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Review
. 2015 Sep;25(3):356-68.
doi: 10.1007/s11065-015-9293-x. Epub 2015 Aug 13.

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

B Nolan Nichols  1 , Kilian M Pohl
Affiliations
Review

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

B Nolan Nichols et al. Neuropsychol Rev. 2015 Sep.

Abstract

Accelerating insight into the relation between brain and behavior entails conducting small and large-scale research endeavors that lead to reproducible results. Consensus is emerging between funding agencies, publishers, and the research community that data sharing is a fundamental requirement to ensure all such endeavors foster data reuse and fuel reproducible discoveries. Funding agency and publisher mandates to share data are bolstered by a growing number of data sharing efforts that demonstrate how information technologies can enable meaningful data reuse. Neuroinformatics evaluates scientific needs and develops solutions to facilitate the use of data across the cognitive and neurosciences. For example, electronic data capture and management tools designed to facilitate human neurocognitive research can decrease the setup time of studies, improve quality control, and streamline the process of harmonizing, curating, and sharing data across data repositories. In this article we outline the advantages and disadvantages of adopting software applications that support these features by reviewing the tools available and then presenting two contrasting neuroimaging study scenarios in the context of conducting a cross-sectional and a multisite longitudinal study.

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Conflict of interest statement

Conflict of interest: Neither author has conflicts of interest with the information presented herein.

Figures

Figure 1
Figure 1
Scenario B: Multisite Longitudinal Study Framework. Each of the NCANDA Data Collection Sites (left) collects form-based clinical and neuropsychological test data and multi-modal neuroimaging data. The form-based data is programmatically converted into a compliant format and automatically uploaded to a central REDCap server using the API. Imaging data is transmitted to a central XNAT server manually or automatically using the DICOM network protocol. Data from both REDCap and XNAT undergo quality control (QC) procedures before analysis and harmonization (center). After this stage, the processed data can be distributed to the broader community (right).
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