Government Agencies

Irish National Open Data Portal

Irish Department of Public Expenditure and Reform

Derilinx provides support to the Department of Public Expenditure and Reform on the national Open Data Initiative including technical support for the national Open Data portal, data.gov.ie. Derilinx also provided guidance to the Department on the technical framework and implementation of the national Open Data Strategy 2017-2022. The Irish Open Data Initiative was recently ranked as the top country for Open Data Maturity across 32 European countries in a European Commission study. Data.gov.ie is a fully managed and cloud-hosted CKAN service for which we provide complete management, development and monitoring services. The development of data.gov.ie involved collecting, importing and harvesting data from a number of diverse sources at various public-sector bodies.

eHealth Ireland Open Data Portal

Health Service Executive

Derilinx is currently working with Health Service Executive (HSE) on their eHealth Ireland Open Data Strategic Programme, including the development of the eHealth Ireland Open Data Portal. As former CIO HSE Richard Corbridge highlighted, ‘the aim of Open Data for Health is twofold; on the one hand facilitating transparency of the Public Sector and on the other providing a valuable resource that can drive innovation. The availability of Open Data can empower citizens and support clinicians, care providers, and researchers make better decisions, spur new innovations and identify efficiencies while ensuring that personal data remains confidential.’ Identifying what data assets are suitable for publication involves carrying out a data audit with a large number of stakeholders.

Linked Open Statistical Data

Central Statistics Office

Derilinx are collaborating with the ADAPT Centre, and Insight Centre for Data Analytics on the provision of a service to Publish and Link Statistical Data on the Semantic Web. The Linked Open Statistical Data (LOSD) service will be adopted by National Statistical Institutes based in Ireland, France, Bulgaria and Italy, to promote the release and dissemination of European statistics to a wide range of users and to improve the innovative use and integration of statistical data. We adopt a practical approach that will deliver a robust and reusable LOSD publication service that is extensible across the European Statistical System (ESS).

> Services: Open Data, Data Sharing

Data Governance and Catalogue Review

Environmental Protection Agency

Derilinx worked with the Environmental Protection Agency’s Data & Information Architecture Team to carry out a complete Data Governance and Catalogue, Standards & Software Review. The solution provided optimised architecture and high level integrated design that closely aligns with EPA’s enterprise strategy and related business architecture. Derilinx engaged closely with the relevant ICT technical resources/ stakeholders and consulted widely with the organisation through workshops and direct meetings. We reviewed the core systems across the organisation, and the logical information architecture across the core functions, taking account of the interdependencies for the solution proposed across the whole infrastructure. We conducted a review of current Data Governance arrangements, and then iterated a plan to identify what the Data Governance strategy needed to be and a clear roadmap on how to get there.

DataSparks BER Hero

Sustainable Energy Authority of Ireland

Derilinx was awarded the SBIR contract launched by the Sustainable Energy Authority of Ireland in collaboration with Enterprise Ireland to develop smart technology solutions to help Building Energy Rating (BER) end users interpret the data and datasets available from the BER scheme. DataSparks, is an innovative and user-friendly web and mobile application that generates customised recommendation to homeowners on how they can improve the energy-efficiency of their property. It uses a large amount of Open Data to make these recommendations, from many different data sources. We used several techniques to find the best recommendation engine for this data, from linear regressions to ‘forest of trees’ algorithms, and finally settled on a simpler ‘human-tuned’ partition/aggregation algorithm.

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