Abstract
Great strides have been made to encourage researchers to archive data created by research and provide the necessary systems to support their storage. Additionally it is recognised that data are meaningless unless their provenance is preserved, through appropriate meta-data. Alongside this is a pressing need to ensure the quality and archiving of the software that generates data, through simulation, control of experiment or data-collection and that which analyses, modifies and draws value from raw data. In order to meet the aims of reproducibility we argue that data management alone is insufficient: it must be accompanied by good software practices, the training to facilitate it and the support of stakeholders, including appropriate recognition for software as a research output.