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TemplateFlow Python Client - TemplateFlow is the Zone of neuroimaging templates.

Project description

Zenodo preprint CircleCI Build Status Pypi

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.

Publishing resources in the TemplateFlow Archive

Please check the Contributing section of the TemplateFlow website.

License information

TemplateFlow adheres to the general licensing guidelines of the NiPreps framework.

License

Copyright (c) 2021, the NiPreps Developers.

The TemplateFlow Python Client is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Acknowledgements

This work is steered and maintained by the NiPreps Community. The development of this resource was supported by the Laura and John Arnold Foundation (RAP and KJG), the NIBIB (R01EB020740, SSG; 1P41EB019936-01A1SSG, YOH), the NIMH (RF1MH121867, RAP, OE; R24MH114705 and R24MH117179, RAP; 1RF1MH121885 SSG), NINDS (U01NS103780, RAP), and NSF (CRCNS 1912266, YOH). OE acknowledges financial support from the SNSF Ambizione project “Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI” (grant number PZ00P2_185872).

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