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740 Research products

  • 2014-2023
  • Research data
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  • European Commission
  • Neuroinformatics

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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: Lumbierres, Maria; Kissling, W. Daniel;

    EuropaBON list of Essential Biodiversity Variables

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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/
    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      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: Lumbierres, Maria; Kissling, W. Daniel;

    Agenda of EuropaBON virtual workshop on EBV workflows

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      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: Chavignon Arthur; Baptiste Heiles; Hingot Vincent; Lopez Pauline; +2 Authors

    Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms. Abstract: Ultrasound Localization Microscopy (ULM) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated in vitro, this technique has matured and sees implementation in vivo for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets in silico and in vivo to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits. This article provides all materials and post-processing scripts and functions. Methods: 200.000 ultrasound images have been acquired in vivo on a rat brain with skull removal at 1000 Hz with a 15 MHz linear probe. This dataset contains raw radiofrequency data (RF) and beamformed images (IQ) of the brain vascularization with flowing microbubbles (ultrasound contrast agent). Article to be cited: Heiles, Chavignon, Hingot, Lopez, Teston and Couture. Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8). Related processing scripts and codes: github.com/AChavignon/PALA Related datasets: doi.org/10.5281/zenodo.4343435 Acknowledgments: We thank Cyrille Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, BB@C, Caen, France) for animals’ preparation and perfusion of contrast agent and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France).

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      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: Canepa, Elisabetta; Condia, Bob; Beighle, Kory; Djebbara, Zakaria; +1 Authors

    This dataset is an output of the ‘Designing Atmospheres: Theory and Science’ Symposium (ATS), an Interfaces event of the Academy of Neuroscience for Architecture (ANFA), sponsored by the EU’s Horizon 2020 MSCA Program — RESONANCES Project, the Perkins Eastman Studio, and the KSTATE APDesign. The symposium was hosted in the College of Architecture, Planning and Design (APDesign), Kansas State University, Manhattan (Kansas, USA), on March 28, 2023. Speakers: Kory Beighle (Kansas State University), Elisabetta Canepa (University of Genoa | Kansas State University), Bob Condia (Kansas State University), Zakaria Djebbara (Aalborg University | TU Berlin), and Harry Francis Mallgrave (Illinois Institute of Technology). Recent advances in science confirm many of the architects’ deep-rooted intuitions, improving knowledge about the perception of space and the meaning of architectural and urban design. The symposium ‘Designing Atmospheres: Theory and Science‘ presented to an audience of students, educators, architects, and scientists a conversation about the experience of design and building, specifically speaking to the significance of atmospheres, affordances, and emotions. This dataset is made of seven files: no. 1 dataset summary (.pdf) no. 1 symposium poster (.pdf) no. 5 videos containing speakers’ presentations (.mp4) Recorded videos of each lecture are also available on the RESONANCES project website (www.resonances-project.com/harvest) and its YouTube channel (@resonancesproject5777). 'Designing Atmospheres: Theory and Science' Symposium is an Interfaces event of the Academy of Neuroscience for Architecture (ANFA), sponsored by the European Union's Horizon 2020 MSCA Program — RESONANCES Project, the Perkins Eastman Studio, and the KSTATE APDesign. The RESONANCES project received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 101025132. The content of the produced and published material reflects only the authors' views. The Research Executive Agency and the European Commission are not responsible for any use that may be made of the information it contains.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO; Sygma
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  • Authors: Cirong Liu;

