Merge remote-tracking branch 'origin/main' into dev

Author: purduercac-docs-bot

docs/snippets/apps/r.md

@@ -45,7 +45,7 @@ For more functions and tutorials:
 **Installing Packages**
 
 - **Step 0: Set up installation preferences.**  
-    Follow the [steps for setting up](../../userguides/gautschi/run_jobs/examples/example_r_profile_setup.md) your `~/.Rprofile` preferences. This step needs to be done only once. If you have created a `~/.Rprofile` file previously on Gautschi, ignore this step.
+    Follow the [steps for setting up](/userguides/gautschi/run_jobs/examples/example_r_profile_setup) your `~/.Rprofile` preferences. This step needs to be done only once. If you have created a `~/.Rprofile` file previously on Gautschi, ignore this step.
 
 - **Step 1: Check if the package is already installed.**  
     As part of the R installations on community clusters, a lot of R libraries are pre-installed. You can check if your package is already installed by opening an R terminal and entering the command `installed.packages()`. For example,

docs/software/ngc_catalog.md

@@ -9,7 +9,7 @@ search:
 
 ## What is NGC?
 
-![Nvidia Logo](../../assets/images/nvidia_logo.png){ align=right width=250px }
+![Nvidia Logo](../assets/images/nvidia_logo.png){ align=right width=250px }
 
 Nvidia GPU cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC offers a comprehensive catalogue of GPU-accelerated containers, so the application runs quickly and reliably on the high performance computing environment. NGC was deployed to extend the cluster capabilities and to enable powerful software and deliver the fastest results. By utilizing Singularity and NGC, users can focus on building lean models, producing optimal solutions and gathering faster insights. For more information, please visit  [https://www.nvidia.com/en-us/gpu-cloud](https://www.nvidia.com/en-us/gpu-cloud/) and [NGC software catalog](https://ngc.nvidia.com/catalog/).
 

docs/software/rocm_catalog.md

@@ -7,7 +7,7 @@ search:
 
 # AMD ROCm containers
 
-![AMD ROCM Logo](../../assets/images/rocm_logo.png){ align=right width=250px }
+![AMD ROCM Logo](../assets/images/rocm_logo.png){ align=right width=250px }
 
 ## What is AMD ROCm
 

docs/userguides/gilbreth/run_jobs/learning/customml.md

@@ -182,4 +182,4 @@ Due to firewall rules, the TensorBoard URL may only be accessible from Gilbreth
 
 For more details, refer to the [TensorBoard User Guide](https://www.tensorflow.org/tensorboard).
 
-[**Back to the Running Jobs section**](index.md)
+[**Back to the Running Jobs section**](../index.md)

docs/userguides/gilbreth/run_jobs/learning/ml_batch.md

@@ -55,4 +55,4 @@ sbatch tensor_hello.sub
 
 Once the job finishes, you will find an output file (`slurm-xxxxx.out`).
 
-[**Back to the Running Jobs section**](index.md)
+[**Back to the Running Jobs section**](../index.md)

docs/userguides/negishi/run_jobs/biocontainers.md

@@ -82,6 +82,6 @@ bowtie2 -p 8 -x ref -1  reads_1.fq -2 reads_2.fq -S align.sam
 
 To help users get started, we provided detailed user guides for each containerized bioinformatics module on the [ReadTheDocs platform](https://biocontainer-doc.readthedocs.io/en/latest/)
 
-![RCAC Biocontainers one ReadTheDocs](../../../../assets/images/userguides/examples/biocontainers.png)
+![RCAC Biocontainers one ReadTheDocs](../../../assets/images/userguides/examples/biocontainers.png)
 
 [**Back to the Running Jobs section**](index.md)

docs/userguides/negishi/run_jobs/windows/launcher.md

@@ -18,7 +18,7 @@ Windows VMs can be easily launched through the [thinlinc](../../../accounts/#thi
 * The "Windows 10" launcher will launch a VM directly on the front-end.
 * Follow the dialogs to set up your VM.
 
-![Thinlinc Applications list](../../../../../assets/images/userguides/examples/windows.png)
+![Thinlinc Applications list](../../../../assets/images/userguides/examples/windows.png)
 
 *Find Windows 10 under the 'Cluster Software' option in the list of Applications.*