cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34810/data72/326
title: 01CO_1.MR.0004.0013.2016.02.04.15.44.11.109375.67134411.IMA
DOI: 10.6084/m9.figshare.11894637
title: naive T cell single-cell RNA-seq, raw counts and annotation

description: naive CD4 and CD8 T cell single-cell RNA-sequencing data from human samples, both raw counts, generated by cellranger, and cell's sample annotations, generated with Demuxlet.
DOI: 10.6084/m9.figshare.11894637.v1
title: naive T cell single-cell RNA-seq, raw counts and annotation

description: naive CD4 and CD8 T cell single-cell RNA-sequencing data from human samples, both raw counts, generated by cellranger, and cell's sample annotations, generated with Demuxlet.
DOI: 10.34810/data72/327
title: 01CO_1.MR.0004.0175.2016.02.04.15.44.11.109375.67140908.IMA
DOI: 10.34810/data72/328
title: 01CO_1.MR.0004.0170.2016.02.04.15.44.11.109375.67140708.IMA
DOI: 10.34810/data72/329
title: 01CO_1.MR.0004.0161.2016.02.04.15.44.11.109375.67140347.IMA
DOI: 10.5519/qd.akmr9404
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (42 records)
DOI: 10.34810/data72/330
title: 01CO_1.MR.0004.0179.2016.02.04.15.44.11.109375.67141069.IMA
DOI: 10.34810/data72/331
title: 01CO_1.MR.0004.0110.2016.02.04.15.44.11.109375.67138302.IMA
DOI: 10.5519/qd.ad765h76
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1149 records)
DOI: 10.34810/data72/332
title: 01CO_1.MR.0004.0041.2016.02.04.15.44.11.109375.67135535.IMA
DOI: 10.34810/data72/333
title: 01CO_1.MR.0004.0032.2016.02.04.15.44.11.109375.67135173.IMA
DOI: 10.34810/data72/334
title: 01CO_1.MR.0004.0026.2016.02.04.15.44.11.109375.67134933.IMA
DOI: 10.34810/data72/335
title: 01CO_1.MR.0004.0099.2016.02.04.15.44.11.109375.67137861.IMA
DOI: 10.34810/data72/336
title: 01CO_1.MR.0004.0188.2016.02.04.15.44.11.109375.67141429.IMA
DOI: 10.34810/data72/337
title: 01CO_1.MR.0004.0098.2016.02.04.15.44.11.109375.67137821.IMA
DOI: 10.34810/data72/338
title: 01CO_1.MR.0004.0050.2016.02.04.15.44.11.109375.67135896.IMA
DOI: 10.34810/data72/339
title: 01CO_1.MR.0004.0116.2016.02.04.15.44.11.109375.67138543.IMA
DOI: 10.25493/wrcy-8z1
title: Probabilistic cytoarchitectonic map of Area hIP7 (IPS) (v7.1)

description: This dataset contains the distinct architectonic Area hIP7 (IPS) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area hIP7 (IPS). The probability map of Area hIP7 (IPS) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area hIP7 (IPS): Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/QN1F-WAF](https://doi.org/10.25493%2FQN1F-WAF) The most probable delineation of Area hIP7 (IPS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/mv3g-ret
title: Probabilistic cytoarchitectonic map of Area TE 1.0 (HESCHL) (v5.1)

description: This dataset contains the distinct architectonic Area TE 1.0 (HESCHL) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area TE 1.0 (HESCHL). The probability map of Area TE 1.0 (HESCHL) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area TE 1.0 (HESCHL): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/CP2T-FYT](https://doi.org/10.25493%2FCP2T-FYT) The most probable delineation of Area TE 1.0 (HESCHL) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/gbt3-576
title: DiFuMo atlas (64 dimensions)

description: DiFuMo (Dictionary of Functional Modes) 64-dimensional atlas presents a parcellation of the brain into 64 elementary structures using sparse dictionary learning. The atlas contains two main components: (1) an image of the 64 spatial components, sampled in MNI space, and (2) anatomical names for these 64 regions. It is meant to serve as an atlas to extract functional signals, eg to create image-derived phenotypes. These modes are optimized to represent well raw BOLD time series, over a with range of experimental conditions.
DOI: 10.25493/15h9-nkc
title: GapMap Frontal to Occipital (v10.0)

description: This dataset contains the “GapMap Frontal to Occipital” in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “GapMap Frontal to Occipital”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “GapMap Frontal to Occipital” is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. New maps are continuously replacing parts of “GapMap Frontal to Occipital” with progress in mapping. Other available data versions of Gap Map Frontal to Occipital: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/PBP4-HE6](https://doi.org/10.25493%2FPBP4-HE6) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/8EHQ-XYY](https://doi.org/10.25493%2F8EHQ-XYY) Amunts et al. (2020) [Data set, v9.2] [DOI: 10.25493/AK8J-WK8](https://doi.org/10.25493%2FAK8J-WK8) The most probable delineation of GapMap Frontal to Occipital derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/qsje-h14
title: Parvalbumin interneuron distribution in the mouse brain

