Updating DSC180 pages

duncanwp · duncanwp · commit 466f7de222b2 · 2023-10-03T20:48:57.000-07:00
diff --git a/_data/navigation.yml b/_data/navigation.yml
@@ -9,3 +9,5 @@ main:
     url: /philosophy/
   - title: "Media"
     url: /media/
+  - title: "Teaching"
+    url: /teaching/
diff --git a/_pages/dsc_180.md b/_pages/dsc_180.md
@@ -1,47 +1,46 @@
 ---
 permalink: /dsc_180/
 title: "Deep Learning for Climate Model Emulation"
-layout: single
+layout: projects
 toc: true
 toc_sticky: true
+featured_figure: 
+    image: /assets/images/climate_change.jpeg
 ---
-### Data Science Capstone Domain - DSC 180AB
-### Section B03 (TA: Yanyi)
-
+# Deep Learning for Climate Model Emulation
+**Data Science Capstone - DSC 180A/B Section B03 (TA: Yanyi)**
 
 ## Introduction to Topic
 
-The choices humanity makes in the next few decades will determine how much warmer the Earth will be by the end of the century, with implications for billions of lives and trillions of dollars in GDP. Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex climate models have simulated a small selection of these, it is impractical to use these computationally expensive models to fully explore the space of possibilities or assess all the associated risks. Our lab has recently developed state-of-the-art climate model emulators to enable fast, accurate and reliable predictions for any given scenario (https://github.com/duncanwp/ClimateBench). 
-
+The choices humanity makes in the next few decades will determine how much warmer the Earth will be by the end of the century, with implications for billions of lives and trillions of dollars in GDP. Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex climate models have simulated a small selection of these, it is impractical to use these computationally expensive models to fully explore the space of possibilities or assess all the associated risks. Our lab has recently developed a state-of-the-art climate model emulation benchmark to enable fast, accurate and reliable predictions for any given scenario: [ClimateBench](<https://github.com/duncanwp/ClimateBench>). 
 
 ## Phase I - Replication
 
 The aim of reproducing a paper's results is to affirm the original authors' findings and methodologies. This process is vital in science to ensure results are robust and reliable, not merely due to chance or error. Reproduction would reinforce the evidence that the constructed emulators are faithfully reproducing the underlying climate model and can be trusted for such tasks. It also provides a deeper understanding of the applied methods like long short-term memory networks. Ultimately, this endeavor seeks to enable fast and efficient sampling of different climate scenarios to improve decision making.
 
 The paper, linked here, we will be working with is:
->  **Watson-Parris, D.**, Rao, Y., Olivié, D., Seland, Ø., ... "ClimateBench v1.0: A benchmark for data-driven climate projections". *Journal of Advances in Modeling Earth Systems 14, e2021MS002954*: <https://doi.org/10.1029/2021MS002954>
+>  Watson-Parris, D., Rao, Y., Olivié, D., Seland, Ø., ... "ClimateBench v1.0: A benchmark for data-driven climate projections". *Journal of Advances in Modeling Earth Systems 14, e2021MS002954*: <https://doi.org/10.1029/2021MS002954>
 
 
 ### Accessing the ClimateBench Dataset
 
 While the processed dataset is publically available, it will be instructive for you to generate it yourselves, and an important part of the replication process. You will be provided with access to [Casper](https://arc.ucar.edu/knowledge_base/70549550) data analysis cluster at the National Center for Atmospheric Research (NCAR) with sufficient resources to perform the analyses throughout the project. Please note this is a national facility with shared resources so be mindful of your requests and be sure to abide by their rules.
 
