cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24272383.v1
title: Supplementary Material(associated data)_reg

description: <b>Female Board Representation and ESG</b>
DOI: 10.5281/zenodo.8412569
title: Tabla 5. Información de artículos - Alfabetización Informacional

description: Tabla 5. Base de datos, autor, país, palabras clave, categoría, metodología, muestra, instrumento/técnica, resultados importantes descritos en cada artículo sobre Alfabetización Informacional.
DOI: 10.5281/zenodo.8412570
title: Tabla 5. Información de artículos - Alfabetización Informacional

description: Tabla 5. Base de datos, autor, país, palabras clave, categoría, metodología, muestra, instrumento/técnica, resultados importantes descritos en cada artículo sobre Alfabetización Informacional.
DOI: 10.6084/m9.figshare.6743207.v9
title: GBIF for CREST database for crestr

description: This database contains all the presence records of plants, beetles, chironomids, foraminifera and diatoms contained in the GBIF database in October 2020. <br> The coordinates of the presence records have been homogenised on a 0.25x0.25° grid, and corresponding bioclimatic values from the Worldclim2.0 database have been added. <br> These data are formatted and ready to use by the <strong>crestr R package</strong>. Additional python scripts have been added to group plant species into pollen types. <br> <strong>To download the latest version of the database, please follow this link: </strong><sub><strong>https://figshare.com/articles/GBIF_for_CREST_database/6743207</strong></sub> <br> Please cite all the appropriate datasets from the following list: GBIF.org, 2020. Lycopodiopsida occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.ydhyhz GBIF.org, 2020. Anthocerotopsida occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.t9zenf GBIF.org, 2020. Liliopsida occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.axv3yd GBIF.org, 2020. Diatoms occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.vfr257 GBIF.org, 2020. Cycadopsidae occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.sfjzxu GBIF.org, 2020. Foraminifera occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.692yg6 GBIF.org, 2020. Magnoliopsida occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.ra49dt GBIF.org, 2020. Pinopsidae occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.x2r7pa GBIF.org, 2020. Rodentia occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.fscw6q GBIF.org, 2020. Gingkoopsidae occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.da9wz8 GBIF.org, 2020. Polypodiopsida occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.87tbp6 GBIF.org, 2020. Beetles occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.nteruy GBIF.org, 2020. Chironomids occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.jv3wsh GBIF.org, 2020. Gnetopsidae occurrence data downloaded on September 24th, 2020. doi: 10.15468/dl.h2kjnc <br> <br>
DOI: 10.5517/ccdc.csd.cc2fpt0z
title: CCDC 2255281: Experimental Crystal Structure Determination

description: Related Article: Takahiro Iwamoto, Yuta Sotome, Youichi Ishii|2023|ACS Org. Inorg. Au|3|305|doi:10.1021/acsorginorgau.3c00021
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fptdc
title: CCDC 2255293: Experimental Crystal Structure Determination

description: Related Article: Takahiro Iwamoto, Yuta Sotome, Youichi Ishii|2023|ACS Org. Inorg. Au|3|305|doi:10.1021/acsorginorgau.3c00021
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fpt98
title: CCDC 2255290: Experimental Crystal Structure Determination

description: Related Article: Takahiro Iwamoto, Yuta Sotome, Youichi Ishii|2023|ACS Org. Inorg. Au|3|305|doi:10.1021/acsorginorgau.3c00021
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fptb9
title: CCDC 2255291: Experimental Crystal Structure Determination

description: Related Article: Takahiro Iwamoto, Yuta Sotome, Youichi Ishii|2023|ACS Org. Inorg. Au|3|305|doi:10.1021/acsorginorgau.3c00021
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fpt87
title: CCDC 2255289: Experimental Crystal Structure Determination

description: Related Article: Takahiro Iwamoto, Yuta Sotome, Youichi Ishii|2023|ACS Org. Inorg. Au|3|305|doi:10.1021/acsorginorgau.3c00021
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24272206
title: Original Data for Sentiment of Smoking Cessation Discussions on Social Media in the Context of EVALI: A Hybrid Machine-Learning-Based Content Analysis Approach, by Yuqi Zhang and Yingning Wang

