WS-PGRADE/gUSE-Based Science Gateways in Teaching - Science Gateways for Distributed Computing Infrastructures - page 222

hours or even days to complete. Science gateways have been used since 2011 for
practical exercises to run the data analysis tools on the Dutch grid infrastructure
from a web interface. In October 2013 the WS-PGRADE-based neuroscience
gateway (NSG) described in Chap. 10 was used for the

first time for these
assignments. The NSG is a facility for large-scale analysis of neuroimaging data
available at the AMC, where the data is stored on an Extensible Neuroimaging
Archive Toolkit (XNAT) data server at the radiology department, and the analysis
software tools are executed on the Dutch grid infrastructure. The NSG is used in
this course to run data analysis software more easily, making it feasible for short-
duration courses such as this one. Additionally, the goal of the lecture is also to
introduce concepts and tools for data management, in particular the XNAT data
archiving system recently deployed for the Brain Imaging Center (BIC) of the
AMC. Finally, tools such as the NSG and XNAT are still considered too advanced
in practice; therefore this course is also used to demystify advanced tools for the
students, preparing them for future generation research methodology.
The course starts with introductory lectures about general concepts of e-science,
the XNAT toolkit, and the NSG. Afterwards, the students are split into groups for
the hands-on exercises during an additional hour. In the previous evening, the
students have images of their own brains acquired with the 3.0 Tesla MRI scanner
of the AMC, and these data are uploaded to the XNAT server and preprocessed by
the instructors. A “ project ” is created for students to share these scans and analysis
results. Each group of three to four students shared an account on XNAT and NSG
during these exercises. The XNAT and NSG accounts are coupled during the course
and deactivated afterwards.
The exercises consist of finding their scanned images, sharing them with others,

and starting the preprocessing step from the NSG. Processing results are retrieved
and visualized by the students using locally installed software tools (Fig. 16.4 ).
Students also download the images and process them locally with a tool that is
practical, but delivers less accurate results than the longer processing performed on
the grid.

Fig. 16.4 Students run neuroimaging applications from the neuroscience gateway ( left ), then
inspect the results with external viewers ( right )