Apply suggestions from code review

Co-authored-by: Troy Raen <raen@ipac.caltech.edu>

Author: jkrick

tutorials/simulated-data/OpenUniverse2024Preview_Firefly.md

@@ -86,7 +86,7 @@ from reproject import reproject_interp
 from io import BytesIO
 ```
 
-## 1. Learn where the OpenUniverse2024 data are hosted in the cloud.
+## 1. Learn where the OpenUniverse2024 data are hosted in the cloud
 
 The OpenUniverse2024 data preview is hosted in the cloud via Amazon Web Services (AWS). To access these data, you need to create a client to read data from Amazon's Simple Storage Service (s3) buckets, and you need to know some information about those buckets. The OpenUniverse2024 data preview contains simulations of the Roman Wide-Area Survey (WAS) and the Roman Time Domain Survey (TDS). In this tutorial, we will focus on the WAS.
 

tutorials/wise/wise-allwise-catalog-demo.md

@@ -135,7 +135,7 @@ print(f"cone_pixels contains {len(cone_pixels)} of a possible {hp.nside_to_npixe
 
 +++
 
-Load using [pandas.read_parquet](https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html).
+Example 1: Load using [pandas.read_parquet](https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html).
 Filter for magnitudes above our w1mpro limit and a sky-area limited to the ra/dec polygon.
 
 Pandas actually uses either pyarrow or fastparquet to interact with parquet files.
@@ -175,7 +175,7 @@ del pandas_df
 
 +++
 
-Load using [pyarrow.dataset.parquet_dataset](https://arrow.apache.org/docs/python/generated/pyarrow.dataset.parquet_dataset.html) and convert to pandas.
+Example 2: Load using [pyarrow.dataset.parquet_dataset](https://arrow.apache.org/docs/python/generated/pyarrow.dataset.parquet_dataset.html) and convert to pandas.
 Useful for:
 
 1. advanced filters that combine, compare, and/or create new columns. (this example)
@@ -255,6 +255,7 @@ del pyarrow_df
 
 +++
 
+Example 3: Spatial search using Astropy.
 Nearest-neighbor searches and cone searches generally use the on-sky separation distance to determine the matches.
 It would be cumbersome to construct the new column and filter on it using the methods shown above because the separation distance is a fairly complicated function of ra and dec.
 However, we can get pretty fast results by filtering down to the HEALPix pixels that cover the region, loading all the data in those partitions, and then using astropy to compute the separations and find the matches.