diff --git a/_includes/auto_threshold/auto_threshold_act1_galaxy.md b/_includes/auto_threshold/auto_threshold_act1_galaxy.md
new file mode 100644
index 00000000..9569b13c
--- /dev/null
+++ b/_includes/auto_threshold/auto_threshold_act1_galaxy.md
@@ -0,0 +1,34 @@
+- Navigate to [Galaxy](https://usergalaxy.eu)
+- Upload an image
+    - In the Tools panel on the left side, click `Upload Data`.
+    - Click the `Paste/Fetch data` button.
+    - Paste the URLs of the images : [xy_8bit__nuclei_without_offset.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_without_offset.tif) and [xy_8bit__nuclei_with_offset.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_with_offset.tif)
+    - Click the `Start` button and wait for the upload to complete.
+    - Once the upload is finished, click the `Close` button. The image will now be available in your Galaxy history.
+- Apply a threshhold
+  - In the Tools panel on the left side, search `Threshold image`.
+  - Choose the tool named `Threshold image with scikit-image`, and click on it.
+    - Manual threshold
+      - `xy_8bit__nuclei_without_offset.tif`
+        - Select the image `xy_8bit__nuclei_without_offset.tif` from the `Select image` dropdown list.
+        - Select `Manual` from the `Thresholding method` dropdown list.
+        - Set `Threshold value` to `20`.
+        - Toggle `Invert output labels` to `Yes`
+        - Click the `Run Tool` button and wait for the job to finish (The job will turn green).
+        - Click on the job in your Galaxy history to download the resulting image.
+      - `xy_8bit__nuclei_with_offset.tif`
+        - Select the image `xy_8bit__nuclei_with_offset.tif` from the `Select image` dropdown list.
+        - Select `Manual` from the `Thresholding method` dropdown list.
+        - Set `Threshold value` to `40`.
+        - Toggle `Invert output labels` to `Yes`
+        - Click the `Run Tool` button and wait for the job to finish (The job will turn green).
+        - Click on the job in your Galaxy history to retrieve the results.   
+    - Auto threshold
+      - `xy_8bit__nuclei_without_offset.tif` 
+        - Select the image `xy_8bit__nuclei_without_offset.tif` from the `Select image` dropdown list.
+        - Select `Otsu` from the `Thresholding Method` dropdown list.
+        - Leave `Offset` value unchanged.
+        - Toggle `Invert output labels` to `Yes`
+        - Click the `Run Tool` button and wait for the job to finish (The job will turn green).
+        - Click on the job in your Galaxy history to retrieve the results.  
+      - Repeat the steps for a different image `xy_8bit__nuclei_with_offset.tif`
diff --git a/_includes/binarization/binarization_act1_galaxy.md b/_includes/binarization/binarization_act1_galaxy.md
new file mode 100644
index 00000000..bfa407cc
--- /dev/null
+++ b/_includes/binarization/binarization_act1_galaxy.md
@@ -0,0 +1,17 @@
+- Navigate to [Galaxy](https://usergalaxy.eu)
+- Upload an image
+    - In the Tools panel on the left side, click `Upload Data`.
+    - Click the `Paste/Fetch data` button.
+    - Paste the URLs of the images : [xy_8bit__two_cells.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__two_cells.tif) 
+    - Click the `Start` button and wait for the upload to complete.
+    - Once the upload is finished, click the `Close` button. The image will now be available in your Galaxy history.
+- Apply a threshhold
+  - In the Tools panel on the left side, search `Threshold image`.
+  - Choose the tool named `Threshold image with scikit-image`, and click on it.
+  - Select the image `xy_8bit__two_cells.tif` from the `Select image` dropdown list.
+  - Select `Manual` from the `Thresholding method` dropdown list.
+  - Set `Threshold value` to `49`.
+  - Toggle `Invert output labels` to `Yes`
+  - Click the `Run Tool` button and wait for the job to finish (The job will turn green).
+  - Click on the job in your Galaxy history to download the resulting image.
+ 
\ No newline at end of file
diff --git a/_includes/binarization/binarization_act2_galaxy.md b/_includes/binarization/binarization_act2_galaxy.md
new file mode 100644
index 00000000..6600f631
--- /dev/null
+++ b/_includes/binarization/binarization_act2_galaxy.md
@@ -0,0 +1,17 @@
+- Navigate to [Galaxy](https://usegalaxy.eu)
+- Upload an image
+    - In the Tools panel on the left side, click `Upload Data`.
+    - Click the `Paste/Fetch data` button.
+    - Paste the URLs of the images : [xy_8bit__PCNA.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__PCNA.tif) 
+    - Click the `Start` button and wait for the upload to complete.
+    - Once the upload is finished, click the `Close` button. The image will now be available in your Galaxy history.
+- Apply a threshhold
+  - In the Tools panel on the left side, search `Threshold image`.
+  - Choose the tool named `Threshold image with scikit-image`, and click on it.
+  - Select the image `xy_8bit__PCNA.tif` from the `Select image` dropdown list.
+  - Select `Manual` from the `Thresholding method` dropdown list.
+  - Experience with different `Threshold value`, e.g. `5`,`44`,`4.5`
+  - Toggle `Invert output labels` to `Yes`
+  - Click the `Run Tool` button and wait for the job to finish (The job will turn green).
+  - Click on the job in your Galaxy history to download the resulting image.
+ 
\ No newline at end of file
diff --git a/_includes/lut/lut_act1_galaxy.md b/_includes/lut/lut_act1_galaxy.md
new file mode 100644
index 00000000..00c25ab4
--- /dev/null
+++ b/_includes/lut/lut_act1_galaxy.md
@@ -0,0 +1,19 @@
+- Upload an [image](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif) to Galaxy
+    - Go to https://usegalaxy.eu
+    - In the Tools panel on the left, click `Upload Data`
+    - Click `Paste/Fetch data` button
+    - Paste the image url: https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif and click the `Start` button
+    - Click the `Close` button after upload finishes, then the image will be available in your Galaxy history.
+- Start the Napari Interactive Tool
+    - In the Tools panel on the left, search for `Run Napari interactive tool`
+    - Select  `xy_8bit_nuclei_high_dynamic_range.tif` from the `Images` dropdown list.
+    - Click the `Run Tool` button. Once the `Open` link appears at the top of the page, click it to open Napari in a separate browser tab.
+    - In the Napari browser tab, navigate to `File -> Open File(s)` and select the image `xy_8bit_nuclei_high_dynamic_range.tif` from the `input` folder.
+- Change the Contrast settings
+	- Experiment with different minimum and maximum values of the `contract limits`.
+	- Notice how, at certain settings, a very dim nucleus becomes visible.
+- Explore different LUTs, e.g.
+	- Go to `File › Open File(s)`
+	- Select the same image `xy_8bit_nuclei_high_dynamic_range.tif` from the `input` folder. A new layer will appear in the bottom left pane.
+    - Change the `colormap` to `turbo`, from the layer options in the top left pane.
+    - Turn on grid mode by clicking the `Grid` button located at the bottom left,second from the right.
\ No newline at end of file
diff --git a/_includes/lut/lut_act2_galaxy.md b/_includes/lut/lut_act2_galaxy.md
new file mode 100644
index 00000000..a9cc1ad2
--- /dev/null
+++ b/_includes/lut/lut_act2_galaxy.md
@@ -0,0 +1,13 @@
+- Upload one of the above pairs of images to Galaxy
+    - Go to https://usegalaxy.eu
+    - In the Tools panel on the left, click `Upload Data`
+    - Click `Paste/Fetch data` button
+    - Paste the URLs of the two images(one line per URL) and click the `Start` button
+    - Click the `Close` button after upload finishes, then the image will be available in your Galaxy history.
+- Start the Napari interactive tool
+    - In the Tools panel on the left, search for `Run Napari interactive tool`
+    - Select the two uploaded images from the `Images` dropdown list
+    - Click the `Run Tool` button. Once the `Open` link appears at the top of the page, click it to open Napari in a separate browser tab.
+    - In the Napari tab, navigate to `File -> Open file(s)`, and select the two images from the `input` folder.
+    -  Turn on grid mode by clicking the `Grid` button located at the bottom left,second from the right. The two images will appear side by side
