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doc.zih.tu-dresden.de/docs/archive/beegfs_on_demand.md

@@ -62,7 +62,7 @@ Check the status of the job with `squeue -u \<username>`.
 ## Mount BeeGFS Filesystem
 
 You can mount BeeGFS filesystem on the partition ml (PowerPC architecture) or on the
-partition haswell (x86_64 architecture), more information [here](../jobs_and_resources/partitions_and_limits.md).
+partition haswell (x86_64 architecture), more information about [partitions](../jobs_and_resources/partitions_and_limits.md).
 
 ### Mount BeeGFS Filesystem on the Partition `ml`
 

doc.zih.tu-dresden.de/docs/jobs_and_resources/overview.md

@@ -62,9 +62,9 @@ Normal compute nodes are perfect for this task.
 **OpenMP jobs:** SMP-parallel applications can only run **within a node**, so it is necessary to
 include the [batch system](slurm.md) options `-N 1` and `-n 1`. Using `--cpus-per-task N` Slurm will
 start one task and you will have `N` CPUs. The maximum number of processors for an SMP-parallel
-program is 896 on partition `julia`, see [here](partitions_and_limits.md).
+program is 896 on partition `julia`, see [partitions](partitions_and_limits.md).
 
-**GPUs** partitions are best suited for **repetitive** and **highly-parallel** computing tasks. If
+Partitions with GPUs are best suited for **repetitive** and **highly-parallel** computing tasks. If
 you have a task with potential [data parallelism](../software/gpu_programming.md) most likely that
 you need the GPUs.  Beyond video rendering, GPUs excel in tasks such as machine learning, financial
 simulations and risk modeling. Use the partitions `gpu2` and `ml` only if you need GPUs! Otherwise

doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md

@@ -6,7 +6,7 @@
 
 [Apache Spark](https://spark.apache.org/), [Apache Flink](https://flink.apache.org/)
 and [Apache Hadoop](https://hadoop.apache.org/) are frameworks for processing and integrating
-Big Data. These frameworks are also offered as software [modules](modules.md) on both `ml` and
+Big Data. These frameworks are also offered as software [modules](modules.md) in both `ml` and
 `scs5` software environments. You can check module versions and availability with the command
 
 ```console

doc.zih.tu-dresden.de/docs/software/machine_learning.md

@@ -35,7 +35,7 @@ The following have been reloaded with a version change:  1) modenv/scs5 => moden
 There are tools provided by IBM, that work on partition ML and are related to AI tasks.
 For more information see our [Power AI documentation](power_ai.md).
 
-## Partition `alpha`
+## Partition: Alpha
 
 Another partition for machine learning tasks is Alpha. It is mainly dedicated to
 [ScaDS.AI](https://scads.ai/) topics. Each node on Alpha has 2x AMD EPYC CPUs, 8x NVIDIA A100-SXM4