Use mintlify components and fix all code snippets

.gitattributes

@@ -0,0 +1 @@
+*.nc filter=lfs diff=lfs merge=lfs -text

api-reference/datasets/accessing-dataset.mdx

@@ -9,12 +9,12 @@ Once you have listed all available datasets, you can access a specific dataset b
 <RequestExample>
 
 ```python Python (Sync)
-dataset = datasets.open_data.asf.sentinel1_sar
+dataset = datasets.open_data.copernicus.sentinel1_sar
 # or any other dataset available to you
 ```
 
 ```python Python (Async)
-dataset = datasets.open_data.asf.sentinel1_sar
+dataset = datasets.open_data.copernicus.sentinel1_sar
 # or any other dataset available to you
 ```
 

api-reference/storage-providers/creating-storage-client.mdx

@@ -6,6 +6,8 @@ icon: database
 
 You can create a cached storage client by importing the respective class and instantiating it.
 
+For a complete example look at the [Accessing Open Data](/datasets/open-data#sample-code) section.
+
 <RequestExample>
 
 ```python Python (Sync)

datasets/collections.mdx

@@ -1,6 +1,7 @@
 ---
 title: Collections
 description: Learn about Time Series Dataset Collections
+icon: layer-group
 ---
 
 Collections are a way of grouping together data points from the same dataset. They are useful for representing
@@ -48,22 +49,24 @@ Each dataset has a list of collections associated with it. You can list the coll
 <CodeGroup>
 
   ```python Python (Sync)
-  dataset = datasets.open_data.asf.sentinel1_sar
+  dataset = datasets.open_data.copernicus.landsat8_oli_tirs
   collections = dataset.collections()
   print(collections)
   ```
 
   ```python Python (Async)
-  dataset = datasets.open_data.asf.sentinel1_sar
+  dataset = datasets.open_data.copernicus.landsat8_oli_tirs
   collections = await dataset.collections()
   print(collections)
   ```
 
 </CodeGroup>
 
 ```txt Output
-{'Sentinel-1A': Collection Sentinel-1A: [2014-06-15T03:44:43.000 UTC, 2022-12-31T23:57:59.000 UTC] (1209636 data points),
- 'Sentinel-1B': Collection Sentinel-1B: [2016-09-26T00:02:34.000 UTC, 2021-12-23T06:53:08.000 UTC] (657674 data points)}
+{'L1GT': Collection L1GT: [2013-03-25T12:08:43.699 UTC, 2024-08-19T12:57:32.456 UTC],
+ 'L1T': Collection L1T: [2013-03-26T09:33:19.763 UTC, 2020-08-24T03:21:50.000 UTC],
+ 'L1TP': Collection L1TP: [2013-03-24T00:25:55.457 UTC, 2024-08-19T12:58:20.229 UTC],
+ 'L2SP': Collection L2SP: [2015-01-01T07:53:35.391 UTC, 2024-08-12T12:52:03.243 UTC]}
 ```
 
 The `collections` variable is a dictionary, where the keys are the names of the collections and the values are
@@ -78,22 +81,24 @@ method.
 <CodeGroup>
 
   ```python Python (Sync)
-  dataset = datasets.open_data.asf.sentinel1_sar
+  dataset = datasets.open_data.copernicus.landsat8_oli_tirs
   collections = dataset.collections(availability=True, count=True)
   print(collections)
   ```
 
   ```python Python (Async)
-  dataset = datasets.open_data.asf.sentinel1_sar
+  dataset = datasets.open_data.copernicus.landsat8_oli_tirs
   collections = await dataset.collections(availability=True, count=True)
   print(collections)
   ```
 
 </CodeGroup>
 
 ```txt Output
-{'Sentinel-1A': Collection Sentinel-1A: [2014-06-15T03:44:43.000 UTC, 2022-12-31T23:57:59.000 UTC] (1209636 data points),
- 'Sentinel-1B': Collection Sentinel-1B: [2016-09-26T00:02:34.000 UTC, 2021-12-23T06:53:08.000 UTC] (657674 data points)}
+{'L1GT': Collection L1GT: [2013-03-25T12:08:43.699 UTC, 2024-08-19T12:57:32.456 UTC] (154288 data points),
+ 'L1T': Collection L1T: [2013-03-26T09:33:19.763 UTC, 2020-08-24T03:21:50.000 UTC] (87958 data points),
+ 'L1TP': Collection L1TP: [2013-03-24T00:25:55.457 UTC, 2024-08-19T12:58:20.229 UTC] (322041 data points),
+ 'L2SP': Collection L2SP: [2015-01-01T07:53:35.391 UTC, 2024-08-12T12:52:03.243 UTC] (191110 data points)}
 ```
 
 ## Accessing individual collections
@@ -107,22 +112,22 @@ You can then use the `info()` method on the collection object to get information
 
   ```python Python (Sync)
   collections = dataset.collections()
-  sat1 = collections["Sat-1"]
-  collection_info = sat1.info(availability=True, count=True)
+  terrain_correction = collections["L1GT"]
+  collection_info = terrain_correction.info(availability=True, count=True)
   print(collection_info)
   ```
 
   ```python Python (Async)
   collections = await dataset.collections()
-  sat1 = collections["Sat-1"]
-  collection_info = await sat1.info(availability=True, count=True)
+  terrain_correction = collections["L1GT"]
+  collection_info = await terrain_correction.info(availability=True, count=True)
   print(collection_info)
   ```
 
 </CodeGroup>
 
