The profile of research software engineering has been greatly enhanced by developments at institutions around the world to form groups and communities that can support effective, sustainable development of research software. We observe, however, that there is still a long way to go to build a clear understanding about what approaches provide the best support for research software developers in different contexts, and how such understanding can be used to suggest more formal structures, models or frameworks that can help to further support the growth of research software engineering. This short paper provides an overview of some preliminary thoughts and proposes an initial high-level framework based on discussions between the authors around the concept of a set of pillars representing key activities and processes that form the core structure of a successful research software engineering offering.

Summary A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Datasets are growing larger and becoming distributed; their location, availability, and properties are often time‐dependent. Collectively, these characteristics give rise to dynamic distributed data‐intensive applications. While “static” data applications have received significant attention, the characteristics, requirements, and software systems for the analysis of large volumes of dynamic, distributed data, and data‐intensive applications have received relatively less attention. This paper surveys several representative dynamic distributed data‐intensive application scenarios, provides a common conceptual framework to understand them, and examines the infrastructure used in support of applications.

HPC software is developed and used in a wide variety of scientific domains including nuclear physics, computational chemistry, crash simulation, satellite data processing, fluid dynamics, climate modeling, bioinformatics, and vehicle development. The increase in the importance of this software motivates the need to identify and understand appropriate software engineering (SE) practices for HPC architectures. Because of the variety of the scientific domains addressed using HPC, existing SE tools and techniques developed for the business/IT community are often not efficient or effective. Appropriate SE solutions must account for the salient characteristics of the HPC, research oriented development environment. This situation creates a need for members of the SE community to interact with members of the scientific and HPC communities to address this need. This workshop facilitates that collaboration by bringing together members of the SE, the scientific, and the HPC communities to share perspectives and present findings relevant to research, practice, and education. A significant portion of the workshop is devoted to focused interaction among the participants with the goal of generating a research agenda to improve tools, techniques, and experimental methods regarding SE for HPC science.

This paper explores what it takes to develop digital objects (software but also other products such as workflows or data collections), which are both sustainable and achieve demonstrable uptake and usage. We focus in particular on objects produced in the context of e-Research and on the specific conditions this context brings with it.

Abstract Initially, Grid technologies were principally associated with supercomputer centres and large‐scale scientific applications in physics and astronomy. They are now increasingly seen as being relevant to many areas of e‐Science and e‐Business. The emergence of the Open Grid Services Architecture (OGSA), to complement the ongoing activity on Web Services standards, promises to provide a service‐based platform that can meet the needs of both business and scientific applications. Early Grid applications focused principally on the storage, replication and movement of file‐based data. Now the need for the full integration of database technologies with Grid middleware is widely recognized. Not only do many Grid applications already use databases for managing metadata, but increasingly many are associated with large databases of domain‐specific information (e.g. biological or astronomical data). This paper describes the design and implementation of OGSA‐DAI, a service‐based architecture for database access over the Grid. The approach involves the design of Grid Data Services that allow consumers to discover the properties of structured data stores and to access their contents. The initial focus has been on support for access to Relational and XML data, but the overall architecture has been designed to be extensible to accommodate different storage paradigms. The paper describes and motivates the design decisions that have been taken, and illustrates how the approach supports a range of application scenarios. The OGSA‐DAI software is freely available from http://www.ogsadai.org.uk . Copyright © 2005 John Wiley & Sons, Ltd.

Modem biological research is data intensive and relies on an efficient informatics infrastructure. Research on the genetic control of embryo development involves large volume image data requiring significant computing resources for reconstruction, mapping and analysis. This poster describes a demonstrator for delivering secure and transparent access to high-performance computing to the laboratory workbench using Grid technologies, in this case for 3D reconstruction from OPT microscopy.