![]() Moreover, repetitive downloading of datasets with different characteristics substantially increases the effort of the potential users. Indeed, even downloading of relevant data from a number of different repositories is challenging because it requires multiple manual steps, which render the process essentially irreproducible. These datasets are heterogeneous, which raises substantial challenges for central organization and harmonization. Medical imaging research is constantly evolving, which led to a dramatic increase in the availability, scale, and number of publicly-available image datasets. The potential of Rxnat is illustrated using an example of neuroimaging data normalization from two neuroimaging repositories, NITRC and HCP. ![]() This provides a lingua franca for the large community of R analysts to interface with multiple XNAT-based publicly available neuroimaging repositories. The Rxnat package can query multiple XNAT repositories and download all or a specific subset of images for further processing. Rxnat was developed to address the increased popularity of R among neuroimaging researchers. ![]() The program has similar capabilities with PyXNAT and XNATpy, which were developed for Python users. We introduce Rxnat ( ), an open-source R package designed to interact with any XNAT-based repository. Some examples include the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, ), the ConnectomeDB for the Human Connectome Project ( ), and XNAT Central ( ). The extensible neuroimaging archive toolkit (XNAT) is a common platform for storing and distributing neuroimaging data and is used by many key repositories of public neuroimaging data.
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