Basic init idear template

README.md

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+# IDEAR FAIRification Toolkit
+
+This is a **containerized, JupyterLab-based data toolkit** developed as part of the IDEAR project. It supports efficient, reproducible, and metadata-enriched data processing workflows for instrument-generated datasets.
+
+---
+
+### Key Features
+
+- Modular pipeline with reusable notebook workflows  
+- Metadata-driven HDF5 outputs for long-term data reuse  
+- Optional network-mounted input for seamless integration with shared drives  
+
+---
+
+### Output Format
+
+- **Self-describing HDF5 files**, including:
+  - Project-level, contextual, and data lineage metadata
+
+---
+
+### Extensibility
+
+New instruments can be supported by extending the file parsing capabilities in the `dima/` module.
+
+
+
+## Repository Structure
+
+<details>
+<summary><b>Click to expand</b></summary>
+
+- `data/` — Input and output datasets (mounted volume)  
+- `figures/` — Output visualizations (mounted volume)  
+- `notebooks/` — Jupyter notebooks for processing and metadata integration  
+- `scripts/` — Supplementary processing logic  
+- `dima/` — Metadata and HDF5 schema utilities (persisted module)  
+- `Dockerfile` — Container image definition  
+- `docker-compose.yaml` — Local and networked deployment options  
+- `env_setup.sh` — Optional local environment bootstrap  
+- `CITATION.cff`, `LICENCE`, `README.md`, `.gitignore`, `.dockerignore` — Project metadata and config  
+- `campaignDescriptor.yaml` — Campaign-specific metadata
+
+</details>
+
+---
+
+## Getting Started
+
+### Requirements
+
+#### For Docker-based usage:
+
+- **Docker Desktop**  
+- **Git Bash** (for running shell scripts on Windows)  
+
+#### Optional for local (non-Docker) usage:
+
+- **Conda** (`miniconda` or `anaconda`)  
+
+#### If accessing network drives (e.g., PSI):
+
+- PSI credentials with access to mounted network shares
+
+---
+
+## Clone the Repository
+
+```bash
+git clone --recurse-submodules <your-repo-url>
+cd <your-repo-name>
+```
+
+## Run with Docker
+
+This toolkit includes a containerized JupyterLab environment for executing the data processing pipeline, plus an optional dashboard for manual flagging.
+
+1. Open **PowerShell as Administrator** and navigate to the `acsmnode` repository.  
+2. Create a `.env` file in the root of `acsmnode/`.  
+3. **Securely store your network drive access credentials** in the `.env` file by adding the following lines: 
+   ```plaintext
+   CIFS_USER=<your-username>
+   CIFS_PASS=<your-password>
+   JUPYTER_TOKEN=my-token
+   ```
+   **To protect your credentials:**
+   - Do not share the .env file with others.
+   - Ensure the file is excluded from version control by adding .env to your .gitignore and .dockerignore files.
+4. Open **Docker Desktop**, then build the container image:
+   ```bash
+   docker build -f Dockerfile -t idear_processor .
+   ```
+5. Start the environment:
+
+- **Locally without network drive mount:**
+
+  ```bash
+  docker compose up idear_processor
+
+- **With network drive mount:**
+
+  ```bash
+  docker compose up idear_processor_networked
+
+6. Access:
+   - **Jupyter Lab**: [http://localhost:8889/lab/tree/notebooks/](http://localhost:8889/lab/tree/notebooks/)
+
+7. Stop the app:
+   In the previously open PowerShell terminal, enter:
+   ```bash
+   Ctrl + C
+   ```
+   After the container is properly Stopped, remove the container process as: 
+   ```bash
+   docker rm $(docker ps -aq --filter ancestor=idear_processor)
+   ```
+
+
+## (Optional) Set Up the Python Environment
+
+   > Required only if you plan to run the toolkit outside of Docker
+
+If **Git Bash** lacks a suitable Python interpreter, run:
+
+```bash
+   bash env_setup.sh
+```
+
+## Citation
+
+## License