+    - Adjust the `contrast limits` and apply the same values to both images to compare them directly
\ No newline at end of file
diff --git a/_includes/median_filter/median_filter_galaxy.md b/_includes/median_filter/median_filter_galaxy.md
new file mode 100644
index 00000000..51f95452
--- /dev/null
+++ b/_includes/median_filter/median_filter_galaxy.md
@@ -0,0 +1,22 @@
+- Upload the following images to Galaxy
+    - Navigate to [Galaxy](https://usegalaxy.eu)
+    - Locate the Tools panel on the left, click the `Upload Data` button.
+    - Within the `Uupload data` pop-up wintow, Click `Paste/Fetch data` button.
+    - In the text box, paste the URLs of the following images. Enter each URL on a new line.
+        - [xy_8bit_binary__squares_different_size.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__squares_different_size.tif)
+        - [xy_8bit_binary__large_spot.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__large_spot.tif)
+        - [xy_8bit__two_noisy_squares_different_size.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__large_spot.tif)
+        - [xy_8bit__PCNA.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__PCNA.tif)
+        - [xy_8bit_binary__test_structures.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__test_structures.tif)
+    
+    - Click the `Start` button to upload the images.
+    - Once the upload is finished, click the `Close` button at the bottom of the upload window
+    - The uploaded images will be available in your Galaxy history on the right panel.
+- Apply Median Filter
+    - In the `Tools` panel, search `Filter 2D image`, and click `Filter 2D image with scikit-image` from the search results
+    - In Galaxy main window,apply the followings
+        - `Filter type`: `Median`
+        - `Radius/Sigma`: Explore different values, such as `1`,`2` or `5`
+        - `Source file`: click the second button to activate `Multiple datasets`. Select images from the dropdown list.
+    - Click `Run Tool`
+    - Depending on the number of input images, you will see the corresponding number of outputs in the `History` panel on the right. Wait for them to turn green and download the resulting images.
diff --git a/_includes/multichannel_images/activity1_galaxy.md b/_includes/multichannel_images/activity1_galaxy.md
new file mode 100644
index 00000000..0edfe352
--- /dev/null
+++ b/_includes/multichannel_images/activity1_galaxy.md
@@ -0,0 +1,14 @@
+- Upload an [image](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyc_16bit__hela-cells.tif) to Galaxy
+    - Navigate to [Galaxy]( https://usegalaxy.eu)
+    - In the Tools panel on the left, click `Upload Data`.
+    - Click the `Paste/Fetch data` button.
+    - Paste the image URL: `https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyc_16bit__hela-cells.tif` and click the `Start` button.
+    - After the upload finishes, click the `Close` button. The image will then be available in your Galaxy history.
+- Start the Napari Interactive Tool
+    - In the Tools panel on the left, search for `Run Napari interactive tool`.
+    - Select `xyc_16bit__hela-cells.tif` from the `Images` dropdown list.
+    - Click the `Run Tool` button.
+    - Once the `Open` link once it appears at the top of the page, click it. This will open Napari in a separate browser tab.
+    - In the Napari tab, select `File -> Open File(s)`, and choose the image `xyc_16bit__hela-cells.tif` from the "input" folder. The image will be displayed in Napari's main window.
+    - In the layer pane located at the bottom left, right-click the image and select `Split RGB`.
+    - Experiment with adjusting the contrast of each channel.
diff --git a/_includes/pixels/pixels_act1_galaxy.md b/_includes/pixels/pixels_act1_galaxy.md
new file mode 100644
index 00000000..594aa6cd
--- /dev/null
+++ b/_includes/pixels/pixels_act1_galaxy.md
@@ -0,0 +1,17 @@
+## Pixel operation in Galaxy
+
+- Navigate to [Galaxy](https://usegalaxy.eu)
+- In the tools panel on the left, click `Upload Data`
+- Click `Paste/Fetch data` button
+- Paste the URL of [xy_8bit__nuclei_noisy_different_intensity.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif) and click `Start` .
+- After the upload finishes, click the `Close` button. The image will then be available in your Galaxy history.
+- Pixel operations
+  - In the `Tools` panel, search for `Operate on pixels with ImageJ2`, and click on it.
+  - In the main window
+    - `Select image`: select the image ```xy_8bit__nuclei_noisy_different_intensity.tif``` from the dropdown list.
+    - `Operation`: Explore different operations from the dropdown list. Refer to the `What it does` section for explainations of each operation.
+    - `Value`:  Some operations requires a value, input corresponding the value.
+    - Click `Run Tool` to start the operation.
+    - Results will be available in the Galaxy History panel once the process bar turns green.
+
+
diff --git a/_includes/tool_installation/galaxy.md b/_includes/tool_installation/galaxy.md
new file mode 100644
index 00000000..9c1b6f00
--- /dev/null
+++ b/_includes/tool_installation/galaxy.md
@@ -0,0 +1,3 @@
+<h4 id="galaxy"><a href="#galaxy">Install Galaxy</a></h4>
+
+Install the Galaxy instance.
diff --git a/_includes/tool_installation/install_galaxy_eu.md b/_includes/tool_installation/install_galaxy_eu.md
new file mode 100644
index 00000000..1b5dd15e
--- /dev/null
+++ b/_includes/tool_installation/install_galaxy_eu.md
@@ -0,0 +1,7 @@
+Galaxy Europe is the biggest Galaxy instance in Europe and one of the biggest worldwide. 
+- Free registration
+    - Navigate to usegalaxy.eu
+    - Click `Log in or Register` from the top menu.
+    - Click `Register here` to start registration process.
+    - Fill in the required fields and click the `Create` button.
+    - A confirmation email will be sent to your email address. Once the email is confirmed, you can start using the Galaxy Europe platform.
\ No newline at end of file
diff --git a/_includes/tool_installation/install_galaxy_local.md b/_includes/tool_installation/install_galaxy_local.md
new file mode 100644
index 00000000..f22a9ae7
--- /dev/null
+++ b/_includes/tool_installation/install_galaxy_local.md
@@ -0,0 +1,38 @@
+  
+
+To setup a Galaxy server locally, we will first clone the Galaxy github repository, make a few small edits to the galaxy.yaml configuration file, and then start the server.
+
+1. Clone the github repository with ```release_23.2``` branch.
+
+        git clone -b release_23.2 https://github.com/galaxyproject/galaxy.git
+        cd galaxy
+
+2. Add yourself as admin user in ```config/galaxy.yaml```
+
+        cp config/galaxy.yml.sample config/galaxy.yml
+
+3. open the ```galaxy.yml``` file with your favorite editor and edit the following line with your email address:
+
+    > admin_users: user@example.org
+.
+4. Start Galaxy
+
+		sh run.sh
+
+5. Galaxy will now install all its requirements, which may take a few minutes, when all is finished installing, you should see something like this in your screen:
+
+	> Starting server in PID 9560.\
+	> serving on http://localhost:8080
+    
+6. Open Galaxy
+- Open a web browser
+- Navigate to ```localhost:8080``` to access Galaxy
+
+7. Register an account on Galaxy using the email address you added to the ```config/galaxy.yml``` file. Once logged in, verify that you have a menu item named ```Admin``` in your top menu bar.
+
+8. Pull Galaxy tools from toolshed
+    - Click on the ```Admin``` menu.
+    - On the left pane, click on ```Install and Uninstall``` under the ```Tool Management``` section.
+    - Search for the tools via the ```Search Repositories`` text box on top of the main window.
+    - Click on the repository name to expand it. 
+    - Click ```Install``` to install the tool into your local Galaxy.
\ No newline at end of file
diff --git a/_modules/auto_threshold.md b/_modules/auto_threshold.md
index 13af5a6f..2dba42e2 100644
--- a/_modules/auto_threshold.md
+++ b/_modules/auto_threshold.md
@@ -20,7 +20,7 @@ figure: /figures/auto_threshold.png
 figure_legend: Input images, histograms (Huang threshold - blue, Otsu threshold - orange),  binary images (Huang), binary images (Otsu).
 
