fix remaining discovered issues

topics/admin/tutorials/sentry/tutorial.md

@@ -53,7 +53,6 @@ To proceed from here it is expected that:
 >
 > 1. You have set up a working Galaxy instance as described in the [ansible-galaxy](../ansible-galaxy/tutorial.html) tutorial.
 >
-
 {: .comment}
 
 # Installing and Configuring
@@ -354,7 +353,7 @@ In addition to sending logging errors to Sentry you can also collect failing too
 >    {: data-commit="Configure error reporting"}
 >
 > 2. Create a testing tool in `files/galaxy/tools/job_properties.xml`.
-.
+>
 >    {% raw %}
 >    ```diff
 >    --- /dev/null
@@ -481,7 +480,6 @@ To generate a tool error, run the job properties testing tool and set the `failb
 
 > <hands-on-title>Open the Galaxy Project in Sentry</hands-on-title>
 > 1. Go to your Sentry instance and click on issues. You should see an issue for the tool run error.
-
 {: .hands_on }
 
 ## Reporting errors from the Pulsar server
@@ -518,7 +516,7 @@ It is also possible to report errors from the Pulsar server. You can either use
 >    ```
 >    {: data-commit="Configure pulsar for error reporting"}
 >
-> > 4. Run the pulsar playbook.
+> 4. Run the pulsar playbook.
 >
 >    > <code-in-title>Bash</code-in-title>
 >    > ```bash

topics/assembly/tutorials/mrsa-illumina/tutorial.md

@@ -58,10 +58,11 @@ In this training you're going to make an assembly of data produced by
 Japan" from {% cite Hikichi_2019 %} which describes:
 
 > Methicillin-resistant *Staphylococcus aureus* (MRSA) is a major pathogen
-causing nosocomial infections, and the clinical manifestations of MRSA
-range from asymptomatic colonization of the nasal mucosa to soft tissue
-infection to fulminant invasive disease. Here, we report the complete
-genome sequences of eight MRSA strains isolated from patients in Japan.
+> causing nosocomial infections, and the clinical manifestations of MRSA
+> range from asymptomatic colonization of the nasal mucosa to soft tissue
+> infection to fulminant invasive disease. Here, we report the complete
+> genome sequences of eight MRSA strains isolated from patients in Japan.
+{: .quote}
 
 > <agenda-title></agenda-title>
 >

topics/assembly/tutorials/mrsa-nanopore/tutorial.md

@@ -61,10 +61,11 @@ In this training you're going to make an assembly of data produced by
 Japan" from {% cite Hikichi_2019 %} which describes:
 
 > Methicillin-resistant *Staphylococcus aureus* (MRSA) is a major pathogen
-causing nosocomial infections, and the clinical manifestations of MRSA
-range from asymptomatic colonization of the nasal mucosa to soft tissue
-infection to fulminant invasive disease. Here, we report the complete
-genome sequences of eight MRSA strains isolated from patients in Japan.
+> causing nosocomial infections, and the clinical manifestations of MRSA
+> range from asymptomatic colonization of the nasal mucosa to soft tissue
+> infection to fulminant invasive disease. Here, we report the complete
+> genome sequences of eight MRSA strains isolated from patients in Japan.
+{: .quote}
 
 > <agenda-title></agenda-title>
 >

topics/assembly/tutorials/unicycler-assembly/tutorial.md

@@ -70,8 +70,7 @@ There are 12,738 [2d-reads](http://www.nature.com/nmeth/journal/v12/n4/fig_tab/n
 You can see that there many reads under the second peak with median of approximately 7.5 kb.
 
