cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8017497
title: Dataset for Analyzing Social Network Posts and Responses Written in Hebrew from the Political Field

description: To collect the dataset from Israel’s most popular social network, we employed the services of an information retrieval company. The data was collected from January 2020 to December 2021; every two weeks we obtained the most popular post (based on the number of comments) for each politician. We focused on the most influential party leaders on the right, left, and center of Israel’s political spectrum. We have published the dataset for the benefit of the academic research community. We describe the columns based on their names: Index – A unique identifier assigned to each row in the dataset by a sequential number. Sub Index – A secondary index assigned to ’Index’ representing a sub comment (a comment made to another comment). Name – The name of the person who wrote the comment, also known as the commenter. Profile ID – A unique identifier assigned to the commenter’s Facebook profile. Date – The date that has been recorded when the comment was published on Facebook. Likes – The number of likes received for a comment. Comment – The content of the comment. URL – The web address or link associated with the comment. Post ID – A unique identifier assigned to a specific post. Politician Name – The name of the politician who wrote the post. Comment ID – A unique identifier assigned to a specific comment. Is Media – A binary feature that indicates where some media (e.g., picture or video) has been part of the user’s comment.
DOI: 10.5281/zenodo.8017496
title: Dataset for Analyzing Social Network Posts and Responses Written in Hebrew from the Political Field

description: To collect the dataset from Israel’s most popular social network, we employed the services of an information retrieval company. The data was collected from January 2020 to December 2021; every two weeks we obtained the most popular post (based on the number of comments) for each politician. We focused on the most influential party leaders on the right, left, and center of Israel’s political spectrum. We have published the dataset for the benefit of the academic research community. We describe the columns based on their names: Index – A unique identifier assigned to each row in the dataset by a sequential number. Sub Index – A secondary index assigned to ’Index’ representing a sub comment (a comment made to another comment). Name – The name of the person who wrote the comment, also known as the commenter. Profile ID – A unique identifier assigned to the commenter’s Facebook profile. Date – The date that has been recorded when the comment was published on Facebook. Likes – The number of likes received for a comment. Comment – The content of the comment. URL – The web address or link associated with the comment. Post ID – A unique identifier assigned to a specific post. Politician Name – The name of the politician who wrote the post. Comment ID – A unique identifier assigned to a specific comment. Is Media – A binary feature that indicates where some media (e.g., picture or video) has been part of the user’s comment.
DOI: 10.6084/m9.figshare.23388113
title: Data used to run multilevel analyses on the research paper titled: <sub>The Effect of Work Autonomy on Value Creation in Organizations: Controlling for Change-Oriented Organizational Citizenship Behavior and Employee Affective States.</sub>

description: Daily data spreadsheet measured our 2 level 1 variables (daily autonomy and daily value creation) for each particpant with a unique code for a full work week. <br> General data spreadsheet measured demographics and trait affectivity in our sample.
DOI: 10.6084/m9.figshare.23388113.v1
title: Data used to run multilevel analyses on the research paper titled: <sub>The Effect of Work Autonomy on Value Creation in Organizations: Controlling for Change-Oriented Organizational Citizenship Behavior and Employee Affective States.</sub>

description: Daily data spreadsheet measured our 2 level 1 variables (daily autonomy and daily value creation) for each particpant with a unique code for a full work week. <br> General data spreadsheet measured demographics and trait affectivity in our sample.
DOI: 10.6084/m9.figshare.23387516
title: Data for publication

