diff --git a/book/_config.yml b/book/_config.yml
index d735714..919ca01 100644
--- a/book/_config.yml
+++ b/book/_config.yml
@@ -11,7 +11,7 @@ parse:
# https://jupyterbook.org/content/content-blocks.html#using-substitutions-in-links
myst_substitutions:
hackweek: "GeoSmart Hackweek"
- dates: "October 23 to 27, 2023"
+ dates: "August 19 to 23, 2024"
github_org_url: "https://github.com/geo-smart"
book_repo: "2023-hackweek-website"
website_url: "https://geosmart.hackweek.io"
diff --git a/cookiecutter.yaml b/cookiecutter.yaml
index 79b8cbd..1a13127 100644
--- a/cookiecutter.yaml
+++ b/cookiecutter.yaml
@@ -15,55 +15,16 @@ banner:
image: https://geohackweek.github.io/assets/images/banner.jpg
about:
description:
- Hackweeks are participant-driven events that strive to create
- welcoming spaces for participants to learn new things, build community and
- gain hands-on experience with collaboration and team science.
-
- The 2024 GeoSmart Hackweek will focus primarily on project work, with an emphasis on applications in Hydrology and Cryosphere science.
- Project ideas will be shared in advance.
- There will be space for new ideas to emerge during the Hackweek based on participant engagement.
- We will provide about 5 hours of data science tutorials spread over the week, including space for participant-led tutorials.
-
- During the week, participants will have the opportunity to collaborate with
- their peers, share ideas, and work on projects leading to exciting results and
- discoveries. This event is open to all experience levels in machine learning
- 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. See the event Jupyter book for more details.
-
- 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: learn about common machine learning workflows,
- computational environments, reproducibility, and workflow management.
- - header: Data preparation
- description: explore datasets to identify and
- engineer relevant variables that can be used to build machine learning models.
- - header: Models
- description: work on building machine learning models using
- popular libraries such as TensorFlow, PyTorch, or scikit-learn.
- - 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
- receive feedback from their peers.
- - header: Networking
- description: facilitated opportunities for networking and community
- building will be provided.
-# learn_more: https://escience.washington.edu/using-data-science/hackweeks
-team:
- !include book/team.yaml
-schedule:
- !include book/schedule.yaml
+ "We are in the early stages of organizing a third event as part of this series, and we need your help! Consider joining the hackweek organizing team. We will partner with you to design tutorials and projects, and provide a variety of professional development opportunities along the way. Click on the button below to learn more:"
+ learn_more: https://escience.notion.site/UW-Hackweek-Organizing-Team-Application-b7e5742a4c7e4cad8b4ece80ee171ab0
+ #links:
+ #- url: https://geosmart.hackweek.io/
+ # title: 2023 Hackweek
+ # new_window: true
+#team:
+# !include book/team.yaml
+#schedule:
+# !include book/schedule.yaml
sponsors:
description: 'This event was made possible by the National Science Foundation (Awards #1829585, #2117834) and the eScience Institute in collaboration with CUAHSI and ESIP. Cloud computing infrastructure provided by CryoCloud.'
organizations:
diff --git a/{{ cookiecutter.repo_directory }}/index.html b/{{ cookiecutter.repo_directory }}/index.html
index 64971ba..3ccf886 100644
--- a/{{ cookiecutter.repo_directory }}/index.html
+++ b/{{ cookiecutter.repo_directory }}/index.html
@@ -138,6 +138,76 @@