diff --git a/Jupyterhub/convert_to_geotiff.qmd b/Jupyterhub/convert_to_geotiff.qmd
deleted file mode 100644
index a192f87..0000000
--- a/Jupyterhub/convert_to_geotiff.qmd
+++ /dev/null
@@ -1,217 +0,0 @@
-# Jupyter Notebook Guide for Disaster COG Processing
-
-This guide helps you get started with converting disaster satellite imagery to Cloud Optimized GeoTIFFs (COGs).
-
-## Quick Start
-
-### 🚀 Option 1: Simple Template (Recommended for Most Users)
-
-Use `templates/simple_disaster_template.ipynb` for a streamlined experience with just 5 cells:
-
-1. **Open the notebook**
-   ```bash
-   jupyter notebook templates/simple_disaster_template.ipynb
-   ```
-
-2. **Configure your event** (Cell 1)
-   - Set `EVENT_NAME` (e.g., '202408_TropicalStorm_Debby')
-   - Set `PRODUCT_NAME` (e.g., 'landsat8')
-   - Modify filename functions to control output names
-
-3. **Run the cells in order**
-   - Cell 2: Imports and initializes
-   - Cell 3: Discovers files and shows preview
-   - Cell 4: Processes all files
-   - Cell 5: Reviews results
-
-### 🎛️ Option 2: Advanced Template (For Power Users)
-
-Use `templates/disaster_processing_template.ipynb` for full control over:
-- Memory management
-- Chunk configurations
-- Processing parameters
-- Verification options
-- Detailed error handling
-
-## Configuration Examples
-
-### Basic Configuration
-
-```python
-EVENT_NAME = '202408_TropicalStorm_Debby'
-PRODUCT_NAME = 'landsat8'
-BUCKET = 'nasa-disasters'
-SOURCE_PATH = f'drcs_activations/{EVENT_NAME}/{PRODUCT_NAME}'
-DESTINATION_BASE = 'drcs_activations_new'
-OVERWRITE = False  # Set True to replace existing files
-```
-
-### Custom Filename Functions
-
-Define how your files are renamed:
-
-```python
-def create_truecolor_filename(original_path, event_name):
-    """Create filename for trueColor products."""
-    filename = os.path.basename(original_path)
-    stem = os.path.splitext(filename)[0]
-    date = extract_date_from_filename(stem)
-
-    if date:
-        stem_clean = re.sub(r'_\d{8}', '', stem)
-        return f"{event_name}_{stem_clean}_{date}_day.tif"
-    return f"{event_name}_{stem}_day.tif"
-```
-
-### Map Products to Filename Functions
-
-```python
-FILENAME_CREATORS = {
-    'trueColor': create_truecolor_filename,
-    'colorInfrared': create_colorinfrared_filename,
-    'naturalColor': create_naturalcolor_filename,
-}
-```
-
-## File Organization
-
-The system automatically:
-- **Discovers** files in your S3 source path
-- **Categorizes** them by product type (trueColor, NDVI, etc.)
-- **Applies** the appropriate filename function
-- **Saves** to organized output directories
-
-### Default Output Structure
-```
-drcs_activations_new/
-├── imagery/
-│   ├── trueColor/
-│   ├── colorIR/
-│   └── naturalColor/
-├── indices/
-│   ├── NDVI/
-│   └── MNDWI/
-└── SAR/
-    └── processed/
-```
-
-## Common Patterns
-
-### Process Multiple Product Types
-
-The system automatically detects and processes different product types:
-
-```python
-# Files are auto-categorized by these patterns:
-- 'trueColor' → imagery/trueColor/
-- 'colorInfrared' → imagery/colorIR/
-- 'NDVI' → indices/NDVI/
-- 'MNDWI' → indices/MNDWI/
-- 'SAR' → SAR/processed/
-```
-
-### Custom No-Data Values
-
-```python
-NODATA_VALUES = {
-    'NDVI': -9999,      # Specific value for NDVI
-    'MNDWI': -9999,     # Specific value for MNDWI
-    'trueColor': None,  # Auto-detect for imagery
-}
-```
-
-### Override Output Directories
-
-```python
-OUTPUT_DIRS = {
-    'trueColor': 'Landsat/trueColor',
-    'colorInfrared': 'Landsat/colorIR',
-    'naturalColor': 'Landsat/naturalColor',
-}
-```
-
-## Troubleshooting
-
-### Issue: "No files found"
-- Check `SOURCE_PATH` is correct
-- Verify files exist: `aws s3 ls s3://bucket/path/`
-
-### Issue: "Failed to connect to S3"
-- Check AWS credentials: `aws configure list`
-- Ensure bucket access permissions
-
-### Issue: Files being skipped
-- Files already exist in destination
-- Set `OVERWRITE = True` to reprocess
-
-### Issue: Wrong filenames
-- Modify filename creator functions
-- Re-run from discovery step to preview
-
-### Issue: Processing is slow
-- Large files take time (normal)
-- System automatically uses GDAL optimization
-- Files >1.5GB use optimized chunking
-
-## Performance Tips
-
-1. **File Size Optimization**
-   - Files <1.5GB: Processed whole (fastest)
-   - Files >1.5GB: Smart chunking
-   - Files >7GB: Ultra-large file handling
-
-2. **Compression**
-   - Uses ZSTD level 22 (maximum compression)
-   - Automatic predictor selection
-   - Intelligent resampling based on data type
-
-3. **Parallel Processing**
-   - For batch processing multiple events, use:
-   ```python
-   from batch_processor_parallel import process_files_parallel
-   ```
-
-## Advanced Features
-
-### Using the Helper Module Directly
-
-```python
-from notebooks.notebook_helpers import quick_process
-
-results = quick_process({
-    'event_name': '202408_TropicalStorm_Debby',
-    'bucket': 'nasa-disasters',
-    'source_path': 'drcs_activations/202408_TropicalStorm_Debby/landsat8',
-    'destination_base': 'drcs_activations_new',
-    'overwrite': False,
-    'filename_creators': FILENAME_CREATORS
-})
-```
-
-### Batch Processing Multiple Events
-
-```python
-events = [
-    '202408_TropicalStorm_Debby',
-    '202409_Hurricane_Example',
-    '202410_Wildfire_Sample'
-]
-
-for event in events:
-    config['event_name'] = event
-    config['source_path'] = f'drcs_activations/{event}/landsat8'
-    processor = SimpleProcessor(config)
-    processor.connect_to_s3()
-    processor.discover_files()
-    processor.process_all()
-```
-
-## Next Steps
-
-1. Start with the simple template
-2. Run a small test batch
-3. Verify output filenames are correct
-4. Process full dataset
-5. Check results in S3
-
-For more details, see the main [README.md](README.md) or review the [RESAMPLING_GUIDE.md](RESAMPLING_GUIDE.md) for data type handling.
\ No newline at end of file
diff --git a/Jupyterhub/jupyterhub-training-guide.qmd b/Jupyterhub/jupyterhub-training-guide.qmd
index 676e39a..0e312a5 100644
--- a/Jupyterhub/jupyterhub-training-guide.qmd
+++ b/Jupyterhub/jupyterhub-training-guide.qmd
@@ -7,13 +7,7 @@
 4. [Working with Jupyter Notebooks](#working-with-jupyter-notebooks)
 5. [Data Management](#data-management)
 6. [Environment and Package Management](#environment-and-package-management)
-7. [Terminal and Command Line Access](#terminal-and-command-line-access)
-8. [Collaboration and Sharing](#collaboration-and-sharing)
-9. [Resource Management](#resource-management)
-10. [Best Practices](#best-practices)
-11. [Troubleshooting](#troubleshooting)
-12. [Keyboard Shortcuts](#keyboard-shortcuts)
-13. [Resources and Links](#resources-and-links)
+7. [Shutting Down](#shutting-down-properly)
 
 ---
 
@@ -45,8 +39,6 @@ The **Disasters Hub** (https://hub.disasters.2i2c.cloud/) is a specialized Jupyt
 ✅ **Pre-configured Environments** - Common packages already installed  
 ✅ **Persistent Storage** - Your work is saved between sessions  
 ✅ **Collaboration Ready** - Share notebooks with team members  
-✅ **Scalable Resources** - Access to GPU and high-memory instances when needed  
-
 ---
 
 ## Getting Started
@@ -58,7 +50,11 @@ The **Disasters Hub** (https://hub.disasters.2i2c.cloud/) is a specialized Jupyt
    - Go to: [https://hub.disasters.2i2c.cloud/](https://hub.disasters.2i2c.cloud/)
    - Bookmark this URL for easy access
 
-2. **Authentication**
+2. **First-Time Login**
+   - Must sign in through [Keycloak - CI Logon](https://cilogon.org/)
+   - After Keycloak has been completed, request to be added to the Disasters Jupyterhub account
+
+3. **Authentication**
    - You'll see a login screen with authentication options
    - Common authentication methods:
      - **GitHub**: Use your GitHub credentials
@@ -66,10 +62,6 @@ The **Disasters Hub** (https://hub.disasters.2i2c.cloud/) is a specialized Jupyt
      - **Institutional Login**: Use your organization's credentials
    - Select your authentication method and follow the prompts
 
-3. **First-Time Login**
-   - Accept terms of service if prompted
-   - Your home directory will be created automatically
-   - Initial setup may take 30-60 seconds
 
 ### Server Selection
 
@@ -78,19 +70,12 @@ After login, you may be presented with server options:
 ```
 Server Options:
 ┌─────────────────────────────────────┐
-│ • Small (2 CPU, 4GB RAM)            │
-│ • Medium (4 CPU, 8GB RAM)           │
-│ • Large (8 CPU, 16GB RAM)           │
-│ • GPU Instance (if available)       │
+│ • Small (4 CPU, 4GB RAM)            │
+│ • Medium (4 CPU, 7GB RAM)           │
+│ • Large (4 CPU, 15GB RAM)           │
+│ • Additional resources if needed    │
 └─────────────────────────────────────┘
 ```
-
-**Tips for Server Selection:**
-- Start with **Small** for basic notebook work
-- Use **Medium** for data processing tasks
-- Choose **Large** for machine learning or big data
-- Select **GPU** only when needed (limited availability)
-
 ---
 
 ## JupyterHub Interface Overview
@@ -136,13 +121,6 @@ Once logged in, you'll see the JupyterLab interface:
    - Line/column position
    - File encoding and type
 
-### Creating Your First Notebook
-
-1. Click the **Python 3** icon in the Launcher
-2. Or: File → New → Notebook
-3. Select kernel (usually Python 3)
-4. Rename your notebook: Right-click on "Untitled.ipynb" → Rename
-
 ---
 
 ## Working with Jupyter Notebooks
@@ -150,6 +128,7 @@ Once logged in, you'll see the JupyterLab interface:
 ### Notebook Basics
 
 A Jupyter notebook consists of **cells** that can contain:
+
 - **Code**: Executable Python (or other language) code
 - **Markdown**: Formatted text, equations, and images
 - **Raw**: Unformatted text
@@ -204,27 +183,6 @@ The **kernel** is the computational engine that executes your code.
 - **[*]**: Cell currently executing
 - **[1]**: Cell execution number
 
-### Notebook Best Practices
-
-1. **Use meaningful cell divisions**
-   - One concept or operation per cell
-   - Separate imports, data loading, processing, visualization
-
-2. **Document your work**
-   ```python
-   # Good practice: Add comments and markdown cells
-   # Load disaster response data
-   df = pd.read_csv('disaster_data.csv')
-   
-   # Data preprocessing
-   df['date'] = pd.to_datetime(df['date'])
-   df = df.dropna()
-   ```
-
-3. **Clear output before sharing**
-   - Kernel → Restart & Clear Output
-   - Reduces file size and removes sensitive output
-
 ---
 
 ## Data Management
@@ -234,22 +192,14 @@ The **kernel** is the computational engine that executes your code.
 #### Uploading Files
 1. **Drag and drop** files directly into the file browser
 2. **Upload button**: Click the ⬆ button in the file browser toolbar
-3. **Terminal upload**: Use `wget` or `curl` in terminal
-   ```bash
-   wget https://example.com/data.csv
-   curl -O https://example.com/data.zip
-   ```
 
 #### Downloading Files
 1. **Right-click** file in browser → Download
-2. **From notebook**:
-   ```python
-   from IPython.display import FileLink
-   FileLink('results.csv')  # Creates downloadable link
-   ```
 