description: In our study, an altered gut microbiota composition featured by the reduction in SCFAs-producing bacteria was demonstrated in the KD mouse model. Next, probiotic <em>C. butyricum</em> and antibiotic cocktails were respectively employed to modulate gut microbiota. The use of <em>C. butyricum </em>significantly increased the abundance of SCFAs-producing bacteria and attenuated the coronary lesions with reduced inflammatory markers IL-1β and IL-6, but antibiotics depleting gut bacteria oppositely deteriorated the inflammation response. The gut leakage induced by dysbiosis to deteriorate the host’s inflammation was confirmed by the decreased intestinal barrier proteins Claudin-1, Jam-1, Occludin, and ZO-1, and increased plasma D-lactate level in KD mice. Mechanistically, SCFAs, the major beneficial metabolites of gut microbes to maintain the intestinal barrier integrity and inhibit inflammation, was also found decreased, especially butyrate, acetate and propionate, in KD mice by gas chromatography-mass spectrometry (GC-MS). Moreover, the reduced expression of SCFAs transporters, monocarboxylate transporter 1 (MCT-1) and sodium-dependent monocarboxylate transporter 1 (SMCT-1), was also shown in KD mice by western blot and RT-qPCR analyses. As expected, the decrease of fecal SCFAs production and barrier dysfunction were improved by <em>C. butyricum</em> treatment but was deteriorated by antibiotics. <em>In vitro</em>, butyrate increased the expression of phosphatase MKP-1 to dephosphorylate activated JNK, ERK1/2 and p38 MAPK against excessive inflammation in RAW264.7 macrophages.
DOI: 10.5281/zenodo.8011148
title: QLKNN11D training set

description: <strong>QLKNN11D training set</strong> This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv https://arxiv.org/abs/1911.05617 and the older dataset on Zenodo https://doi.org/10.5281/zenodo.3497066. For an application example, see Van Mulders et al 2021 https://doi.org/10.1088/1741-4326/ac0d12, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation https://qualikiz.com QuaLiKiz/QLKNN input/output variables naming scheme https://qualikiz.com/QuaLiKiz/Input-and-output-variables Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hype <strong>Data exploration</strong> The data is provided in 43 netCDF files. We advise opening single datasets using xarray or multiple datasets out-of-core using dask. For reference, we give the load times and sizes of a single variable that just depends on the scan size `dimx` below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number. Timing of dataset loading Amount of datasets Final in-RAM memory (GiB) Loading time single var (M:SS) 1 10.3 0:09 5 43.9 1:00 10 63.2 2:01 16 98.0 3:25 17 Out Of Memory x:xx <strong>Full dataset</strong> The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files!
description: EUROfusion - Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium (633053)
description: {"references": ["van de Plassche, K.L. et al. (2019)"]}
DOI: 10.6084/m9.figshare.6176411
title: Juvenile vervet monkeys rely on others when responding to danger

description: Primate alarm calls are mainly hardwired but individuals need to adapt their calling behaviours according to the situation. Such learning necessitates recognising locally relevant dangers and may take place via their own experience or by observing others. To investigate monkeys alarm calling behaviour, we carried out a field experiment in which we exposed juvenile vervet monkeys to unfamiliar raptor models in the presence of audiences that differed in experience and reliability. We used audience age as a proxy for experience and relatedness as a proxy for reliability, while quantifying audience reactions to the models. We found a negative correlation between alarm call production and callers’ age. Adults never alarm called, compared to juveniles. We found no overall effect of audience composition and size, with juveniles calling more when with siblings than mothers or unrelated individuals. Finally, concerning audience reactions to the models, we observed juveniles remained silent with vigilant mothers and only alarm called with ignoring mothers, whereas we observed the opposite for siblings: juveniles remained silent with ignoring siblings and called with vigilant siblings. Despite the small sample size, juvenile vervet monkeys, confronted with unfamiliar and potentially dangerous raptors, seem to rely on others to decide whether to alarm call, demonstrating that the choice of the model may play an important key role in the ontogeny of primate alarm call behaviour.
DOI: 10.6084/m9.figshare.6176411.v1
title: Juvenile vervet monkeys rely on others when responding to danger