 > <warning-title>Oxford Nanopore Data Format</warning-title>
-> Oxford Nanopore machines output
- data in [fast5](http://bioinformatics.cvr.ac.uk/blog/exploring-the-fast5-format/) format that contains additional information besides sequence data. In this tutorial we assume that this data is *already* converted into [fastq](https://en.wikipedia.org/wiki/FASTQ_format). An additional tutorial dedicated to handling fast5 datasets will be developed shortly.
+> Oxford Nanopore machines output data in [fast5](http://bioinformatics.cvr.ac.uk/blog/exploring-the-fast5-format/) format that contains additional information besides sequence data. In this tutorial we assume that this data is *already* converted into [fastq](https://en.wikipedia.org/wiki/FASTQ_format). An additional tutorial dedicated to handling fast5 datasets will be developed shortly.
 {: .warning}
 
 

topics/assembly/tutorials/vgp_workflow_training/tutorial.md

@@ -72,7 +72,7 @@ This tutorial assumes you are comfortable getting data into Galaxy, running jobs
 
 The {VGP} assembly pipeline has a modular organization, consisting in five main subworkflows (fig. 1), each one integrated by a series of data manipulation steps. Firstly, it allows the evaluation of intermediate steps, which facilitates the modification of parameters if necessary, without the need to start from the initial stage. Secondly, it allows to adapt the workflow to the available data.
 
-> ![Figure 1: VGP pipeline modules](../../images/vgp_assembly/VGP_workflow_modules.png "VGP assembly pipeline. The VGP workflow is implemented in a modular fashion: it consists of five independent subworkflows. In addition, it includes some additional workflows (not shown in the figure), required for exporting the results to GenomeArk.")
+![Figure 1: VGP pipeline modules](../../images/vgp_assembly/VGP_workflow_modules.png "VGP assembly pipeline. The VGP workflow is implemented in a modular fashion: it consists of five independent subworkflows. In addition, it includes some additional workflows (not shown in the figure), required for exporting the results to GenomeArk.")
 
 The VGP pipeline first uses an assembly program to generate {contigs}. When {Hi-C} data and Bionano data are avilable, then they are used to generate {scaffolds}. When both data types are available, then Bionano scaffolding is run first before Hi-C scaffolding, but if optical maps are not available then HiC scaffolding can be run on the contigs. 
 

