Payments over €20,000 Q1 2022

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2022	DMOD Ltd		Purchase Order	€43,944.21
31 Mar 2022	Dublin City Council	Property rates	Purchase Order	€45,719.41
31 Mar 2022	Micromail	Microsoft Azure Cloud	Purchase Order	€28,862.80
31 Mar 2022	Commsec Communications & Security Ltd	ICT security	Purchase Order	€29,981.00
28 Feb 2022	CHI Crumlin		Purchase Order	€127,539.96
28 Feb 2022	Client Solutions Ltd	Registration system	Purchase Order	€129,475.87
28 Feb 2022	Cardiff University	Review of standards project	Purchase Order	€40,266.00
31 Jan 2022	Decision Time	Annual subscription	Purchase Order	€35,000.00
31 Jan 2022	Telcom Ltd	Telephone system	Purchase Order	€20,475.96
31 Jan 2022	HEAnet CLG	Microsoft Unified support	Purchase Order	€24,043.00
31 Jan 2022	Grosvenor Cleaning Services	Cleaning fees	Purchase Order	€21,126.06
31 Jan 2022	ENERGIA	Electricity supply	Purchase Order	€53,850.00
31 Jan 2022	Clancourt Management		Purchase Order	€30,760.15
31 Jan 2022	Clancourt Management	Rent	Purchase Order	€356,537.50
31 Jan 2022	Teamsoft	Finance system user licenses	Purchase Order	€66,862.80
31 Jan 2022	RGRE Kingram Limited	Rent	Purchase Order	€827,500.00
31 Jan 2022	DMOD Ltd		Purchase Order	€64,884.96

^* 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.

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