We describe a set of best practices for scientific software development, based on research and experience, that will improve scientists' productivity and the reliability of their software.

This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).

SUMMARYInitially, 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.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.