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| Hauptseite > Publikationsdatenbank > 3D cytoarchitectonic map of the human Area MFG4 of the BigBrain (v2.0) > print |
| Hauptseite > Publikationsdatenbank > 3D cytoarchitectonic map of the human Area MFG4 of the BigBrain (v2.0) > print |
| 001 | 1016809 | ||
| 005 | 20250728202331.0 | ||
| 024 | 7 | _ | |a 10.25493/Y1P3-YYN |2 doi |
| 037 | _ | _ | |a FZJ-2023-03793 |
| 100 | 1 | _ | |a Schiffer, Christian |0 P:(DE-Juel1)170068 |b 0 |e Corresponding author |
| 245 | _ | _ | |a 3D cytoarchitectonic map of the human Area MFG4 of the BigBrain (v2.0) |
| 260 | _ | _ | |c 2023 |b EBRAINS |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Dataset |b dataset |m dataset |0 PUB:(DE-HGF)32 |s 1753698195_3161 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Chart or Table |0 26 |2 EndNote |
| 336 | 7 | _ | |a Dataset |2 DataCite |
| 336 | 7 | _ | |a DATA_SET |2 ORCID |
| 336 | 7 | _ | |a ResearchData |2 DINI |
| 520 | _ | _ | |a This dataset contains cytoarchitectonic maps of the Area MFG4 of the BigBrain cortex and subcortical structures. The maps are based on reference delineations in serial histological sections of the BigBrain at 1 micron resolution in-plane, with a distance of sections typically not lower than 300 micrometres. The 3D cytoarchitectonic maps were created in two different approaches: (1) As first version, a 3D map of the area was created by simple interpolation of the respective reference delineations. (2) As second version, a 3D map was created using deep convolutional neural networks trained on the respective reference delineations. As a result, the latter version also includes delineations of the area in the sections in-between reference delineations. The 3D maps were transformed to the 3D reconstructed BigBrain space and are integrated into the Julich-Brain Cytarchitectonic Atlas. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Neuroscience |2 Other |
| 700 | 1 | _ | |a Bruno, Ariane |0 P:(DE-Juel1)173629 |b 1 |
| 700 | 1 | _ | |a Lothmann, Kimberley |b 2 |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 3 |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 4 |
| 700 | 1 | _ | |a Amunts, Katrin |0 P:(DE-Juel1)131631 |b 5 |
| 773 | _ | _ | |a 10.25493/Y1P3-YYN |
| 909 | C | O | |o oai:juser.fz-juelich.de:1016809 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)170068 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)173629 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)131631 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)165746 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)131631 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
| 980 | _ | _ | |a dataset |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
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