Merge pull request #4571 from galaxyproject/swallow-albacore

Fix sharing snippet

_config.yml

@@ -145,7 +145,7 @@ icon-tag:
   hands_on: fas fa-pencil-alt
   help: far fa-question-circle
   history-annotate: fas fa-comment
-  history-share: fas fa-toggle-on
+  history-share: fas fa-share-alt
   instances: fas fa-globe
   interactive_tour: fas fa-magic
   keypoints: fas fa-key

faqs/galaxy/histories_sharing.md

@@ -9,6 +9,7 @@ contributors: [shiltemann]
 
 Sharing your history allows others to import and access the datasets, parameters, and steps of your history.
 
+Access the history sharing menu via the History Options dropdown ({% icon galaxy-history-options %}), and clicking "{% icon history-share %} Share or Publish"
 
 1. **Share via link**
    - Open the **History Options** {% icon galaxy-gear %} menu (gear icon) at the top of your history panel and select **Share or Publish**

topics/climate/tutorials/fates-jupyterlab/tutorial.md

@@ -506,17 +506,7 @@ p.fig.savefig('CANOPY_HEIGHT_DIST.png')
 
 One of the most important features of Galaxy comes at the end of an analysis. When you have published striking findings, it is important that other researchers are able to reproduce your in-silico experiment. Galaxy enables users to easily share their workflows and histories with others.
 
-To share a history, click on the {% icon galaxy-gear %} icon in the history panel and select `Share or Publish`. On this page you can do 3 things:
-
-1. **Make History Accessible via Link**. This generates a link that you can give out to others. Anybody with this link will be able to view your history.
-2. **Make History Accessible and Publish**. This will not only create a link, but will also publish your history. This means your history will be listed under `Shared Data → Histories` in the top menu.
-3. **Share with a user**. This will share the history only with specific users on the Galaxy instance.
-
-> <comment-title>Permissions</comment-title>
-> Different servers have different default permission settings. Some servers create all of your datasets completely private to you, while others make them accessible if you know the secret ID.
->
-> Be sure to select **Also make all objects within the History accessible** whenever you make a history accessible via link, otherwise whomever you send your link to might not be able to see your history.
-{: .comment}
+{% snippet faqs/galaxy/histories_sharing.md %}
 
 > <hands-on-title>Share history</hands-on-title>
 >

topics/climate/tutorials/fates/tutorial.md

@@ -392,17 +392,7 @@ We would like to run a CLM-FATES case where the atmospheric Carbon Dioxyde Conce
 
 One of the most important features of Galaxy comes at the end of an analysis. When you have published striking findings, it is important that other researchers are able to reproduce your in-silico experiment. Galaxy enables users to easily share their workflows and histories with others.
 
-To share a history, click on the {% icon galaxy-gear %} icon in the history panel and select `Share or Publish`. On this page you can do 3 things:
-
-1. **Make History Accessible via Link**. This generates a link that you can give out to others. Anybody with this link will be able to view your history.
-2. **Make History Accessible and Publish**. This will not only create a link, but will also publish your history. This means your history will be listed under `Shared Data → Histories` in the top menu.
-3. **Share with a user**. This will share the history only with specific users on the Galaxy instance.
-
-> <comment-title>Permissions</comment-title>
-> Different servers have different default permission settings. Some servers create all of your datasets completely private to you, while others make them accessible if you know the secret ID.
->
-> Be sure to select **Also make all objects within the History accessible** whenever you make a history accessible via link, otherwise whomever you send your link to might not be able to see your history.
-{: .comment}
+{% snippet faqs/galaxy/histories_sharing.md %}
 
 > <hands-on-title>Share history</hands-on-title>
 >

topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.md

@@ -737,20 +737,9 @@ With this adjustment, we can reuse our workflow on the data, and analyze and vis
 
 One of the most important features of Galaxy comes at the end of an analysis. When you have published striking findings, it is important that other researchers are able to reproduce your in-silico experiment. Galaxy enables users to easily share their workflows and histories with others.
 
-To share a history, click on the {% icon galaxy-gear %} icon in the history panel and select `Share or Publish`. On this page you can do 3 things:
-
-1. **Make History Accessible via Link**. This generates a link that you can give out to others. Anybody with this link will be able to view your history.
-2. **Make History Accessible and Publish**. This will not only create a link, but will also publish your history. This means your history will be listed under `Shared Data → Histories` in the top menu.
-3. **Share with a user**. This will share the history only with specific users on the Galaxy instance.
-
-> <comment-title>Permissions</comment-title>
-> Different servers have different default permission settings. Some servers create all of your datasets completely private to you, while others make them accessible if you know the secret ID.
->
-> Be sure to select **Also make all objects within the History accessible** whenever you make a history accessible via link, otherwise whomever you send your link to might not be able to see your history.
-{: .comment}
+{% snippet faqs/galaxy/histories_sharing.md %}
 
 > <hands-on-title>Share history</hands-on-title>
->
 > 1. Share your history with your neighbour.
 > 2. Find the history shared by your neighbour. Histories shared with specific users can be accessed by those users under their top masthead "User" menu under `Histories shared with me`.
 {: .hands_on}

topics/introduction/tutorials/galaxy-intro-101/tutorial.md

@@ -616,17 +616,7 @@ Now that we have built our workflow, let's use it on some different data. For ex
 
 One of the most important features of Galaxy comes at the end of an analysis. When you have published striking findings, it is important that other researchers are able to reproduce your in-silico experiment. Galaxy enables users to easily share their workflows and histories with others.
 
