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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bitsch, Lise; Salles, Arleen; Evers, Kathinka; Changeux, Jean-Pierre; +26 Authors

    Attention to ethical and social issues were part of the Human Brain Project’s work from the very beginning in 2013. Accordingly, a group of HBP researchers from the social sciences and the humanities created several structures and mechanisms and used various conceptual and empirical methods to develop activities and to identify, reflect upon, and manage the ethical and social issues raised by brain research, its outputs, and applications. With this collection of essays, we aim to present our work in an accessible format, with the ambition of sharing the research and its outputs with diverse stakeholder communities, including policymakers, civil society -and interest organisations, research, and expert communities outside our peer communities. The collection includes short essays by our HBP colleagues who describe and reflect on their work at different stages of our developmental history. In the process, they offer key findings, reflection points, and lessons learned

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Collection . 2023
    License: CC BY
    Data sources: ZENODO; Sygma
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Collection . 2023
      License: CC BY
      Data sources: ZENODO; Sygma
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bitsch, Lise; Salles, Arleen; Evers, Kathinka; Changeux, Jean-Pierre; +27 Authors

    Attention to ethical and social issues were part of the Human Brain Project's work from the very beginning in 2013. Accordingly, a group of HBP researchers from the social sciences and the humanities created several structures and mechanisms and used various conceptual and empirical methods to develop activities and to identify, reflect upon, and manage the ethical and social issues raised by brain research, its outputs, and applications. With this collection of essays, we aim to present our work in an accessible format, with the ambition of sharing the research and its outputs with diverse stakeholder communities, including policymakers, civil society -and interest organisations, research, and expert communities outside our peer communities. The collection includes short essays by our HBP colleagues who describe and reflect on their work at different stages of our developmental history. In the process, they offer key findings, reflection points, and lessons learned.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Collection . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Collection . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Collection . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Collection . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Claudio Román; Cecilia Hernández; Miguel Figueroa; Josselin Houenou; +3 Authors

    Superficial white matter bundle atlas constructed using fiber clustering algorithms, based on 100 subjects of HCP database (probabilistic tractography). The ROIs of Desikan-Killiany atlas where used to label the final clusters. The atlas is composed of 525 fascicles, 267 bundles in the left hemisphere and 258 bundles in the right hemisphere. It has 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Marios Georgiadis; Miriam Menzel; Jan A Reuter; Donald Born; +8 Authors

    Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in "Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering" deposited in bioRxiv: https://doi.org/10.1101/2022.09.30.510198

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Miriam Menzel; David Gräßel; Ivan Rajkovic; Michael Zeineh; +1 Authors

    This dataset supplements the research article "Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI". It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data. While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic. All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'. In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK). For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane. All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps. We thank Laura Pisani from the Stanford Center for Innovation in In vivo Imaging (SCi3) and Kristin Garlund from Bruker BioSpin USA for assistance with the dMRI measurements, Roger Woods from the UCLA Brain Research Institute and Donald Born from Stanford Pathology for providing the vervet and human brain samples, and the laboratory team from the Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, for the preparation of the vervet and human brain sections for the SLI measurements. This work was supported by the National Institutes of Health (NIH) under award numbers R01NS088040, P41EB017183, R01AG061120-01, R01MH092311, and 5P40OD010965, by the Helmholtz Association port-folio theme "Supercomputing and Modeling for the Human Brain", by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 945539 ("Human Brain Project" SGA3), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). The Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The SSRL Structural Molecular Biology Program is supported by the DOE Office of Biological and Environmental Research, and by the National Institutes of Health, National Institute of General Medical Sciences (P30GM133894). The Pilatus detector at beamline 4-2 at SSRL was funded under National Institutes of Health Grant S10OD021512. M.M. received funding from the Helmholtz Doctoral Prize 2019 and the Klaus Tschira Stiftung gGmbH. {"references": ["Menzel et al. (2022), Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI, bioRxiv, DOI: 10.1101/2022.10.04.509781"]}

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hagen, Espen;