-The data we will use is available from the sixth Coupled Model Intercomparison Project (CMIP6) which represents the combined efforts of dozens of international research laboratories running hundreds of thousands of simulation years of experiments. The data (all 30 petabytes!) is publically archived and available e.g. here: https://esgf-index1.ceda.ac.uk/projects/esgf-ceda/, and also recently mirrored to the cloud here: https://registry.opendata.aws/cmip6/. Fortunately, all the data you will need is already available on Casper so you shouldn't need to download any large datasets, which can be quite cumbersome. 
+The data we will use is available from the sixth Coupled Model Intercomparison Project (CMIP6) which represents the combined efforts of dozens of international research laboratories running hundreds of thousands of simulation years of experiments. The data (all 30 petabytes!) is publically archived and available e.g. [here](https://esgf-index1.ceda.ac.uk/projects/esgf-ceda/), and also recently mirrored to the cloud [here](https://registry.opendata.aws/cmip6/). Fortunately, all the data you will need is already available on Casper so you shouldn't need to download any large datasets, which can be quite cumbersome. 
 
 ### Schedule
 
 Click the "topic" links below for details regarding the readings, questions, and tasks for that week.
 
 | Week | Topic |
 | --- | --- |
-| Summer | [Summer preperation](dsc_180_summer) |
-| 1 | [Introduction to topic, domain, and paper](dsc_180_intro) |
-| 2-3 | [Dive into the ClimateBench dataset](dsc_180_data) |
-| 4-5 | [Begin data preprocessing and learn about xarray](dsc_180_xarray) |
-| 6-7 | [Start implementing regression models](dsc_180_implement) |
-| 8-9 | [Perform validation and testing of baselines](dsc_180_validate) |
-| 10 | [Project wrap up and debrief](dsc_180_debrief) |
-
+| Summer | [Summer preperation](/dsc_180_summer) |
+| 1 | [Introduction to topic, domain, and paper](/dsc_180_intro) |
+| 2-3 | [Dive into the ClimateBench dataset](/dsc_180_data) |
+| 4-5 | [Begin data preprocessing and learn about xarray](/dsc_180_xarray) |
+| 6-7 | [Start implementing regression models](/dsc_180_implement) |
+| 8-9 | [Perform validation and testing of baselines](/dsc_180_validate) |
+| 10 | [Project wrap up and debrief](/dsc_180_debrief) |
 
 
 ## Phase II
diff --git a/_pages/dsc_180_intro.md b/_pages/dsc_180_intro.md
@@ -7,38 +7,37 @@ toc_sticky: true
 ---
 
 
-Welcome, Welcome, Welcome!
+Welcome!
 
-Week 1 is upon us and we are going to spend it getting everyone situated, goiong over the primary paper, and beginning to read up on the domain of climate model simulation and emulation
+Week 1 is upon us and we are going to spend it getting everyone situated, goiong over the primary paper, and beginning to read up on the domain of climate model simulation and emulation.
 
 ### Topics
 
 You will be touching on these topics over the first week
 
-- What is sepsis, how it can be diagnosed/treated and why it is very deadly from an epidemiological point of view
-- Severity of illness scores
-- EHR data
+- What is climate change, and how do we model it?
+- What is CMIP and how does it relate to the IPCC?
+- What is the ClimateBench dataset?
 - Reproducibility/replicability in data science
 
 ### Tasks
 
-- Gain access to the MIMIC-III and MIMIC-IV dataset, see instructions on home pagecritical
-- Complete all assigned readings
-- Begin to familairize yourself with the domain by thumbing through the Domain Expertise page.
-- Submit question answers to the online form (linked below)
-- Join the class Discord Channel: TBD
+- [ ] Gain access to Casper, see instructions on home page
+- [ ] Complete all assigned readings
+- [ ] Submit question answers to the online form (linked below)
+- [ ] Join the class Slack Channel
 
 ### Readings
 
-- Read the capstone primary paper by Zador et al. linked on the home pagecritical
-- MIMIC-III paper by Johnson et al. link - if you already did not finish it
-- MIMIC-III web description link - if you already did not finish it
+- Skim the latest UN Intergovernmental Panel on Climate Change [Synthesis Report](https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf) to get a summary of the latest climate change science, especially the figures.
+- Fully read the ClimateBench primary [paper](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002954) by Watson-Parris et al.
+- Pick two citations within the introduction to the ClimateBench paper and skim-read them (just read the abstract, conclusion and look at the figures. Maybe look at the methods if relevent.). 
 
