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FAIR-sharing of multi-scale, multi-species brain models

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models

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Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.


The rationale behind TemplateFlow and how we envision it as a fundamental instrument to neuroimaging studies is presented in our preprint:

Ciric R. et al., 2021. doi:10.1101/2021.02.10.430678


This work is steered and maintained by the NiPreps Community. The development of this framework is supported the NIMH (RF1MH121867, RAP, OE).

Thanks to the DataLad developers, as we rely on their wonderful tool for the management of the TemplateFlow Archive.

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  1. FAIR-sharing of multi-scale, multi-species brain models

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  2. A python client to query TemplateFlow via pyBIDS

    Python 5 7

  3. A toolbox to upload and update the TemplateFlow repository

    Python 1



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