MOTIVATION: The data that put the 'evidence' into 'evidence-based medicine' are central to developments in public health, primary and hospital care. A fundamental challenge is to site such data in repositories that can easily be accessed under appropriate technical and governance controls which are effectively audited and are viewed as trustworthy by diverse stakeholders. This demands socio-technical solutions that may easily become enmeshed in protracted debate and controversy as they encounter the norms, values, expectations and concerns of diverse stakeholders. In this context, the development of what are called 'Data Safe Havens' has been crucial. Unfortunately, the origins and evolution of the term have led to a range of different definitions being assumed by different groups. There is, however, an intuitively meaningful interpretation that is often assumed by those who have not previously encountered the term: a repository in which useful but potentially sensitive data may be kept securely under governance and informatics systems that are fit-for-purpose and appropriately tailored to the nature of the data being maintained, and may be accessed and utilized by legitimate users undertaking work and research contributing to biomedicine, health and/or to ongoing development of healthcare systems. RESULTS: This review explores a fundamental question: 'what are the specific criteria that ought reasonably to be met by a data repository if it is to be seen as consistent with this interpretation and viewed as worthy of being accorded the status of 'Data Safe Haven' by key stakeholders'? We propose 12 such criteria. CONTACT: [email protected].

MOTIVATION: Very large studies are required to provide sufficiently big sample sizes for adequately powered association analyses. This can be an expensive undertaking and it is important that an accurate sample size is identified. For more realistic sample size calculation and power analysis, the impact of unmeasured aetiological determinants and the quality of measurement of both outcome and explanatory variables should be taken into account. Conventional methods to analyse power use closed-form solutions that are not flexible enough to cater for all of these elements easily. They often result in a potentially substantial overestimation of the actual power. RESULTS: In this article, we describe the Estimating Sample-size and Power in R by Exploring Simulated Study Outcomes tool that allows assessment errors in power calculation under various biomedical scenarios to be incorporated. We also report a real world analysis where we used this tool to answer an important strategic question for an existing cohort. AVAILABILITY AND IMPLEMENTATION: The software is available for online calculation and downloads at http://espresso-research.org. The code is freely available at https://github.com/ESPRESSO-research. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.