description: Paper Abstract:Objective:This study explores the public discourse on Twitter regarding smoking cessation before, during, and after the EVALI outbreak, aiming to discern any changes in sentiment, topics, and content and help with contemporary smoking cessation efforts.Methods:Using snscrape, English tweets from September 1, 2018, to January 31, 2020, were collected and filtered for smoking-cessation-related keywords. Sentiments were evaluated with VADER, classifying tweets into positive, negative, or neutral. Topics were identified through Latent Semantic Analysis, and LexRank extracted representative sentences for content analysis.Results:There was a significant increase in smoking cessation discussions in September 2019, coupled with a decline in average sentiment score. The "Vaping" theme dominated, characterized by a drop in sentiment. Opinions on vaping were divided; some promoted e-cigarettes as a tool for smoking cessation while others expressed negativity, largely criticizing regulations on non-tobacco-flavored e-cigarette products.Conclusions:These findings highlight the urgency for policymakers to intervene on social media, aiming to amplify targeted smoking cessation content and improve the long-lasting less effective communication around e-cigarette policies, ensuring that public discourse is informed and constructive.
DOI: 10.6084/m9.figshare.24272206.v1
title: Original Data for Sentiment of Smoking Cessation Discussions on Social Media in the Context of EVALI: A Hybrid Machine-Learning-Based Content Analysis Approach, by Yuqi Zhang and Yingning Wang

description: Paper Abstract:Objective:This study explores the public discourse on Twitter regarding smoking cessation before, during, and after the EVALI outbreak, aiming to discern any changes in sentiment, topics, and content and help with contemporary smoking cessation efforts.Methods:Using snscrape, English tweets from September 1, 2018, to January 31, 2020, were collected and filtered for smoking-cessation-related keywords. Sentiments were evaluated with VADER, classifying tweets into positive, negative, or neutral. Topics were identified through Latent Semantic Analysis, and LexRank extracted representative sentences for content analysis.Results:There was a significant increase in smoking cessation discussions in September 2019, coupled with a decline in average sentiment score. The "Vaping" theme dominated, characterized by a drop in sentiment. Opinions on vaping were divided; some promoted e-cigarettes as a tool for smoking cessation while others expressed negativity, largely criticizing regulations on non-tobacco-flavored e-cigarette products.Conclusions:These findings highlight the urgency for policymakers to intervene on social media, aiming to amplify targeted smoking cessation content and improve the long-lasting less effective communication around e-cigarette policies, ensuring that public discourse is informed and constructive.
DOI: 10.5281/zenodo.8422373
title: Raw Data for: "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances"

description: This repository contains all the raw data to reproduce the manuscript: D. Schwalbe-Koda et al. "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". arXiv:2307.10935 (2023) The raw data should be used in combination with the code hosted on GitHub: https://github.com/dskoda/Zeolites-AMD. <strong>Description of the data</strong> The data in this link contains all necessary information to reproduce the manuscript. In combination with the code hosted on GitHub, it can be visualized and analyzed accordingly. The full description on the columns and results is available on the GitHub code.<br> The data files in this repository are: - `hparams_rnd_*.json`: results of the hyperparameter optimization of all classifiers studied in this work. The data was produced by randomly sampling the train-validation-test sets. In some cases, the data was normalized (`_norm_`), and the train set was kept `balanced` or `unbalanced`.<br> - `hyp_dm`: distance matrix of all hypothetical zeolites towards the known zeolites<br> - `hyp_predictions`: predictions of the synthesis conditions for all hypothetical zeolites<br> - `xgb_ensembles*`: pickle files containing the serialized ensemble models used in the evaluation of the data in this work. The models can be loaded with the `xgboost` Python package. <strong>License</strong> The data and all the content from this repository is distributed under the Creative Commons Attribution 4.0 (CC-BY 4.0) This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Dataset released as: LLNL-MI-854709.
description: {"references": ["D. Schwalbe-Koda et al. \"Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances\". arXiv:2307.10935 (2023)"]}
DOI: 10.5281/zenodo.8422372
title: Raw Data for: "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances"

description: This repository contains all the raw data to reproduce the manuscript: D. Schwalbe-Koda et al. "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". arXiv:2307.10935 (2023) The raw data should be used in combination with the code hosted on GitHub: https://github.com/dskoda/Zeolites-AMD. <strong>Description of the data</strong> The data in this link contains all necessary information to reproduce the manuscript. In combination with the code hosted on GitHub, it can be visualized and analyzed accordingly. The full description on the columns and results is available on the GitHub code.<br> The data files in this repository are: - `hparams_rnd_*.json`: results of the hyperparameter optimization of all classifiers studied in this work. The data was produced by randomly sampling the train-validation-test sets. In some cases, the data was normalized (`_norm_`), and the train set was kept `balanced` or `unbalanced`.<br> - `hyp_dm`: distance matrix of all hypothetical zeolites towards the known zeolites<br> - `hyp_predictions`: predictions of the synthesis conditions for all hypothetical zeolites<br> - `xgb_ensembles*`: pickle files containing the serialized ensemble models used in the evaluation of the data in this work. The models can be loaded with the `xgboost` Python package. <strong>License</strong> The data and all the content from this repository is distributed under the Creative Commons Attribution 4.0 (CC-BY 4.0) This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Dataset released as: LLNL-MI-854709.
description: {"references": ["D. Schwalbe-Koda et al. \"Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances\". arXiv:2307.10935 (2023)"]}
DOI: 10.6084/m9.figshare.24272050
title: Data analysis: R code

