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@INPROCEEDINGS{Oberstra:1048783,
      author       = {Oberstraß, Alexander and Schiffer, Christian and
                      Matuschke, Felix and Kropp, Jan-Oliver and Thönnißen,
                      Julia and Amunts, Katrin and Axer, Markus and Gui, Xiaoyun
                      and Dickscheid, Timo},
      title        = {tiamat: {T}iled {I}mage {A}ccess, {M}anipulation and
                      {A}nalysis {T}oolkit for {V}isualization and {A}nalysis of
                      {L}arge {S}cientific {I}mage {D}atasets},
      reportid     = {FZJ-2025-04898},
      year         = {2025},
      abstract     = {Large-scale scientific imaging datasets -ranging from
                      terabytes to petabytes- are increasingly central to
                      neuroscience and other scientific fields. These datasets
                      require heterogeneous tools for analysis and visualization,
                      which impose conflicting requirements on file formats,
                      metadata schemas, and storage access patterns. Converting
                      between formats or duplicating data is a common workaround,
                      but this introduces inefficiencies, storage overhead, and
                      potential errors in large-volume workflows.We present
                      tiamat, the Tiled Image Access and Manipulation Toolkit, a
                      flexible and extensible Python framework that facilitates
                      reading, transforming, and exposing large image datasets
                      through a configurable pipeline of readers, transformers,
                      and interfaces.Tiamat supports on-the-fly transformations
                      such as normalization, axis reordering, and colormapping,
                      while streaming data to diverse endpoints—including
                      Napari, Neuroglancer, OpenSeadragon, Python/Numpy scripts,
                      and FUSE—without requiring intermediate file conversion or
                      duplication. We demonstrate its use within the EBRAINS
                      platform, where tiamat delivers 1µm-resolution histological
                      brain images from the BigBrain dataset directly from
                      high-performance GPFS storage to web-based viewers and
                      analysis clients.Tiamat decouples data storage from
                      visualization and analysis workflows, enabling modular,
                      reusable, and domain-agnostic image processing pipelines.Its
                      plugin-based design and compatibility with multiple tools
                      offer a scalable solution for managing large scientific
                      image datasets. Tiamat is implemented in Python, released
                      under the Apache 2.0 license, and deployed via docker. The
                      source code is available here.},
      month         = {Oct},
      date          = {2025-10-27},
      organization  = {9th BigBrain Workshop - HIBALL Closing
                       Symposium, Berlin (Germany), 27 Oct
                       2025 - 29 Oct 2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(EU-Grant)101147319 / G:(DE-HGF)InterLabs-0015 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1048783},
}