    The Marmoset Brain Mapping Project was launched in Nov. 2016, aiming at building comprehensive marmoset brain atlases and tools to facilitate neuroimaging and translational studies.With supports and helps from multiple labs and teams, including- Cirong Liu's lab (ION/CAS): Xiaojia Zhu, Fufui Feng- Afonso Silva's lab (NINDS/NIH): Afonso Silva, John Newman, Cecil Yen, Diego Szczupak, and Xiaoguang Tian;- David Leopold's lab (NIMH/NIH): David Leopold, and Frank Ye;- @MarmosetBrainConnectityAtlas: Marcello Rosa, and Piotr Majka;- AFNI (NIMH/NIH): Daniel Glen;- Zhifeng Liang's lab and CEBSIT MRI Facility (ION/CAS);- Gustavo Deco's lab (UPF): Gustavo Deco and Yonatan Sanz PerlWe are making significant progress during these years, not only in developing atlases for mapping the marmoset brain, but also in pushing the resolution limit of non-human primate MRI. These efforts resulted in useful atlases and tools for the marmoset research and valuable MRI data that can be of interests to researchers who are in the MRI research field.How to CiteFor Atlas-V1 (Cortical parcellaion) and 150um MRI data:Liu C, et al, A digital 3D atlas of the marmoset brain based on multi-modal MRI. Neuroimage (2018) doi: 10.1016/j.neuroimage.2017.12.004.For Atlas-V2 (White matter atlas) and 150um, 60um, 80um and 50um MRI data:Liu C, et al. A resource for detailed 3D mapping of white matter pathways in the marmoset brain. Nature Neuroscience (2020) doi:10.1038/s41593-019-0575-0.For Atlas-V3 (Population templates) :Liu C, et al. Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates. Neuroimage (2021) doi:10.1016/j.neuroimage.2020.117620.For Atlas-V4 (Functional connectivity) and associated in-vivo MRI data:Tian X, et al. An integrated resource for functional and structural connectivity of the marmoset brain. Nature Communications (2022) doi:10.1038/s41467-022-35197-2For Atlas-V5 (Cerebellum):Zhu X, et al. An Anatomical and Connectivity Atlas of the Marmoset Cerebellum. Cell Reports (2023)

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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: Lupori, Leonardo; Totaro, Valentino; Cornuti, Sara; Ciampi, Luca; +10 Authors

    Quality-controlled predictions of deep learning models for cell counting This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper: A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain. The dataset integrates the raw data published on a previous upload on Zenodo. Cell locations were obtained using two deep-learning models for cell counting (publicly available on GitHub, details in the paper by Ciampi et al., 2022). The output of the deep-learning pipeline was filtered based on the score assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. Content The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper. Folder Structure and file naming conventions There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells). This work was funded by: AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911); the Tuscany Health Ecosystem (THE) Project (CUP I53C22000780001), funded by the National Recovery and Resilience Plan (NRPP), within the NextGeneration Europe (NGEU) Program; PRIN2017 2017HMH8FA to T.P., R.M. was supported by Fondazione Umberto Veronesi.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite; Sygma
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite; Sygma
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  • Authors: Xi-Nian Zuo; CCNP Consortium;

    CCNP takes its pilot stage (2013 – 2022) of the first ten-year. It aims at establishing protocols on the Chinese normative brain development trajectories across the human lifespan. It implements a structured multi-cohort longitudinal design (or accelerated longitudinal design), which is particularly viable for lifespan trajectory studies, and optimal for recoverable missing data. The CCNP pilot comprises three connected components: developing CCNP (devCCNP, baseline age = 6-18 years, 12 age cohorts, 3 waves, interval = 15 months), maturing CCNP (matCCNP, baseline age = 18-60 years, 14 age cohorts, 3 waves, interval = 39 months) and ageing CCNP (ageCCNP, baseline age = 60-84 years, 12 age cohorts, 3 waves, interval = 27 months). The developmental component of CCNP (devCCNP, 2013-2022), also known as "Growing Up in China", a ten-year's pilot stage of CCNP, has established follow-up cohorts in Chongqing (CKG, Southwest China) and Beijing (PEK, Northeast China). It is an ongoing project focused on longitudinal developmental research as creating and sharing a large-scale multimodal dataset for typically developing Chinese children and adolescents (ages 6.0-17.9 at enrollment) carried out in both school- and community-based samples. The devCCNP houses longitudinal data about demographics, biophysical measures, psychological and behavioral assessments, cognitive phenotyping, ocular-tracking, as well as multimodal magnetic resonance imaging (MRI) of brain's resting and naturalistic viewing function, diffusion structure and morphometry. With the collection of longitudinal structured images and psychobehavioral samples from school-age children and adolescents in multiple cohorts, devCCNP has constructed a full school-age brain template and its growth curve reference for Han Chinese which demonstrated the difference in brain development between Chinese and American school-aged children.*This dataset contains only T1-weighted MRI, Resting-state fMRI and Diffusion Tensor MRI data of devCCNP.To access the devCCNP Lite data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at http://deepneuro.bnu.edu.cn/?p=163 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). All terms specified by the DUA-CCNP must be complied with. Meanwhile, the baseline CKG Sample on brain imaging are available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn.