-To share a history, click on the {% icon galaxy-gear %} icon in the history panel and select `Share or Publish`. On this page you can do 3 things:
-
-1. **Make History Accessible via Link**. This generates a link that you can give out to others. Anybody with this link will be able to view your history.
-2. **Make History Accessible and Publish**. This will not only create a link, but will also publish your history. This means your history will be listed under `Shared Data → Histories` in the top menu.
-3. **Share with a user**. This will share the history only with specific users on the Galaxy instance.
-
-> <comment-title>Permissions</comment-title>
-> Different servers have different default permission settings. Some servers create all of your datasets completely private to you, while others make them accessible if you know the secret ID.
->
-> Be sure to select **Also make all objects within the History accessible** whenever you make a history accessible via link, otherwise whomever you send your link to might not be able to see your history.
-{: .comment}
+{% snippet faqs/galaxy/histories_sharing.md %}
 
 > <hands-on-title>Share history and workflow</hands-on-title>
 >

topics/single-cell/tutorials/scanpy_parameter_iterator/tutorial.md

@@ -165,7 +165,7 @@ The output of the Parameter Iterator is the list of datasets. We will be working
 >
 > 1. {% tool [Scanpy ComputeGraph](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_compute_graph/scanpy_compute_graph/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `Scanpy RunPCA: AnnData object`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"File with n_neighbours, use with parameter iterator. Overrides the n_neighbors setting"*:
 >       - Click on {% icon param-collection %} (*Dataset collection*)
 >       - Choose `Parameter iterated - n-neighbours`
@@ -196,20 +196,20 @@ Where are we now in our workflow?
 > 1. {% tool [Scanpy RunTSNE](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_run_tsne/scanpy_run_tsne/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-collection %} *"Input object in AnnData/Loom format"* (make sure you choose *Dataset collection*): `Scanpy ComputeGraph on collection X: Graph object AnnData (n-neighbours)`
 >    - *"Use the indicated representation"*: `X_pca`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"The perplexity is related to the number of nearest neighbours, select a value between 5 and 50"*: `30`
 >
 > 1A. **Rename** {% icon galaxy-pencil %} the Anndata object collection output: `Scanpy RunTSNE on collection X: tSNE object AnnData (n-neighbours)` (you have to first click on the collection so that you see the datasets, and then rename it)
 >
 > 2. {% tool [Scanpy RunUMAP](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_run_umap/scanpy_run_umap/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-collection %} *"Input object in AnnData/Loom format"* (make sure you choose *Dataset collection*): `Scanpy RunTSNE on collection X: tSNE object AnnData (n-neighbours)`
->    - *"Use programme defaults"*: {% icon history-share %} `Yes`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `Yes`
 >
 > 2A. **Rename** {% icon galaxy-pencil %} the Anndata object collection output: `Scanpy RunUMAP on collection X: UMAP object AnnData (n-neighbours)` (you have to first click on the collection so that you see the datasets, and then rename it)
 >
 > 3. {% tool [Scanpy FindCluster](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_find_cluster/scanpy_find_cluster/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-collection %} *"Input object in AnnData/Loom format"* (make sure you choose *Dataset collection*): `Scanpy RunUMAP on collection X: UMAP object AnnData`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"Resolution, high value for more and smaller clusters"*: `0.6`
 >
 > 3A. **Rename** {% icon galaxy-pencil %} the Anndata object collection output: `Scanpy FindCluster on collection X: Clusters AnnData (n-neighbours)` (you have to first click on the collection so that you see the datasets, and then rename it)
@@ -279,7 +279,7 @@ The next tool in our workflow is Scanpy RunTSNE, which contains the perplexity p
 > 1. {% tool [Scanpy RunTSNE](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_run_tsne/scanpy_run_tsne/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `Scanpy ComputeGraph on data X and data Y: Graph object AnnData` with the tag `#n-neighbours_15`
 >    - *"Use the indicated representation"*: `X_pca`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"The perplexity is related to the number of nearest neighbours"*:
 >       - Click on {% icon param-collection %} (*Dataset collection*)
 >       - Choose `Parameter iterated - perplexity`
@@ -293,13 +293,13 @@ Changing the value of perplexity will only affect the tSNE graphs, so we can com
 > <hands-on-title> Complete the workflow </hands-on-title>
 > 1. {% tool [Scanpy RunUMAP](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_run_umap/scanpy_run_umap/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-collection %} *"Input object in AnnData/Loom format"* (make sure you choose *Dataset collection*): `Scanpy RunTSNE on collection X: tSNE object AnnData (perplexity)`
->    - *"Use programme defaults"*: {% icon history-share %} `Yes`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `Yes`
 >
 > 1A. **Rename** {% icon galaxy-pencil %} the Anndata object collection output: `Scanpy RunUMAP on collection X: UMAP object AnnData (perplexity)` (you have to first click on the collection so that you see the datasets, and then rename it)
 >
 > 2. {% tool [Scanpy FindCluster](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_find_cluster/scanpy_find_cluster/1.8.1+galaxy9) %} with the following parameters:
 >    - {% icon param-collection %} *"Input object in AnnData/Loom format"* (make sure you choose *Dataset collection*): `Scanpy RunUMAP on collection X: UMAP object AnnData (perplexity)`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"Resolution, high value for more and smaller clusters"*: `0.6`
 >
 > 3A. **Rename** {% icon galaxy-pencil %} the Anndata object collection output: `Scanpy FindCluster on collection X: Clusters AnnData (perplexity)` (you have to first click on the collection so that you see the datasets, and then rename it)
@@ -368,7 +368,7 @@ The last tool that we can use Parameter Iterator for is **Scanpy FindCluster** {
 > <hands-on-title> FindCluster with iterated parameter </hands-on-title>
 > 1. {% tool [Scanpy FindCluster](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_find_cluster/scanpy_find_cluster/1.8.1+galaxy9) %} with the following parameters:
 >   - {% icon param-file %} *"Input object in AnnData/Loom format"*: `Scanpy RunUMAP on data X: UMAP object AnnData` with the tags `#n-neighbours_15` and `#perplexity_30`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"File with resolution, use with parameter iterator. Overrides the resolution setting"*:
 >       - Click on {% icon param-collection %} (*Dataset collection*)
 >       - Choose `Parameter iterated - resolution`

topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md

@@ -616,7 +616,7 @@ We're still looking at around 20 dimensions at this point. We need to identify h
 >
 > 1. {% tool [Scanpy ComputeGraph](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_compute_graph/scanpy_compute_graph/1.8.1+galaxy1) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `output_h5ad` (output of **Scanpy RunPCA** {% icon tool %})
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"Maximum number of neighbours used"*: `15`
 >    - *"Use the indicated representation"*: `X_pca`
 >    - *"Number of PCs to use"*: `20`
@@ -638,12 +638,12 @@ Two major visualisations for this data are tSNE and UMAP. We must calculate the
 > 1. {% tool [Scanpy RunTSNE](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_run_tsne/scanpy_run_tsne/1.8.1+galaxy1) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `output_h5ad` (output of **Scanpy ComputeGraph** {% icon tool %})
 >    - *"Use the indicated representation"*: `X_pca`
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"The perplexity is related to the number of nearest neighbours, select a value between 5 and 50"*: `30`
 >
 > 2. {% tool [Scanpy RunUMAP](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_run_umap/scanpy_run_umap/1.8.1+galaxy0) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `output_h5ad` (output of **Scanpy RunTSNE** {% icon tool %})
->    - *"Use programme defaults"*: {% icon history-share %} `Yes`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `Yes`
 {: .hands_on}
 
 {% icon congratulations %} Congratulations! You have prepared your object and created neighborhood coordinates. We can now use those to call some clusters!
@@ -677,7 +677,7 @@ Finally, let's identify clusters! Unfortunately, it's not as majestic as biologi
 >
 > 1. {% tool [Scanpy FindCluster](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_find_cluster/scanpy_find_cluster/1.8.1+galaxy0) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `output_h5ad` (output of **Scanpy RunUMAP** {% icon tool %})
->    - *"Use programme defaults"*: {% icon history-share %} `No`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `No`
 >    - *"Resolution, high value for more and smaller clusters"*: `0.6`
 {: .hands_on}
 
@@ -699,7 +699,7 @@ Nearly plotting time! But one final piece is to add in SOME gene information. Le
 > 3. {% tool [Scanpy FindMarkers](toolshed.g2.bx.psu.edu/repos/ebi-gxa/scanpy_find_markers/scanpy_find_markers/1.8.1+galaxy0) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in AnnData/Loom format"*: `Final object`
 >    - *"The sample grouping/clustering to use"*: `genotype`
->    - *"Use programme defaults"*: {% icon history-share %} `Yes`
+>    - *"Use programme defaults"*: {% icon param-toggle %} `Yes`
 >
 > 4. **Rename** {% icon galaxy-pencil %} output table (not h5ad) `Markers - genotype`
 >
@@ -821,7 +821,7 @@ The authors weren't interested in further annotation of the DP cells, so neither
 >
 > 2. {% tool [AnnData Operations](toolshed.g2.bx.psu.edu/repos/ebi-gxa/anndata_ops/anndata_ops/1.8.1+galaxy0) %} with the following parameters:
 >    - {% icon param-file %} *"Input object in hdf5 AnnData format"*: `Final object`
->    - *"Copy observations (such as clusters)"*: {% icon history-share %} *Yes*
+>    - *"Copy observations (such as clusters)"*: {% icon param-toggle %} *Yes*
 >    - **Keys from obs to copy**
 >    - *"+ Insert Keys from obs to copy"*
 >    - *"Key contains"*: `louvain`