description: Primate alarm calls are mainly hardwired but individuals need to adapt their calling behaviours according to the situation. Such learning necessitates recognising locally relevant dangers and may take place via their own experience or by observing others. To investigate monkeys alarm calling behaviour, we carried out a field experiment in which we exposed juvenile vervet monkeys to unfamiliar raptor models in the presence of audiences that differed in experience and reliability. We used audience age as a proxy for experience and relatedness as a proxy for reliability, while quantifying audience reactions to the models. We found a negative correlation between alarm call production and callers’ age. Adults never alarm called, compared to juveniles. We found no overall effect of audience composition and size, with juveniles calling more when with siblings than mothers or unrelated individuals. Finally, concerning audience reactions to the models, we observed juveniles remained silent with vigilant mothers and only alarm called with ignoring mothers, whereas we observed the opposite for siblings: juveniles remained silent with ignoring siblings and called with vigilant siblings. Despite the small sample size, juvenile vervet monkeys, confronted with unfamiliar and potentially dangerous raptors, seem to rely on others to decide whether to alarm call, demonstrating that the choice of the model may play an important key role in the ontogeny of primate alarm call behaviour.
DOI: 10.5281/zenodo.8017522
title: QLKNN11D training set

description: <strong>QLKNN11D training set</strong> This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv https://arxiv.org/abs/1911.05617 and the older dataset on Zenodo https://doi.org/10.5281/zenodo.3497066. For an application example, see Van Mulders et al 2021 https://doi.org/10.1088/1741-4326/ac0d12, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation https://qualikiz.com QuaLiKiz/QLKNN input/output variables naming scheme https://qualikiz.com/QuaLiKiz/Input-and-output-variables Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hype <strong>Data exploration</strong> The data is provided in 43 netCDF files. We advise opening single datasets using xarray or multiple datasets out-of-core using dask. For reference, we give the load times and sizes of a single variable that just depends on the scan size `dimx` below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number. Timing of dataset loading Amount of datasets Final in-RAM memory (GiB) Loading time single var (M:SS) 1 10.3 0:09 5 43.9 1:00 10 63.2 2:01 16 98.0 3:25 17 Out Of Memory x:xx <strong>Full dataset</strong> The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files!
description: EUROfusion - Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium (633053)
description: {"references": ["van de Plassche, K.L. et al. (2019)"]}
DOI: 10.5281/zenodo.8011147
title: QLKNN11D training set

description: <strong>QLKNN11D training set</strong> This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv https://arxiv.org/abs/1911.05617 and the older dataset on Zenodo https://doi.org/10.5281/zenodo.3497066. For an application example, see Van Mulders et al 2021 https://doi.org/10.1088/1741-4326/ac0d12, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation https://qualikiz.com QuaLiKiz/QLKNN input/output variables naming scheme https://qualikiz.com/QuaLiKiz/Input-and-output-variables Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hype <strong>Data exploration</strong> The data is provided in 43 netCDF files. We advise opening single datasets using xarray or multiple datasets out-of-core using dask. For reference, we give the load times and sizes of a single variable that just depends on the scan size `dimx` below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number. Timing of dataset loading Amount of datasets Final in-RAM memory (GiB) Loading time single var (M:SS) 1 10.3 0:09 5 43.9 1:00 10 63.2 2:01 16 98.0 3:25 17 Out Of Memory x:xx <strong>Full dataset</strong> The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files!
description: EUROfusion - Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium (633053)
description: {"references": ["van de Plassche, K.L. et al. (2019)"]}
DOI: 10.6084/m9.figshare.23388275
title: 10min_20190601_20200531.csv

description: Solar irradiance, 10-minute mean, 08:00-19:00，June 1, 2019 - May 31, 2020
DOI: 10.6084/m9.figshare.23388275.v1
title: 10min_20190601_20200531.csv

description: Solar irradiance, 10-minute mean, 08:00-19:00，June 1, 2019 - May 31, 2020
DOI: 10.26300/q0vj-b923
title: Data from Role of Crack Interaction on Shear Localization in Porous Granular Rocks Deformed in the Brittle and Ductile Fields
DOI: 10.6084/m9.figshare.23389202
title: Data for publication in <em>Frontiers in immunology </em>(<strong>DOI:</strong> 10.3389/fimmu.2023.1124118)