    Simulated data from the publication: Hagen E, Magnusson SH, Ness TV, Halnes G, Babu PN, et al. (2022) Brain signal predictions from multi-scale networks using a linearized framework. PLOS Computational Biology 18(8): e1010353. https://doi.org/10.1371/journal.pcbi.1010353 The corresponding simulation codes are available from https://github.com/LFPy/LFPykernels (https://doi.org/10.5281/zenodo.5720619)

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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    Authors: Lillehaug, S.; Yetman, M.; Puchades, M.; Checinska, M.; +4 Authors

    Tabular overview (csv format) of brain-wide reporter expression mapped in five commonly used tetracycline-transactivator (tTA) driver lines: neuropsin (Nop); L7/Purkinje cell protein 2 (Pcp2); Pituitary homeobox 3 (Pitx3); cellular prion protein (Prnp), and Ca2+/calmodulin-dependent protein kinase IIa (Camk2a). This analysis is derived from microscopic brain images taken from 12 driver-reporter constructs (DOIs for source data sets are specified in data descriptor file listed below; hbp-00170\_Comparative\_DataDescriptor\_v1p1.txt) in which the different promoters regulate the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Labelling was observed in microscopic images that were spatially registered to the Allen Mouse Common Coordinate Framework (v2; mouse.brain-map.org). The density of labeling was assessed by two independent researchers using a semi-quantitative grading system from 0 – 4, introduced in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Here grade 0 represents absence of labeled cells (less than 1 per 0.01 mm2), grade 1 - low density (few cells, possible to count), grade 2 - medium density (several cells that can be individually discerned, but not readily counted), grade 3 - high density (many labeled cells with large degree of overlap), and grade 4 - very high density (where individual cells cannot be discerned). For each promoter-tTA line one representative case was semi-quantitatively scored and results verified in the other cases. Scores did not vary more than 1 grade between cases or researchers in any regions. The highest numbers were reported. If the density of labeling was found to vary substantially within a region, the highest observed score was recorded.

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    https://doi.org/10.25493/arks-...
    Dataset . 2018
    License: CC BY
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/arks-...
      Dataset . 2018
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    Authors: David, Olivier; Lemaréchal, Jean-Didier; Jedynak, Maciej; Trebaul, Lena; +39 Authors

    F-TRACT atlas release - December 2021 ====================================== The F-TRACT atlas is provided as .csv (comma-separated values) files that can be read in any table editor. In addition, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with subsequent releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (2018) Probabilistic functional tractography of the human cortex revisited, NeuroImage, Lemarechal et al. (2022) A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials, Brain, when using the F-TRACT atlas. - f-tract_v2112 : Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 780 patients, in the AAL, AICHA, Brodmann, Freesurfer, Hammers, HCP-MMP1, Lausanne2008 (resolutions 33, 60, 125, 250, 500) and MarsAtlas parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, duration, integral, velocity estimated from the onset latency and the fibers distance between the parcels and axonal conduction delays. Synaptic excitatory and inhibitory delays are also provided for each parcel. All features have been estimated separately for patients younger than 15 y.o. (group "0-15") and patients older than 15 y.o. (group "15-100"). - Features maps : Images representing the connectivity probability and response features for all the regions in the Lausanne2008-60 parcellation.

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    ZENODO
    Dataset . 2021
    License: CC BY
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    ZENODO
    Dataset . 2021
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    Data sources: Datacite
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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    Authors: Siwiaszczyk, Marine; Yebga Hot, Raïssa; Morisse, Mélody; Calandreau, Ludovic; +7 Authors

    This project was supported by the APR Neuro2Co grant (Convention 2017 00117257; coordinator Elodie Chaillou, France) from Région Centre Val de Loire. Marine Siwiaszczyk's PhD was funded by Région Centre Val de Loire and Beauval Nature Foundation. Cyril Poupon and Raissa Yebga Hot received funding from the European Union's Horizon 2020 Framework Program for Research and Innovation under the specific Grants No. 945539 (HBP SGA3). The authors have no relevant financial or non-financial interests to disclose. A population average MRI brain template computed from 20 male Japanese Quails and a manually segmented atlas containing 194 regions. In this Version 2: the nomenclature in the file siwiaszczyk_LUT-ITK-SNAP_v2.txt was updated one slice of one region was completed in the file siwiaszczyk_atlas_v2.nii.gz.

