Several small improvements

_includes/datatypes/metadata_and_datatype.md

@@ -1,7 +1,5 @@
 <h4 id="datatype_metadata"><a href="#datatype_metadata">Explore image data types</a></h4> 
 
-Explore the datatypes of images! 
-
 Observe that for some software the datatype of the loaded image **does not match** the datatype given in the metadata.
 
 The reason is that some software only support data types where the bit depth is a multiple of 8. For example, unsigned integer 12-bit data may not be supported. 

_includes/datatypes/metadata_and_datatype_imagej_gui.md

@@ -1,13 +1,11 @@
-For any image mentioned in the activity perform the below tasks.
-
+- Download any of the above images
 - Open the image using `Plugins > Bio-Formats > Bio-Formats Importer`
     - Select `[X] Display OME-XML metadata`
     - Click `[ OK ]`
-- Check whether the image data type mentioned in `Image > Type` is the same as the one mentioned in the metadata.
-  - Metadata: look for `SignificantBits` and `Type`
-- Check the maximum value in the image, e.g. using `Analyze > Histogram`
-- How does this maximum value compare the image's datatype?
-    - For example, you may find a value of 4095, which is the maximum of an unsigned integer 12 bit image, which may be the datatype mentioned in the image metadata, however ImageJ may represent this image as a 16 bit image. Appreciate that this can be confusing!
-    - If you find the maximum of the image to be identical to to maximum that the datatype of the image can represent you may have an issue with saturation! Check this
+- Check whether the information in `Image > Type` is the same as the one mentioned in the displayed metadata (look for `SignificantBits` and `Type`)
+- Also check the maximum value in the image, e.g. using `Analyze > Histogram`
+- How does this maximum value compare to the image datatype?
+    - For example, you may find a value of 4095, which is the maximum of an unsigned integer 12-bit image, which may be the datatype mentioned in the image metadata, however ImageJ may represent this image as a 16-bit image. Appreciate that this can be confusing!
+    - If you find the maximum of the image to be identical to maximum that the datatype of the image can represent you may have an issue with saturation! Check this
       - by hovering with the mouse over bright regions
       - using the `HiLo` LUT with appropriate contrast settings, i.e. the maximum should be the maximum of your datatype! 

_includes/image_inspection_and_presentation/inspect_collagen_data.md

@@ -2,15 +2,14 @@
 
 In this activity we inspect images of tissue culture cells expressing collagen.
 
-The below drop down menu allows you to perfom all steps in one go, which requires prior knowledge as 
-mentioned in this module's prerequisites.
+The below drop down menu allows you to perfom all steps in one go, which requires prior knowledge as mentioned in this module's prerequisites.
 
 If you are a beginner, we recommend first learning the individual steps
-  - [Inspect the image pixel values](https://neubias.github.io/training-resources/pixels/index.html#inspect_collagen) 
-  - [Adjust the image display](https://neubias.github.io/training-resources/lut/index.html#configure_luts)
-  - [Inspect and display spatial calibration](https://neubias.github.io/training-resources/spatial_calibration/index.html#scale_bar)
-  - [Inspect image data type](https://neubias.github.io/training-resources/datatypes/index.html#datatype_metadata)
-  - [Inspect microscope settings](https://neubias.github.io/training-resources/image_file_formats/index.html#open)
+  - [Image pixel value inspection](https://neubias.github.io/training-resources/pixels/index.html#inspect_collagen) 
+  - [Image display adjustment](https://neubias.github.io/training-resources/lut/index.html#configure_luts)
+  - [Image spatial calibration](https://neubias.github.io/training-resources/spatial_calibration/index.html#scale_bar)
+  - [Image data types](https://neubias.github.io/training-resources/datatypes/index.html#datatype_metadata)
+  - [Image microscopy metadata](https://neubias.github.io/training-resources/image_file_formats/index.html#open)
     - Check out the collagen image and find important metadata, such as exposure time and illumination intensity
 
 

_includes/lut/lut_act2_imagejgui.md

@@ -1,5 +1,7 @@
-- Open one of the pairs of images mentioned in the activity preface
+- Open one of the above pairs of images
 - Choose a suitable LUT using `Image › Lookup Tables ›  ... `
-- Adjust brightness and contrast in the brighter image using `Image › Adjust › Brightness/Contrast...`
-- Use the `Set` button in `Image › Adjust › Brightness/Contrast...` and check `[X] propagate to ...`
-- Visualise the LUT as an inset in both images using `Analyze › Tools › Calibration Bar...`
+- Adjust brightness and contrast in one image using `Image › Adjust › Brightness/Contrast...`
+  - To avoid intensity clipping one typically sets the contrast on the brightest image (this may depend on your scientific question though...)
+  - To find out which image is brighter you can try to use `Analyze > Histogram` 
+- Use the `Set` button in `Image › Adjust › Brightness/Contrast...` and check `[X] Propagate to all other open images`
+- Visualise the current LUT as an inset in both images using `Analyze › Tools › Calibration Bar...`

