Merge branch 'main' into event-template-updates

.gitignore

@@ -142,3 +142,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# JetBrains
+.idea/

book/application.md

@@ -3,6 +3,9 @@
 We are glad you are interested in applying to participate in {{hackweek}}! 
 
 [Applications](https://washington.co1.qualtrics.com/jfe/form/SV_4GjwMl2RgEpeIoC) are open until September 15, 2023.
+<!---```{warning} Applications not yet open
+Come back soon to apply!
+```-->
 
 ## Considerations
 

book/preliminary/skills_refresher_index.md

@@ -1,7 +1,7 @@
 # Skills Refresher
 
-Our hackweeks focus on applied, hands-on learning, with participants engaging in extended periods of small-group work. Our tutorials are designed to offer a broad snapshot of data science tools to support your applied investigations. Due to the relatively short duration of our events, we are not able to provide comprehensive, in-depth training in fundamental tools. Rather, our goal is to inform you about the types of tools we think are best suited to working with your datasets, leaving details of implementation to be supported through peer-learning and office hours.
+Our GeoSMART hackweeks focus on applied, hands-on learning, with participants engaging in extended periods of small-group work. Our tutorials are designed to offer a broad snapshot of data science tools to support your applied investigations. Due to the relatively short duration of our events, we are not able to provide comprehensive, in-depth training in fundamental tools. Rather, our goal is to inform you about the types of tools we think are best suited to working with your datasets, leaving details of implementation to be supported through peer-learning and office hours.
 
 ## Expectations for the GeoSMART Hackweek
 
-Our projects and tutorials will draw from materials covered in a recent University of Washington course titled "Data Science for Earth and Planetary Systems". Hackweek participants will get the most out of our event if they have some familiarity with the material covered in this class. The curriculum is located in the [Machine Learning in the Geosciences](https://geo-smart.github.io/curriculum-book/about_this_book/about_this_book.html) Jupyter Book. We encourage everyone to review this material and contact us with any questions.
+Our projects and tutorials will draw from materials covered in a recent University of Washington course titled "Data Science for Earth and Planetary Systems". Hackweek participants will get the most out of our event if they have some familiarity with the material covered in this class. The curriculum is located in the [Machine Learning in the Geosciences](https://geo-smart.github.io/curriculum-book/about_this_book/about_this_book.html) Jupyter Book. We encourage everyone to review this material and contact us with any questions For Python, we suggest these resources:  [Pythia Foundations](https://foundations.projectpythia.org/landing-page.html), [Earth Data Science](https://www.earthdatascience.org/), and the data analysis classes taught at the University of Washington [Geospatial Analysis with Python](https://uwgda-jupyterbook.readthedocs.io/en/latest/intro.html) and [Data Analysis in Water Sciences](https://mountain-hydrology-research-group.github.io/data-analysis).

cookiecutter.yaml

@@ -33,29 +33,28 @@ about:
     knowledge, so whether you're a seasoned pro or just starting out, you're
     welcome to join. However, to benefit most from the event, prior knowledge of
     Python programming and data handling using common Python packages (pandas,
-    xarray, etc.) is desired. 
+    xarray, etc.) is desired. See the event Jupyter book for more details. 
+    </br></br>
+    Preliminary project ideas include streamflow prediction from SAR-derived snowmelt timing or snow data, predicting snow water equivalent with machine learning, glacier dh/dt from DEMs using geospatial time series analysis, derivation of snow covered areas from satellite imagery, derivation of snow depth from SAR backscatter and lidar-derived snow data, predicting river discharge from seismic waves and others! Join one of these projects or pitch your own project idea at the event!
   expanded_description:
     header: "Hackweek activities will include:"
     list:
       - header: Brainstorming sessions
         description: participants can join an existing project or come
           up with ideas for projects that can be implemented using machine learning.
       - header: Tutorials
-        description: participants will learn about common machine learning workflows,
+        description: learn about common machine learning workflows,
           computational environments, reproducibility, and workflow management.
       - header: Data preparation
-        description: participants will explore datasets to identify and
+        description: explore datasets to identify and
           engineer relevant variables that can be used to build machine learning models.
       - header: Models
-        description: participants will work on building machine learning models using
+        description: work on building machine learning models using
           popular libraries such as TensorFlow, PyTorch, or scikit-learn.
-      - header: Model validation
-        description: participants can validate their models using
-          cross-validation and other techniques to ensure that their models are
-          robust and accurate.
-      - header: Optimization
-        description: participants can work on optimizing their models by
-          fine-tuning hyperparameters, using feature engineering techniques, or
+      - header: Model validation and optimization
+        description: validate models using
+          cross-validation and other techniques to ensure that models are
+          robust and accurate; fine-tuning hyperparameters, using feature engineering techniques, or
           other methods.
       - header: Presentations
         description: participants can share the results from projects to
@@ -72,14 +71,20 @@ team:
 schedule:
   !include book/schedule.yaml
 sponsors:
-  description: 'This event was made possible by the National Science Foundation (Awards #1829585, #2117834) and the eScience Institute'
+  description: 'This event was made possible by the National Science Foundation (Awards #1829585, #2117834) and the eScience Institute in collaboration with CUAHSI and ESIP'
   organizations:
     - name: eScience Institute
       website: 'https://escience.washington.edu/'
       logo_url: https://escience.washington.edu/wp-content/uploads/2015/10/Logo_eScience-stacked.png
     - name: National Science Foundation
       website: 'https://www.nsf.gov/'
       logo_url: https://new.nsf.gov/themes/custom/nsf_theme/components/images/logo/logo-desktop.svg
+    - name: CUAHSI
+      website: 'https://www.cuahsi.org/'
+      logo_url: https://www.cuahsi.org/uploads/pages/img/Round_Logo_No_Text_-_Transparent.png
+    - name: ESIP
+      website: 'https://www.esipfed.org/'
+      logo_url: https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/esip-logo_0.png
 footer:
   social:
     - icon: github