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@INPROCEEDINGS{Schlegel:1048794,
      author       = {Schlegel, Ulrike and Kleven, Heidi and Gillespie, Tom and
                      Köhnen, Louisa and Gui, Xiaoyun and Gazzotti, Raphael and
                      Schmid, Oliver and Dickscheid, Timo and Amunts, Katrin and
                      Bjaalie, Jan G. and Leergaard, Trygve B. and Zehl, Lyuba},
      title        = {open{MINDS} {SANDS}: making brain atlases machine-
                      actionable using {L}inked {D}ata},
      reportid     = {FZJ-2025-04909},
      year         = {2025},
      abstract     = {<b>INTRODUCTION/MOTIVATION</b><br>Brain atlases underpin
                      many branches of neuroscience by providing a spatial
                      scaffold on whichmultimodal data can be organised and
                      compared. Yet atlas resources are often released as
                      projectspecific archives differing in structure, coordinate
                      space, parcellation logic, and nomenclature.
                      Thisheterogeneity forces developers to write custom loaders,
                      hinders cross-atlas comparison, andpropagates inconsistency.
                      To overcome these barriers we introduced openMINDS
                      $SANDS(RRID:SCR_023498),$ a Linked Data specification that
                      operationalises the Atlas Ontology Model(AtOM) [1] within
                      the openMINDS metadata framework $(RRID:SCR_023173).$ Our
                      goal is to turnstatic atlas downloads into interoperable web
                      resources that can be queried and reused by humansand
                      machines alike.<br><br><b>METHODS</b><br>SANDS was
                      implemented as an openMINDS extension integrating the four
                      AtOM entities (referencedata, coordinate system,
                      annotations, terminology) into the holistic openMINDS model.
                      Schemaswere designed to follow the FAIR principles [2]
                      (e.g., licensing, links to other FAIR resources) and toadopt
                      AtOM suggestions (e.g., standardized atlas structures,
                      versioning). To demonstrate expressiveness, we curated
                      widely used brain atlases in accordance with SANDS for three
                      commonatlas types: (i) discrete atlases where the annotation
                      set contains only discretely defined regions,
                      (ii)probabilistic atlases where the annotation set contains
                      regions defined by statistically-weightedcomposites, and
                      (iii) parcellation models where processive annotation
                      criteria are defined to createspecimen-specific atlases by
                      parcellating a single specimen’s anatomy and mapping it to
                      a definedterminology. Finally, the SANDS compliant Linked
                      Data descriptions of the curated atlases wereshared
                      formatted as JSON-LD files through the openMINDS instance
                      libraries $(RRID:SCR_027358)making$ them serviceable to any
                      software and service developers as standardized
                      atlasrepresentations.<br><br><b>RESULTS AND
                      DISCUSSION</b><br>As of today (2025-08-28), we have applied
                      SANDS to 13 commonly used brain atlases andparcellation
                      models from 3 different species. Their Linked Data
                      representations are provided in theopenMINDS instances
                      libraries. As evidence, we present one example for each
                      atlas type: (i) theWaxholm Space Rat Brain Atlas
                      $(RRID:SCR_017124)$ [3] as example for a discrete atlas,
                      (ii) theJulich-Brain Atlas $(RRID:SCR_023277)$ [4] as
                      example for a probabilistic atlas, and (iii) the
                      DesikanKilliany Atlas [5] as example for a parcellation
                      model. These examples demonstrate harmonized atlasusage
                      between the EBRAINS Knowledge Graph $(RRID:SCR_017612;$
                      EBRAINS central data andknowledge platform), and the siibra
                      toolsuite [6] (EBRAINS software for providing
                      interactivemultilevel brain atlases) enabled by adopting
                      SANDS.By integrating AtOM into the openMINDS metadata
                      framework, SANDS converts existing atlases intoFAIR,
                      machine-actionable web resources. The resulting Linked Data
                      facilitates side-by-sidevisualization, pipeline automation,
                      and atlas-driven analysis. Moreover, SANDS instances can
                      beharvested by search engines, enriched with community
                      annotations, and mirrored acrossrepositories. However, the
                      main limitation remains sociotechnical: atlas providers must
                      supplycompliant metadata, and developers must replace
                      hard-coded templates with dynamic queries. Toease adoption
                      we are developing open-source converters from other
                      standardization efforts such asBIDS [7] and provide
                      integration support through our GitHub (Open Metadata
                      Initiative). By makingbrain atlases first-class Linked-Data
                      citizens, openMINDS SANDS removes the final technical
                      barrierto fully FAIR, automation-ready neuroanatomical
                      workflows.Keywords: Atlas Ontology Model (AtOM), discrete
                      brain atlas, FAIR principles, Linked Data,openMINDS SANDS,
                      parcellation model, probabilistic brain atlas, spatial
                      reference frameworks<br><br><b>ACKNOWLEDGEMENTS</b><br>This
                      work has received funding from the European Union’s
                      Horizon Europe research and innovationprogramme under grant
                      agreement No 101147319 (EBRAINS 2.0). It also was supported
                      by theHelmholtz International BigBrain Analytics and
                      Learning Laboratory
                      (HIBALL).<br><br><b>REFERENCES</b><ol><li>Kleven H,
                      Gillespie TH, Zehl L, et al. AtOM, an ontology model to
                      standardize use of brain atlases in tools, workflows, and
                      data infrastructures. Sci Data. 2023;10:486.
                      https://doi.org/10.1038/s41597-023-02389-4</li><li>Wilkinson
                      MD, Dumontier M, Aalbersberg IJJ, et al. The FAIR guiding
                      principles for scientific datamanagement and stewardship.
                      Sci Data. 2016;3:160018.
                      https://doi.org/10.1038/sdata.2016.18</li><li>Kleven H,
                      Bjerke IE, Clascá F, et al. Waxholm Space atlas of the rat
                      brain: a 3D atlas supporting data analysis and integration.
                      Nat Methods. 2023;20:1822-1829.
                      https://doi.org/10.1038/s41592-023-02034-3</li><li>Amunts K,
                      Mohlberg H, Bludau S, Zilles K. Julich-Brain: a 3D
                      probabilistic atlas of the human brain’s cytoarchitecture.
                      Science. 2020;369(6506):988-992.
                      https://doi.org/10.1126/science.abb4588</li><li>Desikan RS,
                      Ségonne F, Fischl B, et al. An automated labeling system
                      for subdividing the human cerebral cortex on MRI scans into
                      gyral based regions of interest. Neuroimage.
                      2006;31(3):968-980.
                      https://doi.org/10.1016/j.neuroimage.2006.01.021</li><li>Dickscheid
                      T, Gui X, Simsek A, et al. Siibra: a software tool suite for
                      realizing a multilevel human brain atlas from complex data
                      resources. bioRxiv. Published online May 20, 2025.
                      https://doi.org/10.1101/2025.05.20.655042</li><li>Gorgolewski
                      KJ, Auer T, Calhoun VD, et al. The brain imaging data
                      structure, a format for organizing and describing outputs of
                      neuroimaging experiments. Sci Data. 2016;3:160044.
                      https://doi.org/10.1038/sdata.2016.44</li></ol>},
      month         = {Dec},
      date          = {2025-12-08},
      organization  = {EBRAINS summit 2025, Brüssel
                       (Belgium), 8 Dec 2025 - 11 Dec 2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {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)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)101147319 /
                      G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048794},
}