 multiactivities:
-  - ["auto_threshold/auto_threshold_act1.md", [["ImageJ GUI", "auto_threshold/auto_threshold_act1_imagejgui.md", "markdown"], ["skimage napari", "auto_threshold/auto_threshold_act1_skimage_napari.py", "python"]]]
+  - ["auto_threshold/auto_threshold_act1.md", [["ImageJ GUI", "auto_threshold/auto_threshold_act1_imagejgui.md", "markdown"], ["skimage napari", "auto_threshold/auto_threshold_act1_skimage_napari.py", "python"],["Galaxy", "auto_threshold/auto_threshold_act1_galaxy.md"]]]
   - ["auto_threshold/auto_threshold_act2.md", [["ImageJ GUI", "auto_threshold/auto_threshold_act2_imagejgui.md", "markdown"], ["skimage napari", "auto_threshold/auto_threshold_act2_skimage_napari.py", "python"]]]
 
 assessment: >
diff --git a/_modules/binarization.md b/_modules/binarization.md
index 2a8eda03..bc3350f0 100644
--- a/_modules/binarization.md
+++ b/_modules/binarization.md
@@ -8,7 +8,7 @@ prerequisites:
   - "[Data types](../datatypes)"
 objectives:
   - "Describe the relationship between an intensity image and a derived binary image"
-  - "Apply a threshold to segment an image into foreground and background regions"
+  - "Apply threshold to segment an image into foreground and background regions"
 motivation: |
   One strategy to detect objects or specific regions in images is to first distinguish so-called background pixels,
   which do not contain objects or interesting regions from foreground pixels, which mark the areas of interest.
@@ -26,8 +26,8 @@ figure: /figures/binarization.png
 figure_legend: Image before and after applying a threshold of 73 gray values.
 
