Apply suggestions from code review

Author: petrasovaa

content/tutorials/time_series/time_series_aggregations.qmd

@@ -30,7 +30,7 @@ as explained in the [first](time_series_management_and_visualization.qmd)
 time series tutorial.
 :::
 
-There are two main tools to do time series aggregations in GRASS GIS:
+There are two main tools to do time series aggregations in GRASS:
 [t.rast.aggregate](https://grass.osgeo.org/grass-stable/manuals/t.rast.aggregate.html)
 and [t.rast.series](https://grass.osgeo.org/grass-stable/manuals/t.rast.series.html).
 We'll demonstrate their usage in the upcoming sections.
@@ -53,7 +53,7 @@ import grass.jupyter as gj
 
 path_to_project = "eu_laea/italy_LST_daily"
 
-# Start the GRASS GIS Session
+# Start the GRASS session
 session = gj.init(path_to_project);
 ```
 

content/tutorials/time_series/time_series_management_and_visualization.qmd

@@ -61,7 +61,7 @@ scheme. In this way, we have:
 
 ::: {.callout-note title="Setup"}
 To run this tutorial locally or in Google Colab, you should install
-GRASS GIS 8.4+, and download the 
+GRASS 8.4+, and download the 
 [daily MODIS LST project](https://zenodo.org/doi/10.5281/zenodo.3564514).
 This project contains average daily MODIS LST (Land Surface Temperature)
 data reconstructed by [mundialis GmbH & Co. KG](https://www.mundialis.de/en/)