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@MISC{Dickscheid:1031456,
      author       = {Dickscheid, Timo and Bludau, Sebastian},
      title        = {{M}aking the multiscale organization of the human brain
                      accessible to reproducible workflows using siibra-python},
      reportid     = {FZJ-2024-05675},
      year         = {2024},
      abstract     = {Understanding the human brain requires access to
                      experimental data that capture relevant aspects of brain
                      organization across a broad range of scales and modalities,
                      and typically originate from a plethora of resources. To
                      make multimodal and multidimensional measures of brain
                      organization accessible, they need to be integrated into a
                      common reference framework and exposed via suitable software
                      interfaces. This tutorial will introduce participants to
                      siibra toolsuite, which provides access to a multilevel
                      atlas of the human brain built from “big data”. The
                      atlas integrates brain reference templates at different
                      spatial scales, complementary parcellation maps, and a wide
                      range of multimodal data features. It links macroanatomical
                      concepts and their inter-subject variability with
                      measurements of the microstructural composition and
                      intrinsic variance of brain regions, using cytoarchitectonic
                      maps as a reference, and integrating the BigBrain model as
                      microscopic reference template. The tool suite includes a
                      web-based 3D viewer (siibra-explorer) and a Python library
                      (siibra-python) to support a broad range of neuroscientific
                      use cases. It makes use of EBRAINS as a data sharing
                      platform and cloud infrastructure and implements interfaces
                      to other neuroscience resources. The focus of this tutorial
                      will be on building reproducible workflows with BigBrain
                      data using the siibra-python library.},
      month         = {Sep},
      date          = {2024-09-09},
      organization  = {8th BigBrain Workshop, Padua (Italy),
                       9 Sep 2024 - 11 Sep 2024},
      subtyp        = {Invited},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)101147319 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)17},
      url          = {https://juser.fz-juelich.de/record/1031456},
}