 multiactivities:
-  - ["binarization/binarization_act1.md", [["ImageJ GUI", "binarization/binarization_act1_imagejgui.md"], ["ImageJ Macro", "binarization/binarization_act1_imagejmacro.ijm"], ["ImageJ Jython", "binarization/binarization_act1_jython.py"], ["skimage napari", "binarization/binarization_act1_skimage_napari.py", "python"]]]
-  - ["binarization/binarization_act2.md", [["ImageJ GUI", "binarization/binarization_act2_imagejgui.md"], ["ImageJ Macro", "binarization/binarization_act2_imagejmacro.ijm"], ["ImageJ Jython", "binarization/binarization_act2_jython.py"], ["ImageJ Jython + input parameters", "binarization/binarization_act2_jython_inputparameters.py"], ["skimage napari", "binarization/binarization_act2_skimage_napari.py"]]]
+  - ["binarization/binarization_act1.md", [["ImageJ GUI", "binarization/binarization_act1_imagejgui.md"], ["ImageJ Macro", "binarization/binarization_act1_imagejmacro.ijm"], ["ImageJ Jython", "binarization/binarization_act1_jython.py"], ["skimage napari", "binarization/binarization_act1_skimage_napari.py", "python"],["Galaxy", "binarization/binarization_act1_galaxy.md"]]]
+  - ["binarization/binarization_act2.md", [["ImageJ GUI", "binarization/binarization_act2_imagejgui.md"], ["ImageJ Macro", "binarization/binarization_act2_imagejmacro.ijm"], ["ImageJ Jython", "binarization/binarization_act2_jython.py"], ["ImageJ Jython + input parameters", "binarization/binarization_act2_jython_inputparameters.py"], ["skimage napari", "binarization/binarization_act2_skimage_napari.py"],["Galaxy", "binarization/binarization_act2_galaxy.md"]]]
   - ["binarization/binarization_act3.md", [["ImageJ GUI", "binarization/binarization_act3_imagejgui.md"]]]
 
