Merge pull request #5086 from galaxyproject/wampus-porgy

Add news post, pending photo

.github/workflows/ci.yml

@@ -37,30 +37,23 @@ jobs:
       - uses: ruby/setup-ruby@v1
         with:
           ruby-version: "3.2"
-      - uses: actions/setup-node@v4
-        with:
-          node-version: '19'
       - uses: actions/cache@v4
         with:
           path: |
             vendor/bundle
-            ~/.npm
-          key: ${{ runner.os }}-gems-${{ hashFiles('**/Gemfile.lock') }}-node-${{ hashFiles('**/package-lock.json') }}
+          key: ${{ runner.os }}-gems-${{ hashFiles('**/Gemfile.lock') }}-
           restore-keys: |
-            ${{ runner.os }}-gems-${{ hashFiles('**/Gemfile.lock') }}-node-
             ${{ 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
           gem install bundler
           bundle config path vendor/bundle
           bundle install --jobs 4 --retry 3
-          npm install
           bundle pristine ffi
       # END Dependencies
 
-      - name: Build the site
-        run: bundle exec jekyll build --strict_front_matter -d _site/training-material --trace
+      - name: Ensure that the site builds
+        run: bundle exec jekyll build --strict_front_matter -d _site/training-material --trace --future
 
       - name: Validate all links, enforce alt text
         run: |
@@ -72,11 +65,11 @@ jobs:
             --enforce-https=false \
             ./_site
 
-      - name: Ensure no unexpected encoded HTML in output
+      - name: Ensure no unexpected encoded HTML in output, ask a GTN contributor for help with this one.
         run: |
           ! fgrep -R 'lt;blockquote' _site
 
-      - name: Ensure no poorly rendered FAQs
+      - name: Ensure no poorly rendered FAQs, generally this is fixed by removing whitespace/blank lines from the frontmatter
         run: |
           ! fgrep -R -A2 -B2 'box_type:' _site/
 
@@ -88,24 +81,6 @@ jobs:
         run: |
           ! fgrep -R 'site.pages' _site
 
-      - name: Run aXe accessibility testing on some representative URLs
-        run: |
-          node_modules/.bin/http-server _site/ &
-          # We can focus AND scroll these regions. I thus disable their violation warning.
-          # Twitter isn't our fault. Sorry.
-          node_modules/.bin/axe --disable scrollable-region-focusable --exclude '#twitter-widget-0' --chromedriver-path /usr/bin/chromedriver \
-            http://localhost:8080/training-material/ \
-            http://localhost:8080/training-material/hall-of-fame/ \
-            http://localhost:8080/training-material/hall-of-fame/hexylena/ \
-            http://localhost:8080/training-material/topics/introduction/ \
-            http://localhost:8080/training-material/topics/statistics/ \
-            http://localhost:8080/training-material/topics/genome-annotation/ \
-            http://localhost:8080/training-material/topics/admin/ \
-            http://localhost:8080/training-material/topics/dev/ \
-            http://localhost:8080/training-material/topics/introduction/tutorials/galaxy-intro-short/tutorial.html \
-            http://localhost:8080/training-material/topics/introduction/tutorials/galaxy-intro-short/workflows/ \
-            http://localhost:8080/training-material/topics/admin/tutorials/ansible-galaxy/tutorial.html
-
       - name: Validate notebooks (If this fails, ask @hexylena to look at your notebook)
         run: |
           find _site | grep ipynb | xargs bundle exec ruby bin/check-valid-notebook.rb

_includes/contributor-quilt.html

@@ -0,0 +1,12 @@
+<div class="card">
+  <div class="card-header">
+  Thanks to our <b>{{ site.data.contributors | size }} GTN contributors</b>!
+  </div>
+  <div class="card-body">
+    <p class="card-text contributor-quilt" style="margin: 1em;">
+    {% for entity in site.data.contributors -%}
+    <img src="{{ entity[1] | fetch_entity_avatar_url: entity[0], 50 }}" style="width: 33px;margin:0" alt="avatar"/>
+    {%- endfor %}
+    </p>
+  </div>
+</div>

