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PCNtoolkit

Authors: de Boer, Stijn; Rutherford, Saige; Marquand, Andre; Kia, Seyed Mostafa; Tsilimparis, Konstantinos; Wolfers, Thomas; Berthet, Pierre; +12 Authors
Abstract

PCNtoolkit is a lightweight Python package designed to support normative modeling workflows in neuroscience.

If you use this software, please cite it using the metadata below.

Keywords

neuroscience, software toolkit, federated learning, normative modeling, Bayesian modeling

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average