 ```txt Output
-Collection Sat-1: [2019-03-07T16:09:17.773000 UTC, 2021-05-23T19:17:23.472000 UTC] (910245 data points)
+L1GT: [2013-03-25T12:08:43.699 UTC, 2024-08-19T12:57:32.456 UTC] (154288 data points)
 ```
 
 You can also access a specific collection by using the `collection` method on the dataset object as well.
@@ -131,21 +136,21 @@ This has the advantage that you can directly access the collection without havin
 <CodeGroup>
 
   ```python Python (Sync)
-  sat1 = dataset.collection("Sat-1")
-  collection_info = sat1.info(availability=True, count=True)
+  terrain_correction = dataset.collection("L1GT")
+  collection_info = terrain_correction.info(availability=True, count=True)
   print(collection_info)
   ```
 
   ```python Python (Async)
-  sat1 = dataset.collection("Sat-1")
-  collection_info = await sat1.info(availability=True, count=True)
+  terrain_correction = dataset.collection("L1GT")
+  collection_info = await terrain_correction.info(availability=True, count=True)
   print(collection_info)
   ```
 
 </CodeGroup>
 
 ```txt Output
-Collection Sat-1: [2019-03-07T16:09:17.773000 UTC, 2021-05-23T19:17:23.472000 UTC] (910245 data points)
+L1GT: [2013-03-25T12:08:43.699 UTC, 2024-08-19T12:57:32.456 UTC] (154288 data points)
 ```
 
 ## Errors you may encounter
@@ -166,6 +171,10 @@ await dataset.collection("Sat-X").info() # raises NotFoundError: 'No such collec
 
 </CodeGroup>
 
-## Summary
+## Next steps
 
-Great, now you know how to list and access collections. Next you can look at [how to query data points from a collection](/datasets/loading-data).
+<CardGroup cols={2}>
+  <Card title="Loading Data" icon="download" href="/datasets/loading-data" horizontal>
+    How to load data points from a collection.
+  </Card>
+</CardGroup>

datasets/introduction.mdx

@@ -1,17 +1,28 @@
 ---
 title: Introduction
 description: Learn about Tilebox Datasets
+icon: house
 ---
 
 As the name suggests, time series datasets refer to a certain kind of datasets where each data point is associated with a timestamp.
 This is a common format for datasets that are collected over time, such as satellite data.
 
 This section covers:
 
-- [Which timeseries datasets are available](/datasets/timeseries#listing-datasets) and how to list them
-- [Which common fields](/datasets/timeseries#common-fields) all time series datasets share
-- [What collections are](/datasets/collections) and how to access them
-- [How to access data](/datasets/loading-data) from a collection for a given time interval
+<CardGroup cols={2}>
+  <Card title="Available Datasets" icon="list" href="/datasets/timeseries#listing-datasets" horizontal>
+    Which time series datasets are available and how to list them.
+  </Card>
+  <Card title="Common Fields" icon="file-code" href="/datasets/timeseries#common-fields" horizontal>
+    Which common fields all time series datasets share.
+  </Card>
+  <Card title="Collections" icon="layer-group" href="/datasets/collections" horizontal>
+    What collections are and how to access them.
+  </Card>
+  <Card title="Loading Data" icon="download" href="/datasets/loading-data" horizontal>
+    How to access data from a collection for a given time interval.
+  </Card>
+</CardGroup>
 
 <Note>
   If you want to quickly look up the name of some API method or the meaning of a specific parameter [check out the
@@ -22,11 +33,21 @@ This section covers:
 
 Here are some terms used throughout this section.
 
-- **Data points**: time series data points are the individual entities that make up a dataset. Each data point is associated with a timestamp.
-  Each data point consists of a set of fixed [metadata fields](/datasets/timeseries#common-fields) as well as individual fields that are defined on a dataset level.
-- **Datasets**: time series datasets are a container for individual data points. All data points in a time series dataset share the same data type, so all
-  data points in a dataset share the same set of fields.
-- **Collections**: Collections are a way of grouping data points within a dataset. They are useful for representing a logical grouping of data points that are commonly queried together.
+<AccordionGroup>
+  <Accordion title="Data points">
+    Time series data points are the individual entities that make up a dataset. Each data point is associated with a
+    timestamp. Each data point consists of a set of fixed [metadata fields](/datasets/timeseries#common-fields) as well
+    as individual fields that are defined on a dataset level.
+  </Accordion>
+  <Accordion title="Datasets">
+    Time series datasets are a container for individual data points. All data points in a time series dataset share the
+    same data type, so all data points in a dataset share the same set of fields.
+  </Accordion>
+  <Accordion title="Collections">
+    Collections are a way of grouping data points within a dataset. They are useful for representing a logical grouping
+    of data points that are commonly queried together.
+  </Accordion>
+</AccordionGroup>
 
 ## Creating a datasets Client
 
@@ -122,6 +143,8 @@ datasets = await client.datasets() # raises AuthenticationError
 
 ## Next steps
 
-- [Accessing datasets](/datasets/timeseries)
-- [Async support](/sdks/python/async)
-- [Working with Xarray](/sdks/python/xarray)
+<CardGroup cols={2}>
+  <Card title="Accessing datasets" icon="database" href="/datasets/timeseries" horizontal />
+  <Card title="Async support" icon="rotate" href="/sdks/python/async" horizontal />
+  <Card title="Working with Xarray" icon="chart-bar" href="/sdks/python/xarray" horizontal />
+</CardGroup>