Formatting of website

README.md

@@ -16,21 +16,21 @@ We implement a range of deterministic and stochastic regression baselines to hig
 
 ## Getting Started
 
-* [Quick Start](https://leap-stc.github.io/ClimSim/quickstart.html)
+* [Quickstart](https://leap-stc.github.io/ClimSim/quickstart.html)
 * [Dataset Information](https://leap-stc.github.io/ClimSim/dataset.html)
-* [Code installation](https://leap-stc.github.io/ClimSim/installation.html)
+* [Code Installation](https://leap-stc.github.io/ClimSim/installation.html)
 * [Baseline Models](https://leap-stc.github.io/ClimSim/models.html)
 * [Evaluation](https://leap-stc.github.io/ClimSim/evaluating.html)
 
 ## Demo notebooks
-* [Multi-layer Perceptron (MLP) Example](https://leap-stc.github.io/ClimSim/demo_notebooks/mlp_example.html)
+* [Multi-Layer Perceptron (MLP) Example](https://leap-stc.github.io/ClimSim/demo_notebooks/mlp_example.html)
 * [Convolutional Neural Network (CNN) Example](https://leap-stc.github.io/ClimSim/demo_notebooks/cnn_example.html)
-* [Water conservation example](https://leap-stc.github.io/ClimSim/demo_notebooks/water_conservation.html)
+* [Water Conservation Example](https://leap-stc.github.io/ClimSim/demo_notebooks/water_conservation.html)
 
 ## References
 * [ClimSim paper](https://arxiv.org/abs/2306.08754)
 * Youtube video
 
 [![Alt text](https://img.youtube.com/vi/M3Vz0zR1Auc/0.jpg)](https://www.youtube.com/watch?v=M3Vz0zR1Auc)
 
-* [Contributor Guide](https://leap-stc.github.io/ClimSim/demo_notebooks/CONTRIBUTING.html)
+* [Contributor Guide](https://leap-stc.github.io/ClimSim/CONTRIBUTING.html)

demo_notebooks/cnn_example.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# CNN example"
+    "# Convolutional Neural Network (CNN) Example"
    ]
   },
   {

demo_notebooks/mlp_example.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# MLP example"
+    "# Multi-Layer Perceptron (MLP) Example"
    ]
   },
   {

demo_notebooks/water_conservation.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Water conservation example"
+    "# Water Conservation Example"
    ]
   },
   {

website/dataset.md

@@ -21,10 +21,10 @@ Data from multi-scale climate model (E3SM-MMF) simulations were saved at 20-minu
     - 1.9 MB per input file, 1.1 MB per output file
 
 Input files are labeled ```E3SM-MMF.mli.YYYY-MM-DD-SSSSS.nc```, where ```YYYY-MM-DD-SSS``` corresponds to the simulation year (```YYYY```), month (``MM``), day of the month (``DD``), and seconds of the day (```SSSSS```), with timesteps being spaced 1,200 seconds (20 minutes) apart. Target files are labeled the same way, except ```mli``` is replaced by ```mlo```. 
-Scalar variables vary in time and "horizontal" grid ("ncol"), while vertically-resolved variables vary additionally in vertical space ("lev").  For vertically-resolved variables, lower indices of "lev" corresponds to higher levels in the atmosphere. This is because pressure decreases monotonically with altitude.   
+Scalar variables vary in time and "horizontal" grid (`ncol`), while vertically-resolved variables vary additionally in vertical space (`lev`).  For vertically-resolved variables, lower indices of `lev` corresponds to higher levels in the atmosphere. This is because pressure decreases monotonically with altitude.   
 
 The full list of variables can be found in [Supplementary Information](https://arxiv.org/pdf/2306.08754.pdf), Table 1.
 
-There is also a [Quickstart dataset](https://huggingface.co/datasets/LEAP/subsampled_low_res) that contains subsampled and prenormalized data. This data was used for training, validation, and metrics for the ClimSim paper and can be reproduced using the ```preprocessing/create_npy_data_splits.ipynb``` notebook.
+There is also a [**Quickstart dataset**](https://huggingface.co/datasets/LEAP/subsampled_low_res) that contains subsampled and prenormalized data. This data was used for training, validation, and metrics for the ClimSim paper and can be reproduced using the [`preprocessing/create_npy_data_splits.ipynb`](https://github.com/leap-stc/ClimSim/tree/main/preprocessing/create_npy_data_splits.ipynb) notebook.
 
