add instructions for the Galaxy platform

_includes/auto_threshold/auto_threshold_act1_galaxy.md

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+- 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 `ImageJ2`.
+  - Choose the tool named `Adjust threshold with ImageJ2`, 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.
+        - Set the minimum threshold value to `20`
+        - Leave the maximum threshold value unchanged.
+        - Select `Default` from the `Method` dropdown list.
+        - Select `Black and White` as `Display` type
+        - Set `Black background` to `Yes`
+        - Set `Stack histogram` to `No`
+        - 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.
+      - `xy_8bit__nuclei_with_offset.tif`
+        - Select the image `xy_8bit__nuclei_with_offset.tif` from the `Select image` dropdown list.
+        - Set the minimum threshold value to `20`
+        - Set the maximum threshold value to `40`.
+        - Select `Default` from the `Method` dropdown list.
+        - Select `Black and White` as `Display` type.
+        - Set `Black background` to `Yes`
+        - Set `Stack histogram` to `No`
+        - 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.
+      - Leave the minimum threshold value unchanged.
+      - Leave the maximum threshold value unchanged.
+      - Select one of the items from the `Method` dropdown list.
+      - Select `Black and White` as `Display` type
+      - Set `Black background` to `Yes`
+      - Set `Stack histogram` 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.
+    - `xy_8bit__nuclei_with_offset.tif`
+      - Select the image `xy_8bit__nuclei_with_offset.tif` from the `Select image` dropdown list.
+      - Leave the minimum threshold value unchanged.
+      - Leave the maximum threshold value unchanged.
+      - Select one of the items from the `Method` dropdown list.
+      - Select `Black and White` as `Display` type.
+      - Set `Black background` to `Yes`
+      - Set `Stack histogram` 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.   

_includes/auto_threshold/auto_threshold_act2_galaxy.md

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+- 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 URL of the image : [xy_8bit__nuclei_autothresh.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit__nuclei_autothresh.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.
+- Auto Threshold on 3D image
+  - In the Tools panel on the left side, search `ImageJ2`.
+  - Choose the tool named `Adjust threshold with ImageJ2`, and click on it.
+    - Select the image `xy_8bit__nuclei_autothresh.tif` from the `Select image` dropdown list.
+    - Select one of the items from the `Method` dropdown list. e.g. `Otsu`
+    - Select `Black and White` as `Display` type
+    - Set `Black background` to `Yes`
+    - Set `Stack histogram` to `Yes` to compute the histogram of the whole stack and then compute the threshold based on that histogram.
+  - 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.

_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.

_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

_includes/median_filter/median_filter_galaxy.md

@@ -0,0 +1,22 @@
+- Upload the following images to Galaxy
+    - Go to 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.

_includes/multichannel_images/activity1_galaxy.md

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+- 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.

_includes/pixels/pixels_act1_galaxy.md

@@ -0,0 +1,17 @@
+## Pixel operation in Galaxy
+
+- Navigate to [Galaxy](https://usergalaxy.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`, 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.
+
+

_modules/auto_threshold.md

@@ -20,8 +20,8 @@ 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_act2.md", [["ImageJ GUI", "auto_threshold/auto_threshold_act2_imagejgui.md", "markdown"], ["skimage napari", "auto_threshold/auto_threshold_act2_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 ImageJ", "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"],["Galaxy ImageJ", "auto_threshold/auto_threshold_act2_galaxy.md"]]]
 
 assessment: >
 

_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.

_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: |
 

_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: >
 

_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/collagen_inspection.md", [["ImageJ GUI", "pixels/collagen_inspection_imagejgui.md"]]]
 
 assessment: >