 ### Working with Cloud Storage
 
+Credentials for reading from S3 are already integrated within the Disasters Hub!
+
 #### AWS S3 Integration
 ```python
 import boto3
@@ -262,38 +212,11 @@ df = pd.read_csv('s3://bucket-name/path/to/file.csv')
 df.to_csv('s3://bucket-name/output/results.csv', index=False)
 ```
 
-#### Google Cloud Storage
-```python
-# Read from GCS
-df = pd.read_csv('gs://bucket-name/path/to/file.csv')
-
-# Using gsutil in terminal
-!gsutil cp gs://bucket/file.csv ./data/
-```
-
-### Data Organization
-
-Recommended directory structure:
-```
-home/
-├── data/
-│   ├── raw/           # Original, immutable data
-│   ├── processed/     # Cleaned, transformed data
-│   └── external/      # Data from external sources
-├── notebooks/
-│   ├── exploratory/   # Initial explorations
-│   ├── analysis/      # Detailed analysis
-│   └── reports/       # Final reports
-├── scripts/           # Reusable Python scripts
-├── results/           # Output files, figures
-└── requirements.txt   # Package dependencies
-```
-
 ### Data Persistence
 
 ⚠️ **Important**: Your home directory is persistent, but understand the storage limits:
 
-- **Home directory**: Usually 10-100 GB (persistent)
+- **Home directory**: 100 GB/user (persistent)
 - **Shared data**: Read-only datasets available to all users
 - **Temporary storage**: `/tmp` cleared on restart
 - **Best practice**: Store large datasets in cloud storage, not home directory
@@ -372,205 +295,6 @@ pip install -r requirements.txt
 
 ---
 
-## Terminal and Command Line Access
-
-### Opening Terminal
-
-1. **From Launcher**: Click "Terminal" icon
-2. **From menu**: File → New → Terminal
-3. **Keyboard shortcut**: (varies by setup)
-
-### Common Terminal Commands
-
-```bash
-# Navigation
-pwd                     # Print working directory
-ls -la                  # List files with details
-cd ~/notebooks         # Change directory
-
-# File operations
-mkdir project          # Create directory
-cp file1.txt file2.txt # Copy file
-mv oldname newname     # Move/rename
-rm file.txt           # Delete file (careful!)
-
-# File viewing
-cat file.txt          # Display file contents
-head -n 10 data.csv   # First 10 lines
-tail -n 10 log.txt    # Last 10 lines
-less large_file.txt   # Page through file
-
-# Process management
-ps aux                # List processes
-top                   # Monitor resources
-kill -9 PID          # Kill process
-
-# Git operations
-git status
-git add .
-git commit -m "message"
-git push
-```
-
-### Working with Data Files
-
-```bash
-# Count lines in file
-wc -l data.csv
-
-# View CSV structure
-head -1 data.csv | tr ',' '\n' | nl
-
-# Search in files
-grep "pattern" file.txt
-grep -r "pattern" ./directory
-
-# Compress/decompress
-zip archive.zip file1 file2
-unzip archive.zip
-tar -czf archive.tar.gz directory/
-tar -xzf archive.tar.gz
-```
-
----
-
-## Collaboration and Sharing
-
-### Sharing Notebooks
-
-#### Method 1: Direct File Sharing
-1. Download notebook: File → Download as → Notebook (.ipynb)
-2. Share via email, Slack, or file sharing service
-3. Recipient uploads to their JupyterHub
-
-#### Method 2: Using Git
-```bash
-# Initialize repository
-git init
-git add notebook.ipynb
-git commit -m "Add analysis notebook"
-git remote add origin https://github.com/user/repo.git
-git push -u origin main
-```
-
-#### Method 3: Export Formats
-- **HTML**: File → Export Notebook As → HTML
-- **PDF**: File → Export Notebook As → PDF (requires LaTeX)
-- **Python script**: File → Export Notebook As → Python
-- **Markdown**: File → Export Notebook As → Markdown
-
-### Real-time Collaboration
-
-Some JupyterHub deployments support real-time collaboration:
-
-1. **Share workspace link**: Get shareable link from hub admin
-2. **Collaborative editing**: Multiple users can edit simultaneously
-3. **See collaborator cursors**: Real-time cursor positions
-4. **Chat integration**: Built-in chat for discussion
-
-### Version Control Best Practices
-
-1. **Clear outputs before committing**:
-   ```bash
-   jupyter nbconvert --clear-output notebook.ipynb
-   ```
-
-2. **Use .gitignore**:
-   ```
-   .ipynb_checkpoints/
-   __pycache__/
-   *.pyc
-   .DS_Store
-   data/  # Don't commit large data files
-   ```
-
-3. **Notebook diff tools**:
-   ```bash
-   # Install nbdime for better notebook diffs
-   pip install nbdime
-   nbdime config-git --enable
-   ```
-
----
-
-## Resource Management
-
-### Understanding Resource Limits
-
-Your JupyterHub instance has resource limits:
-
-```python
-# Check available resources
-import psutil
-
-# Memory
-memory = psutil.virtual_memory()
-print(f"Total RAM: {memory.total / 1e9:.2f} GB")
-print(f"Available: {memory.available / 1e9:.2f} GB")
-print(f"Used: {memory.percent}%")
-
-# CPU
-print(f"CPU cores: {psutil.cpu_count()}")
-print(f"CPU usage: {psutil.cpu_percent()}%")
-
-# Disk
-disk = psutil.disk_usage('/')
-print(f"Disk space: {disk.total / 1e9:.2f} GB")
-print(f"Disk used: {disk.percent}%")
-```
-
-### Monitoring Resource Usage
-
-#### JupyterLab Extension
-- Install Resource Usage extension
-- Shows real-time memory and CPU usage in status bar
-
-#### Command line monitoring
-```bash
-# Real-time resource monitoring
-top
-htop  # If installed
-
-# Memory usage
-free -h
-
-# Disk usage
-df -h
-du -sh *  # Directory sizes
-```
-
-### Optimizing Resource Usage
-
-1. **Clear variables when done**:
-   ```python
-   # Clear specific variable
-   del large_dataframe
-   
-   # Clear all variables
-   %reset -f
-   
-   # Garbage collection
-   import gc
-   gc.collect()
-   ```
-
-2. **Use efficient data types**:
-   ```python
-   # Use categories for strings with few unique values
-   df['category'] = df['category'].astype('category')
-   
-   # Use smaller numeric types when possible
-   df['count'] = df['count'].astype('int32')  # Instead of int64
-   ```
-
-3. **Process data in chunks**:
-   ```python
-   # Read large CSV in chunks
-   chunk_size = 10000
-   for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
-       process_chunk(chunk)
-   ```
-
 ### Shutting Down Properly
 
