TMT data analysis can be found at [MaxQuant and MSstats for the analysis of TMT data](https://gxy.io/GTN:T00220).
 
-![Data-Interpretation-workflow](../../images/clinical-mp/clinical-mp-data-interpretation.JPG)
+![Data-Interpretation-workflow]({% link topics/proteomics/images/clinical-mp/clinical-mp-data-interpretation.JPG %})
+
 > <agenda-title></agenda-title>
 >
 > In this tutorial, we will cover:
@@ -131,7 +132,7 @@ Unipept serves as a vital bioinformatics platform for the analysis of mass spect
 >
 {: .hands_on}
 
-![Data-Interpretation with Unipept](../../images/clinical-mp/clinical-mp-data-interpretation-figure2.jpg)
+![Data-Interpretation with Unipept]({% link topics/proteomics/images/clinical-mp/clinical-mp-data-interpretation-figure2.jpg %})
 
 ## Extraction of Microbial Sequences
 
@@ -222,7 +223,7 @@ MSstats TMT(Tandem Mass Tag) is a computational tool designed for the robust sta
 The MSstats output typically includes essential information such as estimated fold changes, p-values, and other statistical measures that help identify differentially expressed proteins across experimental conditions or sample groups. It provides a clear picture of the variations in protein expression levels, aiding in the prioritization of biologically relevant targets. MSstats output also often includes visualizations and quality control metrics, making it a valuable resource for researchers in their quest to extract meaningful insights from complex proteomic datasets and understand the underlying biology of their experiments.
 Example of our data interpretation:
 
-![Data-Interpretation results with MSstats](../../images/clinical-mp/clinical-mp-data-interpretation-figure3.jpg)
+![Data-Interpretation results with MSstats]({% link topics/proteomics/images/clinical-mp/clinical-mp-data-interpretation-figure3.jpg %})
 
 
 # Conclusion
diff --git a/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.md b/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.md
index 846d0bdb7cc3d4..1296744da839ed 100644
--- a/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.md
+++ b/topics/sequence-analysis/tutorials/quality-contamination-control/tutorial.md
@@ -487,7 +487,7 @@ __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken
    > > <solution-title></solution-title>
    > >
    > > 1. 119, as the number of lines
-   > > 2. Staphylococcus, with 99.04%!
+   > > 2. Staphylococcaceae, with 99.06%!
    > {: .solution}
    >
    {: .question}
@@ -610,4 +610,4 @@ Additionally, it's important to document and **report the contamination findings
 
 In this tutorial, we inspected the quality of the bacterial isolate sequencing data and checked the expected species and potential contamination. Prepared short reads **can be used in downstream analysis**, like [Genome Assembly]({% link topics/assembly/tutorials/mrsa-illumina/tutorial.md %}). Even after the assembly, you can identify contamination using a tool like **CheckM** ({% cite Parks_2015 %}). **CheckM** {% icon tool %} can be used for screening the 'cleanness' of bacterial assemblies.
 
-To learn more about data quality control, you can follow this tutorial: [Quality Control]({% link topics/sequence-analysis/tutorials/quality-control/tutorial.md %}).
\ No newline at end of file
+To learn more about data quality control, you can follow this tutorial: [Quality Control]({% link topics/sequence-analysis/tutorials/quality-control/tutorial.md %}).
diff --git a/topics/sequence-analysis/tutorials/quality-control/tutorial.md b/topics/sequence-analysis/tutorials/quality-control/tutorial.md
index 2e1d2131178dce..51d8629eac4fef 100644
--- a/topics/sequence-analysis/tutorials/quality-control/tutorial.md
+++ b/topics/sequence-analysis/tutorials/quality-control/tutorial.md
@@ -1,44 +1,41 @@
 ---
 layout: tutorial_hands_on
-
-title: "Quality Control"
-zenodo_link: "https://zenodo.org/records/61771"
+title: Quality Control
+zenodo_link: https://zenodo.org/records/61771
 questions:
-  - How to perform quality control of NGS raw data?
-  - What are the quality parameters to check for a dataset?
-  - How to improve the quality of a dataset?
+- How to perform quality control of NGS raw data?
+- What are the quality parameters to check for a dataset?
+- How to improve the quality of a dataset?
 objectives:
-  - Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
-  - Assess long reads FASTQ quality using Nanoplot and PycoQC
-  - Perform quality correction with Cutadapt (short reads)
-  - Summarise quality metrics MultiQC
-  - Process single-end and paired-end data
+- "Assess short reads FASTQ quality using FASTQE \U0001F9EC\U0001F60E and FastQC"
+- Assess long reads FASTQ quality using Nanoplot and PycoQC
+- Perform quality correction with Cutadapt (short reads)
+- Summarise quality metrics MultiQC
+- Process single-end and paired-end data
 follow_up_training:
-  -
-    type: "internal"
-    topic_name: sequence-analysis
-    tutorials:
-      - mapping
-time_estimation: "1H30M"
+- type: internal
+  topic_name: sequence-analysis
+  tutorials:
+  - mapping
+time_estimation: 1H30M
 level: Introductory
 key_points:
-  - Perform quality control on every dataset before running any other bioinformatics analysis
-  - Assess the quality metrics and improve quality if necessary
-  - Check the impact of the quality control
-  - Different tools are available to provide additional quality metrics
-  - For paired-end reads analyze the forward and reverse reads together
+- Perform quality control on every dataset before running any other bioinformatics
+  analysis
+- Assess the quality metrics and improve quality if necessary
+- Check the impact of the quality control
+- Different tools are available to provide additional quality metrics
+- For paired-end reads analyze the forward and reverse reads together
 contributors:
-  - bebatut
-  - mblue9
-  - alexcorm
-  - abretaud
-  - lleroi
-  - r1corre
-  - stephanierobin
-  - gallantries
-  - neoformit
-
-
+- bebatut
+- mblue9
+- alexcorm
+- abretaud
+- lleroi
+- r1corre
+- stephanierobin
+- gallantries
+- neoformit
 recordings:
 - youtube_id: coaMGvZazoc
   length: 50M
@@ -57,10 +54,21 @@ recordings:
   youtube_id: QJRlX2hWDKM
   speakers:
   - heylf
+- youtube_id: uiWZea53QIA
+  length: 51M
+  galaxy_version: 24.1.2.dev0
+  date: '2024-09-30'
+  speakers: 
+  - dianichj
+  captioners: 
+  - dianichj
+  bot-timestamp: 1727710795
+
 
 ---
 
 
+
 During sequencing, the nucleotide bases in a DNA or RNA sample (library) are determined by the sequencer. For each fragment in the library, a sequence is generated, also called a **read**, which is simply a succession of nucleotides.
 
 Modern sequencing technologies can generate a massive number of sequence reads in a single experiment. However, no sequencing technology is perfect, and each instrument will generate different types and amount of errors, such as incorrect nucleotides being called. These wrongly called bases are due to the technical limitations of each sequencing platform.
diff --git a/topics/single-cell/faqs/single_cell_omics.md b/topics/single-cell/faqs/single_cell_omics.md
index ca249a590734b7..8adb632a0ff96c 100644
--- a/topics/single-cell/faqs/single_cell_omics.md
+++ b/topics/single-cell/faqs/single_cell_omics.md
@@ -10,6 +10,8 @@ Did you know we have a unique Single Cell Omics Lab with all our single cell too
 
 The Single Cell Omics Lab currently uses the main European Galaxy infrastructure and power, it's just organised better for users of particular analyses...like single cell!
 
-Try it out! All your histories/workflows/logins from the general [European Galaxy server](https://usegalaxy.eu) will be there!
+Try it out!
 
- - {% icon subdomain %} [Single Cell Omics Lab](https://singlecell.usegalaxy.eu)
+ - {% icon subdomain %} [Europe | Single Cell Omics Lab](https://singlecell.usegalaxy.eu)
+ - {% icon subdomain %} [USA | Single Cell Omics Lab](https://singlecell.usegalaxy.org)
+ - {% icon subdomain %} [Australia | Single Cell Omics Lab](https://singlecell.usegalaxy.org.au)
diff --git a/topics/single-cell/metadata.yaml b/topics/single-cell/metadata.yaml
index 74be3ee8843e13..56d84d08957db2 100644
--- a/topics/single-cell/metadata.yaml
+++ b/topics/single-cell/metadata.yaml
@@ -26,7 +26,7 @@ editorial_board:
 subtopics:
   - id: scintroduction
     title: "Introduction"
-    description: "Start here if you are new to single cell analysis in Galaxy and want to learn the concepts."
+    description: "Start here if you are new to single cell analysis and want to learn the concepts."
   - id: firstsc
     title: "Your first analysis"
     description: "Start here if you are new to single cell analysis in Galaxy and want to try analysing data."
@@ -51,7 +51,9 @@ subtopics:
   - id: datamanipulation
     title: "Changing data formats & preparing objects"
     description: "These tutorials cover a range of needs for importing data from different sources, to changing data into different formats to move from one analysis to the other."
-
+  - id: exploratory
+    title: "Exploratory Analyses"
+    description: "What do you do with your list of genes? Come here to explore your results more!"
 
 
 references:
diff --git a/topics/single-cell/tutorials/GO-enrichment/tutorial.md b/topics/single-cell/tutorials/GO-enrichment/tutorial.md
index 7fb0e6343736d9..5c48b24872ab73 100644
--- a/topics/single-cell/tutorials/GO-enrichment/tutorial.md
+++ b/topics/single-cell/tutorials/GO-enrichment/tutorial.md
@@ -3,6 +3,7 @@ layout: tutorial_hands_on
 
 title: GO Enrichment Analysis on Single-Cell RNA-Seq Data
 zenodo_link: 'https://zenodo.org/records/13461890'
+subtopic: exploratory
 
 questions:
 - What is Gene Ontology (GO) enrichment analysis, and why should I perform it on my marker genes?
@@ -32,9 +33,13 @@ key_points:
 - GO enrichment helps make sense of your data and understand what makes each cell cluster/condition unique.
 - GO enrichment analysis is used to discover new insights about how cells work, which can lead to better understanding of biological processes and diseases.
   
