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jeffgillan · Sep 5, 2023 · 1ca9960 · 1ca9960
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diff --git a/docs/flatgeobuf.md b/docs/flatgeobuf.md
@@ -28,6 +28,8 @@ Check out this [blog](https://worace.works/2022/02/23/kicking-the-tires-flatgeob
 
 ## [GeoParquet](https://geoparquet.org/){target=_blank}
 
-</br>
+GeoParquet is a geospatial data format that is based on the [Apache Parquet format](https://parquet.apache.org/){target=_blank}. Parquet is a columnar storage format that is designed to be efficient for both reading and writing large datasets. GeoParquet adds support for storing geospatial (i.e., vector data with coordinate information). 
+
+  </br>
 
 ## [Geopackage](https://www.geopackage.org/){target=_blank}
diff --git a/docs/images/zarr.png b/docs/images/zarr.png
diff --git a/docs/index.md b/docs/index.md
@@ -37,10 +37,9 @@ Geospatial data formats are evolving toward being completely cloud-native, meani
 Throughout, we will emphasize Open Science principles such as [FAIR](https://www.go-fair.org/fair-principles/){target=_blank} and [CARE](https://www.gida-global.org/care){target=_blank}, and highlight primarily Open Source tools. 
 
 <br/>
- Helpful skills to have
+### Presentation Slides
 
-* a basic understanding of the [Command Line Interface (UNIX)](https://swcarpentry.github.io/shell-novice/){target=_blank}
-* a basic understanding of [Python3](https://www.geeksforgeeks.org/introduction-to-python3/#:~:text=Python%20is%20a%20high%2Dlevel,them%20readable%20all%20the%20time.){target=_blank}
+This content was delivered at the 2023 Arizona Geographic Information Council meeting in Prescott, AZ in August 2023. Check out the presentation slides [here](https://docs.google.com/presentation/d/1oepKUpMMayO2Qp1APlWHDvRRiiaqcqam5y-KKPhC5V8/edit?usp=sharing){target=_blank}.
 
 ## Let's Use the Cloud!
 

diff --git a/docs/notebooks/copc.ipynb b/docs/notebooks/copc.ipynb
diff --git a/docs/notebooks/geojson.ipynb b/docs/notebooks/geojson.ipynb
diff --git a/docs/notebooks/stac.ipynb b/docs/notebooks/stac.ipynb
diff --git a/docs/stac.md b/docs/stac.md
@@ -31,12 +31,8 @@ Use Case 1: A researcher wants to find all the Sentinel-2 imagery that is availa
 </figure>
 
 
-
 [STAC Spec on GitHub](https://github.com/radiantearth/stac-spec){target=_blank}
 
-The [Radiant Earth STAC Browser](https://radiantearth.github.io/stac-browser/#/){target=_blank} allows users to search, preview, and access these massive geospatial assets hosted over conventional `https://` endpoints and cloud-base object stores (i.e., `s3://` buckets).
-
-[STAC Index](https://stacindex.org/)
 
 There are four components to making a given STAC run. They can be used independently of one another, but most often they are all used together:
 

diff --git a/docs/zarr.md b/docs/zarr.md
@@ -1,28 +1,24 @@
-[Jump to :material-hand-clap: hands-on lesson :material-school: ](#hands-on)
-
 ## Overview of Zarr
 
-`.zarr` is a data format for chunked, N-dimensional arrays.  
+[Zarr](https://zarr.readthedocs.io/en/stable/getting_started.html){target=_blank} is a general-purpose, chunked, compressed, N-dimensional array format. It is designed to be efficient for storing and accessing large datasets. Zarr can be used to store a variety of data types, including geospatial raster and vector data. Zarr is also a Python package that provides an implementation of chunked, compressed, N-dimensional arrays.
+
+
+<figure markdown>
+  ![Image title](images/zarr.png){ width="600" }
+  <figcaption> </figcaption>
+</figure>
+
+Zarr integrates direclty with [Xarray](https://xarray.dev/){target=_blank}.
 
-Zarr integrates direclty with Xarray.
 
-[Zarr website](https://zarr.dev/){target=_blank}
 
 Zarr dependes on [NumPy](https://numpy.org/){target=_blank}
  and can save data from NumPy arrays. When paired with [Dask](https://www.dask.org/){target=_blank}
 , Zarr's parallel processing and file transfers increases the read/write speed of your NumPy array data.
 
-The power of Zarr is most easily explained when working with it on the cloud. 
 
 ## Examples using Zarr
 
 [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-zarr-data/){target=_blank}
 
 
-# Hands On
-
-Let's drop right into a Jupyter Notebook for a high level overview of Zarr
-
-[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/tyson-swetnam/agic-2022/blob/main/docs/notebooks/zarr.ipynb)
-
-<iframe width="500" height="300" src="https://www.youtube.com/embed/sY20UpYCAEE" title="Pangeo Forge: Crowdsourcing Open Data in the Cloud- Ryan Abernathey | SciPy 2022" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -23,12 +23,9 @@ nav:
   - SpatioTemporal Asset Catalog: stac.md
   - Cloud Compute: ide.md
   - Jupyter Notebook Examples:
-    - GeoJSON: notebooks/geojson.ipynb
-    - COPC: notebooks/copc.ipynb
     - COG: notebooks/cog.ipynb
     - XArray: notebooks/xarray.ipynb
     - Zarr: notebooks/zarr.ipynb
-    - STAC: notebooks/stac.ipynb
 
 # Configuration
 theme: