Science gateways for biomedical big data analysis

Biomedical researchers are facing data deluge challenges such as dealing with large volume of complex heterogeneous data and complex and computationally demanding data processing methods. Such scale and complexity of biomedical research requires multi-disciplinary collaboration between scientists from different organizations.
Data-driven or e-Science methods are defined as a combination of Information Technology (IT) and science that enables scientists to tackle the data deluge challenges. The IT infrastructures that address these challenges are known as cyberinfrastructures or e-Infrastructures, which are the environments that provide collaborative sharing of distributed computing and data resources. However, e-Infrastructures fall short of high-level and customized services to support the needs of scientists genuinely, and scientists find interacting with e-Infrastructures challenging, as it requires detailed technical knowledge.
Science Gateway (SG) research addresses these drawbacks. SGs are web-based enterprise information systems that provide scientists with customized and easy access to community-specific data collections, computational tools, and collaborative services on e-Infrastructures.
In this thesis we advanced the understanding of the fundamentals of SGs for biomedical research by organizing the findings about the requirements of biomedical researchers. We also organized the considerations about the design, development, operation, and sustainability of effective SGs. Moreover, we constructed a few successful SGs that were adopted by a large number of scientists and facilitated their biomedical big data analysis on e-Infrastructures. Finally we proposed a reference model that organizes the essential functions of SGs. We think that these efforts will facilitate design, development, operation, sustainability, and most importantly, adoption of SGs for biomedical research.