Fledging content

.github/workflows/deploy-preview.yml

@@ -38,5 +38,5 @@ jobs:
           enable-pull-request-comment: true
           enable-commit-comment: false
           enable-commit-status: true
-          overwrites-pull-request-comment: false
+          overwrites-pull-request-comment: true
         timeout-minutes: 1

fledging.qmd

@@ -4,9 +4,97 @@ title: "Fledging"
 
 Fledging is a goal of these JupyterHubs, so that users who have had success
 and want to continue their adventures in cloud computing, have the knowledge
-and resources to develop their own solutions.
+and resources to develop their own solutions. Fleding work to date has been co-developed 
+with the NASA Openscapes Mentors community, and we continue to work with mentors
+in both NASA and NOAA to identify fledging paths and address challenges.
 
-Here is a [presentation on fledging](https://docs.google.com/presentation/d/1-TiPN8bfY6iDL5EVcEuCQSjqFMs3jPSX2MlNWA6no2E/edit#slide=id.g2ece79fddf8_3_73) given at the Earth Science Information Partners
-(ESIP) conference in July, 2024.
+The Openscapes JupyterHubs aim to provide an analysis-ready computing environment,
+with all (or most) of the software dependencies required for working with 
+cloud-hosted spatial data. This is a big advantage for learning how to 
+access and work with the data, run workshops for many learners, and have early
+success in cloud computing. However, it does hide some of the complexities of 
+setting up your computing environment, and most fledging paths will necessarily 
+involve learning some of these tools and processes.
 
-More resources coming soon...
+### The Cloud: Data vs Compute
+
+When thinking about cloud computing, we consider two components: where is our 
+data? And where is our compute? In general, if our data is already on our local
+computer, we will work with that data on our computer, so the scope we are 
+considering is how to work with *data that is hosted in the cloud*.
+
+![](images/data-compute-cloud-grid.png){width="400" fig-align="center"}
+
+The proliferation of data centres to support cloud computing and data storage 
+has a [significant environmental cost](https://www.goclimate.com/knowledge/servers). 
+While most scientific workflows are small in comparison to other cloud uses 
+(e.g., AI and cryptocurrency mining), it is worth taking this into 
+consideration when thinking about "when to cloud".
+
+### Skills learned for working with **cloud-hosted data**
+
+The JupyterHubs provide a shared computing environment for mentors to learn and teach
+workflows for accessing and analyzing cloud-based data. Some of the skills we learn include:
+
+- Conceptual understanding of data in the cloud, and cloud-native data sources
+- Finding and accessing data in Earthdata cloud with earthaccess (Python) and 
+  earthdatalogin (R)
+- Streaming and subsetting cloud data with xarray (Python) and terra/rstac (R)
+- Authenticating with NASA Earthdata Login
+- Ability to ask questions, teach, and develop learning resources
+
+These skills enable efficient use of cloud-hosted data using either *local* or 
+*cloud* compute resources.
+
+### What are fledging paths for **compute**?
+
+Depending on computing needs and data size, there are many options for fledging.
+These may include:
+
+- Continue compute in the cloud using a shared environment (example: JupyterHub)
+    - If your institution offers a JupyterHub instance, this is a great option 
+      to seamlessly transfer your learnings from the Openscapes Hub to a Hub
+      for "real science".
+    - Not all JupyterHubs will have the same environment (i.e., software and 
+      packages), so users will need to learn how to customize their new 
+      environment to suit their needs.
+- Send large compute jobs to the cloud (example: Coiled)
+    - This is a good option for large compute jobs using Python that are easily 
+      parallelized (see an example 
+      [below](#an-example-path-for-computing-in-the-cloud)).
+- Compute locally with cloud-native workflows (example: streaming cloud data 
+  with earthaccess + xarray)
+    - For data and analysis jobs that can fit within your laptop's resrouces 
+      (RAM and hard disk), working locally with the same tools and workflows 
+      learned from working in the hub is a great option, and computing in the 
+      cloud is likely unnecessary. You can benefit greatly from the effiency of 
+      cloud data access patterns (such as streaming and range requests) without 
+      worrying about cloud costs and administration.
+- Compute locally using a shared environment “container” (example: Docker)
+    - One of the great benefits that the shared JupyterHub offers is a computing
+      environment that is already set up, so you don't have to go through the pain
+      of installing and configuring everything yourself. The images used in the 
+      JupyterHubs can often be used locally using tools like Docker. See some 
+      examples [here](https://pangeo-docker-images.readthedocs.io/en/latest/howto/launch.html#how-to-launch-jupyterlab-locally-with-one-of-these-images).
+
+#### An example path for computing in the cloud
+*from Aronne Merrelli, University of Michigan*
+
+- Learned when and how to cloud in 2i2c JupyterHub 
+  (managed by 2i2c & Openscapes, using NASA credits)
+    - Log into a computing environment in the Cloud. A single environment to teach 
+      and learn without having to know the complex setups
+- Experimented with compute jobs in Coiled 
+  (managed by Coiled & Openscapes, NASA credits)
+    - Log into a computing environment in the Cloud
+    - Learned how to run code in parallel in the Cloud
+- Did real science with University credit card
+  (managed by Coiled & University credit card)
+    - UM’s institutional AWS account is a big help here
+    - Ran parallel compute jobs on big data in the cloud
+- See a [presentation on this fledging story](https://docs.google.com/presentation/d/1-TiPN8bfY6iDL5EVcEuCQSjqFMs3jPSX2MlNWA6no2E/edit#slide=id.g2ece79fddf8_3_73), 
+given at the Earth Science Information Partners (ESIP) conference in July, 2024.
+
+#### Vision for the future
+
+> People have skills and are empowered to do their science with cloud-hosted data efficiently and equitably, based on their computing needs and their values.