diff --git a/WELCOME.ipynb b/WELCOME.ipynb
new file mode 100644
index 0000000..db0c23d
--- /dev/null
+++ b/WELCOME.ipynb
@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🚀 Welcome to the IDEAR Computational Environment\n",
+    "---\n",
+    "This environment helps you **integrate**, **curate**, and **analyze multi-instrument <br>  datasets** stored in **HDF5 format**.\n",
+    "\n",
+    "It also supports you in developing data analysis workflows and others in understanding <br> the research lifecycle of your project.\n",
+    "\n",
+    "---\n",
+    "### 📖 Quick Start\n",
+    "\n",
+    "1. Click a workflow link below to open a demonstration notebook.\n",
+    "2. Follow the step-by-step instructions inside.\n",
+    "3. Run each cell in order using Shift + Enter.\n",
+    "4. Modify and experiment with the examples as you go.\n",
+    "\n",
+    "---\n",
+    "### 📚 Scientific Data Workflows\n",
+    "\n",
+    "Start exploring with these examples:\n",
+    "\n",
+    "1. **[Data integration workflow ↗️](notebooks/demo_data_integration.ipynb)** <br>\n",
+    "   Learn how to integrate and curate multi-instrument datasets using campaignDescriptor.yaml and the DIMA data integration pipeline.\n",
+    "2. **[Metadata revision workflow ↗️](notebooks/demo_metadata_revision.ipynb)** <br>\n",
+    "   Learn how to edit metadata and manage HDF5 attributes effectively.\n",
+    "---\n",
+    "### 🧭 Data Practices\n",
+    "\n",
+    "As your IDEAR project evolves, please follow these key practices to keep your data organized, reproducible, and easy to share.\n",
+    "\n",
+    "1. **Set up your data**\n",
+    "    * Define your multi-instrument data folder using `campaignDescriptor.yaml`.\n",
+    "    * When possible, use the **Data Integration Workflow** to gather data from shared drives and create a unified HDF5 dataset.\n",
+    "\n",
+    "2. **Work with your data**\n",
+    "\n",
+    "    * Use DIMA’s HDF5 Data Manager to read and explore data directly from the integrated HDF5 file.\n",
+    "\n",
+    "    * Keep all analysis scripts and notebooks working with data stored in the `data/` folder.\n",
+    "\n",
+    "3. **Save and document your results**\n",
+    "\n",
+    "    * Save all output figures and analysis results in the `figures/` folder.\n",
+    "\n",
+    "    * Use Git version control to record how your figures and analyses change over time (prospective provenance).\n",
+    "\n",
+    "4. **Refine your metadata**\n",
+    "\n",
+    "    * Use the Metadata Revision Workflow to review and improve metadata and HDF5 attributes for clarity and completeness.\n",
+    "\n",
+    "5. **Extend your project**\n",
+    "\n",
+    "    * As you develop new workflows, add them to the list in this notebook so others can reuse or adapt them.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 🗂️ Project Structure\n",
+    "\n",
+    "```\n",
+    ".\n",
+    "├── WELCOME.ipynb          # This file\n",
+    "├── dima/                  # Reuseable data operations\n",
+    "├── data/                  # Your research data (create as needed)\n",
+    "└── figures/               # Analysis outputs (create as needed)\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 💡 Tips\n",
+    "\n",
+    "- **Save frequently**: Use `Ctrl+S` (or `Cmd+S` on Mac)\n",
+    "- **Restart kernel**: If things break, use `Kernel > Restart Kernel` from the menu\n",
+    "- **File browser**: Use the left sidebar to navigate between notebooks\n",
+    "- **New notebooks**: Click the `+` button in the file browser to create new notebooks\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Need help?** Check the project documentation or contact your research team."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python version: 3.11.13 | packaged by conda-forge | (main, Jun  4 2025, 14:48:23) [GCC 13.3.0]\n",
+      "Environment ready! ✅\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Optional: Add a quick system check\n",
+    "import sys\n",
+    "print(f\"Python version: {sys.version}\")\n",
+    "print(f\"Environment ready! ✅\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/WELCOME.md b/WELCOME.md
new file mode 100644
index 0000000..6fdfc3b
--- /dev/null
+++ b/WELCOME.md
@@ -0,0 +1,77 @@
+### 🚀 Welcome to the IDEAR Computational Environment
+---
+This environment helps you **integrate**, **curate**, and **analyze multi-instrument <br>  datasets** stored in **HDF5 format**.
+
+It also supports you in developing data analysis workflows and others in understanding <br> the research lifecycle of your project.
+
+---
+### 📖 Quick Start
+
+1. Click a workflow link below to open a demonstration notebook.
+2. Follow the step-by-step instructions inside.
+3. Run each cell in order using Shift + Enter.
+4. Modify and experiment with the examples as you go.
+
+---
+### 📚 Scientific Data Workflows
+
+Start exploring with these examples:
+
+1. **[Data integration workflow ↗️](notebooks/demo_data_integration.ipynb)** <br>
+   Learn how to integrate and curate multi-instrument datasets using campaignDescriptor.yaml and the DIMA data integration pipeline.
+2. **[Metadata revision workflow ↗️](notebooks/demo_metadata_revision.ipynb)** <br>
+   Learn how to edit metadata and manage HDF5 attributes effectively.
+---
+### 🧭 Data Practices
+
+As your IDEAR project evolves, please follow these key practices to keep your data organized, reproducible, and easy to share.
+
+1. **Set up your data**
+    * Define your multi-instrument data folder using `campaignDescriptor.yaml`.
+    * When possible, use the **Data Integration Workflow** to gather data from shared drives and create a unified HDF5 dataset.
+
+2. **Work with your data**
+
+    * Use DIMA’s HDF5 Data Manager to read and explore data directly from the integrated HDF5 file.
+
+    * Keep all analysis scripts and notebooks working with data stored in the `data/` folder.
+
+3. **Save and document your results**
+
+    * Save all output figures and analysis results in the `figures/` folder.
+
+    * Use Git version control to record how your figures and analyses change over time (prospective provenance).
+
+4. **Refine your metadata**
+
+    * Use the Metadata Revision Workflow to review and improve metadata and HDF5 attributes for clarity and completeness.
+
+5. **Extend your project**
+
+    * As you develop new workflows, add them to the list in this notebook so others can reuse or adapt them.
+
+---
+
+### 🗂️ Project Structure
+
+```
+.
+├── WELCOME.md          # This file
+├── dima/                  # Reuseable data operations
+├── data/                  # Your research data (create as needed)
+└── figures/               # Analysis outputs (create as needed)
+
+```
+
+---
+
+### 💡 Tips
+
+- **Save frequently**: Use `Ctrl+S` (or `Cmd+S` on Mac)
+- **Restart kernel**: If things break, use `Kernel > Restart Kernel` from the menu
+- **File browser**: Use the left sidebar to navigate between notebooks
+- **New notebooks**: Click the `+` button in the file browser to create new notebooks
+
+---
+
+**Need help?** Check the project documentation or contact your research team.
\ No newline at end of file