edit code examples in getting started guide to avoid key errors

docs/source/getting_started.md

@@ -98,6 +98,7 @@ ds = load_poses.from_dlc_df(df, fps=30)
 ::::
 
 You can also try movement out on some sample data included in the package.
+
 :::{dropdown} Fetching sample data
 :color: primary
 :icon: unlock
@@ -118,7 +119,7 @@ To get the path to one of the sample files,
 you can use the `fetch_pose_data_path` function:
 
 ```python
-file_path = datasets.fetch_pose_data_path("DLC_single-wasp.predictions.h5")
+file_path = datasets.fetch_pose_data_path("DLC_two-mice.predictions.csv")
 ```
 The first time you call this function, it will download the corresponding file
 to your local machine and save it in the `~/.movement/data` directory. On
@@ -184,17 +185,17 @@ You can select subsets of the data using the `sel` method:
 ds_sel = ds.sel(time=slice(0, 100))
 
 # select specific individuals or keypoints
-ds_sel = ds.sel(individuals=["mouse1", "mouse2"])
+ds_sel = ds.sel(individuals=["individual1", "individual2"])
 ds_sel = ds.sel(keypoints="snout")
 
 # combine selections
-ds_sel = ds.sel(time=slice(0, 100), individuals"mouse1", keypoints="snout")
+ds_sel = ds.sel(time=slice(0, 100), individuals=["individual1", "individual2"], keypoints="snout")
 ```
 All of the above selections can also be applied to the data variables,
 resulting in a `DataArray` rather than a `Dataset`:
 
 ```python
-pose_tracks = ds.pose_tracks.sel(individuals="mouse1", keypoints="snout")
+pose_tracks = ds.pose_tracks.sel(individuals="individual1", keypoints="snout")
 ```
 You may also use all the other powerful [indexing and selection](https://docs.xarray.dev/en/latest/user-guide/indexing.html) methods provided by `xarray`.