-contributors:
-- nomadscientist
-- MennaGamal
+contributions:
+ authorship:
+  - MennaGamal
+ editing:
+  - nomadscientist
+  - bgruening
+  - pcm32
 ---
 
 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.
diff --git a/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html b/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html
index dc127f0a5a2338..241bb2e5a83775 100644
--- a/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html
+++ b/topics/single-cell/tutorials/scrna-case-cell-annotation/slides.html
@@ -15,7 +15,7 @@
   - "Recognise the common issues and be able to resolve them"
 
 subtopic: scintroduction
-priority: 4
+priority: 6
 
 tags:
 
diff --git a/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md b/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md
index 75d7764cbf0723..a13ffffb591e55 100644
--- a/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md
+++ b/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md
@@ -1,72 +1,56 @@
 ---
 layout: tutorial_hands_on
-
-title: "Generating a single cell matrix using Alevin"
+title: Generating a single cell matrix using Alevin
 subtopic: single-cell-CS
 priority: 1
-
-zenodo_link: 'https://zenodo.org/record/4574153'
-
+zenodo_link: https://zenodo.org/record/4574153
 redirect_from:
-- /topics/transcriptomics/tutorials/droplet-quantification-preprocessing/tutorial
-- /topics/transcriptomics/tutorials/scrna-case_alevin/tutorial
-
+- "/topics/transcriptomics/tutorials/droplet-quantification-preprocessing/tutorial"
+- "/topics/transcriptomics/tutorials/scrna-case_alevin/tutorial"
 questions:
-  - I have some single cell FASTQ files I want to analyse. Where do I start?
-
+- I have some single cell FASTQ files I want to analyse. Where do I start?
 answer_histories:
-  - label: "UseGalaxy.eu"
-    history: https://usegalaxy.eu/u/j.jakiela/h/generating-a-single-cell-matrix-alevin
-    date: 2024-03-22
-  - label: "Older Alevin version"
-    history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---answer-key
-    date: 2024-01-01
-
+- label: UseGalaxy.eu
+  history: https://usegalaxy.eu/u/j.jakiela/h/generating-a-single-cell-matrix-alevin
+  date: 2024-03-22
+- label: Older Alevin version
+  history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---answer-key
+  date: 2024-01-01
 input_histories:
-  - label: "UseGalaxy.eu"
-    history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---input-1
-
+- label: UseGalaxy.eu
+  history: https://humancellatlas.usegalaxy.eu/u/wendi.bacon.training/h/cs1pre-processing-with-alevin---input-1
 objectives:
-  - Generate a cellxgene matrix for droplet-based single cell sequencing data
-  - Interpret quality control (QC) plots to make informed decisions on cell thresholds
-  - Find relevant information in GTF files for the particulars of their study, and include this in data matrix metadata
-
+- Generate a cellxgene matrix for droplet-based single cell sequencing data
+- Interpret quality control (QC) plots to make informed decisions on cell thresholds
+- Find relevant information in GTF files for the particulars of their study, and include
+  this in data matrix metadata
 time_estimation: 2H
-
 key_points:
-  - Create a scanpy-accessible AnnData object from FASTQ files, including relevant gene metadata
-
+- Create a scanpy-accessible AnnData object from FASTQ files, including relevant gene
+  metadata
 tags:
-  - 10x
-  - paper-replication
-
+- 10x
+- paper-replication
 contributions:
   authorship:
-    - nomadscientist
-    - pinin4fjords
-
+  - nomadscientist
+  - pinin4fjords
   editing:
-    - hexylena
-
+  - hexylena
   testing:
-    - wee-snufkin
-
+  - wee-snufkin
 requirements:
-  - type: "internal"
-    topic_name: single-cell
-    tutorials:
-        - scrna-intro
-        - scrna-umis
-
+- type: internal
+  topic_name: single-cell
+  tutorials:
+  - scrna-intro
+  - scrna-umis
 gitter: Galaxy-Training-Network/galaxy-single-cell
-
 follow_up_training:
-  -
-    type: "internal"
-    topic_name: single-cell
-    tutorials:
-        - scrna-case_alevin-combine-datasets
-
+- type: internal
+  topic_name: single-cell
+  tutorials:
+  - scrna-case_alevin-combine-datasets
 recordings:
 - captioners:
   - nomadscientist
@@ -76,10 +60,21 @@ recordings:
   youtube_id: 3ytm2AU6QUc
   speakers:
   - nomadscientist
+- youtube_id: BQriffof8yM
+  length: 49M
+  galaxy_version: 24.1.2.dev0
+  date: '2024-09-12'
+  speakers:
+  - MarisaJL
+  captioners:
+  - MarisaJL
+  bot-timestamp: 1726104172
+
 
 ---
 
 
+
 This tutorial will take you from raw FASTQ files to a cell x gene data matrix in AnnData format. What's a data matrix, and what's AnnData format? Well you'll find out! Importantly, this is the first step in processing single cell data in order to start analysing it. Currently you have a bunch of strings of `ATGGGCTT` etc. in your sequencing files, and what you need to know is how many cells you have and what genes appear in those cells. These steps are the most computationally heavy in the single cell world, as you're starting with 100s of millions of reads, each with 4 lines of text. Later on in analysis, this data becomes simple gene counts such as 'Cell A has 4 GAPDHs', which is a lot easier to store! Because of this data overload, we have downsampled the FASTQ files to speed up the analysis a bit. Saying that, you're still having to map loads of reads to the massive murine genome, so get yourself a cup of coffee and prepare to analyse!
 
