paper edit (#55)

Author: e-marshall

paper.md

@@ -49,7 +49,7 @@ These educational modules address this need and are guided by principles identif
 # Content
 We present two tutorials focusing on publicly available satellite imagery datasets. Both types of satellite imagery featured in these tutorials are 1) large in volume, 2) accessed as cloud-optimized data types and making use of cloud computing resources, and 3) have associated metadata that is crucial to data management and interpretation but can be complicated to work with. 
 
-The tutorials discuss remote sensing principles and note considerations when evaluating and interpreting data, such as resolution, possible distortions, and noise. We also compare two datasets derived from the same source data but created with slightly different processing pipelines in order to illustrate the impact of dataset selection and processing decisions on analytical outcomes.
+The tutorials discuss remote sensing principles and note considerations when evaluating and interpreting data, such as resolution, possible distortions, and noise. We also compare two datasets derived from the same source data but created with slightly different processing pipelines in order to illustrate the impact of dataset selection and processing decisions on analytical outcomes. Throughout both tutorials, we emphasize the steps and skills involved with multi-dimensional data cube workflows and preparing data for analysis.
 
 # Instructional Design
 We designed these tutorials to be accessible to users with various experiences and backgrounds. Emphasis is placed on discussing how to interact with and examine complex, cloud-optimized datasets rather than simply providing example code snippets. To facilitate skill-building, we include errors encountered during the development of the material and illustrate their solutions. The code examples contained within these tutorials highlight popular, robust, and well-maintained open-source tools and software, with a strong focus on the Python module Xarray, which is designed for working with n-dimensional array objects and well-suited to geospatial applications [@Hoyer_Hamman_2017]. In addition, we use technology such as Jupyter Books, Jupyter Notebooks, and GitHub to make these tutorials accessible, participatory, and flexible [@JupyterBookCommunity_2021;@Kluyver2016jupyter].