_includes/tutorial_list.html

@@ -68,7 +68,7 @@
             {% endif %}
             {% if material.tags %}
               {% for tag in material.tags %}
-              <a href="{{site.baseurl}}/search2?query={{ tag }}" class="label label-default tutorial_tag" style="{{ tag | colour_tag }}" title="Click to show all tutorials tagged {{tag}}">{{ tag }}</a>
+              <a href="{{site.baseurl}}/search2.html?query={{ tag }}" class="label label-default tutorial_tag" style="{{ tag | colour_tag }}" title="Click to show all tutorials tagged {{tag}}">{{ tag }}</a>
               {% endfor %}
             {% endif %}
             </div>

bin/validate-frontmatter.rb

@@ -196,6 +196,14 @@ def self.lint_news_file(fn)
     data = lintable?(fn)
     return data if data.nil? || data.is_a?(Array)
 
+    if data.key?('cover') 
+      if !data['cover'].start_with?('https://')
+        if !File.exist?(data['cover'])
+          errs.push("Cover image #{data['cover']} does not exist")
+        end
+      end
+    end
+
     errs.push(*validate_document(data, @news_validator))
     errs
   end

branding/index.md

@@ -41,8 +41,4 @@ table img {
 
 # Contributor Quilt
 
-<div>
-{% for entity in site.data.contributors %}
-<img src="{{ entity[1] | fetch_entity_avatar_url: entity[0], 50 }}" style="width: 33px;margin:0" alt="avatar"/>
-{% endfor %}
-</div>
+{% include _includes/contributor-quilt.html %}

learning-pathways/building_tutorials.md

@@ -16,7 +16,7 @@ description: |
 pathway:
   - section: "Module 1: What is Github and why do I have to use it?"
     description: |
-      Galaxy training material is hosted on Github. Finding these materials and getting used to the ideas of submitting a 'Pull request' is a crucial starting point for developing training material in Galaxy. You may find that [searching Github on our training materials](../search2?query=github) will also provide further resources on this topic after the following module.
+      Galaxy training material is hosted on Github. Finding these materials and getting used to the ideas of submitting a 'Pull request' is a crucial starting point for developing training material in Galaxy. You may find that [searching Github on our training materials](../search2.html?query=github) will also provide further resources on this topic after the following module.
     tutorials:
       - name: github-interface-contribution
         topic: contributing

news/_posts/2024-07-09-gcc-updates.md

@@ -0,0 +1,43 @@
+---
+title: So many new features! GTN 2023-2024 Updates
+layout: news
+tags:
+- gcc
+- gtn infrastructure
+contributions:
+  authorship:
+  - shiltemann
+  - hexylena
+  infrastructure:
+    - shiltemann
+    - hexylena
+    - bebatut
+cover: news/images/2024-gcc-updates.png
+coveralt: the title slide of the presentation titled Updates from the GTN , Galaxy Community Conference 2024 Brno, Saskia and Helena. The background consists of paper pieced flowers and a butterfly. a CC-BY-SA logo is in the top right corner.
+link: "https://docs.google.com/presentation/d/1xXzd7io07KdBk-vVSh_27wlS5bzlDmGuxxpjWL2vB2w/edit?usp=sharing"
+---
+
+We recently [presented the GTN](https://docs.google.com/presentation/d/1xXzd7io07KdBk-vVSh_27wlS5bzlDmGuxxpjWL2vB2w/edit?usp=sharing) at the [2024 Galaxy Community Conference](https://galaxyproject.org/events/gcc2024/). This [presentation](https://docs.google.com/presentation/d/1xXzd7io07KdBk-vVSh_27wlS5bzlDmGuxxpjWL2vB2w/edit?usp=sharing) was a summary of the updates and new features that were added to the GTN in the past year.
+
+Many of these updates have been covered in previous blog posts on the GTN, but when you put it all in a single presentation it really becomes clear how much the community has done over the last year!
+
+- [Our 400th tutorial]({{ site.baseurl }}/news/2024/06/06/400-tutorials-milestone.html)
+- [Perfectly FAIR training]({{ site.baseurl }}/news/2024/05/13/fair.html)
+- [Teaching you to be perfectly FAIR]({{ site.baseurl }}/news/2024/03/26/FAIR-pointers.html)
+- [📆 GTN Event Pages]({{ site.baseurl }}/news/2024/05/22/introducing-gtn-event-pages.html)
+- [🎞 GTN Video Library]({{ site.baseurl }}/news/2024/06/14/gtn-video-library.html)
+- [🗞 News feeds, automating dissemination]({{ site.baseurl }}/news/2024/06/04/gtn-standards-rss.html)
+- [Training beyond the Galaxy]({% link topics/ai4life/index.md %})
+
+[See the presentation]({{ page.link }}){:.btn.btn-primary}
+
+And we covered a few updates there are still somewhere on their journey from planning to implementation, such as:
+
+- WorkflowHub upload
+- Maintainer Homepages
+- Workflow Testing
+- Automated Recordings
+
+We're so happy for the incredible work our massive community has done, thank you all for your contributions!
+
+{% include _includes/contributor-quilt.html %}