topics/climate/tutorials/pangeo-notebook/tutorial.md

@@ -348,7 +348,6 @@ dset.sel(time=(np.timedelta64(2,'D') + np.timedelta64(12,'h')))['pm2p5_conc'].pl
 plt.title("Copernicus Atmosphere Monitoring Service PM2.5, 2 day forecasts\n 24th December 2021 at 12:00 UTC", fontsize=18)
 plt.savefig("CAMS-PM2_5-fc-20211224.png")
 ```
->    {: .code-in}
 
 And you should get the following plot:
 

topics/computational-chemistry/tutorials/cheminformatics/tutorial.md

@@ -163,56 +163,58 @@ We will generate our compound library by searching ChEMBL for compounds which ha
 
 > <tip-title>Problems using the ChEMBL tool?</tip-title>
 > A number of users encounter issues with the ChEMBL tool - sometimes the tool fails, or the output is returned successfully but is empty. If this happens to you, try the following:
-> > Rerun the tool - if a transient error on the ChEMBL server was at fault, this might be enough to fix it.
-> > Try modifying some of the parameters. For example, reducing the Tanimoto coefficient should increase the number of compounds returned.
-> > If all else fails, you can use the following list of SMILES:
-> ```
-> Cc1n[nH]c(c2ccc(O)c(Cl)c2)c1c3ccc4OCCOc4c3	CHEMBL187670
-> COc1ccc(cc1)c2c(C)[nH]nc2c3ccc(O)cc3O	CHEMBL192894
-> COc1ccc(c(O)c1)c2onc(C)c2c3ccc4OCCOc4c3	CHEMBL1541585
-> CCOc1ccc(c(O)c1)c2[nH]nc(C)c2c3ccccc3OC	CHEMBL1504505
-> CN(CCc1c(C)n[nH]c1C)Cc2cn(C)nc2c3ccc4OCCOc4c3	CHEMBL1560480
-> COc1ccc(c(O)c1)c2[nH]nc(C)c2c3ccc4OCCCOc4c3	CHEMBL362893
-> CCCc1c(OCCCOc2cc(O)c(cc2CC)c3cc[nH]n3)ccc4CCC(Oc14)C(=O)O	CHEMBL81401
-> Cc1cccc(n1)c2[nH]nc(C)c2c3ccnc4ccccc34	CHEMBL129153
-> COc1ccc(cc1OC)c2c(C)n[nH]c2c3ccc(O)cc3O	CHEMBL1595327
-> COc1ccc(c(O)c1)c2noc(C)c2c3ccc4ccccc4n3	CHEMBL1486235
-> CCc1cc(c(O)cc1O)c2[nH]nc(C)c2c3ccc4OCCOc4c3	CHEMBL399530
-> Cc1[nH]nc(c2cc(Cl)c(O)cc2O)c1c3ccc4OCCOc4c3	CHEMBL191074
-> COc1ccc(c(O)c1)c2[nH]ncc2c3ccc4OCCOc4c3	CHEMBL1415374
-> Cc1n[nH]c(c2cc(Cl)ccc2O)c1c3ccc4OCCOc4c3	CHEMBL187678
-> Cc1noc(c2ccc(O)cc2O)c1c3ccc4OCCOc4c3	CHEMBL582320
-> CCOC(=O)c1oc(cc1)c2c(C)[nH]nc2c3cc(CC)c(O)cc3O	CHEMBL3932805
-> NC(=O)c1ccc2[nH]nc(c3ccc4OCCOc4c3)c2c1	CHEMBL3900406
-> COc1ccc(cc1OC)c2cc([nH]n2)c3c(O)c(OC)c4occc4c3OC	CHEMBL1351838
-> CCCc1cc(c(O)cc1OC)c2[nH]ncc2c3ccc4OCCCOc4c3	CHEMBL1443258
-> Oc1cc(O)c(cc1Cl)c2[nH]ncc2c3ccc4OCCOc4c3	CHEMBL191228
-> CCc1cc(c(O)cc1O)c2n[nH]cc2c3ccc4OCCOc4c3	CHEMBL3187010
-> Oc1ccccc1c2cc([nH]n2)c3ccc4OCCOc4c3	CHEMBL1567097
-> CCCc1cc(c(O)cc1O)c2[nH]ncc2c3ccc4OCCCOc4c3	CHEMBL1578064
-> COc1ccc(c(O)c1)c2[nH]nc(C)c2c3ccc(OC)c(OC)c3	CHEMBL1335688
-> COc1ccc(cc1)c2c(N)n[nH]c2c3cc(OC)c4OCCOc4c3	CHEMBL2408971
-> CCC(C)c1cc(c(O)cc1O)c2[nH]ncc2c3ccc4OCOc4c3	CHEMBL187674
-> Cc1noc(c2ccc(O)cc2O)c1c3ccc4OCCCOc4c3	CHEMBL587334
-> OCc1cn(nc1c2ccc3OCCOc3c2)c4ccccc4	CHEMBL1549407
-> Oc1ccc(c(O)c1)c2[nH]ncc2c3cccc4cccnc34	CHEMBL1305951