 
diff --git a/_modules/lut.md b/_modules/lut.md
index 96a75f74..69a54568 100644
--- a/_modules/lut.md
+++ b/_modules/lut.md
@@ -26,10 +26,10 @@ figure_legend:
 multiactivities:
   - ["lut/lut_act1.md", [["ImageJ GUI", "lut/lut_act1_imagejgui.md", "markdown"], 
 	["ImageJ Macro", "lut/lut_act1_imagejmacro.ijm", "java"], 
-	["skimage napari", "lut/lut_act1_skimage_napari.py", "python"]]]
+	["skimage napari", "lut/lut_act1_skimage_napari.py", "python"],["Galaxy Napari","lut/lut_act1_galaxy.md"]]]
   - ["lut/lut_act2.md", [["ImageJ GUI", "lut/lut_act2_imagejgui.md"], 
   ["ImageJ Macro", "lut/lut_act2_imagejmacro.ijm", "java"], 
-	["skimage napari", "lut/lut_act2_skimage_napari.py", "python"]]]
+	["skimage napari", "lut/lut_act2_skimage_napari.py", "python"],["Galaxy Napari","lut/lut_act2_galaxy.md"]]]
 
 keypoints:
   - A LUT has configurable contrast limits that determine the pixel value range that is rendered linearly.
diff --git a/_modules/median_filter.md b/_modules/median_filter.md
index 5cc7aaf3..451df09b 100644
--- a/_modules/median_filter.md
+++ b/_modules/median_filter.md
@@ -22,7 +22,7 @@ figure: /figures/median_filter_grayscale.png
 figure_legend: Median filter example. Left - Raw; Right - After a 5x5 median filter.
 