 > <agenda-title></agenda-title>
diff --git a/topics/single-cell/tutorials/scrna-data-formats/slides.html b/topics/single-cell/tutorials/scrna-data-formats/slides.html
index f7b98fca33e418..598498f21dcd5d 100644
--- a/topics/single-cell/tutorials/scrna-data-formats/slides.html
+++ b/topics/single-cell/tutorials/scrna-data-formats/slides.html
@@ -1,585 +1,590 @@
----
-layout: tutorial_slides
-logo: "GTN"
-
-title: "Single-cell Formats and Resources"
-questions:
-  - "What does single-cell data look like?"
-  - "What are the common single-cell formats?"
-  - "Where can public single-cell data be found?"
-objectives:
-  - "Learn the basic structure of single-cell data"
-  - "Be able to recognise different types of single-cell metadata"
-  - "Learn the common data formats used for storing single-cell data"
-  - "Become familiar with some popular sources for public single-cell data"
-
-subtopic: scintroduction
-priority: 4
-
-tags:
-
-key_points:
-  - "Single-cell data consists of four main sections"
-  - "Single-cell data can come in a variety of formats"
-  - "Different data formats are required for different languages and packages"
-  - "There are various public single-cell atlases that consist of useful data"
-contributors:
-  - hexhowells
-  - nomadscientist
-  - kkamieniecka
-requirements:
-  -
-    type: "internal"
-    topic_name: single-cell
-    tutorials:
-        - scrna-intro
-
----
-
-# Breakdown of single-cell data
-
-.pull-center[
-
-*How is single-cell data structured?*
-
-]
-
----
-
-### Overview of single-cell data
-
-.pull-left[
-
-- Single-cell data consists of 4 main components:
-  - Gene expression matrix
-  - Cell metadata (barcodes)
-  - Gene metadata (features)
-
-  - Unstructured metadata
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
-
-]
-
-???
-
-Single-cell data consists of 4 main components:
-- gene expression data 
-- metadata about each cell
-- metadata about each gene
-- unstructured metadata about the data collected
-
-All complex single-cell data formats will contain these 4 main sections, it's important to understand what these components are, how to identify them, and their importance in single-cell analysis
-
----
-
-### Gene expression matrix (Matrix)
-
-.pull-left[
-
-- 2D matrix of expression values
-  - Rows represent genes
-  - Columns represent cells
-  - Values represent gene expression per cell
-
-- Can be in a full or sparse matrix form
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data with the matrix section highlighted](../../images/scrna-data-formats/gene-expression-diagram-matrix.png)
-
-]
-
-???
-
-The core component of single-cell data is the gene expression matrix. This is a 2D matrix representing the gene expression values for each gene in the sequenced cells.
-
-Typically each row of the matrix represents the genes and the columns represent the cells. However, it is important to note that this is not always the case and some matrices may need to be transposed before further processing.
-
-The matrix can come in two different forms: full or sparse. 
-
-Full matrices act as one large table where every entry contains a value, including genes that were not sequenced (in which case their value will be 0). This is the simplest format but can be inefficient to store as many of the values in the matrix are redundant. 
-
-A more compact method to store these values is with a sparse matrix. This is a compressed representation of the matrix that removes all values that contain no useful information whilst keeping track of the overall structure of the matrix.
-
----
-
-### Cell metadata (Barcodes)
-
-
-.pull-left[
-
-- Cell identifiers/barcodes
-
-- Patient/sample metadata
-
-- Quality control metrics
-
-<br/><br/>
-
-- **Example barcodes:**
-  - AAACCTGAGTCT
-  - TGCATTAGGGA
-  - CCGTTGACCTAC-0
-  - GATCGATCGTCA-0
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data with the cells section highlighted](../../images/scrna-data-formats/gene-expression-diagram-cells.png)
-
-]
-
-???
-
-The first section of metadata that will be explored is the cell metadata, commonly referred to as barcodes when stored in a file. This contains metadata on each cell. This metadata includes the cell identifiers/barcodes, data on the origin of each cell, and quality control metrics generated with further analysis tools.
-
-Cell barcodes consist of the 4 nucleotide letters and occasionally suffixed with a dash and a number (which has various meanings). If a file contains a column containing data in this format, then you are likely looking at cell metadata!
-
----
-
-### Gene metadata (Features)
-
-.pull-left[
-
-- Gene identifiers/Ensembl IDs
-
-- Expression metrics
-
-- Quality control metrics
-
-<br/><br/>
-
-- **Example gene identifiers:**
-
-Gene Symbol | Ensembl ID
----		      | ---
-AIF1        | ENSG00000204472
-ACOXL-AS1	  | ENSG00000204581
-RPS27P25	  | ENSG00000233247
-AGR2	      | ENSG00000106541
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data with the genes section highlighted](../../images/scrna-data-formats/gene-expression-diagram-genes.png)
-
-]
-
-???
-
-The next section of metadata to explore is the gene metadata. Commonly referred to as features when stored in a file. This contains metadata about each gene that was sequenced. This metadata includes gene identifiers/ensembl IDs, expression metrics and quality control metrics, both generated with additional analysis tools. 
-
-Gene identifiers typically consist of a sequence of letters and numbers. If a column in your file contains these types of values then your likely looking at the gene metadata! (If you're unsure, performing an internet search on one of the potential gene names will likely reveal whether that string represents a gene)
-
----
-
-### Unstructured metadata
-
-.pull-left[
-
-- Batch/replicate information
-
-- Sequencing platform
-
-- Data / time
-
-- Tissue source
-
-<br/><br/>
-
-- **Example sequencing platforms:**
-  - 10X Chromium
-
-  - Smart-Seq
-
-  - inDrop
-
-  - Cell-Seq
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data with the other metadata section highlighted](../../images/scrna-data-formats/gene-expression-diagram-metadata.png)
-
-]
-
-???
-
-The last section of metadata to explore is the unstructured metadata. This is data that is not associated with an individual cell or gene but instead the data as a whole. Because of this, the metadata contained here can vary, but may include information such as when the cells were sequenced, what sequencing platform was used, the source of the cell samples, etc.
-
----
-
-# Common single-cell data formats
-
-.pull-center[
-
-*What are the different formats for storing single-cell data?*
-
-]
-
----
-
-### Overview of the most common single-cell data formats
-
-- Tabular
-
-- Matrix Market (MTX)
-
-- AnnData
-
-- Loom
-
-- Seurat
-
-- Single Cell Experiment (SCE)
-
-- CellDataSet (CDS)
-
----
-
-## Basic single-cell formats
-
-???
-
-The following formats are fairly basic and only store sections of single-cell data (expression matrix, metadata, etc.)
-
----
-
-### Tabular
-
-.pull-left[
-
-- Most basic format
-
-- Stores all data in a single file
-
-- Contains:
-  - Gene identifiers
-  - Cell identifiers
-  - Expression values
-
-<br/><br/>
-
-- **File extensions:**
-  - .csv
-  - .tsv
-  - .txt
-
-]
-
-.pull-right[
-
-![Example of some single-cell data in tabular format](../../images/scrna-data-formats/tabular-example.png)
-
-]
-
-???
-
-Tabular files are the most basic format for storing single-cell data. This format stores: expression matrix, cell identifiers, and gene identifiers in a single file. The data is separated with either commas (for .csv) or tabs (for .tsv/Tabular).
-
-Whilst being simplistic and easy to read, the tabular format does have limitations, mainly its inability to store any additional metadata outside of the gene or cell identifiers.
-
----
-
-### Matrix Market (MTX)
-
-.pull-left[
-
-- Sparse matrix format
-
-- More compact way to store larger matrices with many 0 values
-
-- Doesn't include any metadata
-
-<br/><br/>
-
-- **File extensions:**
-  - .mtx
-  - .mm
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data with the matrix section highlighted](../../images/scrna-data-formats/gene-expression-diagram-matrix.png)
-
-]
-
-???
-
-The Matrix Market format (MTX) is common for storing the gene expression matrix. This stores the expression data in the more compressed sparse matrix form. This type of file will likely be found alongside two additional files for the cell metadata (barcodes) and the gene metadata (features).
-
----
-
-## Complex single-cell formats
-
-???
-
-The following formats/objects are more complex and can all support storing all 4 core sections of single-cell data:
-- Gene expression matrix
-- Cell metadata
-- Gene metadata
-- Unstructured metadata
-
----
-
-### AnnData
-
-
-.pull-left[
-
-- Python-based single-cell format
-
-- Cell metadata (obs)
-
-- Gene metadata (var)
-
-<br/><br/>
-
-- **File extensions:**
-  - .h5ad
-
-]
-
-.pull-right[
-
-![Diagram showing an overview of the AnnData format](../../images/scrna-data-formats/anndata-object.png)
-
-]
-
-???
-
-The AnnData format is a Python-based single-cell object built upon the HDF5 format. The primary library for performing single-cell analysis with AnnData objects is Scanpy.
-
-It's important to note that AnnData does not support sparse matrices, therefore large single-cell files with many expression values of zero will not be very efficient to store in memory.
-
-**Supported languages:** Python
-
-**Supported packages:** anndata
-
----
-
-### Loom
-
-.pull-left[
-
-- Based on HDF5 standard
-
-
-- Efficient for large datasets
-
-- Supports sparse matrices and hierarchical data
-
-- Graph objects for graphs and clusters
-
-<br/><br/>
-
-- **File extensions:**
-  - .loom
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
-
-]
-
-???
-
-Loom is another format based on HDF5. Loom objects are supported in various different programming languages and supports sparse matrices making it efficient for large data files.
-
-**Supported languages:** 
-- Python
-- R
-- MATLAB
-- Mathematica
-- C
-- C++
-- Java
-- Ruby
-
-**Supported packages:**
-- LoomPy (Python)
-- LoomR (R)
-
----
-
-### Seurat
-
-.pull-left[
-
-- R-based format used by the Seurat package
-
-- Allows for easy integration with the suite of Seurat tools
-
-<br/><br/>
-
-- **File extensions:**
-  - .rds
-  - .rda
-  - .rdata
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
-
-]
-
-???
-
-Seurat is an R-based format that is commonly used with the Seurat package (the naming convention is a bit confusing!). This is a software package that contains various processing and analysis tools for single-cell data.
-
-**Supported languages:** R
-
-**Supported packages:** seurat
-
----
-
-### Single Cell Experiment (SCE)
-
-.pull-left[
-
-- R-based format provided by the Bioconductor package SingleCellExperiment
-
-- Integrates well with the Bioconductor ecosystem
-
-<br/><br/>
-
-- **File extensions:**
-  - .rds
-  - .rda
-  - .rdata
-
-]
-
-
-.pull-right[
-
-![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
-
-]
-
-???
-
-Single Cell Experiment (SCE) is another R-based format that is widely used within the Bioconductor ecosystem of tools for both processing and analysis of single-cell data.
-
-**Supported languages:** R
-
-**Supported packages:** SingleCellExperiment
-
----
-
-### CellDataSet (CDS)
-
-.pull-left[
-
-- R-based format used by the Monocle package
-
-- Integrates well with the Monocle package for various types of analysis
-
-<br/><br/>
-
-- **File extensions:**
-  - .rds
-  - .rda
-  - .rdata
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
-
-]
-
-???
-
-CellDataSet (CDS) is the last of the R-based formats that is commonly used within the Monocle package which contains tools for performing various types of analysis.
-
-**Supported languages:** R
-
-**Supported packages:** monocle
-
----
-
-# Sources of single-cell data
-
-.pull-center[
-
-*Where are some sources to acquire public single-cell data?*
-
-]
-
----
-
-### Data sources
-
-Repository | Link to resource
---- | ---
-NCBI | https://www.ncbi.nlm.nih.gov/
-Human Cell Atlas: Data Explorer | https://explore.data.humancellatlas.org/projects
-CellXGene Collection | https://cellxgene.cziscience.com/datasets 
-Single Cell Portal | https://singlecell.broadinstitute.org/single_cell
-EBI Single Cell Expression Atlas | https://www.ebi.ac.uk/gxa/sc/home
-
-???
-
-There are many publicly available sources for reusable single-cell data. The table shows some common sources for acquiring this data.
-
-This table is not extensive and there are many other resources available!
-
----
-
-### Compressed Files
-
-.pull-left[
-
-- Some files may be compressed/archived
-
-- Requires decompression/extraction to process the data
-
-- May receive errors if left compressed
-
-<br/><br/>
-
-- **File extensions:**
-  - .zip
-  - .gz
-  - .tar
-  - .tar.gz
-
-]
-
-.pull-right[
-
-![Basic structure of single-cell data](../../images/scrna-data-formats/unzip-tool.png)
-
-]
-
-???
-
-It is common for single-cell data to be stored in a compressed format in order to reduce filesizes and make transferring the data simpler. It is important to recognise when a file is compressed as using compressed data in downstream tools may cause errors to occur.
-
-There are two forms of compression that are common:
-
-- Individual file compression
-- Archive of multiple files
-
-Individual files are typically compressed with zip (.zip) or gzip (.gz) and should be unzipped prior to processing.
-
-When data is stored in multiple files/folders they may be archived into a single file (commonly called a tarball), this is done with the TAR tool (.tar). Like individual file compression, you will need to untar/extract the .tar file before further processing.
-
-Finally, it is also common for multiple files/folders to both be compressed and archived into a single file, this is indicated with the extension .tar.gz. In this case the data will need to be **both** unzipped and then extracted before the data is available in it's original form.
+---
+layout: tutorial_slides
+logo: GTN
+title: Single-cell Formats and Resources
+questions:
+- What does single-cell data look like?
+- What are the common single-cell formats?
+- Where can public single-cell data be found?
+objectives:
+- Learn the basic structure of single-cell data
+- Be able to recognise different types of single-cell metadata
+- Learn the common data formats used for storing single-cell data
+- Become familiar with some popular sources for public single-cell data
+subtopic: scintroduction
+priority: 4
+tags:
+key_points:
+- Single-cell data consists of four main sections
+- Single-cell data can come in a variety of formats
+- Different data formats are required for different languages and packages
+- There are various public single-cell atlases that consist of useful data
+contributors:
+- hexhowells
+- nomadscientist
+- kkamieniecka
+requirements:
+- type: internal
+  topic_name: single-cell
+  tutorials:
+  - scrna-intro
+recordings:
+- youtube_id: _GsIlvWxdFw
+  length: 19M
+  date: '2024-09-30'
+  speakers:
+  - hexhowells
+  captioners:
+  - hexhowells
+  bot-timestamp: 1727719569
+
+
+---
+
+# Breakdown of single-cell data
+
+.pull-center[
+
+*How is single-cell data structured?*
+
+]
+
+---
+
+### Overview of single-cell data
+
+.pull-left[
+
+- Single-cell data consists of 4 main components:
+  - Gene expression matrix
+  - Cell metadata (barcodes)
+  - Gene metadata (features)
+
+  - Unstructured metadata
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
+
+]
+
+???
+
+Single-cell data consists of 4 main components:
+- gene expression data
+- metadata about each cell
+- metadata about each gene
+- unstructured metadata about the data collected
+
+All complex single-cell data formats will contain these 4 main sections, it's important to understand what these components are, how to identify them, and their importance in single-cell analysis
+
+---
+
+### Gene expression matrix (Matrix)
+
+.pull-left[
+
+- 2D matrix of expression values
+  - Rows represent genes
+  - Columns represent cells
+  - Values represent gene expression per cell
+
+- Can be in a full or sparse matrix form
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data with the matrix section highlighted](../../images/scrna-data-formats/gene-expression-diagram-matrix.png)
+
+]
+
+???
+
+The core component of single-cell data is the gene expression matrix. This is a 2D matrix representing the gene expression values for each gene in the sequenced cells.
+
+Typically each row of the matrix represents the genes and the columns represent the cells. However, it is important to note that this is not always the case and some matrices may need to be transposed before further processing.
+
+The matrix can come in two different forms: full or sparse.
+
+Full matrices act as one large table where every entry contains a value, including genes that were not sequenced (in which case their value will be 0). This is the simplest format but can be inefficient to store as many of the values in the matrix are redundant.
+
+A more compact method to store these values is with a sparse matrix. This is a compressed representation of the matrix that removes all values that contain no useful information whilst keeping track of the overall structure of the matrix.
+
+---
+
+### Cell metadata (Barcodes)
+
+
+.pull-left[
+
+- Cell identifiers/barcodes
+
+- Patient/sample metadata
+
+- Quality control metrics
+
+<br/><br/>
+
+- **Example barcodes:**
+  - AAACCTGAGTCT
+  - TGCATTAGGGA
+  - CCGTTGACCTAC-0
+  - GATCGATCGTCA-0
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data with the cells section highlighted](../../images/scrna-data-formats/gene-expression-diagram-cells.png)
+
+]
+
+???
+
+The first section of metadata that will be explored is the cell metadata, commonly referred to as barcodes when stored in a file. This contains metadata on each cell. This metadata includes the cell identifiers/barcodes, data on the origin of each cell, and quality control metrics generated with further analysis tools.
+
+Cell barcodes consist of the 4 nucleotide letters and occasionally suffixed with a dash and a number (which has various meanings). If a file contains a column containing data in this format, then you are likely looking at cell metadata!
+
+---
+
+### Gene metadata (Features)
+
+.pull-left[
+
+- Gene identifiers/Ensembl IDs
+
+- Expression metrics
+
+- Quality control metrics
+
+<br/><br/>
+
+- **Example gene identifiers:**
+
+Gene Symbol | Ensembl ID
+---		      | ---
+AIF1        | ENSG00000204472
+ACOXL-AS1	  | ENSG00000204581
+RPS27P25	  | ENSG00000233247
+AGR2	      | ENSG00000106541
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data with the genes section highlighted](../../images/scrna-data-formats/gene-expression-diagram-genes.png)
+
+]
+
+???
+
+The next section of metadata to explore is the gene metadata. Commonly referred to as features when stored in a file. This contains metadata about each gene that was sequenced. This metadata includes gene identifiers/ensembl IDs, expression metrics and quality control metrics, both generated with additional analysis tools.
+
+Gene identifiers typically consist of a sequence of letters and numbers. If a column in your file contains these types of values then your likely looking at the gene metadata! (If you're unsure, performing an internet search on one of the potential gene names will likely reveal whether that string represents a gene)
+
+---
+
+### Unstructured metadata
+
+.pull-left[
+
+- Batch/replicate information
+
+- Sequencing platform
+
+- Data / time
+
+- Tissue source
+
+<br/><br/>
+
+- **Example sequencing platforms:**
+  - 10X Chromium
+
+  - Smart-Seq
+
+  - inDrop
+
+  - Cell-Seq
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data with the other metadata section highlighted](../../images/scrna-data-formats/gene-expression-diagram-metadata.png)
+
+]
+
+???
+
+The last section of metadata to explore is the unstructured metadata. This is data that is not associated with an individual cell or gene but instead the data as a whole. Because of this, the metadata contained here can vary, but may include information such as when the cells were sequenced, what sequencing platform was used, the source of the cell samples, etc.
+
+---
+
+# Common single-cell data formats
+
+.pull-center[
+
+*What are the different formats for storing single-cell data?*
+
+]
+
+---
+
+### Overview of the most common single-cell data formats
+
+- Tabular
+
+- Matrix Market (MTX)
+
+- AnnData
+
+- Loom
+
+- Seurat
+
+- Single Cell Experiment (SCE)
+
+- CellDataSet (CDS)
+
+---
+
+## Basic single-cell formats
+
+???
+
+The following formats are fairly basic and only store sections of single-cell data (expression matrix, metadata, etc.)
+
+---
+
+### Tabular
+
+.pull-left[
+
+- Most basic format
+
+- Stores all data in a single file
+
+- Contains:
+  - Gene identifiers
+  - Cell identifiers
+  - Expression values
+
+<br/><br/>
+
+- **File extensions:**
+  - .csv
+  - .tsv
+  - .txt
+
+]
+
+.pull-right[
+
+![Example of some single-cell data in tabular format](../../images/scrna-data-formats/tabular-example.png)
+
+]
+
+???
+
+Tabular files are the most basic format for storing single-cell data. This format stores: expression matrix, cell identifiers, and gene identifiers in a single file. The data is separated with either commas (for .csv) or tabs (for .tsv/Tabular).
+
+Whilst being simplistic and easy to read, the tabular format does have limitations, mainly its inability to store any additional metadata outside of the gene or cell identifiers.
+
+---
+
+### Matrix Market (MTX)
+
+.pull-left[
+
+- Sparse matrix format
+
+- More compact way to store larger matrices with many 0 values
+
+- Doesn't include any metadata
+
+<br/><br/>
+
+- **File extensions:**
+  - .mtx
+  - .mm
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data with the matrix section highlighted](../../images/scrna-data-formats/gene-expression-diagram-matrix.png)
+
+]
+
+???
+
+The Matrix Market format (MTX) is common for storing the gene expression matrix. This stores the expression data in the more compressed sparse matrix form. This type of file will likely be found alongside two additional files for the cell metadata (barcodes) and the gene metadata (features).
+
+---
+
+## Complex single-cell formats
+
+???
+
+The following formats/objects are more complex and can all support storing all 4 core sections of single-cell data:
+- Gene expression matrix
+- Cell metadata
+- Gene metadata
+- Unstructured metadata
+
+---
+
+### AnnData
+
+
+.pull-left[
+
+- Python-based single-cell format
+
+- Cell metadata (obs)
+
+- Gene metadata (var)
+
+<br/><br/>
+
+- **File extensions:**
+  - .h5ad
+
+]
+
+.pull-right[
+
+![Diagram showing an overview of the AnnData format](../../images/scrna-data-formats/anndata-object.png)
+
+]
+
+???
+
+The AnnData format is a Python-based single-cell object built upon the HDF5 format. The primary library for performing single-cell analysis with AnnData objects is Scanpy.
+
+It's important to note that AnnData does not support sparse matrices, therefore large single-cell files with many expression values of zero will not be very efficient to store in memory.
+
+**Supported languages:** Python
+
+**Supported packages:** anndata
+
+---
+
+### Loom
+
+.pull-left[
+
+- Based on HDF5 standard
+
+
+- Efficient for large datasets
+
+- Supports sparse matrices and hierarchical data
+
+- Graph objects for graphs and clusters
+
+<br/><br/>
+
+- **File extensions:**
+  - .loom
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
+
+]
+
+???
+
+Loom is another format based on HDF5. Loom objects are supported in various different programming languages and supports sparse matrices making it efficient for large data files.
+
+**Supported languages:**
+- Python
+- R
+- MATLAB
+- Mathematica
+- C
+- C++
+- Java
+- Ruby
+
+**Supported packages:**
+- LoomPy (Python)
+- LoomR (R)
+
+---
+
+### Seurat
+
+.pull-left[
+
+- R-based format used by the Seurat package
+
+- Allows for easy integration with the suite of Seurat tools
+
+<br/><br/>
+
+- **File extensions:**
+  - .rds
+  - .rda
+  - .rdata
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
+
+]
+
+???
+
+Seurat is an R-based format that is commonly used with the Seurat package (the naming convention is a bit confusing!). This is a software package that contains various processing and analysis tools for single-cell data.
+
+**Supported languages:** R
+
+**Supported packages:** seurat
+
+---
+
+### Single Cell Experiment (SCE)
+
+.pull-left[
+
+- R-based format provided by the Bioconductor package SingleCellExperiment
+
+- Integrates well with the Bioconductor ecosystem
+
+<br/><br/>
+
+- **File extensions:**
+  - .rds
+  - .rda
+  - .rdata
+
+]
+
+
+.pull-right[
+
+![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
+
+]
+
+???
+
+Single Cell Experiment (SCE) is another R-based format that is widely used within the Bioconductor ecosystem of tools for both processing and analysis of single-cell data.
+
+**Supported languages:** R
+
+**Supported packages:** SingleCellExperiment
+
+---
+
+### CellDataSet (CDS)
+
+.pull-left[
+
+- R-based format used by the Monocle package
+
+- Integrates well with the Monocle package for various types of analysis
+
+<br/><br/>
+
+- **File extensions:**
+  - .rds
+  - .rda
+  - .rdata
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data](../../images/scrna-data-formats/gene-expression-diagram.png)
+
+]
+
+???
+
+CellDataSet (CDS) is the last of the R-based formats that is commonly used within the Monocle package which contains tools for performing various types of analysis.
+
+**Supported languages:** R
+
+**Supported packages:** monocle
+
+---
+
+# Sources of single-cell data
+
+.pull-center[
+
+*Where are some sources to acquire public single-cell data?*
+
+]
+
+---
+
+### Data sources
+
+Repository | Link to resource
+--- | ---
+NCBI | https://www.ncbi.nlm.nih.gov/
+Human Cell Atlas: Data Explorer | https://explore.data.humancellatlas.org/projects
+CellXGene Collection | https://cellxgene.cziscience.com/datasets
+Single Cell Portal | https://singlecell.broadinstitute.org/single_cell
+EBI Single Cell Expression Atlas | https://www.ebi.ac.uk/gxa/sc/home
+
+???
+
+There are many publicly available sources for reusable single-cell data. The table shows some common sources for acquiring this data.
+
+This table is not extensive and there are many other resources available!
+
+---
+
+### Compressed Files
+
+.pull-left[
+
+- Some files may be compressed/archived
+
+- Requires decompression/extraction to process the data
+
+- May receive errors if left compressed
+
+<br/><br/>
+
+- **File extensions:**
+  - .zip
+  - .gz
+  - .tar
+  - .tar.gz
+
+]
+
+.pull-right[
+
+![Basic structure of single-cell data](../../images/scrna-data-formats/unzip-tool.png)
+
+]
+
+???
+
+It is common for single-cell data to be stored in a compressed format in order to reduce filesizes and make transferring the data simpler. It is important to recognise when a file is compressed as using compressed data in downstream tools may cause errors to occur.
+
+There are two forms of compression that are common:
+
+- Individual file compression
+- Archive of multiple files
+
+Individual files are typically compressed with zip (.zip) or gzip (.gz) and should be unzipped prior to processing.
+
+When data is stored in multiple files/folders they may be archived into a single file (commonly called a tarball), this is done with the TAR tool (.tar). Like individual file compression, you will need to untar/extract the .tar file before further processing.
+
+Finally, it is also common for multiple files/folders to both be compressed and archived into a single file, this is indicated with the extension .tar.gz. In this case the data will need to be **both** unzipped and then extracted before the data is available in it's original form.
diff --git a/topics/single-cell/tutorials/scrna-preprocessing-tenx/tutorial.md b/topics/single-cell/tutorials/scrna-preprocessing-tenx/tutorial.md
index 8bf1f96f0c83f2..eb50634c83eaf0 100644
--- a/topics/single-cell/tutorials/scrna-preprocessing-tenx/tutorial.md
+++ b/topics/single-cell/tutorials/scrna-preprocessing-tenx/tutorial.md
@@ -64,6 +64,10 @@ recordings:
   speakers:
   - nomadscientist
 