_includes/pixels/collagen_inspection_imagejgui.md

@@ -4,9 +4,9 @@
 - Read image dimensions in image header
 - Mouse hover to see gray value and pixel position in the ImageJ status bar
 - Zoom in and out using the arrow up and down keys
-- Line profile: Draw a line ROI and use **[Analyze > Plot Profile]** or **[Ctrl + K]**.
-  - Use the ` Live ` button to explore different image regions
-- Histogram: **[Analyze > Histogram]** or **[Ctrl + H]**
+- Draw a line ROI and use `Analyze > Plot Profile`
+  - Use the `Live` button to explore different image regions
+- Create a histogram using `Analyze > Histogram`
   - Draw a rectangluar ROI to restrict the histogram computation to a small region
-  - Use the ` Live ` button to explore different image regions
+  - Use the `Live` button to explore different image regions
     - Understand what you see in the histogram

_includes/spatial_calibration/scale_bar_imagej_gui.md

@@ -1,8 +1,9 @@
-- Open one or more of the images that are mentioned in the activity preface, using `Plugins › Bio-Formats › Bio-Formats Importer`
-    - `[X] Display metadata`
+- Open one of the above images using `Plugins › Bio-Formats › Bio-Formats Importer`
     - `[X] Display OME-XML metadata`
-- Find the pixel calibration in the metadata text windows
-- Also inspect the pixel size using `Image > Properties`
-- Check that all information are consistent
+- Find the pixel calibration in the metadata text
+- Also inspect the pixel size in `Image > Properties`
+- Check that those information are consistent
 - Add a scale bar to the image using `Analyze › Tools › Scale Bar...`
     - Explore the various options for where and how to place the scale bar
+- Open the same image again using `File > Open...`
+  - Are you getting the same pixel calibration?

_includes/tool_installation/install_fiji.md

@@ -1,5 +1,6 @@
-Fiji bundles the ImageJ GUI, a scripting editor and various plugins available as optional update sites.
+Fiji bundles the ImageJ GUI, a scripting editor and various plugins; additional plugins can be readily installed via so-called update sites.
 
 1. Install Fiji on your machine, as described [here](https://imagej.net/software/fiji/downloads).
-2. To exectue the training modules, please add the following updates sites, as described [here](https://imagej.net/update-sites/following).
+2. Add the following Fiji updates sites, as described [here](https://imagej.net/update-sites/following).
   - IJPB-Plugins
+  - BioVoxxel

_includes/workflow_segment_2d_nuclei_measure_shape/segment_2d_nuclei_imagejgui.md

@@ -1,19 +1,18 @@
-- [ Process › Binary › Options... ]
-  - [X] Black background
-- Open one of the input image (see above)
-- [ Image › Duplicate... ]
-  - Title = binary
+- `Process › Binary › Options...`
+  - `[X] Black background`, because we work with fluorescence data
+- Open one of the above images
+- `Image › Duplicate...`
+  - `Title = binary`
 - Draw a line profile to find a good threshold
   - Use the straight line tool in the Fiji menu bar
-  - [ Analyze › Plot Profile ]
-    - [ Live ] and move the line around, including nuclei and background pixels
-- [ Image › Adjust › Manual Threshold... ]
-  - Min = 25
-  - Max = 255
-- [ Process › Binary › Convert to Mask ]
-- [ Plugins › MorphoLibJ › Binary Images › Connected Components Labeling ]
-  - connectivity = 4 
-  - type= 8 bits
-- [ Image › Lookup Tables › glasbey_on_dark ]
-- [ Plugins › MorphoLibJ › Analyze › Analyze Regions ]
-  - [X] area
+  - `Analyze › Plot Profile`
+    - `Live` and move the line around, including nuclei and background pixels
+- `Image › Adjust › Manual Threshold...`
+  - `Min = 25`
+  - `Max = 255`, because this is the maximum of the image data-type
+- `Process › Binary › Convert to Mask`
+- `Plugins › MorphoLibJ › Binary Images › Connected Components Labeling`
+  - `connectivity = 4`, for no good reason... 
+  - `type = 8 bits`, because we will have less than 255 objects
+- `Plugins › MorphoLibJ › Analyze › Analyze Regions`
+  - You may subset the measurements if you are not interested in all