An interactive dashboard developed using Dash and Plotly to support material discovery workflows at Lawrence Berkeley National Lab.
It brings together synthesis and characterization data in one place to help researchers:

• Compare experimental samples
• Identify trends or anomalies
• Explore ideas for new analyses

🎥 Watch the video demo:

To keep credentials secure, this project uses environment variables loaded from a .env file.

git clone https://github.com/your-username/ALAB_DASHBOARD_NEW.git
cd ALAB_DASHBOARD_NEW

Copy the template:

cp .env.example .env

Then open .env and fill in your MongoDB credentials:

SAURON_DB_USER=your_sauron_username
SAURON_DB_PASS=your_sauron_password
SAURON_DB_HOST=your_sauron_host
SAURON_DB_NAME=your_sauron_database

DARA_DB_USER=your_dara_username
DARA_DB_PASS=your_dara_password
DARA_DB_HOST=your_dara_host
DARA_DB_NAME=your_dara_database
DARA_COLLECTION=results

🔒 Note: The .env file is ignored via .gitignore and will not be tracked by git.

python -m venv venv
source venv/bin/activate        # macOS/Linux
# OR
venv\Scripts\activate           # Windows

pip install -r requirements.txt

Once the environment is set up:

python app.py

Then open your browser and go to:
http://127.0.0.1:8050

├── app.py                # Entry point for the dashboard
├── data.py               # MongoDB access and caching
├── layout.py             # Layout and UI components
├── callbacks.py          # App interactivity
├── utils.py              # Helper functions
├── datasets/             # Local CSV cache
├── assets/               # Dash assets (e.g., custom CSS)
├── .env.example          # Template for secrets
├── requirements.txt      # Dependencies
└── README.md