description: Distribution of parvalbumin interneurons in the mouse brain obtained with confocal light sheet microscopy on transgenic mouse.
DOI: 10.25493/e7vg-4ng
title: Probabilistic cytoarchitectonic map of Area FG3 (FusG) (v6.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area FG3 (FusG) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area FG3 (FusG). The probability map of Area FG3 (FusG) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area FG3 (FusG): Lorenz et al. (2019) [Data set, v6.1] [DOI: 10.25493/Z0F6-0SY](https://doi.org/10.25493%2FZ0F6-0SY) The most probable delineation of Area FG3 (FusG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/v715-bqu
title: Probabilistic cytoarchitectonic map of Area OP3 (POperc) (v11.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP3 (POperc) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area OP3 (POperc). The probability map of Area OP3 (POperc) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area OP3 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/6PRT-2PS](https://doi.org/10.25493%2F6PRT-2PS) Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/1Z8F-PX4](https://doi.org/10.25493%2F1Z8F-PX4) The most probable delineation of Area OP3 (POperc) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/d5cg-sx6
title: Probabilistic map of the left inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/qx63-fnp
title: Probabilistic map of the left uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left uncinate fasciculus (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/aeq9-d0k
title: Probabilistic cytoarchitectonic map of Area hIP3 (IPS) (v9.0)

description: This dataset contains the distinct architectonic Area hIP3 (IPS) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area hIP3 (IPS). The probability map of Area hIP3 (IPS) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area hIP3 (IPS): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/J9T6-TX9](https://doi.org/10.25493%2FJ9T6-TX9) Scheperjans et al. (2018) [Data set, v8.4] [DOI: 10.25493/P8X0-V1G](https://doi.org/10.25493%2FP8X0-V1G) The most probable delineation of Area hIP3 (IPS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/eeaq-xwr
title: Probabilistic cytoarchitectonic map of Area 44 (IFG) (v9.0)

description: This dataset contains the distinct architectonic Area 44 (IFG) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area 44 (IFG). The probability map of Area 44 (IFG) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area 44 (IFG): Amunts et al. (2018) [Data set, v7.2] [DOI: 10.25493/J2KZ-AZW](https://doi.org/10.25493%2FJ2KZ-AZW) Amunts et al. (2019) [Data set, v7.4] [DOI: 10.25493/F9P8-ZVW](https://doi.org/10.25493%2FF9P8-ZVW) The most probable delineation of Area 44 (IFG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/ejtm-ndy
title: Probabilistic cytoarchitectonic map of Area hIP2 (IPS) (v6.1)

description: This dataset contains the distinct architectonic Area hIP2 (IPS) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area hIP2 (IPS). The probability map of Area hIP2 (IPS) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area hIP2 (IPS): Choi et al. (2018) [Data set, v6.0] [DOI: 10.25493/FR6P-6HW](https://doi.org/10.25493%2FFR6P-6HW) The most probable delineation of Area hIP2 (IPS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.34810/data72/340
title: 01CO_1.MR.0004.0154.2016.02.04.15.44.11.109375.67140066.IMA
DOI: 10.25493/4kr1-14s
title: Probability map of bundle rh_Op-PrC_0 (atlas of superficial white matter fibre bundles, version 2018)

description: ##Summary This data contains the probability map of a short fibre bundle connecting the right hemisphere Pars Opercularis and Precentral regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/4khy-kpu
title: Probability map of bundle rh_CMF-RMF_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the right hemisphere Caudal Middle Frontal and Rostral Middle Frontal regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/h2n2-6j2
title: Probabilistic cytoarchitectonic map of Area Fo1 (OFC) (v3.4)

description: This dataset contains the distinct architectonic Area Fo1 (OFC) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area Fo1 (OFC). The probability map of Area Fo1 (OFC) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area Fo1 (OFC): Henssen et al. (2018) [Data set, v3.2] [DOI: 10.25493/2NSZ-PGW](https://doi.org/10.25493%2F2NSZ-PGW) The most probable delineation of Area Fo1 (OFC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/76f0-87n
title: Purkinje cell distribution in mouse cerebellum

description: The dataset contains the complete reconstruction of the cerebellum from an L7-GFP mouse (PND 10), where all Purkinje cells are fluorescently labeled. The dataset includes raw imaging data obtained with confocal light sheet microscopy of cleared sample and cell localization (position of somata expressed as xyz coordinates).