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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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    Authors: Zilles, K.; Palomero-Gallagher, N.; Amunts, K.;

    This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area 45 (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_** \| _[3H]AMPA_ **kainate** \| **_glutamate_** \| _[3H]kainate_ **NMDA** \| **_glutamate_** \| _[3H]MK-801_ **mGluR2/3** \| **_glutamate_** \| _[3H]LY341495_ **GABAA** \| **_GABA_** \| _[3H]muscimol_ **GABAB** \| **_GABA_** \| _[3H]CGP54626_ **GABAA/BZ** \| **_GABA_** \| _[3H]flumazenil_ **muscarinic M1** \| **_acetylcholine_** \| _[3H]pirenzepine_ **muscarinic M2** \| **_acetylcholine_** \| _[3H]oxotremorine-M_ **muscarinic M3** \| **_acetylcholine_** \| _[3H]4-DAMP_ **nicotinic α4β2** \| **_acetylcholine_** \| _[3H]epibatidine_ **α1** \| **_noradrenalin/norepinephrine_** \| _[3H]prazosin_ **α2** \| **_noradrenalin/norepinephrine_** \| _[3H]UK-14,304_ **5-HT1A** \| **_serotonin_** \| _[3H]8-OH-DPAT_ **5-HT2** \| **_serotonin_** \| _[3H]ketanserin_ **D1** \| **_dopamine_** \| _[3H]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

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    https://doi.org/10.25493/qfsy-...
    Dataset . 2021
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/qfsy-...
      Dataset . 2021
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    Authors: Bitsch, Lise; Salles, Arleen; Evers, Kathinka; Changeux, Jean-Pierre; +26 Authors

    Attention to ethical and social issues were part of the Human Brain Project’s work from the very beginning in 2013. Accordingly, a group of HBP researchers from the social sciences and the humanities created several structures and mechanisms and used various conceptual and empirical methods to develop activities and to identify, reflect upon, and manage the ethical and social issues raised by brain research, its outputs, and applications. With this collection of essays, we aim to present our work in an accessible format, with the ambition of sharing the research and its outputs with diverse stakeholder communities, including policymakers, civil society -and interest organisations, research, and expert communities outside our peer communities. The collection includes short essays by our HBP colleagues who describe and reflect on their work at different stages of our developmental history. In the process, they offer key findings, reflection points, and lessons learned

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    ZENODO
    Collection . 2023
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    Authors: Bitsch, Lise; Salles, Arleen; Evers, Kathinka; Changeux, Jean-Pierre; +27 Authors

    Attention to ethical and social issues were part of the Human Brain Project's work from the very beginning in 2013. Accordingly, a group of HBP researchers from the social sciences and the humanities created several structures and mechanisms and used various conceptual and empirical methods to develop activities and to identify, reflect upon, and manage the ethical and social issues raised by brain research, its outputs, and applications. With this collection of essays, we aim to present our work in an accessible format, with the ambition of sharing the research and its outputs with diverse stakeholder communities, including policymakers, civil society -and interest organisations, research, and expert communities outside our peer communities. The collection includes short essays by our HBP colleagues who describe and reflect on their work at different stages of our developmental history. In the process, they offer key findings, reflection points, and lessons learned.