 Always shut down kernels and terminals when done:
@@ -580,369 +304,11 @@ Always shut down kernels and terminals when done:
 3. **Hub Control Panel**: File → Hub Control Panel → Stop My Server
 4. **Logout**: File → Log Out
 
-⚠️ **Important**: Idle servers may be automatically culled after a period of inactivity (usually 1-2 hours).
-
----
-
-## Best Practices
-
-### Project Organization
-
-1. **Use consistent naming**:
-   ```
-   2024-01-15_earthquake_analysis.ipynb  # Good
-   untitled1.ipynb                       # Bad
-   ```
-
-2. **Create project templates**:
-   ```python
-   # notebook_template.ipynb
-   
-   # 1. Imports
-   import pandas as pd
-   import numpy as np
-   import matplotlib.pyplot as plt
-   
-   # 2. Configuration
-   pd.set_option('display.max_columns', None)
-   plt.style.use('seaborn')
-   
-   # 3. Data Loading
-   
-   # 4. Data Exploration
-   
-   # 5. Analysis
-   
-   # 6. Results
-   ```
-
-3. **Document dependencies**:
-   ```python
-   # Generate requirements.txt
-   !pip freeze > requirements.txt
-   ```
-
-### Security Considerations
-
-1. **Never commit credentials**:
-   ```python
-   # Bad
-   api_key = "sk-abc123def456"
-   
-   # Good - Use environment variables
-   import os
-   api_key = os.environ.get('API_KEY')
-   ```
-
-2. **Use secrets management**:
-   ```python
-   # Store secrets in .env file
-   from dotenv import load_dotenv
-   load_dotenv()
-   
-   # Access secrets
-   secret = os.getenv('SECRET_KEY')
-   ```
-
-3. **Be careful with outputs**:
-   - Clear cells containing sensitive information
-   - Review notebooks before sharing
-
-### Performance Tips
-
-1. **Vectorize operations**:
-   ```python
-   # Slow
-   results = []
-   for i in range(len(df)):
-       results.append(df.iloc[i]['column'] * 2)
-   
-   # Fast
-   results = df['column'] * 2
-   ```
-
-2. **Use built-in functions**:
-   ```python
-   # Use pandas/numpy operations instead of loops
-   df['new_col'] = df['col1'] + df['col2']  # Vectorized
-   ```
-
-3. **Profile your code**:
-   ```python
-   %%time  # Time entire cell
-   
-   %timeit function()  # Time single line
-   
-   # Detailed profiling
-   %load_ext line_profiler
-   %lprun -f function_to_profile function_to_profile()
-   ```
-
----
-
-## Troubleshooting
-
-### Common Issues and Solutions
-
-#### Kernel Won't Start
-- **Check resources**: Server might be full
-- **Try different kernel**: Some kernels may be broken
-- **Restart server**: Hub Control Panel → Stop → Start
-
-#### Package Import Errors
-```python
-# Check if package is installed
-import importlib
-if importlib.util.find_spec("package_name") is None:
-    !pip install package_name
-    
-# Restart kernel after installation
-from IPython import get_ipython
-get_ipython().kernel.do_shutdown(True)
-```
-
-#### Out of Memory Errors
-1. Clear unnecessary variables: `del variable_name`
-2. Use smaller data samples for testing
-3. Request larger server instance
-4. Process data in chunks
-
-#### Notebook Won't Save
-- **Check disk space**: `df -h` in terminal
-- **Check file permissions**: `ls -la notebook.ipynb`
-- **Save with new name**: File → Save As
-- **Download backup**: File → Download
-
-#### Connection Issues
-- **Check internet connection**
-- **Try different browser**
-- **Clear browser cache**
-- **Check if hub is under maintenance**
-
-### Getting Help
-
-1. **Built-in help**:
-   ```python
-   help(function_name)
-   function_name?  # Quick help
-   function_name??  # Source code
-   ```
-
-2. **Documentation**:
-   - JupyterHub docs: https://jupyterhub.readthedocs.io
-   - JupyterLab docs: https://jupyterlab.readthedocs.io
-   - 2i2c docs: https://docs.2i2c.org
-
-3. **Community support**:
-   - Discourse forum
-   - GitHub issues
-   - Stack Overflow with tags: `jupyter`, `jupyterhub`
-
----
-
-## Keyboard Shortcuts
-
-### Command Mode (Blue cell border)
-Press `Esc` to enter command mode
-
-| Shortcut | Action |
-|----------|--------|
-| `Enter` | Enter edit mode |
-| `A` | Insert cell above |
-| `B` | Insert cell below |
-| `D,D` | Delete cell |
-| `Y` | Change to code cell |
-| `M` | Change to markdown cell |
-| `Shift+Up/Down` | Select multiple cells |
-| `Shift+M` | Merge selected cells |
-| `C` | Copy cell |
-| `X` | Cut cell |
-| `V` | Paste cell below |
-| `Shift+V` | Paste cell above |
-| `Z` | Undo cell deletion |
-| `0,0` | Restart kernel |
-| `I,I` | Interrupt kernel |
-
-### Edit Mode (Green cell border)
-Press `Enter` to enter edit mode
-
-| Shortcut | Action |
-|----------|--------|
-| `Esc` | Enter command mode |
-| `Ctrl+Enter` | Run cell |
-| `Shift+Enter` | Run cell, select below |
-| `Alt+Enter` | Run cell, insert below |
-| `Ctrl+S` | Save notebook |
-| `Tab` | Code completion |
-| `Shift+Tab` | Tooltip |
-| `Ctrl+]` | Indent |
-| `Ctrl+[` | Dedent |
-| `Ctrl+A` | Select all |