-> Cc1n[nH]c(c2ccc(O)cc2O)c1c3ccc4OCCOc4c3	CHEMBL188965
-> Cc1[nH]nc(c2ccc3OCC(=O)Nc3c2)c1c4ccc(F)cc4	CHEMBL3337723
-> CCOc1ccc(c(O)c1)c2n[nH]c(C)c2c3ccc(OC)cc3	CHEMBL1698243
-> COc1ccc(cc1)c2cc([nH]n2)c3c(O)c(OC)c4occc4c3OC	CHEMBL1402615
-> CCc1cc(c(O)cc1O)c2[nH]ncc2c3ccc4OCOc4c3	CHEMBL1412538
-> CCOc1cc(O)c(cc1CC)c2nc(N)ncc2c3ccc4OCCOc4c3	CHEMBL547662
-> COc1ccc(cc1)c2c(N)onc2c3cc(OC)c4OCCOc4c3	CHEMBL3113121
-> CCCc1cc(c(O)cc1O)c2n[nH]cc2c3ccc4OCCOc4c3	CHEMBL3956397
-> CCC(C)c1cc(c(O)cc1O)c2[nH]nc(C)c2c3ccc4OCCOc4c3	CHEMBL190919
-> CCC(C)c1cc(c(O)cc1OC)c2[nH]ncc2c3ccc4OCCOc4c3	CHEMBL435501
-> Cn1cc(CNCc2c[nH]nc2c3ccc(F)cc3)c(n1)c4ccc5OCCOc5c4	CHEMBL1537178
-> Oc1ccc(F)cc1c2cc([nH]n2)c3ccc4OCCOc4c3	CHEMBL1451528
-> CCc1cc(c(O)cc1OCC(=O)O)c2n[nH]c(C)c2c3ccc4OCCCOc4c3	CHEMBL3952001
-> Cc1[nH]nc(c2ccc(O)c(O)c2O)c1c3ccc(Cl)cc3	CHEMBL1092945
-> COc1cc(cc(OC)c1OC)c2n[nH]nc2c3ccc4OCCOc4c3	CHEMBL3740841
-> Oc1ccc(c(O)c1)c2n[nH]cc2c3ccc4OCOc4c3	CHEMBL577176
-> ```
+> 1.  Rerun the tool - if a transient error on the ChEMBL server was at fault, this might be enough to fix it.
+> 1.  Try modifying some of the parameters. For example, reducing the Tanimoto coefficient should increase the number of compounds returned.
+> 1.  If all else fails, you can use the following list of SMILES:
+>
+>     ```
+>     Cc1n[nH]c(c2ccc(O)c(Cl)c2)c1c3ccc4OCCOc4c3	CHEMBL187670
+>     COc1ccc(cc1)c2c(C)[nH]nc2c3ccc(O)cc3O	CHEMBL192894
+>     COc1ccc(c(O)c1)c2onc(C)c2c3ccc4OCCOc4c3	CHEMBL1541585
+>     CCOc1ccc(c(O)c1)c2[nH]nc(C)c2c3ccccc3OC	CHEMBL1504505
+>     CN(CCc1c(C)n[nH]c1C)Cc2cn(C)nc2c3ccc4OCCOc4c3	CHEMBL1560480
+>     COc1ccc(c(O)c1)c2[nH]nc(C)c2c3ccc4OCCCOc4c3	CHEMBL362893
+>     CCCc1c(OCCCOc2cc(O)c(cc2CC)c3cc[nH]n3)ccc4CCC(Oc14)C(=O)O	CHEMBL81401
+>     Cc1cccc(n1)c2[nH]nc(C)c2c3ccnc4ccccc34	CHEMBL129153
+>     COc1ccc(cc1OC)c2c(C)n[nH]c2c3ccc(O)cc3O	CHEMBL1595327
+>     COc1ccc(c(O)c1)c2noc(C)c2c3ccc4ccccc4n3	CHEMBL1486235
+>     CCc1cc(c(O)cc1O)c2[nH]nc(C)c2c3ccc4OCCOc4c3	CHEMBL399530
+>     Cc1[nH]nc(c2cc(Cl)c(O)cc2O)c1c3ccc4OCCOc4c3	CHEMBL191074
+>     COc1ccc(c(O)c1)c2[nH]ncc2c3ccc4OCCOc4c3	CHEMBL1415374
+>     Cc1n[nH]c(c2cc(Cl)ccc2O)c1c3ccc4OCCOc4c3	CHEMBL187678
+>     Cc1noc(c2ccc(O)cc2O)c1c3ccc4OCCOc4c3	CHEMBL582320
+>     CCOC(=O)c1oc(cc1)c2c(C)[nH]nc2c3cc(CC)c(O)cc3O	CHEMBL3932805
+>     NC(=O)c1ccc2[nH]nc(c3ccc4OCCOc4c3)c2c1	CHEMBL3900406
+>     COc1ccc(cc1OC)c2cc([nH]n2)c3c(O)c(OC)c4occc4c3OC	CHEMBL1351838
+>     CCCc1cc(c(O)cc1OC)c2[nH]ncc2c3ccc4OCCCOc4c3	CHEMBL1443258
+>     Oc1cc(O)c(cc1Cl)c2[nH]ncc2c3ccc4OCCOc4c3	CHEMBL191228
+>     CCc1cc(c(O)cc1O)c2n[nH]cc2c3ccc4OCCOc4c3	CHEMBL3187010
+>     Oc1ccccc1c2cc([nH]n2)c3ccc4OCCOc4c3	CHEMBL1567097
+>     CCCc1cc(c(O)cc1O)c2[nH]ncc2c3ccc4OCCCOc4c3	CHEMBL1578064