+answer_histories:
+  - label: "UseGalaxy.org"
+    history: https://usegalaxy.org/u/videmp/h/pre-processing-of-10x-single-cell-rna-datasets
+    date: 2024-10-04
 ---
 
 
@@ -512,7 +516,7 @@ The knee and inflection points on the curve mark the transition between two comp
 >
 > > <solution-title></solution-title>
 > > 1. We see the blue knee line cross the threshold of barcodes at just below than the 10000 Rank on the horizontal log scale, which is shown in the expanded view of our data as `"knee = 4861"`. This line intersects with the ranked cells near 100 on x-axis. We can expect 100-200 cells with high RNA content.
-> > 1. This threshold is given by the inflection line, which is given at `"inflection = 260"`. The vertical drop in the ranked cells at the inflection line is between 100 and 10000 on the x-axis. The axis is log scaled it is close to 100. Hence, we can expect between 200 and 400 cells.  
+> > 1. This threshold is given by the inflection line, which is given at `"inflection = 260"`. The vertical drop in the ranked cells at the inflection line is between 100 and 10000 on the x-axis. The axis is log scaled it is close to 100. Hence, we can expect between 200 and 400 cells.
 > {: .solution}
 {: .question}
 
@@ -544,7 +548,7 @@ Here we recover 282 high quality cells instead of the 272 detected via the defau
 
 In this workflow we have learned to quickly perform mapping and quantification of scRNA-seq FASTQ data in a single step via **RNA STARsolo**, and have reproduced a *Cell Ranger* workflow using the **DropletUtils** suite, where we further explored the use of barcode rankings to determine better filtering thresholds to generate a high quality count matrix.
 
-A full pipeline which produces both an AnnData and tabular file for inspection is provided [in this workflow](workflows/scRNA-seq-Preprocessing-TenX.ga).
+A full pipeline which produces both an AnnData and tabular file for inspection is provided [in this workflow](workflows/scRNA-seq-Preprocessing-TenX.ga)
 
 Note that, since version *2.7.7a* of the tool, the entire *Cell Ranger* pipeline including the filtering can be performed natively within **RNA STARsolo**. As this is still a relatively new feature, we do not use it here in this tutorial, but eager users are encouraged to try it out.
 
