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