README

Repository: IRIS
Description: Monorepo containing the IRIS frontend and backend services.
SPDX-License-Identifier: Apache-2.0 AND OGL-UK-3.0

Overview

IRIS is a digital tool designed to support data-driven decision-making for retrofitting domestic properties by identifying homes that could benefit from energy efficiency improvements. It enables stakeholders to assess housing stock based on energy performance data to help target funding schemes and policy interventions more effectively. IRIS is part of the NDTP Demonstrator Programme.

This monorepo contains:

• frontend/ — the IRIS visualisation client (Angular).
• backend/ — the IRIS API service (Python).
• data-tools/data-cleanser/ — ETL pipeline using Airbyte + dbt to prepare data for IRIS.
• data-tools/data-pipeline/ — data pipelines to process EPC and geographic data.

Repository Structure

.
├── frontend/  # IRIS visualisation client
├── backend/   # IRIS API service
├── data-tools/data-cleanser/ # ETL pipeline for IRIS data preparation
└── data-tools/data-pipeline/ # Data pipelines for EPC and geographic processing

Prerequisites

Common:

• Git

Frontend:

• NVM (recommended)
• Node.js (via .nvmrc)
• Angular CLI

Backend:

• Python 3.12
• Docker
• make

Data cleanser:

• Python
• Poetry
• Postgres
• Airbyte
• dbt
• Docker

Data pipeline:

• Python 3.12+
• Docker
• Kafka
• Zookeeper

Quick Start

1. Clone

git clone https://github.com/National-Digital-Twin/IRIS.git
cd IRIS

2. Frontend (visualisation)

cd frontend
nvm install
nvm use

If you need access to GitHub Packages, create a classic token with read:packages and export it:

export GITHUB_ACCESS_TOKEN=ghp_xxxxxxx

npm install
ng build

System requirements (frontend):

• Dual-Core CPU (Intel i5 or AMD Ryzen 3 equivalent), 8GB RAM, SSD/HDD with 10GB free space

For more details, see frontend/INSTALLATION.md and frontend/UNINSTALL.md.

3. Backend (API)

cd backend
pip install -f requirements.txt
make run-api

System requirements (backend):

• Dual-Core CPU (Intel i5 or AMD Ryzen 3 equivalent), 8GB RAM, SSD/HDD with 10GB free space

For more details, see backend/INSTALLATION.md and backend/UNINSTALL.md.

4. Data cleanser (ETL)

cd data-tools/data-cleanser
poetry --version

For detailed install and run steps, see data-tools/data-cleanser/INSTALLATION.MD and data-tools/data-cleanser/UNINSTALL.md.

5. Data pipeline

cd data-tools/data-pipeline
python --version

For detailed install and run steps, see data-tools/data-pipeline/INSTALLATION.md and data-tools/data-pipeline/UNINSTALL.md.

Data Cleanser (ETL)

The data cleanser is an ETL pipeline that fetches and transforms EPC and OS data for IRIS. It uses Airbyte for extraction and dbt for transformations, with outputs persisted to S3.

Key components:

• Airbyte sources and destinations in data-tools/data-cleanser/airbyte/
• dbt models in data-tools/data-cleanser/dbt-pipeline/

For full details, see data-tools/data-cleanser/README.md.

Data Pipeline

The data pipeline contains two major pipelines:

• address-profiling-pipeline for EPC assessment data processing.
• lat-long-pipeline for geographic coordinate processing.

Both pipelines use adapters and mappers to ingest source data (CSV or S3) and publish to Kafka topics as RDF outputs.

For full details, see data-tools/data-pipeline/README.md.

Features

Frontend:

• Core functionality Visualises housing data on a map, including materials and EPC ratings.
• Scalability & performance Code optimised for scalability and performance.

Backend:

• Core functionality Provides API routes to serve and route data to and from the visualisation client.
• Key integrations REST interface to query and write data to the IA node.
• Scalability & performance Optimised for scalability and performance.

Data cleanser:

• Core functionality ETL pipeline to ingest EPC/OS data and prepare datasets for IRIS.
• Key integrations Airbyte sources/destinations and dbt transformations with S3 outputs.

Data pipeline:

• Core functionality Address profiling and lat/long pipelines to transform EPC and geographic data into RDF.
• Key integrations Kafka topics for adapter/mapper components.

API Documentation

The API documentation can be accessed by running the backend and navigating to:

• /api-docs
• /api-docs/openapi.json

Public Funding Acknowledgment

This repository has been developed with public funding as part of the National Digital Twin Programme (NDTP), a UK Government initiative. NDTP, alongside its partners, has invested in this work to advance open, secure, and reusable digital twin technologies for any organisation, whether from the public or private sector, irrespective of size.

License

This repository contains both source code and documentation, which are covered by different licenses:

• Code: Originally developed by Coefficient Systems, Ove Arup & Partners, Telicent, and Informed Solutions, now maintained by National Digital Twin Programme. Licensed under the Apache License 2.0.
• Documentation: Licensed under the Open Government Licence v3.0.

See LICENSE.md, OGL_LICENSE.md, and NOTICE.md for details. Service-specific notes remain in frontend/README.md and backend/README.md.

Security and Responsible Disclosure

We take security seriously. If you believe you have found a security vulnerability in this repository, please follow our responsible disclosure process outlined in SECURITY.md.

Software Bill of Materials (SBOM)

Download the latest SBOM for this codebase to view the current list of components used in this repository.

Contributing

We welcome contributions that align with the Programme’s objectives. Please read CONTRIBUTING.md before submitting pull requests.

Acknowledgements

For a list of acknowledgments, see ACKNOWLEDGEMENTS.md.

Support and Contact

For questions or support, check Issues or contact the NDTP team on ndtp@businessandtrade.gov.uk.

Maintained by the National Digital Twin Programme (NDTP).

© Crown Copyright 2025. This work has been developed by the National Digital Twin Programme and is legally attributed to the Department for Business and Trade (UK) as the governing entity.

Licensed under the Open Government Licence v3.0. For full licensing terms, see OGL_LICENSE.md.