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For many medical applications, it's challenging to access large datasets, which are often hosted across different domains on heterogeneous infrastructures. Homogenizing the infrastructure to simplify data access is unrealistic; therefore, it's important to develop distributed storage that doesn't introduce added complexity. Here, a solution is investigated that flexibly federates data on clouds or grids without changing the existing infrastructure, instead scaling with the increasing data demand. Unlike existing approaches, this method isn't bound to a specific technology and doesn't require additional effort from administrators, users, or application developers to federate and use the storage infrastructure.

In this article, the authors propose a new approach to distributed computing with Web browsers and introduce the WeevilScout prototype framework. The proliferation of Web browsers and the performance gains being achieved by current JavaScript virtual machines raises the question whether Internet browsers can become yet another middleware for distributed computing. With 2 billion users online, computing through Internet browsers has the potential to amass immense resources, thus transforming the Internet into a distributed computer ideal for common classes of distributed scientific applications such as parametric studies. As a proof of concept, the authors demonstrate how a cluster of globally distributed Internet browsers is used to compute thousands of bio-informatics tasks.

Recent advances in Internet and grid technologies have greatly enhanced scientific experiments' life cycle. In addition to compute- and data-intensive tasks, large-scale collaborations involving geographically distributed scientists and e-infrastructure are now possible. Scientific workflows, which encode the logic of experiments, are becoming valuable resources. Sharing these resources and letting scientists worldwide work together on one experiment is essential for promoting knowledge transfer and speeding up the development of scientific experiments. Here, the authors discuss the challenges involved in supporting collaborative e-Science experiments and propose support for different phases of the scientific experimentation life cycle.