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    ZENODO
    Collection . 2023
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    ZENODO
    Collection . 2023
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    Authors: Claudio Román; Cecilia Hernández; Miguel Figueroa; Josselin Houenou; +3 Authors

    Superficial white matter bundle atlas constructed using fiber clustering algorithms, based on 100 subjects of HCP database (probabilistic tractography). The ROIs of Desikan-Killiany atlas where used to label the final clusters. The atlas is composed of 525 fascicles, 267 bundles in the left hemisphere and 258 bundles in the right hemisphere. It has 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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    Authors: Marios Georgiadis; Miriam Menzel; Jan A Reuter; Donald Born; +8 Authors

    Small-angle X-ray scattering datasets for resolving crossing fibers (myelinated neuronal axon bundles), as described in "Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering" deposited in bioRxiv: https://doi.org/10.1101/2022.09.30.510198

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
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      ZENODO
      Dataset . 2022
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    Authors: Miriam Menzel; David Gräßel; Ivan Rajkovic; Michael Zeineh; +1 Authors

    This dataset supplements the research article "Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI". It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data. While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic. All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'. In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK). For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane. All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps. We thank Laura Pisani from the Stanford Center for Innovation in In vivo Imaging (SCi3) and Kristin Garlund from Bruker BioSpin USA for assistance with the dMRI measurements, Roger Woods from the UCLA Brain Research Institute and Donald Born from Stanford Pathology for providing the vervet and human brain samples, and the laboratory team from the Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, for the preparation of the vervet and human brain sections for the SLI measurements. This work was supported by the National Institutes of Health (NIH) under award numbers R01NS088040, P41EB017183, R01AG061120-01, R01MH092311, and 5P40OD010965, by the Helmholtz Association port-folio theme "Supercomputing and Modeling for the Human Brain", by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 945539 ("Human Brain Project" SGA3), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). The Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The SSRL Structural Molecular Biology Program is supported by the DOE Office of Biological and Environmental Research, and by the National Institutes of Health, National Institute of General Medical Sciences (P30GM133894). The Pilatus detector at beamline 4-2 at SSRL was funded under National Institutes of Health Grant S10OD021512. M.M. received funding from the Helmholtz Doctoral Prize 2019 and the Klaus Tschira Stiftung gGmbH. {"references": ["Menzel et al. (2022), Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI, bioRxiv, DOI: 10.1101/2022.10.04.509781"]}

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
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      ZENODO
      Dataset . 2022
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    Authors: Hagen, Espen;

    Simulated data from the publication: Hagen E, Magnusson SH, Ness TV, Halnes G, Babu PN, et al. (2022) Brain signal predictions from multi-scale networks using a linearized framework. PLOS Computational Biology 18(8): e1010353. https://doi.org/10.1371/journal.pcbi.1010353 The corresponding simulation codes are available from https://github.com/LFPy/LFPykernels (https://doi.org/10.5281/zenodo.5720619)

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    ZENODO
    Dataset . 2022
    License: CC BY
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
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    Authors: Lillehaug, S.; Yetman, M.; Puchades, M.; Checinska, M.; +4 Authors

    Tabular overview (csv format) of brain-wide reporter expression mapped in five commonly used tetracycline-transactivator (tTA) driver lines: neuropsin (Nop); L7/Purkinje cell protein 2 (Pcp2); Pituitary homeobox 3 (Pitx3); cellular prion protein (Prnp), and Ca2+/calmodulin-dependent protein kinase IIa (Camk2a). This analysis is derived from microscopic brain images taken from 12 driver-reporter constructs (DOIs for source data sets are specified in data descriptor file listed below; hbp-00170\_Comparative\_DataDescriptor\_v1p1.txt) in which the different promoters regulate the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Labelling was observed in microscopic images that were spatially registered to the Allen Mouse Common Coordinate Framework (v2; mouse.brain-map.org). The density of labeling was assessed by two independent researchers using a semi-quantitative grading system from 0 – 4, introduced in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Here grade 0 represents absence of labeled cells (less than 1 per 0.01 mm2), grade 1 - low density (few cells, possible to count), grade 2 - medium density (several cells that can be individually discerned, but not readily counted), grade 3 - high density (many labeled cells with large degree of overlap), and grade 4 - very high density (where individual cells cannot be discerned). For each promoter-tTA line one representative case was semi-quantitatively scored and results verified in the other cases. Scores did not vary more than 1 grade between cases or researchers in any regions. The highest numbers were reported. If the density of labeling was found to vary substantially within a region, the highest observed score was recorded.

