Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies. In The Fourth Paradigm: Data-Intensive Scientific Discovery, the collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.

Mission: • To inspire new activities and facilitate knowledge exchange between Nordic/Baltic • stakeholder, and to increase the international visibility of Nordic and Baltic policies and initiatives • To stress the importance of Open Access in the Nordic and Baltic countries and to describe both theoretical and best-practice models for financing, rights management and other fundamental issues. • To disseminate to both a Nordic/Baltic and an international readership information about successful initiatives and other activities in the Nordic and Baltic countries.

Sharing data is good. But sharing your own data? That can get complicated. As two research communities who held meetings in May on the issue report their proposals to promote data sharing in biology, a special issue of Nature examines the cultural and technical hurdles that can get in the way of good intentions.

The Science Collaboration Framework (SCF) is a software toolkit to establish web-based virtual team organizations for researchers in biomedicine. It enables researchers to publish and discuss on-line content such as articles, news, and perspectives, and to provide shared semantic context for this content using established scientific vocabularies and automated text mining.

# We should create an open central repository location at which authors can release software and documentation. # Software release should be an integral and funded part of projects. # Software release should become an integral part of the publication process. # The barriers to publication of methods and descriptive papers should be lower. # Programming, statistics and data analysis should be an integral part of the curriculum. # There should be more opportunities to fund grass-roots software projects of use to the wider community. # We should develop institutional support for science programs that attract and support talented scientists who generate software for public release.

Experimental processes in the life sciences are becoming increasingly complex. As a result, recording, archiving and sharing descriptions of these processes and of the results of experiments is becoming ever more challenging. However, validation of results, sharing of best practice and integrated analysis all require systematic description of experiments at carefully determined levels of detail. The present paper discusses issues associated with the management of experimental data in the life sciences, including: the different tasks that experimental data and metadata can support, the role of standards in informing data sharing and archiving, and the development of effective databases and tools, building on these standards.