cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6919170
title: COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces

description: Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science. <strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong> <strong>Please note that version 2 corrected some bugs in the data of participants 17 to 24 for which we sincerely apologize. The Notebook file is updated accordingly.</strong>
description: This database has been created as part of a project validated by the local ethical committee of the University of Toulouse (CER number 2021-342).
DOI: 10.5281/zenodo.6923638
title: COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces

description: Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science. <strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong> <strong>Please note that version 3 corrected several bugs in the data of participants 17 to 24, for which we sincerely apologize. The Notebook file is updated accordingly. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong>
description: This database has been created as part of a project validated by the local ethical committee of the University of Toulouse (CER number 2021-342).
DOI: 10.5281/zenodo.6874128
title: COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces

description: Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science. <strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong> <strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong>
description: This database has been created as part of a project validated by the local ethical committee of the University of Toulouse (CER number 2021-342).
DOI: 10.5281/zenodo.7413650
title: COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces

description: Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science. <strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong> <strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong>
description: This database has been created as part of a project validated by the local ethical committee of the University of Toulouse (CER number 2021-342).
DOI: 10.6084/m9.figshare.21673769
title: Interview Guide in English

description: This is the interview guide used in the study titled ' ‘<strong>Because people here are ignorant’: The failure of a community intervention to prevent mycetoma in Sudan'.</strong>
DOI: 10.34740/kaggle/dsv/4687295
title: US Board on Geographic Names
DOI: 10.5281/zenodo.7389786
title: Example data set for the R package riversCentralAsia

description: This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/).
description: The description of the data set is minimal as the data is only used for demonstration of the functionalities of the riversCentralAsia R package. This data should not be used for anything else than experimenting with the package.
description: {"references": ["Tobias Siegfried, & Bea Marti. (2022). hydrosolutions/caham_book: Initial Release (v_2022_1-beta.2). Zenodo. https://doi.org/10.5281/zenodo.6349984"]}
DOI: 10.5281/zenodo.7389785
title: Example data set for the R package riversCentralAsia

description: This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/).
description: The description of the data set is minimal as the data is only used for demonstration of the functionalities of the riversCentralAsia R package. This data should not be used for anything else than experimenting with the package.
description: {"references": ["Tobias Siegfried, & Bea Marti. (2022). hydrosolutions/caham_book: Initial Release (v_2022_1-beta.2). Zenodo. https://doi.org/10.5281/zenodo.6349984"]}
DOI: 10.5281/zenodo.7390116
title: Example data set for the R package riversCentralAsia

description: This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/).
description: The description of the data set is minimal as the data is only used for demonstration of the functionalities of the riversCentralAsia R package. This data should not be used for anything else than experimenting with the package.
description: {"references": ["Tobias Siegfried, & Bea Marti. (2022). hydrosolutions/caham_book: Initial Release (v_2022_1-beta.2). Zenodo. https://doi.org/10.5281/zenodo.6349984"]}
DOI: 10.5281/zenodo.7413906
title: Example data set for the R package riversCentralAsia

description: This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/).
description: The description of the data set is minimal as the data is only used for demonstration of the functionalities of the riversCentralAsia R package. This data should not be used for anything else than experimenting with the package.
description: {"references": ["Tobias Siegfried, & Bea Marti. (2022). hydrosolutions/caham_book: Initial Release (v_2022_1-beta.2). Zenodo. https://doi.org/10.5281/zenodo.6349984"]}
DOI: 10.6084/m9.figshare.21695066
title: 权重问卷数据.xlsx

description: questionnaire data
DOI: 10.6084/m9.figshare.21695066.v1
title: 权重问卷数据.xlsx

description: questionnaire data
DOI: 10.6084/m9.figshare.21624998
title: Sound Velocities and Densities of Liquid Fe-8.6wt% Si under Earth’s Outer Core Conditions: Implications for a Si-rich Core

description: Using a two-stage light gas gun, the sound velocities of Fe-8.6 Si (weight percent) were measured up to 298 GPa and 5500 K. A discontinuity in sound velocities along Hugoniot indicates the onset of melting at 216.4 GPa (4200 K) and complete melting at 260.5 GPa (4530 K). When extrapolated the melting temperature via Lindemann Law, the melting temperature of Fe-8.6Si is 5000 K at the inner core boundary pressure of 330 GPa. Combing the measurements of Hugoniot data and sound velocities, we established a thermodynamic model to calculate the equation of state (EOS) along solidus, liquidus and liquid Fe-Si alloys. The calculated EOSs and sound velocities for Fe and Fe-8.6 Si are consistent with measurements by static and dynamic compression. Under the outer condition, densities and sound velocities of Fe-8.6Si simultaneously match the seismic data of the outer core. The maximum amount of Si in the outer core is 7.6~8.6 wt % for a pure Fe-Si core.
DOI: 10.6084/m9.figshare.21624998.v1
title: Sound Velocities and Densities of Liquid Fe-8.6wt% Si under Earth’s Outer Core Conditions: Implications for a Si-rich Core