 multiactivities:
-  - ["median_filter/median_filter.md", [["ImageJ Macro", "median_filter/median_filter_imagejmacro.ijm", "java"], ["skimage napari", "median_filter/median_filter_skimage_napari.py", "python"]]]
+  - ["median_filter/median_filter.md", [["ImageJ Macro", "median_filter/median_filter_imagejmacro.ijm", "java"], ["skimage napari", "median_filter/median_filter_skimage_napari.py", "python"],["Galaxy", "median_filter/median_filter_galaxy.md"]]]
 
 assessment: |
 
diff --git a/_modules/multichannel_images.md b/_modules/multichannel_images.md
index b9072071..561dbfda 100644
--- a/_modules/multichannel_images.md
+++ b/_modules/multichannel_images.md
@@ -36,6 +36,7 @@ activities:
     - ["ImageJ GUI - Inspect/view channels", "multichannel_images/activity1_imagejgui.md", "markdown"]
     #- ["binarization/binarization_act1.md", [["ImageJ GUI", "binarization/binarization_act1_imagejgui.md"], ["ImageJ Macro", "binarization/binarization_act1_imagejmacro.ijm"], ["ImageJ Jython", "binarization/binarization_act1_jython.py"], ["skimage napari", "binarization/binarization_act1_skimage_napari.py", "python"]]]
     - ["ImageJ GUI - Save channels as Tiff/RGB image", "multichannel_images/activity2_imagejgui.md", "markdown"]
+    - ["Galaxy Napari - Inspect/view channels", "multichannel_images/activity1_galaxy.md", "markdown"]
 
 assessment: >
 
diff --git a/_modules/pixels.md b/_modules/pixels.md
index 89b818d2..5bf0469c 100644
--- a/_modules/pixels.md
+++ b/_modules/pixels.md
@@ -26,7 +26,7 @@ figure_legend:  Digital image pixel array and gray-scale rendering. This array (
 
 
 multiactivities:
-  - ["pixels/pixels_act1.md", [["ImageJ GUI", "pixels/pixels_act1_imagejgui.md"], ["skimage napari", "pixels/pixels_act1_skimage_napari.py"], ["MATLAB", "pixels/pixels_act1_matlab.m"]]]
+  - ["pixels/pixels_act1.md", [["ImageJ GUI", "pixels/pixels_act1_imagejgui.md"], ["skimage napari", "pixels/pixels_act1_skimage_napari.py"], ["MATLAB", "pixels/pixels_act1_matlab.m"], ["Galaxy", "pixels/pixels_act1_galaxy.md"]]]
   - ["pixels/pixels_3d_image_inspection.md", [["skimage napari", "pixels/pixels_3d_image_inspection_skimage_napari.py"]]]
   - ["pixels/collagen_inspection.md", [["ImageJ GUI", "pixels/collagen_inspection_imagejgui.md"]]]
   
diff --git a/_modules/tool_installation.md b/_modules/tool_installation.md
index 81ef2c77..f1af193e 100644
--- a/_modules/tool_installation.md
+++ b/_modules/tool_installation.md
@@ -23,6 +23,8 @@ multiactivities:
   - ["tool_installation/imagej.md", [["Fiji", "tool_installation/install_fiji.md"], ["IntelliJ", "tool_installation/install_intellij.md"] ]]
   - ["tool_installation/skimage_napari.md", [["conda", "tool_installation/install_skimage_napari_conda.md"], ["BAND", "tool_installation/install_skimage_napari_BAND.md"] ]]
   - ["tool_installation/napari_standalone.md", [["desktop download", "tool_installation/install_napari_standalone.md"]]]
+  - ["tool_installation/galaxy.md", [["Start a local Galaxy", "tool_installation/install_galaxy_local.md"],["Using Galaxy EU", "tool_installation/install_galaxy_eu.md"]]]
+
 assessment:
 
 learn_next: