cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/fs3t-2r8
title: Density measurements of different receptors for Area PFm (IPL) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area PFm (IPL) obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **mGluR2/3** \| **_glutamate_** \| _[<sup>3</sup>H]LY341495_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[<sup>3</sup>H]flumazenil_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]4-DAMP_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]epibatidine_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[<sup>3</sup>H]SCH23390_ For exemplary samples, we also provide laminar _profiles_ (**pr**) and/or color-coded laminar _autoradiography_ images (**ar**). The density profiles provide, for a single tissue sample, an exemplary density distribution for a single receptor from the pial surface to the border between layer VI and the white matter. The autoradiography images show an exemplary density distribution of a single receptor for one laminar cross-section in a single tissue sample. Information on the used tissue samples and corresponding subjects for the receptor fingerprints, profiles and autoradiographs as well as a list of analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387
DOI: 10.25493/2ayk-4jg
title: Density measurements of different receptors for Area Oc1M [rat, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of Area Oc1M obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[<sup>3</sup>H]flumazenil_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]4-DAMP_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]epibatidine_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[<sup>3</sup>H]SCH23390_ Information on the used tissue samples and corresponding subjects, as well as analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387
DOI: 10.25493/kr92-c33
title: Brain-wide quantitative data on parvalbumin positive neurons in the rat

description: This dataset contains quantitative data extracted from four sets of parvalbumin stained sections covering the whole brain of normal, adult Sprague-Dawley rats. Section images from the dataset “Distribution of parvalbumin-positive interneurons in the normal adult rat brain” [1] were analysed using the QUINT workflow. The dataset includes numbers and densities of cells for brain regions defined according to the Waxholm Space atlas of the rat brain and point coordinate data representing labelled cells across the brain. A detailed description of the number and distribution on parvalbumin neurons in hippocampal regions is given in the related publication. To facilitate re-use of the data, the dataset furthermore includes atlas maps and segmented images in .png format and .nut files used to run the analyses. References: 1. Laja, A., Bjerke, I., Leergaard, T. & Witter, M. Distribution of parvalbumin-positive interneurons in the normal adult rat brain. EBRAINS (2020) https://doi.org/10.25493/8KCQ-3C7.
DOI: 10.25493/4962-ycm
title: Interpolated 3D map of Area STS1 (STS) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area STS1 (STS) in the Big Brain dataset [Amunts et al. 2013]. The mappings were created using the semi-automatic method presented in Schleicher et al. 1999, based on coronal histological sections on 1 micron resolution. Mappings are available on approximately every 15-60th section of this region. They were then aligned to the corresponding sections of the 3D reconstructed Big Brain space, using the transformations used in Amunts et al. 2013, kindly provided by Claude Lepage (McGill University). From these delineations, a preliminary 3D map of Area STS1 (STS) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below. A full mapping of this area in every histological section using a Deep Learning approach is in progress. **Additional information:** The reference delineations used for this map are part of the work for the corresponding probabilistic map of Area STS1 (STS) of the JuBrain Cytoarchitectonic Atlas, published in: Zachlod et al. (2019) [Data set,v3.0] [DOI: 10.25493/2G11-1WA](https://doi.org/10.25493%2F2G11-1WA) Zachlod et al. (2019) [Data set,v3.1] [DOI: 10.25493/F6DF-H8P](https://doi.org/10.25493%2FF6DF-H8P)
DOI: 10.25493/p82m-pvm
title: Density measurements of different receptors for ventral part of Area 44 (IFG) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in ventral part of Area 44 (IFG) obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **mGluR2/3** \| **_glutamate_** \| _[<sup>3</sup>H]LY341495_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[<sup>3</sup>H]flumazenil_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]4-DAMP_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]epibatidine_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[<sup>3</sup>H]SCH23390_ Information on the used tissue samples and corresponding subjects for the receptor fingerprints, profiles and autoradiographs as well as a list of analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387
DOI: 10.25493/m8y0-47
title: Probability map of bundle rh_PoCi-PrCu_1 (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 Posterior Cingulate and Precuneus 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/dgez-q93
title: Ultrahigh resolution 3D cytoarchitectonic map of Area hOc1 (V1, 17, CalcS) created by a Deep-Learning assisted workflow

description: This dataset contains automatically created cytoarchitectonic maps of Area hOc1 (V1, 17, CalcS) in the BigBrain dataset [Amunts et al. 2013]. The mappings were created using Deep Convolutional Neural networks based on the idea presented in Spitzer et al. 2017 and Spitzer et al. 2018, which were trained on delineations on every 120th section created using the semi-automatic method presented in Schleicher et al. 1999. Mappings are available on every section. Their quality was observed by a trained neuroscientist to exclude sections with low quality results from further processing. Automatic mappings were then transformed to the 3D reconstructed BigBrain space using transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill). Individual sections were used to assemble a 3D volume of the area, low quality results were replaced by interpolations between nearest neighboring sections. The volume was then smoothed using an 11³ median filter and largest connected components were identified to remove false positive results of the classification algorithm. The dataset consists of a single HDF5 file containing the volume in RAS dimension ordering and 20 micron isotropic resolution in the dataset “volume” and affine transformation matrix in the dataset “affine”. An additional dataset “interpolation_info” contains a vector with an integer value for each section which indicates if a section was interpolated due to low quality results (value 2) or not (value 1). Due to the large size of the volume, it’s recommended to view the data online using the provided viewer link.
DOI: 10.25493/wb6k-v72
title: Neuropsin tetracycline-transactivator expression: coronal sections (case 2849)

