Improve workflows pages

.vale.ini

@@ -15,7 +15,7 @@ mdx = md
 # Ignore react components starting with export const
 # Ignore code blocks in triple backticks
 # Ignore inline code blocks in backticks
-BlockIgnores = (export const (.|\n)*), CodeGroup(.|\n)*CodeGroup, ```(.|\n)*?```, `.*?`, \[.*\]\(http[^\)]*?\), \(\/.*\)
+BlockIgnores = (export const (.|\n)*), (?s)<CodeGroup>.*?</CodeGroup>, (?s)```.*?```, `.*?`, \[.*\]\(http[^\)]*?\), \(\/.*\)
 BasedOnStyles = Vale, Google, write-good, docs
 
 write-good.Passive = NO

api-reference/tilebox.workflows/Client.mdx

@@ -47,7 +47,7 @@ A client for scheduling recurrent tasks.
 def Client.runner(...) -> TaskRunner
 ```
 
-A client is also used to instantiate task runners. Check out the [`Client.runner` API reference](/api-reference/workflows/client.runner) for more information.
+A client is also used to instantiate task runners. Check out the [`Client.runner` API reference](/api-reference/tilebox.workflows/Client.runner) for more information.
 
 <RequestExample>
 ```python Python

assets/workflows/diagrams/movies-failed.d2

@@ -18,7 +18,7 @@ title: {
   style.font-color: "black"
 }
 01916b1f-524b-7e2c-ef0e-ff006e44508c: {
-  label: "MovieStats"
+  label: "Shrek 2"
   style.fill: HoneyDew
 }
 01916b1f-4769-73e9-886e-ae0b1dd3ae3d -> 01916b1f-524b-7e2c-ef0e-ff006e44508c
@@ -28,7 +28,7 @@ title: {
 }
 01916b1f-4769-73e9-886e-ae0b1dd3ae3d -> 01916b1f-524b-2e44-9bdf-b7d9c38d736c
 01916b1f-4769-73e9-886e-ae0b1dd3ae3d: {
-  label: "Shrek 2"
+  label: "MovieStats"
   style.fill: HoneyDew
 }
 01916b1f-524b-8be2-ee23-e0a93a4c2a3a: {

assets/workflows/diagrams/movies-retried.d2

@@ -18,7 +18,7 @@ title: {
   style.font-color: "black"
 }
 01916b1f-524b-7e2c-ef0e-ff006e44508c: {
-  label: "MovieStats"
+  label: "Shrek 2"
   style.fill: HoneyDew
 }
 01916b1f-4769-73e9-886e-ae0b1dd3ae3d -> 01916b1f-524b-7e2c-ef0e-ff006e44508c
@@ -28,7 +28,7 @@ title: {
 }
 01916b1f-4769-73e9-886e-ae0b1dd3ae3d -> 01916b1f-524b-2e44-9bdf-b7d9c38d736c
 01916b1f-4769-73e9-886e-ae0b1dd3ae3d: {
-  label: "Shrek 2"
+  label: "MovieStats"
   style.fill: HoneyDew
 }
 01916b1f-524b-8be2-ee23-e0a93a4c2a3a: {

datasets/introduction.mdx

@@ -24,7 +24,7 @@ This section covers:
 </CardGroup>
 
 <Note>
-  For a quick reference to API methods or specific parameter meanings, [check out the complete time series API Reference](/api-reference/datasets/).
+  For a quick reference to API methods or specific parameter meanings, [check out the complete time series API Reference](/api-reference/tilebox.datasets/Client).
 </Note>
 
 ## Terminology
@@ -53,26 +53,22 @@ Prerequisites
 After meeting these prerequisites, you can create a client instance to interact with Tilebox Datasets.
 
 <CodeGroup>
+```python Python
+from tilebox.datasets import Client
 
-    ```python Python
-    from tilebox.datasets import Client
-
-    client = Client(token="YOUR_TILEBOX_API_KEY")
-    ```
-
+client = Client(token="YOUR_TILEBOX_API_KEY")
+```
 </CodeGroup>
 
-Alternatively, you can set the `TILEBOX_API_KEY` environment variable to your API key. You can then instantiate the client without passing the `token` argument. Python will automatically use this environment variable for authentication.
+You can also set the `TILEBOX_API_KEY` environment variable to your API key. You can then instantiate the client without passing the `token` argument. Python will automatically use this environment variable for authentication.
 
