DFBIdb: A Software Package for Neuroimaging Data Management
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DFBIdb: A Software Package for Neuroimaging Data Management Christopher L. Adamson & Amanda G. Wood
Published online: 14 September 2010 # Springer Science+Business Media, LLC 2010
Abstract We present DFBIdb: a suite of tools for efficient management of neuroimaging project data. Specifically, DFBIdb was designed to allow users to quickly perform routine management tasks of sorting, archiving, exploring, exporting and organising raw data. DFBIdb was implemented as a collection of Python scripts that maintain a project-based, centralised database that is based on the XCEDE 2 data model. Project data is imported from a filesystem hierarchy of raw files, which is an often-used convention of imaging devices, using a single script that catalogues meta-data into a modified XCEDE 2 data model. During the import process data are reversibly anonymised, archived and compressed. The import script was designed to support multiple file formats and features an extensible framework that can be adapted to novel file formats. An ACL-based security model, with accompanying graphical management tools, was implemented to provide a straightforward method to restrict access to raw and meta-data.
C. L. Adamson : A. G. Wood Critical Care and Neurosciences, Murdoch Childrens Research Institute, Level 10, Royal Children’s Hospital, Flemington Road, Parkville 3052, Melbourne, Australia A. G. Wood Department of Medicine, Southern Clinical School, Monash University, 246 Clayton Rd, Clayton, Melbourne, Australia A. G. Wood (*) School of Psychology, University of Birmingham, Birmingham B15 2TT, UK e-mail: [email protected]
Graphical user interfaces are provided for data exploration. DFBIdb includes facilities to export, convert and organise customisable subsets of project data according to userspecified criteria. The command-line interface was implemented to allow users to incorporate database commands into more complex scripts that may be utilised to automate data management tasks. By using DFBIdb, neuroimaging laboratories will be able to perform routine data management tasks in an efficient manner. Keywords Data management . Python . XCEDE 2 . Neuroimaging . Archiving
Introduction Neuroimaging data is complex due to the diverse nature of imaging modalities, imaging sequences, file formats, experimental protocols and subject groups employed in neuroimaging experiments (Toga 2002). For example, a single project may contain Magnetic Resonance (MR), Positron Emission Tomography (PET), histological and Computed Tomography (CT) image data. Additionally, the ever-increasing volume of research data translates into growing data management burdens. Automated data management tools, with simple and accessible data models, are required to minimise labour and maximise research output. The multidisciplinary nature of neuroimaging research adds the requirement that the tools used to manage data are accessible to users with a range of skill levels. Numerous computerised tools have been developed to automate neuroimaging data management tasks such as catal
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