diff --git a/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX-test.yml b/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX-tests.yml
similarity index 100%
rename from topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX-test.yml
rename to topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX-tests.yml
diff --git a/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX.ga b/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX.ga
index 267a519357d775..ee32e7b52e8b3b 100644
--- a/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX.ga
+++ b/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX.ga
@@ -1,6 +1,7 @@
 {
     "a_galaxy_workflow": "true",
     "annotation": "",
+    "comments": [],
     "creator": [
         {
             "class": "Person",
@@ -31,6 +32,9 @@
     "format-version": "0.1",
     "license": "CC-BY-4.0",
     "name": "Preprocessing of 10X scRNA-seq data",
+    "report": {
+        "markdown": "\n# Workflow Execution Report\n\n## Workflow Inputs\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n```galaxy\nworkflow_display()\n```\n"
+    },
     "steps": {
         "0": {
             "annotation": "",
@@ -229,7 +233,12 @@
                     "output_name": "output"
                 }
             },
-            "inputs": [],
+            "inputs": [
+                {
+                    "description": "runtime parameter for tool RNA STARSolo",
+                    "name": "sc"
+                }
+            ],
             "label": null,
             "name": "RNA STARSolo",
             "outputs": [
@@ -270,7 +279,7 @@
                 "owner": "iuc",
                 "tool_shed": "toolshed.g2.bx.psu.edu"
             },
-            "tool_state": "{\"__input_ext\": \"input\", \"chromInfo\": \"/opt/galaxy/tool-data/shared/ucsc/chrom/?.len\", \"outWig\": {\"outWigType\": \"None\", \"__current_case__\": 0, \"outWigStrand\": \"false\"}, \"refGenomeSource\": {\"geneSource\": \"indexed\", \"__current_case__\": 0, \"GTFconditional\": {\"GTFselect\": \"without-gtf-with-gtf\", \"__current_case__\": 1, \"genomeDir\": \"hg19full\", \"sjdbGTFfile\": {\"__class__\": \"ConnectedValue\"}, \"sjdbOverhang\": \"100\"}}, \"sc\": {\"solo_type\": \"CB_UMI_Simple\", \"__current_case__\": 0, \"input_types\": {\"use\": \"repeat\", \"__current_case__\": 0, \"input1\": {\"__class__\": \"ConnectedValue\"}, \"input2\": {\"__class__\": \"ConnectedValue\"}}, \"soloCBwhitelist\": {\"__class__\": \"ConnectedValue\"}, \"params\": {\"chemistry\": \"Cv3\", \"__current_case__\": 1}, \"soloBarcodeReadLength\": true, \"umidedup\": {\"soloUMIdedup\": \"1MM_CR\", \"__current_case__\": 4, \"soloUMIfiltering\": \"-\"}, \"soloCBmatchWLtype\": \"1MM_multi\"}, \"solo\": {\"soloStrand\": \"Forward\", \"soloFeatures\": \"Gene\", \"filter\": {\"filter_type\": \"no_filter\", \"__current_case__\": 3, \"output_raw\": \"true\"}, \"soloOutFormatFeaturesGeneField3\": \"Gene Expression\", \"outSAMattributes\": [\"NH\", \"HI\", \"AS\", \"nM\"], \"quantModeGene\": false, \"outSAMunmapped\": false, \"outSAMmapqUnique\": \"60\", \"junction_limits\": {\"limitOutSJoneRead\": \"1000\", \"limitOutSJcollapsed\": \"1000000\", \"limitSjdbInsertNsj\": \"1000000\"}}, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
+            "tool_state": "{\"outWig\": {\"outWigType\": \"None\", \"__current_case__\": 0, \"outWigStrand\": \"false\"}, \"refGenomeSource\": {\"geneSource\": \"indexed\", \"__current_case__\": 0, \"GTFconditional\": {\"GTFselect\": \"without-gtf-with-gtf\", \"__current_case__\": 1, \"genomeDir\": \"hg19\", \"sjdbGTFfile\": {\"__class__\": \"RuntimeValue\"}, \"sjdbOverhang\": \"100\"}}, \"sc\": {\"solo_type\": \"CB_UMI_Simple\", \"__current_case__\": 0, \"input_types\": {\"use\": \"repeat\", \"__current_case__\": 0, \"input1\": {\"__class__\": \"RuntimeValue\"}, \"input2\": {\"__class__\": \"RuntimeValue\"}}, \"soloCBwhitelist\": {\"__class__\": \"RuntimeValue\"}, \"params\": {\"chemistry\": \"Cv3\", \"__current_case__\": 1}, \"soloBarcodeReadLength\": true, \"umidedup\": {\"soloUMIdedup\": \"1MM_CR\", \"__current_case__\": 4, \"soloUMIfiltering\": \"-\"}, \"soloCBmatchWLtype\": \"1MM_multi\"}, \"solo\": {\"soloStrand\": \"Forward\", \"soloFeatures\": \"Gene\", \"filter\": {\"filter_type\": \"no_filter\", \"__current_case__\": 3, \"output_raw\": \"true\"}, \"soloOutFormatFeaturesGeneField3\": \"Gene Expression\", \"outSAMattributes\": [\"NH\", \"HI\", \"AS\", \"nM\"], \"quantModeGene\": false, \"outSAMunmapped\": false, \"outSAMmapqUnique\": \"60\", \"junction_limits\": {\"limitOutSJoneRead\": \"1000\", \"limitOutSJcollapsed\": \"1000000\", \"limitSjdbInsertNsj\": \"1000000\"}}, \"__page__\": null, \"__rerun_remap_job_id__\": null}",
             "tool_version": "2.7.10b+galaxy3",
             "type": "tool",
             "uuid": "6ee8c4c5-1926-4e1f-af1e-5a17f515b85f",
@@ -296,7 +305,20 @@
                     "output_name": "output_genes"
                 }
             },
-            "inputs": [],
+            "inputs": [
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                },
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                },
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                }
+            ],
             "label": null,
             "name": "DropletUtils",
             "outputs": [
@@ -359,7 +381,20 @@
                     "output_name": "output_genes"
                 }
             },
-            "inputs": [],
+            "inputs": [
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                },
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                },
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                }
+            ],
             "label": null,
             "name": "DropletUtils",
             "outputs": [
@@ -414,7 +449,20 @@
                     "output_name": "output_genes"
                 }
             },
-            "inputs": [],
+            "inputs": [
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                },
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                },
+                {
+                    "description": "runtime parameter for tool DropletUtils",
+                    "name": "tenx_format"
+                }
+            ],
             "label": null,
             "name": "DropletUtils",
             "outputs": [
@@ -447,6 +495,6 @@
         "single-cell",
         "transcriptomics"
     ],
-    "uuid": "99173958-d701-47db-be9c-e23ecfaf4f1c",
-    "version": 6
+    "uuid": "de7e8328-f483-4724-93dc-4526a6332f59",
+    "version": 1
 }
\ No newline at end of file
diff --git a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md
index a17ee4506230c2..49b5930c51dca1 100644
--- a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md
+++ b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/tutorial.md
@@ -1,12 +1,11 @@
 ---
 layout: tutorial_hands_on
-
-title: "Clustering 3K PBMCs with Scanpy"
+title: Clustering 3K PBMCs with Scanpy
 subtopic: firstsc
 priority: 2
 redirect_from:
-  - /topics/transcriptomics/tutorials/scrna-scanpy-pbmc3k/tutorial
-zenodo_link: 'https://zenodo.org/record/3581213'
+- "/topics/transcriptomics/tutorials/scrna-scanpy-pbmc3k/tutorial"
+zenodo_link: https://zenodo.org/record/3581213
 questions:
 - What are the steps to prepare single-cell RNA-Seq data for clustering?
 - How to cluster cells in single-cell RNA-Seq data?
@@ -14,7 +13,8 @@ questions:
 objectives:
 - Describe an AnnData object to store single-cell data
 - Explain the preprocessing steps for single-cell data
-- Evaluate quality of single-cell data and apply steps to select and filter cells and genes based on QC
+- Evaluate quality of single-cell data and apply steps to select and filter cells
+  and genes based on QC
 - Execute data normalization and scaling
 - Identify highly variable genes
 - Construct and run a dimensionality reduction using Principal Component Analysis
@@ -26,12 +26,11 @@ key_points:
 - scRNA-seq data analysis is complex and exploratory process, still in development
 - Different tools and parameters should be tested for each step of the process
 requirements:
--
-    type: "internal"
-    topic_name: single-cell
-    tutorials:
-        - scrna-preprocessing
-        - scrna-preprocessing-tenx
+- type: internal
+  topic_name: single-cell
+  tutorials:
+  - scrna-preprocessing
+  - scrna-preprocessing-tenx
 tags:
 - 10x
 contributors:
@@ -40,10 +39,7 @@ contributors:
 - mtekman
 - pavanvidem
 - dianichj
-
 gitter: Galaxy-Training-Network/galaxy-single-cell
-
-
 recordings:
 - captioners:
   - hrhotz
@@ -52,10 +48,24 @@ recordings:
   youtube_id: nefB35Bi1l4
   speakers:
   - nomadscientist
-
+- youtube_id: P8AucugNlLc
+  length: 1H39M
+  galaxy_version: 24.1.2.dev0
+  date: '2024-09-26'
+  speakers:
+  - pavanvidem
+  captioners:
+  - pavanvidem
+  bot-timestamp: 1727373432
+
+answer_histories:
+  - label: "Scanpy_1.10.2_Anndata_0.10.9"
+    history: https://singlecell.usegalaxy.eu/u/videmp/h/clustering-of-3k-pbmcs-tutorial-from-workflow-october-2024
+    date: 2024-10-04
 ---
 
 
+
 Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq.
 
 In this tutorial, we will investigate clustering of single-cell data from 10x Genomics, including preprocessing, clustering and the identification of cell types via known marker genes, using [Scanpy](https://scanpy.readthedocs.io/en/stable/index.html) ({% cite wolf2018scanpy %}). It will be illustrated using a dataset of Peripheral Blood Mononuclear Cells (PBMC), containing 2,700 single cells.
@@ -1873,9 +1883,7 @@ With the annotated cell types, we can also visualize the expression of their can
 {: .question}
 
 # Conclusion
-{% icon congratulations %} Well done, you’ve made it to the end! You might want to consult your results with this [control history](https://singlecell.usegalaxy.eu/u/videmp/h/clustering-3k-pbmcs-with-scanpy-196), or check out the [full workflow](https://singlecell.usegalaxy.eu/u/videmp/w/clustering-3k-pbmc-with-scanpy) for this tutorial.
-
-In this tutorial, we investigated clustering and annotation of single-cell data from 10x Genomics using Scanpy. This workflow used here was typical for scRNA-seq data analysis:
+{% icon congratulations %} Well done, you’ve made it to the end! In this tutorial, we investigated clustering and annotation of single-cell data from 10x Genomics using Scanpy. This workflow used here was typical for scRNA-seq data analysis:
 
