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on distributed infrastructures and to enhance data management and collaboration
for scienti
c research. The gateway is based on the WS-PGRADE/gUSE generic
science gateway framework as platform for distributed computing, and it is con-
nected to a data server based on the eXtensible Neuroimaging Archive Toolkit
(XNAT). This chapter presents the design and architecture of the gateway with
focus on the utilization of the WS-PGRADE/gUSE framework, and the lessons
learned during its implementation and operation.
10.1 Introduction
Data- and computation-intensive tools and techniques are increasingly used in
computational neuroscience studies to manage and process large volumes of
medical images. Additionally, the demand for the computational power required for
such studies is increasing. For example, a typical neuroscience study includes a few
hundred of human subjects; for each individual subject there are usually a few
image sessions that may include a series of scans. Moreover, it may take between
12 and 72 h to analyze each scan with the common image processing toolkits.
Performing data processing of such studies on desktop computers or even on
computer clusters would take too much time. Distributed computing infrastructures
(DCIs), such as grids, provide the computational power to cope with these
demands. Unfortunately, neuroscientists need advanced technical knowledge to
perform and manage their data processing on DCIs. These technical skills are
speci
cally scarce among biomedical researchers. Science gateways (SGs) are easy-
to-use graphical user interfaces that support the data-intensive scienti
c discovery
by facilitating access to the community-speci
c collection of data and tools and
DCIs without having to know the technical details of the underlying infrastructure.
The above problems are also faced by the community of neuroscientists that
participate in the Brain Imaging Centre (BIC) [BIC] of the Academic Medical
Centre (AMC) of the University of Amsterdam. The goal of the AMC computa-
tional NeuroScience Gateway (AMC-NSG) is to facilitate large-scale data pro-
cessing on distributed infrastructures and to enhance data management and
collaboration for this research community, and potentially also for other external
communities.
The ideal SG for computational neuroscience should support all of the typical
neuroscience research study phases, from study design, data acquisition, data
handling, processing, and analysis, up to publication (Shahand 2012a). The AMC-
NSG is focused on the data acquisition, data handling, and processing phases,
because of their data- and compute-intensive requirements. Therefore, it should
have the following properties to be effective: enable sharing of data and method-
ology; facilitate metadata, data, processing, and provenance management; satisfy
security and privacy regulations; and offer scalable,
transparent, and flexible
management of storage and computing resources.
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