-| `Ctrl+Z` | Undo |
-| `Ctrl+Y` | Redo |
-
-### JupyterLab Shortcuts
-
-| Shortcut | Action |
-|----------|--------|
-| `Ctrl+Shift+C` | Command palette |
-| `Ctrl+B` | Toggle left sidebar |
-| `Ctrl+Shift+D` | Toggle file browser |
-| `Ctrl+Shift+F` | Find and replace |
-| `Ctrl+Shift+[` | Previous tab |
-| `Ctrl+Shift+]` | Next tab |
-| `Alt+W` | Close tab |
-
----
-
-## Resources and Links
-
-### Official Documentation
-
-- **JupyterHub Documentation**: https://jupyterhub.readthedocs.io
-- **JupyterLab Documentation**: https://jupyterlab.readthedocs.io
-- **Jupyter Notebook Documentation**: https://jupyter-notebook.readthedocs.io
-- **2i2c Infrastructure Guide**: https://docs.2i2c.org
-
-### Tutorials and Learning Resources
-
-- **Jupyter Tutorial**: https://jupyter.org/try
-- **Real Python Jupyter Guide**: https://realpython.com/jupyter-notebook-introduction/
-- **DataCamp Jupyter Tutorial**: https://www.datacamp.com/tutorial/tutorial-jupyter-notebook
-- **Official Jupyter Examples**: https://github.com/jupyter/jupyter/wiki/Gallery-of-Jupyter-Notebooks
-
-### Disaster Response Specific Resources
-
-- **NASA Disasters Program**: https://disasters.nasa.gov
-- **USGS Hazards Data**: https://www.usgs.gov/natural-hazards
-- **NOAA Disaster Data**: https://www.ncdc.noaa.gov/billions/
-- **Copernicus Emergency Management**: https://emergency.copernicus.eu
-
-### Python Libraries for Disaster Analysis
-
-```python
-# Geospatial analysis
-import geopandas as gpd
-import rasterio
-import xarray as xr
-import folium
-
-# Data processing
-import pandas as pd
-import numpy as np
-import dask.dataframe as dd
-
-# Visualization
-import matplotlib.pyplot as plt
-import seaborn as sns
-import plotly.express as px
-
-# Machine learning
-from sklearn import *
-import tensorflow as tf
-import torch
-
-# Earth observation
-import ee  # Google Earth Engine
-import planetary_computer as pc
-import pystac_client
-```
-
-### Helpful Extensions
-
-Install JupyterLab extensions for enhanced functionality:
-
-```bash
-# Variable inspector
-jupyter labextension install @lckr/jupyterlab_variableinspector
-
-# Table of contents
-jupyter labextension install @jupyterlab/toc
-
-# Git integration
-pip install jupyterlab-git
-
-# Code formatter
-pip install jupyterlab-code-formatter
-```
-
-### Community and Support
-
-- **Jupyter Discourse Forum**: https://discourse.jupyter.org
-- **Stack Overflow**: https://stackoverflow.com/questions/tagged/jupyter
-- **GitHub Issues**: https://github.com/jupyterhub/jupyterhub/issues
-- **2i2c Support**: https://2i2c.org/support
-- **Gitter Chat**: https://gitter.im/jupyterhub/jupyterhub
-
-### Quick Reference PDFs
-
-- **JupyterLab Cheat Sheet**: https://www.datacamp.com/cheat-sheet/jupyterlab-cheat-sheet
-- **Jupyter Shortcuts PDF**: https://www.cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/
-- **Markdown Guide**: https://www.markdownguide.org/cheat-sheet/
-
----
-
-## Appendix: Sample Workflow
-
-Here's a complete example workflow for disaster analysis:
-
-```python
-# 1. Setup and Imports
-import pandas as pd
-import geopandas as gpd
-import matplotlib.pyplot as plt
-import folium
-from datetime import datetime, timedelta
-import warnings
-warnings.filterwarnings('ignore')
-
-# 2. Load Data
-# Earthquake data
-earthquakes = pd.read_csv('https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/4.5_month.csv')
-earthquakes['time'] = pd.to_datetime(earthquakes['time'])
-
-# 3. Data Processing
-# Filter recent events
-recent = earthquakes[earthquakes['time'] > datetime.now() - timedelta(days=7)]
-
-# Convert to GeoDataFrame
-geometry = gpd.points_from_xy(recent.longitude, recent.latitude)
-geo_df = gpd.GeoDataFrame(recent, geometry=geometry, crs='EPSG:4326')
-
-# 4. Analysis
-print(f"Total earthquakes in last 7 days: {len(recent)}")
-print(f"Average magnitude: {recent['mag'].mean():.2f}")
-print(f"Largest earthquake: {recent['mag'].max():.2f}")
-
-# 5. Visualization
-# Static plot
-fig, ax = plt.subplots(figsize=(12, 8))
-world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
-world.plot(ax=ax, color='lightgray', edgecolor='black')
-geo_df.plot(ax=ax, color='red', markersize=geo_df['mag']**2, alpha=0.6)
-plt.title('Recent Earthquakes (M4.5+)')
-plt.show()
-
-# Interactive map
-m = folium.Map(location=[0, 0], zoom_start=2)
-for idx, row in geo_df.iterrows():
-    folium.CircleMarker(
-        location=[row['latitude'], row['longitude']],
-        radius=row['mag']*2,
-        popup=f"M{row['mag']} - {row['place']}",
-        color='red',
-        fill=True
-    ).add_to(m)
-m.save('earthquake_map.html')
-
-# 6. Export Results
-geo_df.to_csv('processed_earthquakes.csv', index=False)
-print("Analysis complete! Results saved.")
-```
+⚠️ **Important**: Idle servers will be automatically culled after a period of inactivity (usually 1-2 hours).
 