 1. Preprocessing with
     1. Selection and filtration of cells and genes based on quality metrics
diff --git a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy-test.yml b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy-tests.yml
similarity index 100%
rename from topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy-test.yml
rename to topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy-tests.yml
diff --git a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy.ga b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy.ga
index 19be5ce85ba373..46585d041a4ce1 100644
--- a/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy.ga
+++ b/topics/single-cell/tutorials/scrna-scanpy-pbmc3k/workflows/Clustering-3k-PBMC-with-Scanpy.ga
@@ -1,6 +1,7 @@
 {
     "a_galaxy_workflow": "true",
     "annotation": "Workflow based on clustering 3K PBMCs with Scanpy tutorial",
+    "comments": [],
     "creator": [
         {
             "class": "Person",
@@ -26,6 +27,9 @@
     "format-version": "0.1",
     "license": "CC-BY-4.0",
     "name": "Clustering 3k PBMC with Scanpy",
+    "report": {
+        "markdown": "\n# Workflow Execution Report\n\n## Workflow Inputs\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n```galaxy\nworkflow_display()\n```\n"
+    },
     "steps": {
         "0": {
             "annotation": "",
@@ -44,7 +48,7 @@
             "outputs": [],
             "position": {
                 "left": 0,
-                "top": 786
+                "top": 434
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             "tool_state": "{\"optional\": false, \"tag\": null}",
@@ -71,7 +75,7 @@
             "outputs": [],
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@@ -98,7 +102,7 @@
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-                "top": 990
+                "top": 638
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@@ -110,53 +114,31 @@
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-            "inputs": [
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-                    "description": "",
-                    "name": "mito header"
-                }
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-            "label": "mito header",
-            "name": "Input dataset",
-            "outputs": [],
-            "position": {
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-                "top": 1106
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-            "tool_id": null,
-            "tool_state": "{\"optional\": false, \"tag\": null}",
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-            "id": 4,
             "input_connections": {
-                "hd5_format|in|matrix": {
+                "in|matrix": {
                     "id": 2,
                     "output_name": "output"
                 },
-                "hd5_format|in|tenx|barcodes": {
+                "in|tenx|barcodes": {
                     "id": 0,
                     "output_name": "output"
                 },
-                "hd5_format|in|tenx|genes": {
+                "in|tenx|genes": {
                     "id": 1,
                     "output_name": "output"
                 }
             },
-            "inputs": [],
+            "inputs": [
+                {
+                    "description": "runtime parameter for tool Import Anndata",
+                    "name": "in"
+                }
+            ],
             "label": null,
-            "name": "Import Anndata and loom",
+            "name": "Import Anndata",
             "outputs": [
                 {
                     "name": "anndata",
@@ -164,8 +146,8 @@
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             ],
             "position": {
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+                "left": 280.0002611990579,
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@@ -206,37 +188,84 @@
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             "outputs": [
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-                    "type": "tabular"
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+                    "type": "txt"
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@@ -245,37 +274,37 @@
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             "label": null,
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-                "changeset_revision": "9b0cdb8cf6be",
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-            "tool_version": "1.8+galaxy0",
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             "when": null,
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+            "workflow_outputs": [
+                {
+                    "label": "pl_dotplot_marker_genes",
+                    "output_name": "out_png",
+                    "uuid": "a3ceb616-64b8-4c79-ba94-4272a5bd747f"
+                }
+            ]
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     "tags": [
         "name:scRNA-seq",
         "name:single-cell"
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-    "uuid": "6f4b07a9-1759-45d4-9f97-53cf4070327f",
-    "version": 43
+    "uuid": "3f780362-6836-4434-bde4-aede26480a05",
+    "version": 5
 }
\ No newline at end of file
diff --git a/topics/statistics/tutorials/CNN/tutorial.md b/topics/statistics/tutorials/CNN/tutorial.md
index c9633eebe9d2ba..b215d4584024ff 100644
--- a/topics/statistics/tutorials/CNN/tutorial.md
+++ b/topics/statistics/tutorials/CNN/tutorial.md
@@ -1,6 +1,5 @@
 ---
 layout: tutorial_hands_on
-
 title: Deep Learning (Part 3) - Convolutional neural networks (CNN)
 zenodo_link: https://zenodo.org/record/4697906
 questions:
@@ -10,19 +9,18 @@ objectives:
 - Understand the inspiration behind CNN and learn the CNN architecture
 - Learn the convolution operation and its parameters
 - Learn how to create a CNN using Galaxy's deep learning tools
-- Solve an image classification problem on MNIST digit classification dataset using CNN in Galaxy
+- Solve an image classification problem on MNIST digit classification dataset using
+  CNN in Galaxy
 requirements:
-  -
-    type: internal
-    topic_name: statistics
-    tutorials:
-      - intro_deep_learning
-      - FNN
-      - RNN
+- type: internal
+  topic_name: statistics
+  tutorials:
+  - intro_deep_learning
+  - FNN
+  - RNN
 time_estimation: 2H
 contributors:
 - kxk302
-
 recordings:
 - captioners:
   - FilipposZ
@@ -32,10 +30,21 @@ recordings:
   youtube_id: P1NVYOJrv_4
   speakers:
   - kxk302
+- youtube_id: vp6YF2NRIZk
+  length: 45M
+  galaxy_version: 24.1.3.dev0
+  date: '2024-10-05'
+  speakers:
+  - hujambo-dunia
+  captioners:
+  - hujambo-dunia
+  bot-timestamp: 1728137291
+
 
 ---
 
 
+
 Artificial neural networks are a machine learning discipline that have been successfully applied to problems
 in pattern classification, clustering, regression, association, time series prediction, optimiztion, and control {% cite JainEtAl %}.
 With the increasing popularity of social media in the past decade, image and video processing tasks have become very
diff --git a/topics/statistics/tutorials/regression_machinelearning/tutorial.md b/topics/statistics/tutorials/regression_machinelearning/tutorial.md
index 7f5d6828327909..a894bb003a7949 100755
--- a/topics/statistics/tutorials/regression_machinelearning/tutorial.md
+++ b/topics/statistics/tutorials/regression_machinelearning/tutorial.md
@@ -320,7 +320,6 @@ In the final step, we will create a pipeline learner with the **Pipeline builder
 >        - *"Choose the module that contains target estimator"*: `sklearn.ensemble`
 >            - *"Choose estimator class"*: `GradientBoostingRegressor`
 >            - *"Type in parameter settings if different from default"*: `random_state=42`
->        - In *"Output parameters for searchCV?"*: `Yes`
 > 
 >      `random_state` could be set to any arbitrary integer number; its purpose is to ensure a determistic and therefore reproducible result.
 >
diff --git a/topics/transcriptomics/tutorials/ref-based/tutorial.md b/topics/transcriptomics/tutorials/ref-based/tutorial.md
index 47b1d68c3ccec9..fec923c19b51c7 100644
--- a/topics/transcriptomics/tutorials/ref-based/tutorial.md
+++ b/topics/transcriptomics/tutorials/ref-based/tutorial.md
@@ -1489,6 +1489,8 @@ We can now run **DESeq2**:
 >                        - In *"Count file(s)"*: `Select all the single-end count files (GSM461176, GSM461179, GSM461182)`
 >    - *"Files have header?"*: `Yes`
 >    - *"Choice of Input data"*: `Count data (e.g. from HTSeq-count, featureCounts or StringTie)`
+>    - In *"Advanced options"*:
+>        - *"Use beta priors"*: `Yes`
 >    - In *"Output options"*:
 >        - *"Output selector"*: `Generate plots for visualizing the analysis results`, `Output normalised counts`
 >
@@ -1566,6 +1568,8 @@ We can now run **DESeq2**:
 >                        - *"Select groups that correspond to this factor level"*: `Tags: single`
 >    - *"Files have header?"*: `Yes`
 >    - *"Choice of Input data"*: `Count data (e.g. from HTSeq-count, featureCounts or StringTie)`
+>    - In *"Advanced options"*:
+>        - *"Use beta priors"*: `Yes`
 >    - In *"Output options"*:
 >        - *"Output selector"*: `Generate plots for visualizing the analysis results`, `Output normalised counts`
 >
diff --git a/topics/variant-analysis/tutorials/aiv-analysis/tutorial.md b/topics/variant-analysis/tutorials/aiv-analysis/tutorial.md
index fed901c6f86f8f..515e16bb7e1491 100644
--- a/topics/variant-analysis/tutorials/aiv-analysis/tutorial.md
+++ b/topics/variant-analysis/tutorials/aiv-analysis/tutorial.md
@@ -14,6 +14,11 @@ objectives:
 - Construct a sample consensus genome from mapped reads
 - Generate per-segment phylogenetic trees of AIV consensus sequences
 time_estimation: 4H
+input_histories:
+- label: "UseGalaxy.eu"
+  history: https://usegalaxy.eu/published/history?id=0fc6b637aadbc8a2
+- label: "UseGalaxy.org"
+  history: https://usegalaxy.org/published/history?id=31cb33f8e6126823
 key_points:
 - Reassortment of gene segments makes reference-based mapping of influenza sequencing data challenging
 - An alternative to *de-novo* assembly can be mapping to a dynamically chosen reference genome
@@ -75,7 +80,10 @@ Instead, to save some time, you can copy an existing shared history on Galaxy Eu
 
 > <hands-on-title>Prepare the Galaxy history</hands-on-title>
 >
-> 1. Open the [shared history](https://usegalaxy.eu/u/wolfgang-maier/h/influenza-resources) with pre-prepared INSAFlu reference data
+> 1. On Galaxy Europe, open the [shared history](https://usegalaxy.eu/published/history?id=0fc6b637aadbc8a2) with pre-prepared INSAFlu reference data.
+>
+>    On the Galaxy US server, use this [shared history](https://usegalaxy.org/published/history?id=31cb33f8e6126823) instead.
+>
 >
 > 2. Rename the history
 >
@@ -148,7 +156,7 @@ The tool **fastp** lets us perform these tasks and obtain a nice quality report
 
