Environmental science researchers are now using and generating ever-increasing volumes of data and information about our natural world. It is estimated that the Environmental Protection Agency’s (EPA's) STRIVE (Science, Technology, Research and Innovation for the Environment) research funding programme will “involve more than 1,000 researchers and company-based scientists over its seven-year lifetime”1. The EPA's Environmental Research Centre (ERC) expects that large volumes of environmental data and information will be generated by projects funded by STRIVE. One of the key objectives of the STRIVE programme is to make the outcomes and data from this research available “in a coherent and timely manner which will ensure synergies across the wider research agenda and early availability of these outputs into the formulation of policy”2. Consequently, the STRIVE programme must adopt best international practice in environmental research data management. Management of these environmental research data is a core activity for the ERC with particular emphasis on the application of appropriate data management techniques to ensure their long-term availability and accessibility. Environmental research data are often irreplaceable; they are always unique particularly in the spatial location and temporal characteristics of their collection. They can also be extremely expensive and difficult to collect or generate. For these reasons the EPA and the ERC attach great importance to the ongoing development of systems that will ensure that maximum benefits are derived from research data once acquired.