news/_posts/2024-07-17-google-forms.md

@@ -0,0 +1,33 @@
+---
+title: Simplifying GTN contribution with Google Forms
+layout: news
+tags:
+- gtn infrastructure
+- new feature
+- automation
+contributions:
+  authorship:
+  - hexylena
+  - shiltemann
+  infrastructure:
+  - shiltemann
+  - hexylena
+cover: news/images/2024-google-forms.png
+coveralt: A screenshot of the GTN Google Form for news contributions. The form is titled 'GTN News' and has fields for 'Title', with a screenshot of a galaxy single cell news post as the header image.
+---
+
+Since the last time we [announced GTN news posting via Google Form]({{ site.baseurl }}/news/2024/01/29/simplified-gtn-news-submission-via-google-form.html), we've found that this has been an excellent fit for the community and decided to greatly expand the use of Google Forms for GTN contributions.
+
+Our users were clear: *GitHub, pull requests, commits, and YAML files are not always a pleasant or user-friendly way to contribute.*
+
+After the success of news contributions via form we have expanded that process significantly. Now you can contribute the following all via Google Form:
+
+- [News](https://forms.gle/TqGTr6y46wrJDri7A)
+- [FAQs](https://forms.gle/2JVMfd1AgtenZPvv9)
+- [Recordings](https://forms.gle/qNG8FkTN1yRZPNZY6)
+- [Events](https://forms.gle/M6ECp1e3pZoFGYnV8)
+
+## The Details
+
+Each of these works similarly: you fill out the form, and then in the background a GTN GitHub Action will create a pull request for you.
+The GTN maintainers will review the pull request, and when everything is in order and tests pass, merge it. Your contribution will be in the GTN without you needing to learn development tooling.

topics/introduction/tutorials/galaxy-intro-peaks2genes/tutorial.md

@@ -87,7 +87,7 @@ Let's start with a fresh history.
 >    >
 >    > * Click on the title of the history (by default the title is `Unnamed history`)
 >    >
->    >   ![Renaming history](../../../../shared/images/rename_history.png)
+>    >   ![Renaming history]({% link shared/images/rename_history.png %})
 >    >
 >    > * Type `Galaxy Introduction` as the name
 >    > * Press <kbd>Enter</kbd>

topics/introduction/tutorials/galaxy-intro-short/tutorial.md

@@ -88,7 +88,7 @@ Your "History" is in the panel at the right.
 > 1. Go to the **History** panel (on the right)
 > 2. Click on {% icon galaxy-pencil %} (**Edit**) next to the history name (which by default is "Unnamed history")
 >
->    ![Screenshot of the galaxy interface with the history name being edited, it currently reads "Unnamed history", the default value. An input box is below it.](../../../../shared/images/rename_history.png){:width="250px"}
+>    ![Screenshot of the galaxy interface with the history name being edited, it currently reads "Unnamed history", the default value. An input box is below it.]({% link shared/images/rename_history.png %}){:width="250px"}
 >
 >    > <comment-title></comment-title>
 >    >

topics/introduction/tutorials/galaxy-intro-short/tutorial_ES.md

@@ -81,7 +81,7 @@ Tu “Historial” está en el panel de la derecha.
 > 1. Ve al panel **History** (a la derecha)
 > 2. Haz clic en el nombre del historial (que por defecto es "Unnamed history")
 >
->    ![name history](../../../../shared/images/rename_history.png){:width="320px"}
+>    ![name history]({% link shared/images/rename_history.png %}){:width="320px"}
 >
 > 3. Teclea el nuevo nombre, por ejemplo, "Mi-Analisis"
 > 4. Presiona <kbd>Enter</kbd> en tu teclado para guardar

topics/introduction/tutorials/galaxy-intro-strands/tutorial.md

@@ -310,7 +310,7 @@ You don't have to do this.  Galaxy is quite happy for you to have an infinite nu
 >
 > 1. **Name your history** to be meaningful and easy to find.
 >    - *Click* on the title of the history and enter something like **Intro - Strands** as the name.  Hit the `enter` key on your keyboard to save it.
->   ![Rename the history](../../../../shared/images/rename_history.png)
+>   ![Rename the history]({% link shared/images/rename_history.png %})
 > 1. **Rename your dataset**
 >    - *Click* on the **pencil icon** to edit the dataset attributes.
 >    - In the next screen change the name of the dataset to something like `Genes` or `Genes chr22`.

