001     1007218
005     20230711151709.0
024 7 _ |2 doi
|a 10.1101/2023.01.22.525049
024 7 _ |2 Handle
|a 2128/34381
037 _ _ |a FZJ-2023-01990
100 1 _ |0 0000-0002-3710-321X
|a Kleven, Heidi
|b 0
|e Corresponding author
245 _ _ |a AtOM, an ontology model for standardizing use of brain atlases in tools, workflows, and data infrastructures
260 _ _ |c 2023
336 7 _ |0 PUB:(DE-HGF)25
|2 PUB:(DE-HGF)
|a Preprint
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|m preprint
|s 1683545230_32507
336 7 _ |2 ORCID
|a WORKING_PAPER
336 7 _ |0 28
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|a Electronic Article
336 7 _ |2 DRIVER
|a preprint
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 DataCite
|a Output Types/Working Paper
520 _ _ |a Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardization impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We propose minimum requirements for FAIR atlases and discuss how AtOM may facilitate atlas interoperability and data integration. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.
536 _ _ |0 G:(DE-HGF)POF4-5254
|a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
|c POF4-525
|f POF IV
|x 0
536 _ _ |0 G:(EU-Grant)785907
|a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
|c 785907
|f H2020-SGA-FETFLAG-HBP-2017
|x 1
536 _ _ |0 G:(EU-Grant)945539
|a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
|c 945539
|f H2020-SGA-FETFLAG-HBP-2019
|x 2
536 _ _ |0 G:(DE-HGF)InterLabs-0015
|a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
|c InterLabs-0015
|x 3
588 _ _ |a Dataset connected to CrossRef
700 1 _ |0 0000-0002-7509-4801
|a Gillespie, Thomas H.
|b 1
|e Corresponding author
700 1 _ |0 P:(DE-Juel1)145394
|a Zehl, Lyuba
|b 2
700 1 _ |0 P:(DE-Juel1)165746
|a Dickscheid, Timo
|b 3
700 1 _ |0 0000-0001-7899-906X
|a Bjaalie, Jan G.
|b 4
700 1 _ |0 0000-0002-8406-3871
|a Martone, Maryann E.
|b 5
700 1 _ |0 0000-0001-5965-8470
|a Leergaard, Trygve B.
|b 6
773 _ _ |a 10.1101/2023.01.22.525049
|p 24
|t bioarxiv
|y 2023
856 4 _ |u https://juser.fz-juelich.de/record/1007218/files/2023.01.22.525049v1.full.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1007218
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910 1 _ |0 I:(DE-588b)5008462-8
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|a Forschungszentrum Jülich
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
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913 1 _ |0 G:(DE-HGF)POF4-525
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|a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|v Decoding Brain Organization and Dysfunction
|x 0
914 1 _ |y 2023
915 _ _ |0 StatID:(DE-HGF)0510
|2 StatID
|a OpenAccess
915 _ _ |0 LIC:(DE-HGF)CCBY4
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920 _ _ |l yes
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980 _ _ |a preprint
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980 _ _ |a I:(DE-Juel1)INM-1-20090406
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