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    https://doi.org/10.25493/arks-...
    Dataset . 2018
    License: CC BY
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/arks-...
      Dataset . 2018
      License: CC BY
      Data sources: Datacite; Sygma
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    Authors: David, Olivier; Lemaréchal, Jean-Didier; Jedynak, Maciej; Trebaul, Lena; +39 Authors

    F-TRACT atlas release - December 2021 ====================================== The F-TRACT atlas is provided as .csv (comma-separated values) files that can be read in any table editor. In addition, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with subsequent releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (2018) Probabilistic functional tractography of the human cortex revisited, NeuroImage, Lemarechal et al. (2022) A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials, Brain, when using the F-TRACT atlas. - f-tract_v2112 : Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 780 patients, in the AAL, AICHA, Brodmann, Freesurfer, Hammers, HCP-MMP1, Lausanne2008 (resolutions 33, 60, 125, 250, 500) and MarsAtlas parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, duration, integral, velocity estimated from the onset latency and the fibers distance between the parcels and axonal conduction delays. Synaptic excitatory and inhibitory delays are also provided for each parcel. All features have been estimated separately for patients younger than 15 y.o. (group "0-15") and patients older than 15 y.o. (group "15-100"). - Features maps : Images representing the connectivity probability and response features for all the regions in the Lausanne2008-60 parcellation.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
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    Authors: Siwiaszczyk, Marine; Yebga Hot, Raïssa; Morisse, Mélody; Calandreau, Ludovic; +7 Authors

    This project was supported by the APR Neuro2Co grant (Convention 2017 00117257; coordinator Elodie Chaillou, France) from Région Centre Val de Loire. Marine Siwiaszczyk's PhD was funded by Région Centre Val de Loire and Beauval Nature Foundation. Cyril Poupon and Raissa Yebga Hot received funding from the European Union's Horizon 2020 Framework Program for Research and Innovation under the specific Grants No. 945539 (HBP SGA3). The authors have no relevant financial or non-financial interests to disclose. A population average MRI brain template computed from 20 male Japanese Quails and a manually segmented atlas containing 194 regions. In this Version 2: the nomenclature in the file siwiaszczyk_LUT-ITK-SNAP_v2.txt was updated one slice of one region was completed in the file siwiaszczyk_atlas_v2.nii.gz.

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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
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    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
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      Dataset . 2021
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      ZENODO
      Dataset . 2021
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    Authors: Zilles, K.; Palomero-Gallagher, N.; Amunts, K.;

    This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area 45 (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_** \| _[3H]AMPA_ **kainate** \| **_glutamate_** \| _[3H]kainate_ **NMDA** \| **_glutamate_** \| _[3H]MK-801_ **mGluR2/3** \| **_glutamate_** \| _[3H]LY341495_ **GABAA** \| **_GABA_** \| _[3H]muscimol_ **GABAB** \| **_GABA_** \| _[3H]CGP54626_ **GABAA/BZ** \| **_GABA_** \| _[3H]flumazenil_ **muscarinic M1** \| **_acetylcholine_** \| _[3H]pirenzepine_ **muscarinic M2** \| **_acetylcholine_** \| _[3H]oxotremorine-M_ **muscarinic M3** \| **_acetylcholine_** \| _[3H]4-DAMP_ **nicotinic α4β2** \| **_acetylcholine_** \| _[3H]epibatidine_ **α1** \| **_noradrenalin/norepinephrine_** \| _[3H]prazosin_ **α2** \| **_noradrenalin/norepinephrine_** \| _[3H]UK-14,304_ **5-HT1A** \| **_serotonin_** \| _[3H]8-OH-DPAT_ **5-HT2** \| **_serotonin_** \| _[3H]ketanserin_ **D1** \| **_dopamine_** \| _[3H]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

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    https://doi.org/10.25493/qfsy-...
    Dataset . 2021
    License: CC BY NC SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.25493/qfsy-...
      Dataset . 2021
      License: CC BY NC SA
      Data sources: Datacite; Sygma
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