 <CodeGroup>
+```python Python
+from tilebox.datasets import Client
 
-    ```python Python
-    from tilebox.datasets import Client
-
-    # requires a TILEBOX_API_KEY environment variable
-    client = Client()
-    ```
-
+# requires a TILEBOX_API_KEY environment variable
+client = Client()
+```
 </CodeGroup>
 
 <Tip>

datasets/loading-data.mdx

@@ -146,7 +146,7 @@ Data variables: (12/30)
 ```
 
 <Note>
-  Tilebox uses millisecond precision for timestamps. To load all data points for a specific second, it is a [time interval](/datasets/loading-data#time-intervals) request. Refer to the examples below for details.
+  Tilebox uses millisecond precision for timestamps. To load all data points for a specific second, it's a [time interval](/datasets/loading-data#time-intervals) request. Refer to the examples below for details.
 </Note>
 
 The output of the `load` method is an `xarray.Dataset` object. To learn more about Xarray, visit the dedicated [Xarray page](/sdks/python/xarray).
@@ -184,7 +184,7 @@ This feature works by constructing a `TimeInterval` object from the first and la
 
 ## Fetching only metadata
 
-Sometimes, it may be useful to load only the [time series metadata](/datasets/timeseries#common-fields) without the actual data fields. This can be done by setting the `skip_data` parameter to `True` when using `load`. Here’s an example.
+Sometimes, it may be useful to load only the [time series metadata](/datasets/timeseries#common-fields) without the actual data fields. This can be done by setting the `skip_data` parameter to `True` when using `load`. Here's an example.
 
 <CodeGroup>
     ```python Python

datasets/storage-clients.mdx

@@ -198,13 +198,3 @@ Contents:
 ```
 </CodeGroup>
 
-## Further Reading
-
-<CardGroup cols={2}>
-  <Card
-    title="Storage Providers API Reference"
-    icon="database"
-    href="/api-reference/storage-providers/creating-storage-client"
-    horizontal
-  />
-</CardGroup>

sdks/python/async.mdx

@@ -81,7 +81,7 @@ datapoint = await collection.find(datapoint_uuid)
 ## Fetching data concurrently
 
 The primary benefit of the async client is that it allows concurrent requests, enhancing performance.
-In below example, data is fetched from various collections. The synchronous approach retrieves data sequentially, while the async approach does so concurrently, resulting in faster execution.
+In below example, data is fetched from multiple collections. The synchronous approach retrieves data sequentially, while the async approach does so concurrently, resulting in faster execution.
 
 <CodeGroup>
 ```python Python (Sync)
@@ -166,7 +166,7 @@ Fetching data took 7.45 seconds
 
 ## Async workflows
 
-The Tilebox workflows Python client does not have an async client. This is because workflows are designed for distributed and concurrent execution outside a single async event loop. However, within a single task, you may use `async` code to benefit from asynchronous execution, such as parallel data loading. You can achieve this by wrapping your async code in `asyncio.run`.
+The Tilebox workflows Python client does not have an async client. This is because workflows are designed for distributed and concurrent execution outside a single async event loop. But within a single task, you may use still use`async` code to take advantage of asynchronous execution, such as parallel data loading. You can achieve this by wrapping your async code in `asyncio.run`.
 
 Below is an example of using async code within a workflow task.
 

sdks/python/geometries.mdx

@@ -272,7 +272,7 @@ Correct centroid coordinates (antimeridian taken into account)
 
 Another approach to handle the antimeridian issue is performing all coordinate-related calculations, such as polygon intersections, in a [spherical coordinate system](https://en.wikipedia.org/wiki/Spherical_coordinate_system).
 
-One useful library for this is [spherical_geometry](https://spherical-geometry.readthedocs.io/en/latest/). Here’s an example.
+One useful library for this is [spherical_geometry](https://spherical-geometry.readthedocs.io/en/latest/). Here's an example.
 
 ```python Spherical Geometry
 # pip install spherical-geometry

vale/styles/config/vocabularies/docs/accept.txt

@@ -5,6 +5,7 @@ Tilebox
 NumPy
 (?i)async
 (?i)antimeridian
+(?i)cloud
 Datalore
 Colab
 Tropomi
@@ -25,6 +26,8 @@ subclassing
 Unary
 iterable
 iterables
+inclusivity
+requirement
 equivalent
 monitor
 SAR's
@@ -42,3 +45,4 @@ coroutines
 (?i)LLMs
 (?i)Imager
 (?i)pushbroom
+rollout