Sea Spray Aerosol: Where Marine Biology Meets Atmospheric Chemistry.

Atmospheric aerosols have long been known to alter climate by scattering incoming solar radiation and acting as seeds for cloud formation. These processes have vast implications for controlling the chemistry of our environment and the Earth's climate. Sea spray aerosol (SSA) is emitted over nearly three-quarters of our planet, yet precisely how SSA impacts Earth's radiation budget remains highly uncertain. Over the past several decades, studies have shown that SSA particles are far more complex than just sea salt. Ocean biological and physical processes produce individual SSA particles containing a diverse array of biological species including proteins, enzymes, bacteria, and viruses and a diverse array of organic compounds including fatty acids and sugars. Thus, a new frontier of research is emerging at the nexus of chemistry, biology, and atmospheric science. In this Outlook article, we discuss how current and future aerosol chemistry research demands a tight coupling between experimental (observational and laboratory studies) and computational (simulation-based) methods. This integration of approaches will enable the systematic interrogation of the complexity within individual SSA particles at a level that will enable prediction of the physicochemical properties of real-world SSA, ultimately illuminating the detailed mechanisms of how the constituents within individual SSA impact climate

Improving data workflow systems with cloud services and use of open data for bioinformatics research

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum

Work flows in life science

The introduction of computer science technology in the life science domain has resulted in a new life science discipline called bioinformatics. Bioinformaticians are biologists who know how to apply computer science technology to perform computer based experiments, also known as in-silico or dry lab experiments. Various tools, such as databases, web applications and scripting languages, are used to design and run in-silico experiments. As the size and complexity of these experiments grow, new types of tools are required to design and execute the experiments and to analyse the results. Workflow systems promise to fulfill this role. The bioinformatician composes an experiment by using tools and web services as building blocks, and connecting them, often through a graphical user interface. Workflow systems, such as Taverna, provide access to up to a few thousand resources in a uniform way. Although workflow systems are intended to make the bioinformaticians' work easier, bioinformaticians experience difficulties in using them. This thesis is devoted to find out which problems bioinformaticians experience using workflow systems and to provide solutions for these problems.\u

Generic Metadata Handling in Scientific Data Life Cycles

Scientific data life cycles define how data is created, handled, accessed, and analyzed by users. Such data life cycles become increasingly sophisticated as the sciences they deal with become more and more demanding and complex with the coming advent of exascale data and computing. The overarching data life cycle management background includes multiple abstraction categories with data sources, data and metadata management, computing and workflow management, security, data sinks, and methods on how to enable utilization. Challenges in this context are manifold. One is to hide the complexity from the user and to enable seamlessness in using resources to usability and efficiency. Another one is to enable generic metadata management that is not restricted to one use case but can be adapted with limited effort to further ones. Metadata management is essential to enable scientists to save time by avoiding the need for manually keeping track of data, meaning for example by its content and location. As the number of files grows into the millions, managing data without metadata becomes increasingly difficult. Thus, the solution is to employ metadata management to enable the organization of data based on information about it. Previously, use cases tended to only support highly specific or no metadata management at all. Now, a generic metadata management concept is available that can be used to efficiently integrate metadata capabilities with use cases. The concept was implemented within the MoSGrid data life cycle that enables molecular simulations on distributed HPC-enabled data and computing infrastructures. The implementation enables easy-to-use and effective metadata management. Automated extraction, annotation, and indexing of metadata was designed, developed, integrated, and search capabilities provided via a seamless user interface. Further analysis runs can be directly started based on search results. A complete evaluation of the concept both in general and along the example implementation is presented. In conclusion, generic metadata management concept advances the state of the art in scientific date life cycle management