The following manual will guide you through XNAT (website) & Jupyter (website), their setup, use, and many other handy features.
If you are more of a visual learner check out the video guide! If you encounter unfamiliar words - like DICOM - just click & you can find them in the glossary. Alternatively, for selected words you can hover with your cursor over the word for a short definition. For further help visit the FAQ.
Introduction
XNAT is an open source imaging informatics platform developed by the Neuroinformatics Research Group at Washington University. It facilitates common management, productivity, and quality assurance tasks for imaging and associated data. Its main tasks are to store and analyse the imaging data generated by projects. Jupyter notebook is a convenient interface to automatize, document and visualize imaging analysis steps.
What does XNAT do?
The XNAT neuroimaging pipeline provides a complete data workflow. Collected varieties of data types such as neuroimaging data, metadata, questionnaires and genetics are funneled through XNAT’s symbiosis with Docker and Jupyter to provide automated analysis and documentation. As we promote the concept of open science, we especially highlight that XNAT allows the sharing of data, code and results with fellow researchers.
Why should I use XNAT?
General advantages includes its open-source user management while strictly abiding to data security and privacy. Collected data can be stored, backed up, anonymized and prepared for analysis through automatic processing with preconfigured software packages. The process of automatization not only accelerates processing, it also reduces individual and manual errors. The project and user specific output, i.e., preprocessed files, code, figures, derivatives, statistics, etc., can immediately be made available for further analysis as well as export to other researchers. In the light of these upsides the unpleasantry of configurating and maintenance of software and server poses only a minor inconvenience. In order to make is as easy as possible, the following documentation serves as a step-by-step manual which provides a comprehensive guide.
How does XNAT work?
Its architecture constitutes a pipeline which allows for data to be processed on multiple levels starting from the naming of components and ranging to their automated analyzation and association with related data. Information are stored in BIDS which are accessible to preconfigured python scripts and documentation. In order to get a grasp of XNAT’s workflow we provide you with a test data set that you can download. Furthermore, the entirety of this documentation is available as a pdf version.
Disclaimer: The XNAT service relies on a connection to the internet. Please consult your local data privacy department before uploading data to XNAT.