description: Bright-field microscopy images of serial coronal brain sections showing neuropsin (Nop) promoter expression in a bigenic Nop-tetracycline-transactivator (tTA) mouse brain, (case 2849 adult female), using a driver-reporter construct in which the Nop-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Our data show that the Nop-tTA promoter mainly is expressed in the entorhinal cortex, but also in several other cortical regions.
DOI: 10.25493/r28n-2td
title: Probabilistic cytoarchitectonic map of Area TE 2.1 (STG) (v5.1)

description: This dataset contains the distinct architectonic Area TE 2.1 (STG) 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 2.1 (STG). The probability map of Area TE 2.1 (STG) 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. The most probable delineation of Area TE 2.1 (STG) 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)
DOI: 10.25493/myk5-zj
title: Distribution and density of Complex basket formations in the human cerebral cortex

description: This dataset contains the density of VGAT-ir Complex basket formations (Cbk-formations) in various cytoarchitectonic areas of the human cerebral cortex. Cell density was analyzed on cell-body-stained histological sections of 5 human postmortem brains that were supplied by Dr R. Alcaraz, Forensic Pathology Service, Basque Institute of Legal Medicine, Bilbao, Spain.
DOI: 10.25493/v09x-3ew
title: Probabilistic cytoarchitectonic map of Area TE 3 (STG) (v5.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area TE 3 (STG) 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 TE 3 (STG). The probability map of Area TE 3 (STG) 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 TE 3 (STG): Morosan et al. (2019) [Data set, v5.1] [DOI: 10.25493/BN5J-JT8](https://doi.org/10.25493%2FBN5J-JT8) The most probable delineation of Area TE 3 (STG) 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/data72/347
title: 01CO_1.MR.0004.0054.2016.02.04.15.44.11.109375.67136056.IMA
DOI: 10.25493/d2m9-bsk
title: Comparability of basal ganglia delineations across different rat brain atlases

description: The dataset contains an evaluation of the comparability of basal ganglia delineations in eight commonly used rat brain atlases to similarly named, or in other ways corresponding, regions in the Waxholm Space atlas of the rat brain. The degree of spatial correspondence between delineations was semi-quantitatively scored by inspection of serial atlas images from different atlases co-registered to the Waxholm Space atlas of the rat brain. The dataset consists of an .xlxs file summarising spatial relationships and a .pdf file with accompanying descriptions. This provides an overview of the degree of overlap among basal ganglia delineations, which can be used to determine whether data defined by use of different atlases are fully or partly comparable.
DOI: 10.25493/t9p1-wh4
title: Prion promoter tetracycline-transactivator expression: coronal sections (case 388.12)

description: Bright-field microscopy images of serial coronal brain sections showing prion promoter (Prnp) promoter expression in a bigenic Prnp-tetracycline-transactivator (tTA) mouse brain (case 388.12, adult female), using a driver-reporter construct in which the Prnp-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Boy et al., Neuroimage 33:449-62, 2006. Our data show that the Prnp-tTA promoter is spatially expressed in multiple brain regions, including the cerebral cortex, several basal ganglia regions, hippocampus, tectum, and cerebellum.
DOI: 10.25493/2yww-q24
title: Probability map of bundle lh_CAC-PrCu_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Caudal Anterior Cingulate and Precuneus 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/gcfg-2mw
title: Metadata Schema of the Common Data Elements for Dementia (v3)

description: Data in the Medical Informatics Platform (MIP) resides in hospital servers and never leaves the hospital. Analyses and experiments are executed with respect to that principle while preserving patients’ anonymity making it infeasible for their identity to be inferred. Hospitals importing their data to the Medical Informatics Platform join a federation so as to run analyses on data from other hospital nodes as well. Each federation in the MIP refers to a specific Medical Condition. Here the metadata for the federation of hospitals that studies dementia is presented. This metadata schema, consisting of a total of 180 variables, has the following main variable categories: **1. PET** - 3 variables (AV45, FDG-PET, PIB SUVR) which are average scores of measurements collected by a Positron Emission Tomography **2. Brain Anatomy** - 135 volumetric variables based on a brain atlas. Values have been generated from the brain feature extraction pipeline which uses SPM12 **3. Diagnosis** - 12 polynomial variables related to medical conditions like Alzheimer and Parkinson **4. Neuropsychology** - 4 variables for scores in Mini Mental State Examination , 1 Montreal Cognitive Assessment, Hoehn and Yahr scale, Unified Parkinson Disease Rating Scale **5. Demographics** - 5 variables for basic demographic information that does not reveal patient’s identity **6. Genetic** - 14 variables for Single Nucleotide Polymorphisms which are for genetic variation **7. Proteome** - 6 variables which depict the level of some proteins The metadata viewing and management is done by Data Catalogue, a central web portal of MIP. Data Catalogue offers presentation, search and hierarchical visualisation of metadata information for datasets imported into the MIP while providing metadata management features to authorized users. One of its features is parsing metadata descriptions in XLSX files and generating their equivalent in a hierarchical JSON format which the MIP uses. In this repository we upload metadata in both XLSX and the generated JSON format.
DOI: 10.25493/j5s0-qsb
title: Detailed dynamical laminar organisation in different cortical areas (in ferret in vitro)

description: The objective of the study was to investigate the detailed dynamic laminar organisation in different cortical areas to highlight inter-area differences. Functional studies using electrophysiology will be paralleled with structural information from the same areas in collaboration with T1.4.1<sup>[1]</sup> devoted to the detailed cellular and anatomical structure. Data was obtained from the optopharmacological manipulation of activity propagation in ferret V1 coronal slices (in vitro in ferrets). <sup>[1]</sup> HBP funded projects with task ID T1.4.1 “Microanatomical and functional design of the cortical column” (SGA2)