 ---
 
-*Last Updated: 2024*  
+*Last Updated: 2025*  
 *Version: 1.0*  
 *Disasters Hub Training Guide*
 
diff --git a/Jupyterhub/setup-disaster-repo.qmd b/Jupyterhub/setup-disaster-repo.qmd
deleted file mode 100644
index fa4f4b5..0000000
--- a/Jupyterhub/setup-disaster-repo.qmd
+++ /dev/null
@@ -1,492 +0,0 @@
-# Setting Up Disaster Repository - Step-by-Step Guide
-
-## Table of Contents
-1. [Prerequisites](#prerequisites)
-2. [GitHub Account Setup](#github-account-setup)
-3. [Configure Git Identity](#configure-git-identity)
-4. [GitHub Authentication Setup](#github-authentication-setup)
-5. [Clone the Repository](#clone-the-repository)
-6. [Working with Branches](#working-with-branches)
-7. [Making Changes and Pushing](#making-changes-and-pushing)
-8. [Troubleshooting Common Issues](#troubleshooting-common-issues)
-
----
-
-## Prerequisites
-
-Before starting, ensure you have:
-- Git installed in your JupyterHub environment
-- Access to terminal in JupyterHub
-- Internet connection
-- GitHub account (we'll create one if needed)
-
-Check if Git is installed:
-```bash
-git --version
-```
-
-If not installed, contact your JupyterHub administrator.
-
----
-
-## GitHub Account Setup
-
-### Step 1: Create GitHub Account (if you don't have one)
-
-1. Visit [https://github.com](https://github.com)
-2. Click **Sign up**
-3. Enter your details:
-   - **Username**: Choose carefully (this is permanent and public)
-   - **Email**: Use your professional/institutional email
-   - **Password**: Create a strong password
-4. Verify your email address
-5. Complete profile setup
-
-### Step 2: Enable Two-Factor Authentication (Recommended)
-
-1. Go to **Settings** → **Password and authentication**
-2. Click **Enable two-factor authentication**
-3. Use an authenticator app (Google Authenticator, Authy, or Microsoft Authenticator)
-4. Save backup codes securely
-
----
-
-## Configure Git Identity
-
-Configure Git with your GitHub account information:
-
-```bash
-# Set your name (visible in commits)
-git config --global user.name "Your Full Name"
-
-# Set your email (MUST match your GitHub account email)
-git config --global user.email "your.email@example.com"
-
-# Set default branch name to main
-git config --global init.defaultBranch main
-
-# Enable colored output for better readability
-git config --global color.ui auto
-
-# Verify your configuration
-git config --list
-```
-
-**Example:**
-```bash
-git config --global user.name "Kyle Lesinger"
-git config --global user.email "kyle.lesinger@example.com"
-```
-
----
-
-## GitHub Authentication Setup
-
-Since GitHub no longer supports password authentication, you need to use either:
-1. **Personal Access Token** (Easier for JupyterHub)
-2. **SSH Keys** (More secure, one-time setup)
-3. **GitHub CLI** (Recommended - handles auth automatically)
-
-### Option 1: GitHub CLI Authentication (Recommended)
-
-```bash
-# Authenticate with GitHub CLI
-gh auth login
-
-# Follow the prompts:
-# 1. Choose: GitHub.com
-# 2. Choose: HTTPS (recommended for JupyterHub)
-# 3. Choose: Login with a web browser
-# 4. Copy the one-time code shown
-# 5. Press Enter to open browser (or manually visit https://github.com/login/device)
-# 6. Enter the code and authorize
-
-# Verify authentication
-gh auth status
-```
-
-### Option 2: Personal Access Token
-
-1. Go to GitHub.com → **Settings** → **Developer settings**
-2. Click **Personal access tokens** → **Tokens (classic)**
-3. Click **Generate new token** → **Generate new token (classic)**
-4. Name it: "JupyterHub Access"
-5. Set expiration (90 days recommended)
-6. Select scopes:
-   - ✅ `repo` (Full control of private repositories)
-   - ✅ `workflow` (Update GitHub Action workflows)
-7. Click **Generate token**
-8. **COPY THE TOKEN IMMEDIATELY** (you won't see it again!)
-
-Store the token securely for use when pushing:
-```bash
-# Store credentials (will be saved after first use)
-git config --global credential.helper store
-```
-
-### Option 3: SSH Key Setup
-
-```bash
-# Generate SSH key
-ssh-keygen -t ed25519 -C "your.email@example.com"
-# Press Enter for default location
-# Optionally set a passphrase
-
-# Display your public key
-cat ~/.ssh/id_ed25519.pub
-
-# Copy the entire output, then:
-# 1. Go to GitHub.com → Settings → SSH and GPG keys
-# 2. Click "New SSH key"
-# 3. Paste your key and save
-
-# Test SSH connection
-ssh -T git@github.com
-```
-
----
-
-## Clone the Repository
-
-### Step 1: Clone the Repository
-
-```bash
-# Navigate to your workspace
-cd ~/
-
-# Clone the repository (creates a new folder called 'conversion_scripts')
-git clone https://github.com/kyle-lesinger/conversion_scripts.git
-
-# Navigate into the repository
-cd conversion_scripts
-
-# Verify the clone
-ls -la
-git status
-```
-
-### Step 2: Verify Remote Configuration
-
-```bash
-# Check current remotes
-git remote -v
-
-# You should see:
-# origin  https://github.com/kyle-lesinger/conversion_scripts.git (fetch)
-# origin  https://github.com/kyle-lesinger/conversion_scripts.git (push)
-```
-
-### Step 3: (Optional) Switch to SSH Remote
-
-If you set up SSH keys and prefer using SSH:
-
-```bash
-# Remove HTTPS remote
-git remote remove origin
-
-# Add SSH remote
-git remote add origin git@github.com:kyle-lesinger/conversion_scripts.git
-
-# Verify the change
-git remote -v
-```
-
----
-
-## Working with Branches
-
-### Create a New Branch
-
-Always create a new branch for your work instead of committing directly to main:
-
-```bash
-# Make sure you're on the main branch
-git checkout main
-
-# Pull latest changes
-git pull origin main
-
-# Create and switch to a new branch
-git checkout -b feature/your-feature-name
-
-# Example branch names:
-# git checkout -b feature/add-preprocessing
-# git checkout -b bugfix/fix-data-pipeline
-# git checkout -b docs/update-readme
-```
-
-### Verify Your Branch
-
-```bash
-# Check which branch you're on
-git branch
-
-# List all branches (local and remote)
-git branch -a
-```
-
----
-
-## Making Changes and Pushing
-
-### Step 1: Make Your Changes
-
-```bash
-# Create or edit files
-echo "# Conversion Scripts" > README.md
-echo "This repository contains data conversion scripts." >> README.md
-
-# Check what files have changed
