Purchase Orders over €20,000 Q4 2023

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Dec 2023	University of Limerick Research	Longitudinal Research Project	Purchase Order	€350,000.00
31 Dec 2023	Silverbear Limited	Registration Database maintenance & development	Purchase Order	€36,051.00
31 Dec 2023	Real Nation	Event Management Services	Purchase Order	€90,238.00
31 Dec 2023	New Graphic	Website Software Costs	Purchase Order	€25,212.00
31 Dec 2023	Mary Immaculate College	Research Funding	Purchase Order	€35,000.00
31 Dec 2023	Lisglassock Holdings Ltd t/a DGS Business Consulting	Organisation review & Workforce Planning	Purchase Order	€49,749.00
31 Dec 2023	K-MAC Facilities Management Services Ltd	Facilities Management & Maintenance	Purchase Order	€27,913.00
31 Dec 2023	Gwen Malone Stenography Services	Stenography Services	Purchase Order	€20,163.00
31 Dec 2023	Fieldfisher	Legal Advice	Purchase Order	€285,017.00
31 Dec 2023	Ernst & Young Business Consulting Services	Research Strategy Review	Purchase Order	€25,750.00
31 Dec 2023	Digitary	Online portal fees	Purchase Order	€28,536.00
31 Dec 2023	Clarion Consulting	Project Management services	Purchase Order	€21,424.00
31 Dec 2023	Abtran	Managed Administration Services	Purchase Order	€375,545.00

^* Payment date may be the end of the reporting quarter when the entity does not publish an exact date.

How to read these figures

These are reported values from published purchase order and payment records — not verified cash paid, and not total spending by this organisation. A row may be a purchase order, a payment, or another reported transaction; the source files often do not say which.

Coverage and quality vary. Some fields are blank, supplier names are free text (the same firm can appear under many labels), and descriptions are inconsistent rather than a controlled taxonomy. An amount alone says nothing about value for money and it may or may not include VAT.

Records are retrieved from each organisation’s published files such as spreadsheets, PDFs, and other formats of uneven quality. Extraction techniques (including automated parsing) can introduce gaps, misreads, or incomplete rows when source layout or quality is poor. Treat the data as a starting point for investigation, not a definitive ledger.

Here is a project that encountered similar issues