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    Authors: Migliorini, Duccio; Auger-Rozenberg, Marie-Anne; Battisti, Andrea; Brockerhoff, Eckehard; +11 Authors

    File 1

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    ZENODO
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      ZENODO
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    Authors: Migliorini, Duccio; Auger-Rozenberg, Marie-Anne; Battisti, Andrea; Brockerhoff, Eckehard; +11 Authors

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    ZENODO
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      ZENODO
      Dataset . 2023
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      Data sources: ZENODO
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    Authors: Migliorini, Duccio; Auger-Rozenberg, Marie-Anne; Battisti, Andrea; Brockerhoff, Eckehard; +11 Authors

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    ZENODO
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      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: Lumbierres, Maria; Kissling, W. Daniel;

    EuropaBON list of Essential Biodiversity Variables

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    ZENODO
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      ZENODO
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    Authors: Lumbierres, Maria; Kissling, W. Daniel;

    Agenda of EuropaBON virtual workshop on EBV workflows

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    ZENODO
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      ZENODO
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    Authors: Chavignon Arthur; Baptiste Heiles; Hingot Vincent; Lopez Pauline; +2 Authors

    Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms. Abstract: Ultrasound Localization Microscopy (ULM) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated in vitro, this technique has matured and sees implementation in vivo for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets in silico and in vivo to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits. This article provides all materials and post-processing scripts and functions. Methods: 200.000 ultrasound images have been acquired in vivo on a rat brain with skull removal at 1000 Hz with a 15 MHz linear probe. This dataset contains raw radiofrequency data (RF) and beamformed images (IQ) of the brain vascularization with flowing microbubbles (ultrasound contrast agent). Article to be cited: Heiles, Chavignon, Hingot, Lopez, Teston and Couture. Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8). Related processing scripts and codes: github.com/AChavignon/PALA Related datasets: doi.org/10.5281/zenodo.4343435 Acknowledgments: We thank Cyrille Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, BB@C, Caen, France) for animals’ preparation and perfusion of contrast agent and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France).

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    ZENODO
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    ZENODO
    Dataset . 2023
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      ZENODO
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      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: Canepa, Elisabetta; Condia, Bob; Beighle, Kory; Djebbara, Zakaria; +1 Authors

    This dataset is an output of the ‘Designing Atmospheres: Theory and Science’ Symposium (ATS), an Interfaces event of the Academy of Neuroscience for Architecture (ANFA), sponsored by the EU’s Horizon 2020 MSCA Program — RESONANCES Project, the Perkins Eastman Studio, and the KSTATE APDesign. The symposium was hosted in the College of Architecture, Planning and Design (APDesign), Kansas State University, Manhattan (Kansas, USA), on March 28, 2023. Speakers: Kory Beighle (Kansas State University), Elisabetta Canepa (University of Genoa | Kansas State University), Bob Condia (Kansas State University), Zakaria Djebbara (Aalborg University | TU Berlin), and Harry Francis Mallgrave (Illinois Institute of Technology). Recent advances in science confirm many of the architects’ deep-rooted intuitions, improving knowledge about the perception of space and the meaning of architectural and urban design. The symposium ‘Designing Atmospheres: Theory and Science‘ presented to an audience of students, educators, architects, and scientists a conversation about the experience of design and building, specifically speaking to the significance of atmospheres, affordances, and emotions. This dataset is made of seven files: no. 1 dataset summary (.pdf) no. 1 symposium poster (.pdf) no. 5 videos containing speakers’ presentations (.mp4) Recorded videos of each lecture are also available on the RESONANCES project website (www.resonances-project.com/harvest) and its YouTube channel (@resonancesproject5777). 'Designing Atmospheres: Theory and Science' Symposium is an Interfaces event of the Academy of Neuroscience for Architecture (ANFA), sponsored by the European Union's Horizon 2020 MSCA Program — RESONANCES Project, the Perkins Eastman Studio, and the KSTATE APDesign. The RESONANCES project received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 101025132. The content of the produced and published material reflects only the authors' views. The Research Executive Agency and the European Commission are not responsible for any use that may be made of the information it contains.

