Head in the cloud: accessing distributed data and services through XNAT
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1
Washington University School of Medicine, Mallinckrodt Institute of Radiology, United States
The XNAT open-source imaging informatics platform provides a user-friendly web application for uploading, processing, browsing, searching, and downloading imaging-related data and associated metadata. XNAT also has a RESTful web services API for programmatic access. pyxnat is a Python language library that uses the XNAT REST API to facilitate scripted access to the contents of an XNAT server.
We have extended pyxnat to abstract over the location of the data managed by an XNAT server. Queries against XNAT can be used to access data stored on the server itself; to use a local mirror of those data, either prepopulated (e.g., the Human Connectome Project's "Connectome In a Box") or built incrementally by downloading on demand files from the XNAT server; or to index into cloud-based or other third-party network storage (e.g., Amazon S3, INCF Dataspace, NCI TCIA collections).
This abstraction over storage makes it straightforward to write a script that identifies a set of subjects based on search criteria such as age, sex, neurological assessments, or behavioral measures; locates imaging data for those subjects; and applies standard or custom analysis tools to those data, using computing resources nearest to the data or otherwise best suited to the task.
In continuing work we are building an abstraction over computational services, so that only small, parametric script modifications will be required to move data analysis operations between the XNAT server and client-managed computing resources or public clouds.
Keywords:
XNAT,
pyxnat,
Cloud computing,
Open Source Software,
python
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Demo
Topic:
Infrastructural and portal services
Citation:
Archie
KA and
Marcus
DS
(2013). Head in the cloud: accessing distributed data and services through XNAT.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00071
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Received:
29 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Dr. Kevin A Archie, Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, MO, United States, karchie@wustl.edu