+>     COc1ccc(c(O)c1)c2[nH]nc(C)c2c3ccc(OC)c(OC)c3	CHEMBL1335688
+>     COc1ccc(cc1)c2c(N)n[nH]c2c3cc(OC)c4OCCOc4c3	CHEMBL2408971
+>     CCC(C)c1cc(c(O)cc1O)c2[nH]ncc2c3ccc4OCOc4c3	CHEMBL187674
+>     Cc1noc(c2ccc(O)cc2O)c1c3ccc4OCCCOc4c3	CHEMBL587334
+>     OCc1cn(nc1c2ccc3OCCOc3c2)c4ccccc4	CHEMBL1549407
+>     Oc1ccc(c(O)c1)c2[nH]ncc2c3cccc4cccnc34	CHEMBL1305951
+>     Cc1n[nH]c(c2ccc(O)cc2O)c1c3ccc4OCCOc4c3	CHEMBL188965
+>     Cc1[nH]nc(c2ccc3OCC(=O)Nc3c2)c1c4ccc(F)cc4	CHEMBL3337723
+>     CCOc1ccc(c(O)c1)c2n[nH]c(C)c2c3ccc(OC)cc3	CHEMBL1698243
+>     COc1ccc(cc1)c2cc([nH]n2)c3c(O)c(OC)c4occc4c3OC	CHEMBL1402615
+>     CCc1cc(c(O)cc1O)c2[nH]ncc2c3ccc4OCOc4c3	CHEMBL1412538
+>     CCOc1cc(O)c(cc1CC)c2nc(N)ncc2c3ccc4OCCOc4c3	CHEMBL547662
+>     COc1ccc(cc1)c2c(N)onc2c3cc(OC)c4OCCOc4c3	CHEMBL3113121
+>     CCCc1cc(c(O)cc1O)c2n[nH]cc2c3ccc4OCCOc4c3	CHEMBL3956397
+>     CCC(C)c1cc(c(O)cc1O)c2[nH]nc(C)c2c3ccc4OCCOc4c3	CHEMBL190919
+>     CCC(C)c1cc(c(O)cc1OC)c2[nH]ncc2c3ccc4OCCOc4c3	CHEMBL435501
+>     Cn1cc(CNCc2c[nH]nc2c3ccc(F)cc3)c(n1)c4ccc5OCCOc5c4	CHEMBL1537178
+>     Oc1ccc(F)cc1c2cc([nH]n2)c3ccc4OCCOc4c3	CHEMBL1451528
+>     CCc1cc(c(O)cc1OCC(=O)O)c2n[nH]c(C)c2c3ccc4OCCCOc4c3	CHEMBL3952001
+>     Cc1[nH]nc(c2ccc(O)c(O)c2O)c1c3ccc(Cl)cc3	CHEMBL1092945
+>     COc1cc(cc(OC)c1OC)c2n[nH]nc2c3ccc4OCCOc4c3	CHEMBL3740841
+>     Oc1ccc(c(O)c1)c2n[nH]cc2c3ccc4OCOc4c3	CHEMBL577176
+>     ```
+>
 > Don't worry if you can't get it to work - successfully generating this list is a very minor part of the tutorial!
 {: .tip}
 

topics/computational-chemistry/tutorials/setting-up-molecular-systems/tutorial.md

@@ -78,10 +78,10 @@ In this section we'll access the PDB, download the correct structure, import it
 >
 > More resources:
 >
-  - [https://en.wikipedia.org/wiki/Cellulase](https://en.wikipedia.org/wiki/Cellulase)
-  - [https://en.wikipedia.org/wiki/Biofuel](https://en.wikipedia.org/wiki/Biofuel)
-  - [Fungal Cellulases](https://pubs.acs.org/doi/full/10.1021/cr500351c)
-  - [Cellobiohydrolase I Induced Conformational Stability and Glycosidic Bond Polarization ](https://pubs.acs.org/doi/10.1021/ja103766w)
+>  - [https://en.wikipedia.org/wiki/Cellulase](https://en.wikipedia.org/wiki/Cellulase)
+>  - [https://en.wikipedia.org/wiki/Biofuel](https://en.wikipedia.org/wiki/Biofuel)
+>  - [Fungal Cellulases](https://pubs.acs.org/doi/full/10.1021/cr500351c)
+>  - [Cellobiohydrolase I Induced Conformational Stability and Glycosidic Bond Polarization ](https://pubs.acs.org/doi/10.1021/ja103766w)
 {: .details}
 
 ## Get data
@@ -237,11 +237,11 @@ Go to the correct section depending on which MD engine you will be using.
 
 ### Upload to Galaxy
 > <hands-on-title>Upload files to Galaxy</hands-on-title>
-Upload the following files to your Galaxy instance and ensure the correct datatype is selected:
- - step3_pbcsetup.psf -> xplor psf input (psf format)
- - step3_pbcsetup.pdb -> pdb input (pdb format)
- - Checkfft.str -> PME grid specs (txt format)
-- step2.1_waterbox.prm -> waterbox prm input (txt format)
+> Upload the following files to your Galaxy instance and ensure the correct datatype is selected:
+> - step3_pbcsetup.psf -> xplor psf input (psf format)
+> - step3_pbcsetup.pdb -> pdb input (pdb format)
+> - Checkfft.str -> PME grid specs (txt format)
+> - step2.1_waterbox.prm -> waterbox prm input (txt format)
 {: .hands_on}
 
 You are now ready to run the NAMD workflow, which is discussed in another [tutorial]({% link topics/computational-chemistry/tutorials/md-simulation-namd/tutorial.md %}).

topics/contributing/tutorials/learning-principles/tutorial.md

@@ -32,10 +32,12 @@ contributions:
 
 
 ---
+
 > *Learning results from what the student does and thinks and only from what the student does and thinks. The teacher can advance learning only by influencing what the student does to learn*
 >
 > [H.A. Simon](https://en.wikipedia.org/wiki/Herbert_A._Simon) (one of the founders of the field of [Cognitive Science](https://en.wikipedia.org/wiki/Cognitive_science) and Nobel Laureate)
->
+{: .quote}
+
 ---
 
 This quotation from Herbert A. Simon clearly indicates that we cannot talk about teaching, teaching practices or effective teaching techniques if we don't understand first how people learn.
@@ -211,6 +213,7 @@ We can use metaphors to shape and reveal our way of thinking about learning and,
 >
 > Plutard
 >
+{: .quote}
 ---
 
 How the mind-as-a-vessel-to-be-filled metaphor may affect your way of teaching? You are likely to spend your time in the class at the blackboard, trying to 'transmit' to the students your own knowledge.

topics/data-science/tutorials/bash-git/tutorial.md

@@ -170,7 +170,7 @@ Before diving in the tutorial, we need to open {% tool [RStudio](interactive_too
 > > $ conda create -n name_of_your_env nano git
 > > $ conda activate name_of_your_env
 > > ```
-> {: .code_in}
+> {: .code-in}
 >
 >
 > | Software | Version | Manual | Available for | Description |
@@ -359,10 +359,9 @@ same commands to choose another editor or update your email address.
 > More generally, you can get the list of available `git` commands and further resources of the Git manual typing:
 >
 > > <code-in-title>Access available commands</code-in-title>
-> >```bash
-> >$ git help
-> >```
->
+> > ```bash
+> > $ git help
+> > ```
 > {: .code-in}
 >
 {: .tip}
@@ -499,7 +498,6 @@ wording of the output might be slightly different.
 > > to the `suspects` directory.
 > >
 > {: .solution}
-
 {: .question}
 
 > <tip-title>"Nested" repositories</tip-title>
@@ -1143,7 +1141,7 @@ Let's save our changes:
 >    > ```
 >    {: .code-in}
 >
->    > Note, our newly created empty directory `mysteries` does not appear in