description: Using a two-stage light gas gun, the sound velocities of Fe-8.6 Si (weight percent) were measured up to 298 GPa and 5500 K. A discontinuity in sound velocities along Hugoniot indicates the onset of melting at 216.4 GPa (4200 K) and complete melting at 260.5 GPa (4530 K). When extrapolated the melting temperature via Lindemann Law, the melting temperature of Fe-8.6Si is 5000 K at the inner core boundary pressure of 330 GPa. Combing the measurements of Hugoniot data and sound velocities, we established a thermodynamic model to calculate the equation of state (EOS) along solidus, liquidus and liquid Fe-Si alloys. The calculated EOSs and sound velocities for Fe and Fe-8.6 Si are consistent with measurements by static and dynamic compression. Under the outer condition, densities and sound velocities of Fe-8.6Si simultaneously match the seismic data of the outer core. The maximum amount of Si in the outer core is 7.6~8.6 wt % for a pure Fe-Si core.
DOI: 10.6084/m9.figshare.21695198
title: FGD Guide in English

description: The FGD guide for the study titled ' ‘<strong>Because people here are ignorant’: The failure of a community intervention to prevent mycetoma in Sudan'</strong>
DOI: 10.6084/m9.figshare.21695198.v2
title: FGD Guide in English

description: The FGD guide for the study titled ' ‘<strong>Because people here are ignorant’: The failure of a community intervention to prevent mycetoma in Sudan'</strong>
DOI: 10.6084/m9.figshare.21695198.v1
title: FGD Guide

description: The FGD guide for the study titled ' ‘<strong>Because people here are ignorant’: The failure of a community intervention to prevent mycetoma in Sudan'</strong>
DOI: 10.5281/zenodo.7413918
title: Test-particle simulations for "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies"

description: This contains the test-particle simulations of barred galaxies with varying bar properties (bar strength, pattern speed) which have been used in the manuscript "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies" by Ghosh, S., Trick, W. H., Green G. M. (2022). For details for the datasets, please see the README.md file. For any further information, please feel free to contact Ghosh, Soumavo (ghosh@mpia.de).
DOI: 10.5281/zenodo.7413917
title: Test-particle simulations for "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies"

description: This contains the test-particle simulations of barred galaxies with varying bar properties (bar strength, pattern speed) which have been used in the manuscript "Quantifying the influence of bars on action-based dynamical modelling of disc galaxies" by Ghosh, S., Trick, W. H., Green G. M. (2022). For details for the datasets, please see the README.md file. For any further information, please feel free to contact Ghosh, Soumavo (ghosh@mpia.de).
DOI: 10.5281/zenodo.7404779
title: Sound pressure measurements and calculation of the cavitation volume for a full-scale 3500 TEU container vessel

description: Supplementary material comprising measurement data and MATLAB code corresponding to the published research article.
DOI: 10.5281/zenodo.7404780
title: Sound pressure measurements and calculation of the cavitation volume for a full-scale 3500 TEU container vessel

description: Supplementary material comprising measurement data and MATLAB code corresponding to the published research article.
DOI: 10.6084/m9.figshare.21695246
title: 权重问卷数据

description: 问卷数据
DOI: 10.6084/m9.figshare.21695246.v1
title: 权重问卷数据

description: 问卷数据
DOI: 10.5281/zenodo.6576681
title: Extensive data for training neural networks for radiation. Code and data used in: "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System"

description: Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em> 1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by ml_train.py. 2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the RTE+RRTMGP-NN repository on Github, used in a previous paper where different emulation methods were compared) 3) The Fortran and Python code used for data generation and NN training are found in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> on the main branch on Github; an archived version is also included here (<strong>rte-rrtmgp-nn.zip</strong>). See the readme in the above sub-directory for further information.
DOI: 10.5281/zenodo.7413952
title: Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""

description: Data and code used in a paper submitted to JAMES titled :<em> Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System</em> 1) The files <strong>ml_training_*.7z</strong> contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by ml_train.py. 2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside <strong>inputs_to_RRTMGP.zip </strong>by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em>, which call the RRTMGP gas optics scheme, The input profiles contain <strong>millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions.</strong> They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the RTE+RRTMGP-NN repository on Github, used in a previous paper where different emulation methods were compared) 3) The Fortran and Python code used for data generation and NN training are found in <em>rte-rrtmgp-nn/examples/rrtmgp-nn-training</em> on the main branch on Github; <strong>an archived version is also included here </strong>(<strong>rte-rrtmgp-nn-2.0.zip</strong>). See the readme in the above sub-directory for further information.