 ### Questions
 
-Answer the following questions using this google form link
+Answer the following questions using this [form](https://forms.office.com/r/xR1LFNZ6Tg).
 
 1. What are the primary goals of the research paper we aim to replicate? Additionally, what do you anticipate to be the major hurdles in this process? Lastly, define what outcomes would you consider as a successful completion of Phase I in this capstone project.
-2. Provide a detailed explanation of what an Electronic Health Record (EHR) is. How does EHR relate to and integrate with the MIMIC datasets?
-3. Explain the concept and utility of Illness Scores in healthcare. Why do multiple illness scores exist? Select one illness score from the following - OASIS, SAPS II, or SOFA. Write a brief overview, including its applications and key attributes. Note: The aim is for each team member to gain expertise in at least one illness score, so ensure that all three scores are explored by different members.
+2. Provide a brief explanation of each of the ClimateBench inputs, what they represent, and the relative magnitude of their climate impact.
+3. Write a brief summary of each citation you read and how it relates to the ClimateBench paper. Note: You will be doing this for each paper we read in the course, so it is good to get some practice in now. The aim is for each team to gain a broad understanding of the field, so ensure that each team member chooses different citations.
 
diff --git a/_pages/dsc_180_summer.md b/_pages/dsc_180_summer.md
@@ -6,6 +6,6 @@ layout: single
 
 I don't expect much over the summer but the following activities would be useful and allow you to hit the ground running in Fall:
 
-- Skim the latest UN Intergovernmental Panel on Climate Change Synthesis Report to get a summary of the latest climate change science, especially the figures: https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf
-- Read the ClimateBench paper: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002954
-- Try out the xarray python library for working with climate data: https://docs.xarray.dev/en/stable/
+- Skim the latest UN Intergovernmental Panel on Climate Change Synthesis Report to get a summary of the latest climate change science, especially the figures: <https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf>
+- Read the ClimateBench paper: <https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002954>
+- Try out the xarray python library for working with climate data: <https://docs.xarray.dev/en/stable/>
diff --git a/_pages/projects.md b/_pages/projects.md
@@ -4,20 +4,11 @@ layout: posts
 permalink: /projects/
 entries_layout: grid
 classes: wide
-
-header:
-  overlay_color: "#5e616c"
-  overlay_image: /assets/images/podcast-header.png
-  caption: "Photo credit: [**Third Pod from the Sun**](https://thirdpodfromthesun.com/)"
-
-
-# https://mmistakes.github.io/minimal-mistakes/portfolio/
 ---
 
 # Projects
 
-Samples of published and ongoing projects by CCOG. Sort them by computational or Earth Science tags.\
-Exciting new CCOG focus areas include sea level and large scale climate dynamics.
+Samples of published and ongoing projects by the Climate Analytics Lab. Sort them by computational or Earth Science tags.
 
 {% case site.tag_archive.type %}
   {% when "liquid" %}
diff --git a/_pages/teaching.md b/_pages/teaching.md
@@ -0,0 +1,21 @@
+---
+permalink: /teaching/
+title: "Teaching"
+layout: archive
+author_profile: true
+---
+
+## HDSI Courses
+
+### DSC 200
+
+Computing structures and programming concepts such as object orientation, data structures such as queues, heaps, lists, search trees and hash tables. Laboratory skills include data analysis with pandas and xarray in Jupyter notebooks.
+
+ - Please see course canvas page for details: <https://canvas.ucsd.edu/courses/49102>
+ - Github repo: <https://github.com/climate-analytics-lab/dsc200-fa23-public/>
+
+## DSC 180A
+
+Data science capstone course. Students work in teams to complete a climate related data science project. Project management, communication, and teamwork skills are emphasized. 
+
+ - Please see course page [here](/dsc_180) for details.
diff --git a/assets/images/climate_change.jpeg b/assets/images/climate_change.jpeg