 > <hands-on-title>QC and read trimming/filtering with fastp</hands-on-title>
 >
-> 1. {% tool [fastp](toolshed.g2.bx.psu.edu/repos/iuc/fastp/fastp/0.23.2+galaxy0) %}
+> 1. {% tool [fastp](toolshed.g2.bx.psu.edu/repos/iuc/fastp/fastp/0.23.4+galaxy1) %}
 >    - *"Single-end or paired reads"*: `Paired Collection`
 >    - *"Select paired collection(s)"*: the uploaded paired collection of sequenced reads
 >    - In *"Filter Options"* under *"Length filtering options"*:
@@ -239,7 +247,7 @@ Now that we have established that things *may* make sense, we can use the output
 
 > <hands-on-title>Obtaining sequences of top hits identified by VAPOR</hands-on-title>
 >
-> 1. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4) %} to extract just the name of the sequence from each line of VAPOR's output
+> 1. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/9.3+galaxy1) %} to extract just the name of the sequence from each line of VAPOR's output
 >    - {% icon param-collection %} *"File to process"*: collection of score outputs of **VAPOR**
 >    - In {% icon param-repeat %} *"1. Find and Replace"*:
 >      - *"Find pattern"*: `^.+\t>(.+)$`
@@ -250,7 +258,7 @@ Now that we have established that things *may* make sense, we can use the output
 >    - *"Select first"*: `1` lines
 >    - {% icon param-collection %} *"from"*: output of **Replace**
 >
-> 3. {% tool [seqtk_subseq](toolshed.g2.bx.psu.edu/repos/iuc/seqtk/seqtk_subseq/1.3.1) %} to extract the reference sequences based on their names reported by VAPOR
+> 3. {% tool [seqtk_subseq](toolshed.g2.bx.psu.edu/repos/iuc/seqtk/seqtk_subseq/1.4+galaxy0) %} to extract the reference sequences based on their names reported by VAPOR
 >    - {% icon param-collection %} *"Input FASTA/Q file"*: `References per segment (INSAFlu)`
 >    - *"Select source of sequence choices"*: `FASTA/Q ID list`
 >    - {% icon param-collection %} *"Input ID list"*: the collection output of **Select first**
@@ -268,7 +276,7 @@ If things went well, the hybrid reference we just obtained should be close enoug
 
 > <hands-on-title>Shortening sequence titles</hands-on-title>
 >
-> 1. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4) %}
+> 1. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/9.3+galaxy1) %}
 >    - {% icon param-file %} *"File to process"*: the hybrid reference genome; output of **Collapse Collection**
 >    - In {% icon param-repeat %} *"1. Find and Replace"*:
 >      - *"Find pattern"*: `^>([^|]+).+$`
@@ -291,13 +299,13 @@ Having polished the titles of the segments in our hybrid reference genome we are
 
 > <hands-on-title>Read mapping and quality control</hands-on-title>
 >
-> 1. {% tool [Map with BWA-MEM](toolshed.g2.bx.psu.edu/repos/devteam/bwa/bwa_mem/0.7.17.2) %}
+> 1. {% tool [Map with BWA-MEM](toolshed.g2.bx.psu.edu/repos/devteam/bwa/bwa_mem/0.7.18) %}
 >    - *"Will you select a reference genome from your history or use a built-in index?"*: `Use a genome from history and build index`
 >    - {% icon param-file %} *"Use the following dataset as the reference sequence"*: hybrid reference genome with shortened names; output of **Replace**
 >    - *"Single or Paired-end reads"*: `Paired Collection`
 >    - *"Select a paired collection"*: quality-trimmed reads; output of **fastp** in QC and Trimming
 >
-> 2. {% tool [Samtools view](toolshed.g2.bx.psu.edu/repos/iuc/samtools_view/samtools_view/1.15.1+galaxy0) %}
+> 2. {% tool [Samtools view](toolshed.g2.bx.psu.edu/repos/iuc/samtools_view/samtools_view/1.20+galaxy3) %}
 >    - {% icon param-file %} *"SAM/BAM/CRAM data set"*: mapped reads BAM dataset; output of **BWA-MEM**
 >    - *"What would you like to look at?"*: `A filtered/subsampled selection of reads`
 >    - In *"Configure filters"*:
@@ -337,7 +345,7 @@ Unfortunately, the tool we are going to use for this, **ivar consensus**, is not
 
 > <hands-on-title>Splitting mapped reads by genome segment</hands-on-title>
 >
-> 1. {% tool [Split BAM by Reference](toolshed.g2.bx.psu.edu/repos/iuc/bamtools_split_ref/bamtools_split_ref/2.5.2+galaxy1) %}
+> 1. {% tool [Split BAM by Reference](toolshed.g2.bx.psu.edu/repos/iuc/bamtools_split_ref/bamtools_split_ref/2.5.2+galaxy2) %}
 >    - {% icon param-file %} *"BAM dataset to split by reference"*: filtered mapped reads; output of **Samtools view**
 >
 {: .hands_on}
@@ -353,7 +361,7 @@ The output from this step has the desired collection structure, but the names of
 >    - *"that"*: `Matching`
 >    - *"the pattern"*: `^>.+`
 >
-> 2. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4) %}
+> 2. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/9.3+galaxy1) %}
 >    - {% icon param-file %} *"File to process"*: output of **Select**
 >    - In {% icon param-repeat %} *"1. Find and Replace"*:
 >      - *"Find pattern"*: `^>(.+)$`
@@ -383,7 +391,7 @@ Now what if we cannot obtain a consensus base for a position with the above crit
 
 > <hands-on-title>Per-segment consensus construction</hands-on-title>
 >
-> 1. {% tool [ivar consensus](toolshed.g2.bx.psu.edu/repos/iuc/ivar_consensus/ivar_consensus/1.4.2+galaxy0) %}
+> 1. {% tool [ivar consensus](toolshed.g2.bx.psu.edu/repos/iuc/ivar_consensus/ivar_consensus/1.4.3+galaxy0) %}
 >    - {% icon param-collection %} *"Bam file"*: the relabeled collection of mapped reads; output of **Relabel identifiers**
 >    - *"Minimum quality score threshold to count base"*: `20`
 >    - *"Minimum frequency threshold"*: `0.7`
@@ -394,7 +402,7 @@ Now what if we cannot obtain a consensus base for a position with the above crit
 >    The output is a consensus sequence in FASTA format, one per segment, with
 >    the names just providing a bit too much detail for our purpose.
 >
-> 2. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4) %}
+> 2. {% tool [Replace parts of text](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/9.3+galaxy1) %}
 >    - {% icon param-collection %} *"File to process"*: consensus sequences; output of **ivar consensus**
 >    - In {% icon param-repeat %} *"1: Find and Replace"*:
 >      - *"Find pattern"*: `^>Consensus_(.*)_threshold_.*`
@@ -423,7 +431,7 @@ To do so, we are going to combine the reference sequences of all segments with t
 
 > <hands-on-title>Exploring phylogeny</hands-on-title>
 >
-> 1. {% tool [MAFFT](toolshed.g2.bx.psu.edu/repos/rnateam/mafft/rbc_mafft/7.520+galaxy0) %}
+> 1. {% tool [MAFFT](toolshed.g2.bx.psu.edu/repos/rnateam/mafft/rbc_mafft/7.526+galaxy0) %}
 >    - *"For multiple inputs generate"*: `one or several MSAs depending on input structure`
 >      - In *"Input batch"*:
 >        - {% icon param-repeat %} *"1: Input batch"*
@@ -434,12 +442,20 @@ To do so, we are going to combine the reference sequences of all segments with t
 >
 >    Because both input batches are collections of eight elements each, the result is also a collection of eight MSAs, each aligning all reference sequences of one genome segment plus the consensus sequence we have obtained for that segment against each other.
 >
-> 2. {% tool [IQ-TREE](toolshed.g2.bx.psu.edu/repos/iuc/iqtree/iqtree/2.1.2+galaxy2) %}
+> 2. {% tool [IQ-TREE](toolshed.g2.bx.psu.edu/repos/iuc/iqtree/iqtree/2.3.6+galaxy0) %}
 >    - {% icon param-collection %} *"Specify input alignment file in PHYLIP, FASTA, NEXUS, CLUSTAL or MSF format."*: output of **MAFFT**
 >    - *"Specify sequence type ..."*: `DNA`
 >
 > 3. {% icon galaxy-eye %} Explore each of the final trees produced by IQTree for the different segments
 >
+>    There are two ways to explore the trees in Galaxy:
+>
+>    One option is to open the *Report and Final Tree* datasets produced by IQ-Tree.
+>    When you scroll down through their content, you will find that these feature simple text representations of the trees.
+>
+>    Another option is to expand the *MaxLikelihood Tree* datasets generated by IQ-Tree, then click on {% icon galaxy-barchart %} **Visualize**.
+>    This will let you launch the *Phylogenetic Tree Visualization* in the middle panel, which lets you explore the tree interactively and in different representations.
+>
 >    > <question-title></question-title>
 >    >
 >    > What are your conclusions about the sample in general and its HA and NA segments in particular?