+>    Note, our newly created empty directory `mysteries` does not appear in
 >    the list of untracked files even if we explicitly add it (_via_ `git add`) to our
 >    repository. This is the reason why you will sometimes see `.gitkeep` files
 >    in otherwise empty directories. Unlike `.gitignore`, these files are not special

topics/data-science/tutorials/bash-variant-calling/tutorial.md

@@ -124,7 +124,7 @@ The alignment process consists of two steps:
 > > $ mv sub/ ~/dc_workshop/data/trimmed_fastq_small
 > > ```
 > {: .code-in}
-> >
+>
 > You will also need to create directories for the results that will be generated as part of this workflow. We can do this in a single line of code, because `mkdir` can accept multiple new directory names as input.
 >
 > > <code-in-title>Create result directories</code-in-title>

topics/data-science/tutorials/python-conda/tutorial.md

@@ -135,7 +135,7 @@ They also enable you to use a specific older version of a package for your proje
 
 > <tip-title>A Specific Package Version is Only Ever Installed Once</tip-title>
 > Note that you will not have a separate package installations for each of your projects - they will only
-ever be installed once on your system (in `$CONDA/pkgs`) but will be referenced from different environments.
+> ever be installed once on your system (in `$CONDA/pkgs`) but will be referenced from different environments.
 {: .tip}
 
 ### Managing Conda Environments

topics/data-science/tutorials/python-iterables/tutorial.md

@@ -573,8 +573,9 @@ print(f'second time: {values}') # should print [3, 5]
 > > lithium
 > >
 > > ```
-> The first statement prints the whole string, since the slice goes beyond the total length of the string.
-> The second statement returns an empty string, because the slice goes "out of bounds" of the string.
+> >
+> > The first statement prints the whole string, since the slice goes beyond the total length of the string.
+> > The second statement returns an empty string, because the slice goes "out of bounds" of the string.
 > {: .solution}
 {: .question}
 

topics/data-science/tutorials/python-linting/tutorial.md

@@ -51,6 +51,7 @@ worth spending some time learning a bit about Python
 coding style conventions to make sure that your code is consistently formatted and readable by yourself and others.
 
 > *"Any fool can write code that a computer can understand. Good programmers write code that humans can understand."* - [Martin Fowler](https://en.wikiquote.org/wiki/Martin_Fowler), British software engineer, author and international speaker on software development
+{: .quote}
 
 > <comment-title></comment-title>
 >
@@ -483,4 +484,4 @@ PyCharm also displays the docstring for a function/module in a little help popup
 
 ```python
 help(fibonacci)
-```
+```