description: In our study, an altered gut microbiota composition featured by the reduction in SCFAs-producing bacteria was demonstrated in the KD mouse model. Next, probiotic <em>C. butyricum</em> and antibiotic cocktails were respectively employed to modulate gut microbiota. The use of <em>C. butyricum</em> significantly increased the abundance of SCFAs-producing bacteria and attenuated the coronary lesions with reduced inflammatory markers IL-1β and IL-6, but antibiotics depleting gut bacteria oppositely deteriorated the inflammation response. The gut leakage induced by dysbiosis to deteriorate the host’s inflammation was confirmed by the decreased intestinal barrier proteins Claudin-1, Jam-1, Occludin, and ZO-1, and increased plasma D-lactate level in KD mice. Mechanistically, SCFAs, the major beneficial metabolites of gut microbes to maintain the intestinal barrier integrity and inhibit inflammation, was also found decreased, especially butyrate, acetate and propionate, in KD mice by gas chromatography-mass spectrometry (GC-MS). Moreover, the reduced expression of SCFAs transporters, monocarboxylate transporter 1 (MCT-1) and sodium-dependent monocarboxylate transporter 1 (SMCT-1), was also shown in KD mice by western blot and RT-qPCR analyses. As expected, the decrease of fecal SCFAs production and barrier dysfunction were improved by <em>C. butyricum</em> treatment but was deteriorated by antibiotics. <em>In vitro</em>, butyrate increased the expression of phosphatase MKP-1 to dephosphorylate activated JNK, ERK1/2 and p38 MAPK against excessive inflammation in RAW264.7 macrophages.
DOI: 10.5281/zenodo.8017507
title: Composition variations in Cu(In,Ga)(S,Se)2 solar cells: not a gradient, but an interlaced network of two phases

description: <strong>Abstract</strong> Record efficiency in chalcopyrite-based solar cells Cu(In,Ga)(S,Se)<sub>2</sub> is achieved using a gallium gradient to increase the band gap of the absorber towards the back side. Although this structure has successfully reduced recombination at the back contact, we demonstrate that in industrial absorbers grown in the pilot line of Avancis, the back part is a source of non-radiative recombination. Depth-resolved photoluminescence (PL) measurements reveal two main radiative recombination paths at 1.04 eV and 1.5-1.6 eV, attributed to two phases of low and high band gap material, respectively. Instead of a continuous change in the band gap throughout the thickness of the absorber, we propose a model where discrete band gap phases interlace, creating an apparent gradient. Cathodoluminescence and Raman scattering spectroscopy confirm this result. Additionally, deep defects associated to the high gap phase reduce the absorber performance. Etching away the back part of the absorber leads to an increase of one order of magnitude in the PL intensity, i.e., 60 meV in quasi Fermi level splitting. Non-radiative voltage losses correlate linearly with the relative contribution of the high energy PL peak, suggesting that reducing the high gap phase could increase the open circuit voltage by up to 180 mV.
DOI: 10.5281/zenodo.8017506
title: Composition variations in Cu(In,Ga)(S,Se)2 solar cells: not a gradient, but an interlaced network of two phases

description: <strong>Abstract</strong> Record efficiency in chalcopyrite-based solar cells Cu(In,Ga)(S,Se)<sub>2</sub> is achieved using a gallium gradient to increase the band gap of the absorber towards the back side. Although this structure has successfully reduced recombination at the back contact, we demonstrate that in industrial absorbers grown in the pilot line of Avancis, the back part is a source of non-radiative recombination. Depth-resolved photoluminescence (PL) measurements reveal two main radiative recombination paths at 1.04 eV and 1.5-1.6 eV, attributed to two phases of low and high band gap material, respectively. Instead of a continuous change in the band gap throughout the thickness of the absorber, we propose a model where discrete band gap phases interlace, creating an apparent gradient. Cathodoluminescence and Raman scattering spectroscopy confirm this result. Additionally, deep defects associated to the high gap phase reduce the absorber performance. Etching away the back part of the absorber leads to an increase of one order of magnitude in the PL intensity, i.e., 60 meV in quasi Fermi level splitting. Non-radiative voltage losses correlate linearly with the relative contribution of the high energy PL peak, suggesting that reducing the high gap phase could increase the open circuit voltage by up to 180 mV.