The successful application of Grid and Web Service technologies to real-world problems, such as e-Science [1], requires not only the development of a common vocabulary and meta-data framework as the basis for inter-agent communication and service integration but also the access and use of a rich repository of domain-specific knowledge for problem solving. Both requirements are met by the respective outcomes of ontological and knowledge engineering initiatives. In this paper we discuss a novel, knowledge-based approach to resource synthesis (service composition), which draws on the functionality of semantic web services to represent and expose available resources. The approach we use exploits domain knowledge to guide the service composition process and provide advice on service selection and instantiation. The approach has been implemented in a prototype workflow construction environment that supports the runtime recommendation of a service solution, service discovery via semantic service descriptions, and knowledge-based configuration of selected services. The use of knowledge provides a basis for full automation of service composition via conventional planning algorithms. Workflows produced by this system can be executed through a domain-specific direct mapping mechanism or via a more fluid approach such as WSDL-based service grounding. The approach and prototype have been used to demonstrate practical benefits in the context of the Geodise initiative [2].

Google and its competitors have created a new class of large-scale computer systems to support Internet search. These “Data-Intensive Super Computing ” (DISC) systems differ from conventional supercomputers in their focus on data: they acquire and maintain continually changing data sets, in addition to performing large-scale computations over the data. With the massive amounts of data arising from such diverse sources as telescope imagery, medical records, online transaction records, and web pages, DISC systems have the potential to achieve major advances in science, health care, business efficiencies, and information access. DISC opens up many important research topics in system design, resource management, programming models, parallel algorithms, and applications. By engaging the academic research community in these issues, we can more systematically and in a more open forum explore fundamental aspects of a societally important style of computing. Keywords: parallel computing, data storage, web searchWhen a teenage boy wants to find information about his idol by using Google with the search query “Britney Spears, ” he unleashes the power of several hundred processors operating on a data set of over 200 terabytes. Why then can’t a scientist seeking a cure for cancer invoke large amounts of computation over a terabyte-sized database of DNA microarray data at the click of a button? Recent papers on parallel programming by researchers at Google [13] and Microsoft [19] present the results of using up to 1800 processors to perform computations accessing up to 10 terabytes of data. How can university researchers demonstrate the credibility of their work without having comparable computing facilities available? 1

Computer science provides the language needed to study and understand complex multiscale, multiscience systems.ViroLab, a grid-based decision-support system, demonstrates how researchers can now study diseases from the DNA level all the way up to medical responses to treatment. Complex human systems include unique and distinguishable components—from biological cells made of thousands of molecules, to immune systems built from billions of cells, to our society of more than 6 billion interacting individuals. Each gene in a cell, each cell in an immune system, and each individual in a society possesses characteristic behavior and provides unique contributions to the system. The complete cascade—from genome, proteome, metabolome, and physiome to health—forms multiscale,

doi:10.1017/S1351324905004031

This paper describes an ongoing collaborative effort across digital library and scientific communities in the UK to improve access to research data. A prototype demonstrator service supporting the discovery and retrieval of detailed results of crystallography experiments has been deployed within an Open Archives digital library service model. Early challenges include the understanding of requirements in this specialized area of chemistry and reaching consensus on the design of a metadata model and schema. Future plans encompass the exploration of commonality and overlap with other schemas and across disciplines, working with publishers to develop mutually beneficial service models, and investigation of the pedagogical benefits. The potential improved access to experimental data to enrich scholarly communication from the perspective of both research and learning provides the driving force to continue exploring these issues.

Abstract. This paper first explores some of the reasons why collaboration is becoming increasingly important in supporting scientific data curation, digital preservation initiatives and institutional repository development. It then investigates the concepts of trust and control used in the organisation science literature and attempts to apply them to the work on trustworthy repositories being carried out by various international initiatives. 1

Abstract—Integrated Asset Management (IAM) is the vision of IT-enabled transformation of oilfield operations where information integration from a variety of tools for reservoir modeling, simulation, and performance prediction will lead to rapid decision making for continuous production optimization. This paper discusses the similarities and differences of our applications to typical e-Science applications. We then propose an architecture for the system based on the three key goals of the system: support for integrating legacy tools and analysis modules, support for audit trails and data quality indicators for data objects and usability. Our architecture builds upon the rich research in the scientific workflow area and applies many of its learnings to address the unique requirements of our domain. We discuss the implementation strategies and technologies to achieve it and identify some of the key research challenges in realizing our architectural vision. I.

21 st May 2007 Manjula Patel Outline structure of report; audit and certification; representation information; preservation metadata; eprints.org platform 22 nd May 2007 Manjula Patel Scoping of additional subsections under each section heading 23 rd May 2007 Manjula Patel Started to add text into sections on audit and certification 28 th May 2007 Manjula Patel Revision of structure and text for discussion at F2F meeting with Simon Coles on 29 th May 2007.

Institutional Open Access repositories are becoming established as an important part of the university library and information services infrastructure. While early efforts to populate them with content have concentrated on the deposit of peer-reviewed research papers, there is a growing awareness of their potential as repositories of data and other non-text materials, and consequently a need to develop strategies and procedures that can realise this potential. Chemistry as a discipline has been slower than the physical and biomedical sciences to adopt and exploit Open Access concepts in the handling of experimental data and research publications. Chemical information is essential to many sciences outside chemistry, and the reporting of the synthesis and properties of new chemical compounds is central to this. But most of the essential experimental data associated with peer-reviewed publications from chemistry departments are never communicated to the scientific community. These data are all available in high-quality electronic form in the laboratories but there is no effective method for archiving them or making them openly accessible. The SPECTRa (Submission, Preservation, and Exposure of Chemistry Teaching and

Through a series of e-Science projects we have explored the creation of a complete digital chain of knowledge from the scientific laboratory through to scholarly research output. In this paper we describe this experience and we discuss three perspectives on collaborative knowledge acquisition within the context of this cyberinfrastructure: Publication at Source, Record and Reuse, and Annotation. 1