topics/statistics/tutorials/CNN/tutorial.md

@@ -220,7 +220,7 @@ to compare various Machine Learning techniques.
 >    >
 >    > * Click on the title of the history (by default the title is `Unnamed history`)
 >    >
->    >   ![Renaming history](../../../../shared/images/rename_history.png)
+>    >   ![Renaming history]({% link shared/images/rename_history.png %})
 >    >
 >    > * Type `Galaxy Introduction` as the name
 >    > * Press <kbd>Enter</kbd>

topics/statistics/tutorials/fruit_360/tutorial.md

@@ -202,7 +202,7 @@ Orange:8, Peach:9.
 >    >
 >    > * Click on the title of the history (by default the title is `Unnamed history`)
 >    >
->    >   ![Renaming history](../../../../shared/images/rename_history.png)
+>    >   ![Renaming history]({% link shared/images/rename_history.png %})
 >    >
 >    > * Type `Galaxy Introduction` as the name
 >    > * Press <kbd>Enter</kbd>
@@ -255,7 +255,7 @@ train_y_10 file has 3 columns: Label_name (string representation of label), file
 image file), and Label (integer representation of label). We extract Label from train_y_10, and then calcuate
 its OHE representation.
 
-### **Extract the Label column from train_y_10**
+### Extract the Label column from train_y_10
 
 > <hands-on-title>Advanced Cut</hands-on-title>
 >
@@ -269,7 +269,7 @@ its OHE representation.
 >
 {: .hands_on}
 
-### **Create One-Hot Encoding (OHE) representation of training labels**
+### Create One-Hot Encoding (OHE) representation of training labels
 
 > <hands-on-title>One-Hot Encoding</hands-on-title>
 >
@@ -281,7 +281,7 @@ its OHE representation.
 >
 {: .hands_on}
 
-### **Create a deep learning model architecture**
+### Create a deep learning model architecture
 
 > <hands-on-title>Model config</hands-on-title>
 >
@@ -344,7 +344,7 @@ nodes and relu activation function. Finally, we add a fully connected layers wit
 softmax activation function to get the probability of each fruit/vegetable. Fruit/vegetable with the
 highest probability is predicted by CNN. The model config can be downloaded as a JSON file.
 
-### **Create a deep learning model**
+### Create a deep learning model
 
 > <hands-on-title>Model builder (Optimizer, loss function, and fit parameters)</hands-on-title>
 >
@@ -369,7 +369,7 @@ weights/biases after all the training data is feed to the network, the training
 in our dataset). To speed up the training, we present only a subset of the training examples to the network, after which we update
 the weights/biases. *batch_size* decides the size of this subset. The model builder can be downloaded as a zip file.
 
-### **Deep learning training and evaluation**
+### Deep learning training and evaluation
 
 > <hands-on-title>Training the model</hands-on-title>
 >
@@ -389,7 +389,7 @@ the weights/biases. *batch_size* decides the size of this subset. The model buil
 The training step generates 3 datasets. 1) accuracy of the trained model, 2) the trained model, downloadable as a zip file, and
 3) the trained model weights, downloadable as an hdf5 file. These files are needed for prediction in the next step.
 
-### **Model Prediction**
+### Model Prediction
 
 > <hands-on-title>Testing the model</hands-on-title>
 >
@@ -407,7 +407,7 @@ The training step generates 3 datasets. 1) accuracy of the trained model, 2) the
 The prediction step generates 1 dataset. It's a file that has predictions (0 to 9 for the predicted fruit/vegetable) for every image
 in the test dataset.
 
-### **Machine Learning Visualization Extension**
+### Machine Learning Visualization Extension
 
 > <hands-on-title>Creating the confusion matrix</hands-on-title>
 >