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    ZENODO
    Dataset . 2023
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    ZENODO
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  • Authors: Cirong Liu;

    The Marmoset Brain Mapping Project was launched in Nov. 2016, aiming at building comprehensive marmoset brain atlases and tools to facilitate neuroimaging and translational studies.With supports and helps from multiple labs and teams, including- Cirong Liu's lab (ION/CAS): Xiaojia Zhu, Fufui Feng- Afonso Silva's lab (NINDS/NIH): Afonso Silva, John Newman, Cecil Yen, Diego Szczupak, and Xiaoguang Tian;- David Leopold's lab (NIMH/NIH): David Leopold, and Frank Ye;- @MarmosetBrainConnectityAtlas: Marcello Rosa, and Piotr Majka;- AFNI (NIMH/NIH): Daniel Glen;- Zhifeng Liang's lab and CEBSIT MRI Facility (ION/CAS);- Gustavo Deco's lab (UPF): Gustavo Deco and Yonatan Sanz PerlWe are making significant progress during these years, not only in developing atlases for mapping the marmoset brain, but also in pushing the resolution limit of non-human primate MRI. These efforts resulted in useful atlases and tools for the marmoset research and valuable MRI data that can be of interests to researchers who are in the MRI research field.How to CiteFor Atlas-V1 (Cortical parcellaion) and 150um MRI data:Liu C, et al, A digital 3D atlas of the marmoset brain based on multi-modal MRI. Neuroimage (2018) doi: 10.1016/j.neuroimage.2017.12.004.For Atlas-V2 (White matter atlas) and 150um, 60um, 80um and 50um MRI data:Liu C, et al. A resource for detailed 3D mapping of white matter pathways in the marmoset brain. Nature Neuroscience (2020) doi:10.1038/s41593-019-0575-0.For Atlas-V3 (Population templates) :Liu C, et al. Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates. Neuroimage (2021) doi:10.1016/j.neuroimage.2020.117620.For Atlas-V4 (Functional connectivity) and associated in-vivo MRI data:Tian X, et al. An integrated resource for functional and structural connectivity of the marmoset brain. Nature Communications (2022) doi:10.1038/s41467-022-35197-2For Atlas-V5 (Cerebellum):Zhu X, et al. An Anatomical and Connectivity Atlas of the Marmoset Cerebellum. Cell Reports (2023)

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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: Lupori, Leonardo; Totaro, Valentino; Cornuti, Sara; Ciampi, Luca; +10 Authors

    Quality-controlled predictions of deep learning models for cell counting This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper: A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain. The dataset integrates the raw data published on a previous upload on Zenodo. Cell locations were obtained using two deep-learning models for cell counting (publicly available on GitHub, details in the paper by Ciampi et al., 2022). The output of the deep-learning pipeline was filtered based on the score assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. Content The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper. Folder Structure and file naming conventions There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells). This work was funded by: AI4Media - A European Excellence Centre for Media, Society and Democracy (EC, H2020 n. 951911); the Tuscany Health Ecosystem (THE) Project (CUP I53C22000780001), funded by the National Recovery and Resilience Plan (NRPP), within the NextGeneration Europe (NGEU) Program; PRIN2017 2017HMH8FA to T.P., R.M. was supported by Fondazione Umberto Veronesi.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite; 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
      Dataset . 2023
      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/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite; Sygma