This thesis describes the development of Beatbox — a simulation environment for computational biology of the heart. Beatbox aims to provide an adaptable, approachable simulation tool and an extensible framework with which High Performance Computing may be harnessed by researchers. Beatbox is built upon the QUI software package, which is studied in Chapter 2. The chapter discusses QUI’s functionality and common patterns of use, and describes its underlying software architecture, in particular its extensibility through the addition of new software modules called ‘devices’. The chapter summarises good practice for device developers in the Laws of Devices. Chapter 3 discusses the parallel architecture of Beatbox and its implementation for distributed memory clusters. The chapter discusses strategies for domain decomposition, halo swapping and introduces an efficient method for exchange of data with diagonal neighbours called Magic Corners. The development of Beatbox’s parallel Input/Output facilities is detailed, and its impact on scaling performance discussed. The chapter discusses the way in which parallelism can be hidden from the user, even while permitting the runtime execution user-defined functions. The chapter goes on to show how QUI’s extensibility can be continued in a parallel environment by providing implicit parallelism for devices and defining Laws of Parallel Devices to guide third-party developers. Beatbox’s parallel performance is evaluated and discussed. Chapter 4 describes the extension of Beatbox to simulate anatomically realistic tissue geometry. Representation of irregular geometries is described, along with associated user controls. A technique to compute no-flux boundary conditions on irregular boundaries is introduced. The Laws of Devices are further developed to include irregular geometries. Finally, parallel performance of anatomically realistic meshes is evaluated.

Background: Clinical research registries are rarely driven by data quality assurance. However, quality of data can have a huge impact on the performance and outcome of any given trial using registry data. Therefore, data quality assurance procedures for cost reduction and data process improvements have to be implemented in research registries. Hypothesis: This research proposes that web-based data quality feedback can motivate registry users, increase their contributions and ultimately improve the quality of registry data and its (re-)use to support clinical trials; thereby reducing the costs and need for study monitors. Method: To explore causes of low data quality and user motivation, a survey and an assessment of quality indicators in a multicentre clinical setting was performed. Subsequently, a development and evaluation of a web-based feedback framework was conducted. This was explored in the international Niemann-Pick disease registry (INPDR) and two clinical trials associated with the European Network for the Study of Adrenal Tumours (ENSAT). Results: The survey and framework evaluation highlight effectiveness of web-based automated data quality feedback. Case studies showed an increase of data quality within observation time. Conclusion: Centralised data monitoring requires a general framework that can be adjusted for a variety of trials and studies. This research highlights how biomedical research registries have to be designed with focus on data quality and feedback mechanisms.