-git status
-```
-
-### Step 2: Stage and Commit Changes
-
-```bash
-# Add specific files
-git add README.md
-
-# Or add all changes
-git add .
-
-# Commit with descriptive message
-git commit -m "Add README with project description"
-
-# View commit history
-git log --oneline
-```
-
-### Step 3: Push to GitHub
-
-#### First Time Push (new branch):
-```bash
-# Push and set upstream branch
-git push -u origin feature/your-feature-name
-
-# If using Personal Access Token, enter:
-# Username: your-github-username
-# Password: your-personal-access-token (NOT your GitHub password!)
-```
-
-#### Subsequent Pushes:
-```bash
-# After upstream is set, simply:
-git push
-```
-
-### Step 4: Create Pull Request
-
-```bash
-# Using GitHub CLI (if authenticated)
-gh pr create --title "Add README documentation" --body "Added project description"
-
-# Or manually:
-# 1. Visit https://github.com/kyle-lesinger/conversion_scripts
-# 2. Click "Compare & pull request" button
-# 3. Add title and description
-# 4. Click "Create pull request"
-```
-
----
-
-## Complete Workflow Example
-
-Here's a complete example workflow from start to finish:
-
-```bash
-# 1. Configure Git (one-time setup)
-git config --global user.name "Kyle Lesinger"
-git config --global user.email "kyle.lesinger@example.com"
-
-# 2. Authenticate with GitHub CLI
-gh auth login
-# Follow the interactive prompts
-
-# 3. Clone the repository
-cd ~/
-git clone https://github.com/kyle-lesinger/conversion_scripts.git
-cd conversion_scripts
-
-# 4. Create a new branch
-git checkout -b feature/add-conversion-script
-
-# 5. Create a new file
-cat > convert_data.py << 'EOF'
-#!/usr/bin/env python3
-"""
-Data conversion utility script
-"""
-
-def convert_format(input_file, output_file):
-    """Convert data from one format to another"""
-    print(f"Converting {input_file} to {output_file}")
-    # Add conversion logic here
-
-if __name__ == "__main__":
-    convert_format("input.txt", "output.json")
-EOF
-
-# 6. Stage and commit
-git add convert_data.py
-git commit -m "Add data conversion utility script"
-
-# 7. Push to GitHub
-git push -u origin feature/add-conversion-script
-
-# 8. Create pull request
-gh pr create --title "Add data conversion script" --body "Initial conversion utility"
-```
-
----
-
-## Troubleshooting Common Issues
-
-### Issue 1: Authentication Failed
-
-**Error:** `remote: Invalid username or password`
-
-**Solution:**
-```bash
-# Use Personal Access Token instead of password
-# When prompted for password, paste your token
-
-# Or use GitHub CLI
-gh auth login
-```
-
-### Issue 2: Permission Denied (publickey)
-
-**Error:** `git@github.com: Permission denied (publickey)`
-
-**Solution:**
-```bash
-# Check if SSH key exists
-ls -la ~/.ssh/
-
-# Generate new key if needed
-ssh-keygen -t ed25519 -C "your.email@example.com"
-
-# Add to SSH agent
-eval "$(ssh-agent -s)"
-ssh-add ~/.ssh/id_ed25519
-
-# Add public key to GitHub account
-cat ~/.ssh/id_ed25519.pub
-# Copy output and add to GitHub.com → Settings → SSH Keys
-```
-
-### Issue 3: Remote Already Exists
-
-**Error:** `error: remote origin already exists`
-
-**Solution:**
-```bash
-# Remove existing remote
-git remote remove origin
-
-# Add new remote
-git remote add origin https://github.com/kyle-lesinger/conversion_scripts.git
-```
-
-### Issue 4: Rejected Push (Non-fast-forward)
-
-**Error:** `! [rejected] main -> main (non-fast-forward)`
-
-**Solution:**
-```bash
-# Pull latest changes first
-git pull origin main --rebase
-
-# Then push
-git push origin main
-```
-
-### Issue 5: Wrong Branch
-
-**Error:** Working on main branch instead of feature branch
-
-**Solution:**
-```bash
-# Create new branch with current changes
-git checkout -b feature/my-changes
-
-# Push to new branch
-git push -u origin feature/my-changes
-```
-
----
-
-## Best Practices
-
-1. **Always work in branches** - Never commit directly to main
-2. **Pull before pushing** - Always sync with remote before pushing
-3. **Use descriptive commit messages** - Explain what and why
-4. **Commit frequently** - Small, logical commits are better
-5. **Keep tokens secure** - Never commit tokens or passwords
-6. **Test locally** - Run your code before committing
-
----
-
-## Quick Command Reference
-
-```bash
-# Clone repository
-git clone https://github.com/kyle-lesinger/conversion_scripts.git
-
-# Create branch
-git checkout -b feature/new-feature
-
-# Check status
-git status
-
-# Add files
-git add .
-
-# Commit
-git commit -m "Description of changes"
-
-# Push new branch
-git push -u origin feature/new-feature
-
-# Push existing branch
-git push
-
-# Pull latest changes
-git pull origin main
-
-# Switch branches
-git checkout branch-name
-
-# List branches
-git branch -a
-
-# Delete local branch
-git branch -d branch-name
-
-# View commit history
-git log --oneline --graph
-```
-
----
-
-## Additional Resources
-
-- [GitHub Docs](https://docs.github.com)
-- [Git Documentation](https://git-scm.com/doc)
-- [GitHub CLI Manual](https://cli.github.com/manual)
-- [Pro Git Book (Free)](https://git-scm.com/book)
-
----
-
-## Getting Help
-
-If you encounter issues not covered here:
-
-1. Check the repository issues: https://github.com/kyle-lesinger/conversion_scripts/issues
-2. Ask in the JupyterHub support channel
-3. Consult the comprehensive [Git/GitHub guide](../Github/git-github-comprehensive-guide.md)
-
----
-
-*Last Updated: 2024*
-*Version: 1.0*
\ No newline at end of file
diff --git a/_quarto.yml b/_quarto.yml
index 79d5c13..a21761a 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -56,9 +56,7 @@ website:
         text: JupyterHub
         contents:
           - Jupyterhub/jupyterhub-training-guide.qmd
-          - Jupyterhub/setup-disaster-repo.qmd
-          - Jupyterhub/convert_to_geotiff.qmd
-          - Jupyterhub/simple_disaster_template.ipynb
+          - Jupyterhub/clone_conversion_repo.ipynb
       -  section: workflow2.qmd
          text: Data Workflow Diagrams
          contents:
diff --git a/workflow.qmd b/workflow.qmd
index c6572af..df2c26f 100644
--- a/workflow.qmd
+++ b/workflow.qmd
@@ -1,52 +1,13 @@
 ---
-title: "U.S. Greenhouse Gas Center: Data Flow Diagrams"
+title: "NASA Disasters: Data Flow Diagrams"
 ---
 