description: The complexity of contexts and varied purposes for which biome donation are requested is unknown in South Africa. The aim of this study was to provide strategic data towards actualisation of whether a gastrointestinal (GIT) stool donor bank may be established as a collaborative between Western Cape Blood Services (WCBS) and the University of Cape Town (UCT).We designed a cross-sectional, questionnaire-based survey to determine willingness of WCBS blood donors to donate stool specimens for microbiome biobanking. The prospective observational pilot study was conducted between 1 June 2022 and 1 July 2022 at three WCBS donation centres in Cape Town, South Africa. Anonymous blood donors who met the inclusion criteria were provided with infographics on stool donation and a stool collection kit. Anonymised demographic and interview data was aggregated for descriptive purposes, and for statistical analysis.Analysis of responses from 209/231 blood donors demonstrated in a logistic regression model that compensation (p = 3.139e<sup>-05</sup>) and ' societal benefit outweighs inconvenience’ beliefs (p = 7.751e<sup>-05</sup>) were covariates significantly associated with willingness to donate stool. Age was borderline significant at a 5% level (p = 0.0556). Most willing stool donors indicated that donating stool samples would not affect blood donations (140/157, 90%). Factors decreasing willingness to donate were stool collection being unpleasant or embarrassing.The survey provides strategic data for the WCBS and UCT towards establishment of a stool bank and provided an understanding of the underlying determinants governing participants decision process with regards to becoming potential donors.
DOI: 10.6084/m9.figshare.24271969
title: Metadata

description: The complexity of contexts and varied purposes for which biome donation are requested is unknown in South Africa. The aim of this study was to provide strategic data towards actualisation of whether a gastrointestinal (GIT) stool donor bank may be established as a collaborative between Western Cape Blood Services (WCBS) and the University of Cape Town (UCT).We designed a cross-sectional, questionnaire-based survey to determine willingness of WCBS blood donors to donate stool specimens for microbiome biobanking. The prospective observational pilot study was conducted between 1 June 2022 and 1 July 2022 at three WCBS donation centres in Cape Town, South Africa. Anonymous blood donors who met the inclusion criteria were provided with infographics on stool donation and a stool collection kit. Anonymised demographic and interview data was aggregated for descriptive purposes, and for statistical analysis.Analysis of responses from 209/231 blood donors demonstrated in a logistic regression model that compensation (p = 3.139e<sup>-05</sup>) and ' societal benefit outweighs inconvenience’ beliefs (p = 7.751e<sup>-05</sup>) were covariates significantly associated with willingness to donate stool. Age was borderline significant at a 5% level (p = 0.0556). Most willing stool donors indicated that donating stool samples would not affect blood donations (140/157, 90%). Factors decreasing willingness to donate were stool collection being unpleasant or embarrassing.The survey provides strategic data for the WCBS and UCT towards establishment of a stool bank and provided an understanding of the underlying determinants governing participants decision process with regards to becoming potential donors.
DOI: 10.6084/m9.figshare.22183411.v2
title: DOPAMINE RECEPTOR D4R DOWN-REGULATES SODIUM CHLORIDE COTRANSPORTER IN RENAL DISTAL CONVOLUTED TUBULES VIA UBIQUITINATION-ASSOCIATED LYSOSOME DEGRADATION

description: uncropped western blot, immunofluorescence and raw data of figure1, figure2, figure3, figure4, figure5, figure7, figure8
DOI: 10.6084/m9.figshare.22183411.v1
title: DOPAMINE RECEPTOR D4R DOWN-REGULATES SODIUM CHLORIDE COTRANSPORTER IN RENAL DISTAL CONVOLUTED TUBULES VIA UBIQUITINATION-ASSOCIATED LYSOSOME DEGRADATION

description: uncropped western blot, immunofluorescence and raw data of figure1, figure2, figure3, figure4, figure5, figure7, figure8
DOI: 10.5517/ccdc.csd.cc2bs47j
title: CCDC 2168147: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bs6kx
title: CCDC 2168219: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bs6ly
title: CCDC 2168220: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bs6q2
title: CCDC 2168224: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bs6s4
title: CCDC 2168226: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bs7zc
title: CCDC 2168263: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bz6vd
title: CCDC 2173994: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bz6wf
title: CCDC 2173995: Experimental Crystal Structure Determination

description: Related Article: Emmanouil Broumidis, Christopher G. Thomson, Brendan Gallagher, Lia Sotorríos, Kenneth G. McKendrick, Stuart A. Macgregor, Martin J. Paterson, Janet E. Lovett, Gareth O. Lloyd, Georgina M. Rosair, Andreas S. Kalogirou, Panayiotis A. Koutentis, Filipe Vilela|2023|Org.Lett.|25|6907|doi:10.1021/acs.orglett.3c02673
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.