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[Preprint]. 2023 Aug 11:arXiv:2306.02183v3. [Version 3]

Figure 2. The brainlife.io platform concepts, architecture, and approach.

Figure 2.

a. brainlife.io’s Amaretti links data archives, software libraries, and computing resources. Specifically, ‘Apps’ (containerized services defined on GitHub.com) are automatically matched with data stored in the ‘Warehouse’ with computing resources. Statistical analyses can be implemented using Jupyter Notebooks. b. brainlife.io provides efficient docking between data archives, processing apps, and compute resources via a centralized service. c. Apps use standardized Datatypes and allow “smart docking” only with compatible data objects. App outputs can be docked by other Apps for further processing. d. brainlife.io’s Map step takes MRI, MEG and EEG data and processes them to extract statistical features of interest. brainlife.io’s reduce step takes the extracted features and serves them to Jupyter Notebooks for statistical analysis. PS: parc-stats Datatype; TM: tractmeasures Datatype; NET: network Datatype; PSD: power-spectrum density Datatype. CLI: Common Line Interface.