 

website/evaluating.md

@@ -6,7 +6,7 @@ Four different evaluation metrics were calculated:
 3. Root mean squared error (RMSE)
 4. Continuous ranked probability score (CRPS)
 
-Evaluation and comparison of the different baseline models are found in the ```[evaluation/](https://github.com/leap-stc/ClimSim/tree/main/evaluation)``` folder on GitHub. All variables are converted to a common energy unit (i.e., W/m²) for scoring. The scoring is done using the functions in ```[climsim_utils/data_utils.py](https://github.com/leap-stc/ClimSim/tree/main/climsim_utils)```. 
+Evaluation and comparison of the different baseline models are found in the [```evaluation/```](https://github.com/leap-stc/ClimSim/tree/main/evaluation) folder on GitHub. All variables are converted to a common energy unit (i.e., W/m²) for scoring. The scoring is done using the functions in [`climsim_utils/data_utils.py`](https://github.com/leap-stc/ClimSim/tree/main/climsim_utils). 
 
 [This notebook](./evaluation/main_figure_generation.ipynb) calculates and plots MAE, R², RMSE, and CRPS scores for each baseline model. The separate R² for *longitudinally-averaged* and time-averaged 3D variables is found in [this notebook](./evaluation/plot_R2_analysis.ipynb).
 

website/installation.md

@@ -1,4 +1,4 @@
-# Installation & setup
+# Code Installation
 
 For preprocessing and evaluation, please install the `climsim_utils` python tools, by running the following code from the root of this repo:
 

website/quickstart.md

@@ -2,13 +2,13 @@
 
 **Step 1**
 
-The first step is to download the subsampled low-resolution real-geography version of the data [here](https://huggingface.co/datasets/LEAP/subsampled_low_res/tree/main). This contains subsampled and prenormalized data that was used for training, validation, and metrics for the ClimSim paper. It can be reproduced with the full version of the [dataset](https://huggingface.co/datasets/LEAP/ClimSim_low-res) using the [preprocessing/create_npy_data_splits.ipynb](https://github.com/leap-stc/ClimSim/blob/main/preprocessing/create_npy_data_splits.ipynb) notebook.
+The first step is to download the subsampled low-resolution real-geography version of the data [here](https://huggingface.co/datasets/LEAP/subsampled_low_res/tree/main). This contains subsampled and prenormalized data that was used for training, validation, and metrics for the ClimSim paper. It can be reproduced with the full version of the [dataset](https://huggingface.co/datasets/LEAP/ClimSim_low-res) using the [`preprocessing/create_npy_data_splits.ipynb`](https://github.com/leap-stc/ClimSim/blob/main/preprocessing/create_npy_data_splits.ipynb) notebook.
 
 Training data corresponds to **train_input.npy** and **train_target.npy**. Validation data corresponds to **val_input.npy** and **val_target.npy**. Scoring data (which can be treated as a test set) corresponds to **scoring_input.npy** and **scoring_target.npy**. We have an additional held-out test set that we will use for an upcoming online competition. Keep an eye out! 😉
 
 **Step 2**
 
-Install the `climsim_utils` python tools, by running the following code from the root of this repo:
+Install the `climsim_utils` python tools, by running the following code from the root of the [GitHub repo](https://github.com/leap-stc/ClimSim/tree/main):
 
 ```
 pip install .
@@ -22,9 +22,9 @@ pip install . --no-deps
 
 **Step 3**
 
-Train your model on the training data and validate using the validation data. If you wish to use something like a CNN, you will probably want to separate the variables into channels and broadcast scalars into vectors of the same dimension as vertically-resolved variables. Methods to do this can be found in the [climsim_utils/data_utils.py](https://github.com/leap-stc/ClimSim/blob/main/climsim_utils/data_utils.py) script.
+Train your model on the training data and validate using the validation data. If you wish to use something like a CNN, you will probably want to separate the variables into channels and broadcast scalars into vectors of the same dimension as vertically-resolved variables. Methods to do this can be found in the [`climsim_utils/data_utils.py`](https://github.com/leap-stc/ClimSim/blob/main/climsim_utils/data_utils.py) script.
 
 **Step 4**
 
-Evaluation time! Use the [evaluation/main_figure_generation.ipynb](https://github.com/leap-stc/ClimSim/blob/main/evaluation/main_figure_generation.ipynb) notebook to see how your model does! Use the **calc_MAE**, **calc_RMSE**, and **calc_R2** methods in the [climsim_utils/data_utils.py](https://github.com/leap-stc/ClimSim/blob/main/climsim_utils/data_utils.py) script to see how your model does on point estimates and use the calc_CRPS method to check how well-calibrated your model is if it's stochastic. 😊
+Evaluation time! Use the [`evaluation/main_figure_generation.ipynb`](https://github.com/leap-stc/ClimSim/blob/main/evaluation/main_figure_generation.ipynb) notebook to see how your model does! Use the **calc_MAE**, **calc_RMSE**, and **calc_R2** methods in the [`climsim_utils/data_utils.py`](https://github.com/leap-stc/ClimSim/blob/main/climsim_utils/data_utils.py) script to see how your model does on point estimates and use the `calc_CRPS` method to check how well-calibrated your model is if it's stochastic. 😊