small edits

docs/clearml_data/data_management_examples/data_man_folder_sync.md

@@ -51,7 +51,7 @@ As can be seen, the `clearml-data sync` command creates the dataset, then upload
 Now we'll modify the folder:
 1. Add another line to one of the files in the `data_samples` folder.
 1. Add a file to the sample_data folder.<br/> 
-   Run`echo "data data data" > data_samples/new_data.txt` (this will create the file `new_data.txt` and put it in the `data_samples` folder)
+   Run `echo "data data data" > data_samples/new_data.txt` (this will create the file `new_data.txt` and put it in the `data_samples` folder)
 
 
 We'll repeat the process of creating a new dataset with the previous one as its parent, and syncing the folder.

docs/clearml_sdk/task_sdk.md

@@ -645,7 +645,7 @@ See more details in the [Artifacts Reporting example](../guides/reporting/artifa
 A task's artifacts are accessed through the task’s *artifact* property which lists the artifacts’ locations.
 
 The artifacts can subsequently be retrieved from their respective locations by using:
-* `get_local_copy()`- Downloads the artifact and caches it for later use, returning the path to the cached copy.
+* `get_local_copy()` - Downloads the artifact and caches it for later use, returning the path to the cached copy.
 * `get()` - Returns a Python object constructed from the downloaded artifact file.
 
 The code below demonstrates how to access a file artifact using the previously generated preprocessed data:

docs/guides/pipeline/pipeline_functions.md

@@ -83,7 +83,7 @@ logged as required packages for the pipeline execution step.
    ```
    
    The third step in the pipeline uses the `step_three` function and uses as its input the second step’s output. This 
-   reference implicitly defines the pipeline structure, making `step_two`the parent step of `step_three`.
+   reference implicitly defines the pipeline structure, making `step_two` the parent step of `step_three`.
    
    Its return object will be stored as an artifact under the name `model`:
    

docs/guides/storage/examples_storagehelper.md

@@ -28,7 +28,7 @@ StorageManager.get_local_copy(remote_url="s3://MyBucket/MyFolder/file.zip")
 ```
 
 :::note
-Zip and tar.gz files will be automatically extracted to cache. This can be controlled with the`extract_archive` flag.
+Zip and tar.gz files will be automatically extracted to cache. This can be controlled with the `extract_archive` flag.
 :::
 
 To download a file to a specific context in cache, specify the name of the context as the `cache_context` argument: