Non-Invasive Brain Stimulation Data Analysis Structure (NIBS-DAS): A Template for the Layout, Management, and Analysis of NIBS Data
Barham, M. P.; Morrison-Ham, J.; Greenwood, C. J.; Bertazzoli, G.; Rogasch, N. C.; Bereznicki, H. G.; Younger, E. F.; Ellis, E. G.; Graeme, L. G.; Cunningham, D. A.; Liao, W.-Y.; Fried, P. J.; Pascual-Leone, A.; Enticott, P. G.; Corp, D. T.
Show abstract
Currently, there is no consensus about how investigators should format their NIBS data for sharing. This presents a barrier to the advancement of big data analyses because it requires time-consuming operations to generate consistent formats across different shared datasets. Recently, we launched Big non-invasive brain stimulation data (Big NIBS data), an open-access platform and repository for NIBS data (https://www.bignibsdata.com/), providing a structured mechanism for researchers to share NIBS data. However, the reusability and interoperability of data uploaded to Big NIBS data is restricted by the absence of a common data structure. The current paper addresses this problem by creating the NIBS data analysis structure (NIBS-DAS), a template pipeline for the layout, management, and analysis of collated NIBS outcome data. While its primary purpose is to provide a template layout for uploading collated data to the Big NIBS data repository, NIBS-DAS also offers guidelines for the management and analysis of collated NIBS data, thereby forming a data analysis pipeline that can be freely used by the NIBS field in general. We anticipate that NIBS-DAS will serve to facilitate data sharing on the Big NIBS data platform and promote greater standardisation of data management and analytical practices in the NIBS field.
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