[DATALAD RUNCMD] chore: run codespell throughout fixing a few new typos automagically

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

Signed-off-by: Yaroslav Halchenko <debian@onerussian.com>

Author: yarikoptic

docs/catalog-data/advanced_data_catalog_usage.md

@@ -103,7 +103,7 @@ catalog["cars"] = ["Ferrari", "Audi"]  # Add raw data
 ```
 When raw data is added, it's automatically wrapped in a `MemoryDataset`.
 
-## How to iterate trough datasets in the catalog
+## How to iterate through datasets in the catalog
 
 `DataCatalog` supports iteration over dataset names (keys), datasets (values), and both (items). Iteration defaults to dataset names, similar to standard Python dictionaries:
 

docs/create/new_project.md

@@ -130,7 +130,7 @@ kedro run
 ```
 
 ```{warning}
-`kedro run` requires at least one pipeline with nodes. Please define a pipeline before running this command and ensure it is registred in `pipeline_registry.py`.
+`kedro run` requires at least one pipeline with nodes. Please define a pipeline before running this command and ensure it is registered in `pipeline_registry.py`.
 ```
 
 ## Visualise a Kedro project

docs/deploy/distributed.md

@@ -40,4 +40,4 @@ We encourage you to play with different ways of parameterising your runs as you
 
 ## 4. (Optional) Create starters
 
-You may opt to [build your own Kedro starter](../tutorials/settings.md) if you regularly have to deploy in a similar environment or to a similar platform. The starter enables you to re-use any deployment scripts written as part of step 2.
+You may opt to [build your own Kedro starter](../tutorials/settings.md) if you regularly have to deploy in a similar environment or to a similar platform. The starter enables you to reuse any deployment scripts written as part of step 2.

docs/deploy/supported-platforms/kubeflow.md

@@ -8,7 +8,7 @@ Here are the main reasons to use Kubeflow Pipelines:
 
 - It is cloud-agnostic and can run on any Kubernetes cluster
 - Kubeflow is tailored towards machine learning workflows for model deployment, experiment tracking, and hyperparameter tuning
-- You can re-use components and pipelines to create E2E solutions
+- You can reuse components and pipelines to create E2E solutions
 
 
 ## The `kedro-kubeflow` plugin