cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13370801.v1
title: Completo Base consolidado.xlsx

description: Dataset including genetics and clinical response to chemotherapy in patients diagnosed with hematologic malignacies.
DOI: 10.7910/dvn/dpjwol
title: Executive Agreements Database, Statement Concerning Protocol with China On Cooperation In The Fields Of Metrology and Standards. Signed At Washington December 9, 2003. Entered Into Force December 9, 2003.

description: KAV 8526 cover memo
DOI: 10.12751/g-node.4ivuv8
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - MRI data according to the Brain Imaging Data Structure (BIDS)

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.0vmyuh
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - MRI quality metrics and reports

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.0ft06t
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Preprocessed MRI data using fMRIPrep

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.omirok
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Anatomical binarized masks

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.d21zpv
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - First-level GLM results

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.9zft1r
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Multivariate pattern classification results

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.p7dabb
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Behavioral data

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.12751/g-node.eqqdtg
title: Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Statistical analyses

description: Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications. ()
description: IsSupplementTo: Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667. (https://doi.org/10.1101/2020.02.15.950667)
DOI: 10.17632/t5x3gpmrgj.5
title: Data for: A Computational Model for Understanding the Oligomerization Mechanisms of TNF Receptor Superfamily

description: The zip file contains the snapshots taken from the trajectory of kinetic Monte-Carlo simulation for TNF receptor oligomerization.
DOI: 10.5281/zenodo.4319930
title: Panama Papers

description: The Panama Papers dataset originates from the website https://offshoreleaks.icij.org/pages/database where it can be downloaded as Neo4j graph. Proceeding from the Neo4j graph, it has been exported as RDF dump file in Turtle format which thus represents the file that is hosted on this site.<br> The dataset contains offshore business links of wealthy and prominent individuals that have been kept private for reasons of tax evasion and general fraud.
DOI: 10.5281/zenodo.4319929
title: Panama Papers

description: The Panama Papers dataset originates from the website https://offshoreleaks.icij.org/pages/database where it can be downloaded as Neo4j graph. Proceeding from the Neo4j graph, it has been exported as RDF dump file in Turtle format which thus represents the file that is hosted on this site.<br> The dataset contains offshore business links of wealthy and prominent individuals that have been kept private for reasons of tax evasion and general fraud.
DOI: 10.6078/m76h4fjg
title: Data Table of Čḯxʷicən Fish Bones
DOI: 10.5517/ccdc.csd.cc25x8x9
title: CCDC 2023181: Experimental Crystal Structure Determination

description: Related Article: Flávia F. Silveira, Juliana O. de Souza, Lucas V.B. Hoelz, Vinícius R. Campos, Valquíria A.P. Jabor, Anna C.C. Aguiar, M. Cristina Nonato, Magaly G. Albuquerque, Rafael V.C. Guido, Nubia Boechat, Luiz C.S. Pinheiro|2020|Eur.J.Med.Chem.|209|112941|doi:10.1016/j.ejmech.2020.112941
DOI: 10.5517/ccdc.csd.cc25x8yb
title: CCDC 2023182: Experimental Crystal Structure Determination

description: Related Article: Flávia F. Silveira, Juliana O. de Souza, Lucas V.B. Hoelz, Vinícius R. Campos, Valquíria A.P. Jabor, Anna C.C. Aguiar, M. Cristina Nonato, Magaly G. Albuquerque, Rafael V.C. Guido, Nubia Boechat, Luiz C.S. Pinheiro|2020|Eur.J.Med.Chem.|209|112941|doi:10.1016/j.ejmech.2020.112941
DOI: 10.6084/m9.figshare.6822707
title: Database_Leiva-Dueñas et al_Global Planetary Change

description: Database used in Leiva-Dueñas et al. "Millennial-scale trends and controls in Posidonia oceanica (L. Delile) ecosystem productivity". <i>Posidonia oceanica </i>is a marine phanerogam that buries a significant part of its belowground production forming an organic bioconstruction known as mat. <i>Posidonia </i>seagrass mats have proven to be reliable archives of long-term environmental change. Here we reconstruct four millennia of environmental dynamics in the NE coast of Spain by analysing the carbon and nitrogen elemental and stable isotopic composition of <i>P. oceanica </i>sheaths, the proportion of different seagrass organs, the carbonates and organic matter content as well as the grain-size distribution throughout the seagrass mat.<br>
DOI: 10.6084/m9.figshare.6822707.v2
title: Database_Leiva-Dueñas et al_Global Planetary Change

description: Database used in Leiva-Dueñas et al. "Millennial-scale trends and controls in Posidonia oceanica (L. Delile) ecosystem productivity". <i>Posidonia oceanica </i>is a marine phanerogam that buries a significant part of its belowground production forming an organic bioconstruction known as mat. <i>Posidonia </i>seagrass mats have proven to be reliable archives of long-term environmental change. Here we reconstruct four millennia of environmental dynamics in the NE coast of Spain by analysing the carbon and nitrogen elemental and stable isotopic composition of <i>P. oceanica </i>sheaths, the proportion of different seagrass organs, the carbonates and organic matter content as well as the grain-size distribution throughout the seagrass mat.<br>
DOI: 10.6084/m9.figshare.13370870
title: Patient outcomes following monofer or venofer for iron deficiency anaemia in pregnancy

description: Data spreadsheet comparing patient outcomes between groups of patients receiving monofer or venofer for iron deficiency anaemia in pregnancy
DOI: 10.6084/m9.figshare.13312040
title: CNN Models, metadata and global trait distribution maps

description: CNN models, metadata and global trait distribution maps.<br>Files included are:<br>la_inception_resnet_v2.hdf5 + la_xception.hdf5 + la_mobilenet_v2.hdf5: trained models for leaf area predictions<br>gh_inception_resnet_v2.hdf5 + gh_xception.hdf5 + gh_mobilenet_v2.hdf5: trained models for growth height predictions<br>sla_inception_resnet_v2.hdf5 + sla_xception.hdf5 + sla_mobilenet_v2.hdf5: trained models for specific leaf area predictions<br>nm_inception_resnet_v2.hdf5 + nm_xception.hdf5 + nm_mobilenet_v2.hdf5: trained models for leaf nitrogen content predictions<br>sm_inception_resnet_v2.hdf5 + sm_xception.hdf5 + sm_mobilenet_v2.hdf5: trained models for seed mass predictions<br>ssd_inception_resnet_v2.hdf5 + ssd_xception.hdf5 + ssd_mobilenet_v2.hdf5: trained models for stem specific density predictions<br>metadata.csv: <br>1. minimum and maximum values of training targets used for normalisation of reference values (can be used to reconvert predictions to original scale when applied to equation (1), see Methods)<br>2. minimum and maximum values of bioclimatic variables used for normalisation of auxiliary data (needed to predict trait values from photographs, see equation (1) in Methods).<br>The order of the bioclimatic variables in the .csv-file corresponds to the order of the auxiliary input to the model (see also R-script '7_train_CNN.R', accessible via https://github.com/ChrSchiller/cnn_traits)<br>gtdm_la.tif, gtdm_gh.tif, gtdm_sla.tif, gtdm_nm.tif, gtdm_sm.tif, gtdm_ssd.tif: global trait distribution maps for the six plant functional traits<br>gtdm_la_quantile_range.tif, gtdm_gh_quantile_range.tif, gtdm_sla_quantile_range.tif, gtdm_nm_quantile_range.tif, gtdm_sm_quantile_range.tif, gtdm_ssd_quantile_range.tif: global trait quantile range maps for the six plant functional traits<br>