Large-scale international collaborations are increasingly managing large volumes of sensitive health data for research purposes. The use of such infrastructure requires fulfilling various technical and organizational requirements to ensure usability and security. Before data scientists can access the data, it must be imported into the infrastructure with high data security requirements. For federated analysis workflows, additional criteria, such as data harmonization and prevention of individual patient information disclosure, must also be met. This paper outlines the key components of the data infrastructure implemented in the European research project ORCHESTRA and elaborates on the methods that support federated analysis workflows in a heterogeneous legal environment. Special attention is given to data security, interoperability, and usability, from which data scientists and researchers would benefit. We demonstrate the usability of data infrastructure on a federated analysis and machine learning use cases on remote datasets which satisfies the previously mentioned requirements. Furthermore, we propose organizational measures to optimize the process, reducing the time between a data access request and granting access.

This paper focuses on the link between a visible linear and local distortion in a pictorial scene and its cost in terms of information theory quantities. In particular, a formal relation between the Michelson visual contrast and the Jensen-Shannon divergence (JSD) will be provided. A universal just noticeable threshold is also derived by maximizing JSD constrained to the greatest Michelson contrast of an invisible distortion. Such a threshold is independent of both distortion parameters and the probability density function of the degraded image. It is then able to measure the bits budget to be spent for leaving the distortion invisible. Some tests on both synthetic and real distorted images validate the theoretical findings. They confirm that the proposed universal threshold is really able to get the lower bound of human perception and then it may be useful for many applications, such as bit allocation in coding procedures and automatic parameters setting in restoration algorithms.