Responsive dashboards for XNAT: A flexible framework
Presenting author:
As one of the most popular open-source data management platforms available to the neuroinformatics community, XNAT provides an efficient solution for collecting, managing, reviewing and sharing large neuroimaging datasets.
However, XNAT comes with a rather limited capability for monitoring and exploring large-sized datasets through its built-in graphical user interface. To address this, we present a new framework for the creation of custom dashboards based on XNAT data. Such dashboards can be adjusted to the needs and specifications of the hosted studies, with applications ranging from system monitoring to data quality check. The framework, based on pyxnat and flask on the backend and plotly on the frontend, provides a lightweight example of how to create and deploy interactive customized plots based on XNAT data. It is currently used at the BarcelonaBeta Brain Research Center (BBRC) for monitoring multiple sources of data at once (e.g. raw imaging, processed derivatives, QC checks), reducing time and effort spent navigating through large cohorts.
The project started in 2020 during the Google Summer of Code program. The codebase is currently maintained by the BBRC although it is open for external contributions.
However, XNAT comes with a rather limited capability for monitoring and exploring large-sized datasets through its built-in graphical user interface. To address this, we present a new framework for the creation of custom dashboards based on XNAT data. Such dashboards can be adjusted to the needs and specifications of the hosted studies, with applications ranging from system monitoring to data quality check. The framework, based on pyxnat and flask on the backend and plotly on the frontend, provides a lightweight example of how to create and deploy interactive customized plots based on XNAT data. It is currently used at the BarcelonaBeta Brain Research Center (BBRC) for monitoring multiple sources of data at once (e.g. raw imaging, processed derivatives, QC checks), reducing time and effort spent navigating through large cohorts.
The project started in 2020 during the Google Summer of Code program. The codebase is currently maintained by the BBRC although it is open for external contributions.