docs: fix typos pointed out by Hugo

paper/paper.md

@@ -65,12 +65,12 @@ Additionally, `PolarToolkit` provides an easy means for exploring datasets with
 
 # Statement of need
 <!-- clearly illustrates the research purpose of the software and places it in the context of related work. -->
-A common workflow for a geospatial scientist might be; navigate to a online repository and download a dataset, place this downloaded file in a local folder on their computer, perform some preprocessing steps, such a re-projecting, or interpolating the data, possibly using tools like GMT [@wesselgeneric2019], perform their scientific analysis on the data, and then create a map with this data using a graphical user interface (GUI) application such as QGIS [@qgisdevelopmentteamqgis2024].
+A common workflow for a geospatial scientist might be: navigate to an online repository and download a dataset, place this downloaded file in a local folder on their computer, perform some preprocessing steps, such a re-projecting, or interpolating the data, possibly using tools like GMT [@wesselgeneric2019], perform their scientific analysis on the data, and then create a map with this data using a graphical user interface (GUI) application such as QGIS [@qgisdevelopmentteamqgis2024].
 These workflows typically require many separate tools (i.e. internet browser, file browser, spatial analysis software, and mapping software), and are often manually repeated many times throughout a manuscript revision process, and throughout the career of the scientist.
 
 `PolarToolkit` aims to consolidate this workflow to be entirely contained within Python, making it both easier and faster to perform all these steps.
-Scripting workflows like this has several advantages; 1) it decreases the chance of human errors, for example using an old-version of the downloaded data or accidentally altering a pre-processing steps, such as referencing a raster of elevation data to the geoid instead of the ellipsoid, and 2) it allows entire workflows to be shared easily between collaborators with a single python file or Jupyter Notebook.
-Although a popular and well-designed similar package exists [Antarctic Mapping Tools, @greeneantarctic2017], PolarToolkit is unique in it's open-access without the need for a paid MatLab license.
+Scripting workflows like this has several advantages: 1) it decreases the chance of human errors, for example using an old-version of the downloaded data or accidentally altering a pre-processing steps, such as referencing a raster of elevation data to the geoid instead of the ellipsoid, and 2) it allows entire workflows to be shared easily between collaborators with a single python file or Jupyter Notebook.
+Although a popular and well-designed similar package exists [Antarctic Mapping Tools, @greeneantarctic2017], PolarToolkit is unique in its open-access without the need for a paid MatLab license.
 
 Written in easy-to-learn Python, and utilizing common geospatial data structures, `PolarToolkit` is designed to be familiar to use for experienced Python users, while also being approachable for beginner coders.
 It is built upon several open-source packages, such a [Pooch](https://www.fatiando.org/pooch/latest/) for data downloading [@uiedapooch2020], [PyGMT](https://www.pygmt.org/latest/) for creating figures [@uiedapygmt2021], and [xarray](https://docs.xarray.dev/en/stable/) and [verde](https://www.fatiando.org/verde/latest/) for geospatial data processing [@hoyerxarray2017; @uiedaverde2018].
@@ -131,7 +131,7 @@ water_thickness = utils.mask_from_shp(
 
 # plot map and set options
 fig = maps.plot_grd(
-    grid=water_thickness, 
+    grid=water_thickness,
     cmap="dense",
     title="Saunders Coast Ice Shelves",
     cbar_label="Water column thickness (m)",