-Welcome to the homepage for [U.S. Greenhouse Gas (GHG) Center](https://earth.gov/ghgcenter) data flow diagrams. These diagrams summarize the process a dataset goes through from acquisition to integration in the U.S. GHG Center.
+Welcome to the homepage for [NASA Disasters](https://appliedsciences.nasa.gov/what-we-do/disasters) data flow diagrams. These diagrams summarize the process of how to find, download, and process data for NASA Disasters.
 
 Click on a dataset name to view the data flow diagram for that dataset.
 
-[View the US GHG Center Data Catalog](https://earth.gov/ghgcenter/data-catalog)
+[View the NASA Disasters Resources](https://appliedsciences.nasa.gov/what-we-do/disasters/practitioner-resources#portal)
 
-- [Air-Sea CO₂ Flux, ECCO-Darwin Model v5 Data Flow Diagram](data_workflow/eccodarwin-co2flux-monthgrid-v5_Data_Flow.qmd)
+- [NRT Data Download](data_workflow2/NRT_data_download.qmd)
 
-- [Atmospheric Carbon Dioxide Concentrations from the NOAA Global Monitoring Laboratory Data Flow Diagram](data_workflow/noaa-gggrn-co2-concentrations_Data_Flow.qmd)
-
-- [Atmospheric Methane Concentrations from the NOAA Global Monitoring Laboratory Data Flow Diagram](data_workflow/noaa-gggrn-ch4-concentrations_Data_Flow.qmd)
-
-- [Carbon Dioxide and Methane Concentrations from the Indianapolis Flux Experiment (INFLUX) Data Flow Diagram](data_workflow/influx-testbed-ghg-concentrations_Data_Flow.qmd)
-
-- [Carbon Dioxide and Methane Concentrations from the Los Angeles Megacity Carbon Project Data Flow Diagram](data_workflow/lam-testbed-ghg-concentrations_Data_Flow.qmd)
-
-- [Carbon Dioxide and Methane Concentrations from the Northeast Corridor (NEC) Urban Test Bed Data Flow Diagram](data_workflow/nec-testbed-ghg-concentrations_Data_Flow.qmd)
-
-- [CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Data Flow Diagram](data_workflow/ct-ch4-monthgrid-v2023_Data_Flow.qmd)
-
-- [EMIT Methane Point Source Plume Complexes Data Flow Diagram](data_workflow/emit-ch4plume-v1_Data_Flow.qmd)
-
-- [Geostationary Satellite Observations of Extreme and Transient Methane Emissions from Oil and Gas Infrastructure Complexes Data Flow Diagram](data_workflow/goes-ch4plume-v1_Data_Flow.qmd)
-
-- [GOSAT-based Top-down Total and Natural Methane Emissions Data Flow Diagram](data_workflow/gosat-based-ch4budget-yeargrid-v1_Data_Flow.qmd)
-
-- [GRA²PES Greenhouse Gas and Air Quality Species Data Flow Diagram](data_workflow/gra2pes-ghg-monthgrid-v1_Data_Flow.qmd)
-
-- [MiCASA Land Carbon Flux Data Flow Diagram](data_workflow/micasa-carbonflux-daygrid-v1_Data_Flow.qmd)
-
-- [OCO-2 GEOS Column CO₂ Concentrations Data Flow Diagram](data_workflow/oco2geos-co2-daygrid-v10r_Data_Flow.qmd)
-
-- [OCO-2 MIP Top-Down CO₂ Budgets Data Flow Diagram](data_workflow/oco2-mip-co2budget-yeargrid-v1_Data_Flow.qmd)
-
-- [ODIAC Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/odiac-ffco2-monthgrid-v2024_Data_Flow.qmd)
-
-- [SEDAC Gridded World Population Density Data Flow Diagram](data_workflow/sedac-popdensity-yeargrid5yr-v4.11_Data_Flow.qmd)
-
-- [U.S. Gridded Anthropogenic Methane Emissions Inventory Data Flow Diagram](data_workflow/epa-ch4emission-grid-v2express_Data_Flow.qmd)
-
-- [Vulcan Fossil Fuel CO₂ Emissions Data Flow Diagram](data_workflow/vulcan-ffco2-yeargrid-v4_Data_Flow.qmd)
-
-- [Wetland Methane Emissions, LPJ-EOSIM Model Data Flow Diagram](data_workflow/lpjeosim-wetlandch4-grid-v1_Data_Flow.qmd)
-
-
-## Contact
-
-For technical help or general questions, please contact the support team using the [feedback form](https://docs.google.com/forms/d/e/1FAIpQLSeVWCrnca08Gt_qoWYjTo6gnj1BEGL4NCUC9VEiQnXA02gzVQ/viewform).
\ No newline at end of file
+- [NRT Directory Structure](data_workflow2/NRT_directory_structure.qmd)
\ No newline at end of file
diff --git a/workflow2.qmd b/workflow2.qmd
deleted file mode 100644
index df2c26f..0000000
--- a/workflow2.qmd
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: "NASA Disasters: Data Flow Diagrams"
----
-
-Welcome to the homepage for [NASA Disasters](https://appliedsciences.nasa.gov/what-we-do/disasters) data flow diagrams. These diagrams summarize the process of how to find, download, and process data for NASA Disasters.
-
-Click on a dataset name to view the data flow diagram for that dataset.
-
-[View the NASA Disasters Resources](https://appliedsciences.nasa.gov/what-we-do/disasters/practitioner-resources#portal)
-
-- [NRT Data Download](data_workflow2/NRT_data_download.qmd)
-
-- [NRT Directory Structure](data_workflow2/NRT_directory_structure.qmd)
\ No newline at end of file