The main challenge of the contemporary research ecosystem is to enable optimal use of the complex research landscape, both in terms of research data and the analytical methods involved. Computational analysis to discover meaningful patterns in massive, interlinked datasets is rapidly becoming a routine research activity. Providing machine-readable data as the main substrate for knowledge discovery and for enabling these complex scientific processes to run smoothly and sustainably is one of the grand challenges of current research infrastructures. This is most relevant for the interdisciplinary post-genomics biomedical research domain. The motivation that guides our approach is to offer simple bridging among existing gaps in research without trying to overshadow other initiatives and without willing to offer another monolithic introvert environment that demands from users to do science in a predefined way.

Our approach in OpenBio.eu aims to include existing bridging solutions and promote, engage and guide users to create new ones. The existing gaps to bridge are:

• the data gap, which is due to the existence of many unlinked and diverse data sources – to remedy this, our solution offers detailed data description, provides data provenance, links data sources with tools to import them, and connects data with formats, ontologies and tools; it also makes visible which computational environments are more appropriate for specific data analysis and which are the available visualization options;
• the semantic gap, which is due to the existence of multiple vocabularies and unmerged ontologies – to remedy this, users can include existing ontologies and tools for the semantic annotations to be attached to data and tools, and provide statistics on which ontologies are most used and which are obsolete or incomplete according to user comments and ratings;
• the knowledge gap, which is due to the existence of heterogeneous, fast growing and fragmented knowledge resources – to remedy this, data, tools and workflows are linked to published papers and online documentation; rich text description and keywords can be attached to every object in the platform; relevant information is made visible and searchable; and simple recommendations for data/tools/workflows integration are offered;
• the collaboration gap, which is due to the existence of differences in user interests, objectives and methodologies – to remedy this, users are allowed to: build personal profiles where they state their expertise, publications and preferences in tools, languages and operating environments; build reputation according to their contributions similar to popular services (i.e. stackexchange.com
  stackexchange.com
  , biostars.com); express opinions and rate every object in the environment including the comments of other users; and search and locate other users according to their profile or contributions.

The foreseen OpenBio.eu platform will empower biomedical and post-genomics research communities to efficiently and effectively collaborate thanks to reliable and user-friendly access to integrated and interoperable resources of different types. The platform integrates and manages heterogeneous data and knowledge sources, as well as respective processing methodologies and tools, through a service-oriented Scientific Workflow editing and execution environment. The overall approach builds on the synergy between human and machine reasoning capabilities to tame information overload and cognitive complexity in biomedical and post-genomics research settings. It provides researchers with the required capacity to easily assemble their own working environment using their preferred tools, and exploit the wealth of available resources and competences.