cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6401727
title: Daily-resolution pan-Arctic Radar and Laser Freeboard

description: This dataset contains daily-resolution pan-Arctic laser / radar freeboard and uncertainty estimates, created following the methodology outlined in Nab et al. (2022): "Synoptic variability in satellite radar altimeter-derived sea ice thickness" Please refer to this publication for a full description of the data and cite this publication when using these data. Variables: Laser / Radar Freeboard (m), Uncertainty (m), Longitude, Latitude, Day Data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years Days where no data is available are filled with NaNs
DOI: 10.5281/zenodo.7215084
title: Daily-resolution pan-Arctic Radar and Laser Freeboard

description: This dataset contains daily-resolution pan-Arctic laser / radar freeboard and uncertainty estimates, created following the methodology outlined in Nab et al. (2022): "Synoptic variability in satellite radar altimeter-derived sea ice thickness" Please refer to this publication for a full description of the data and cite this publication when using these data. Variables: Laser / Radar Freeboard (m), Uncertainty (m), Longitude, Latitude, Day Data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years Days where no data is available are filled with NaNs
DOI: 10.5281/zenodo.6401726
title: Daily-resolution pan-Arctic Radar and Laser Freeboard

description: This dataset contains daily-resolution pan-Arctic laser / radar freeboard and uncertainty estimates, created following the methodology outlined in <strong>Nab et al. (2023): "Synoptic variability in satellite altimeter-derived radar freeboard of Arctic sea ice"</strong> Please refer to this publication for a full description of the data and cite this publication when using these data. Variables: Laser / Radar Freeboard (m), Uncertainty (m), Longitude, Latitude, Day Data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years Days where no data is available are filled with NaNs
DOI: 10.5281/zenodo.7215093
title: Daily-resolution pan-Arctic Radar and Laser Freeboard

description: This dataset contains daily-resolution pan-Arctic laser / radar freeboard and uncertainty estimates, created following the methodology outlined in <strong>Nab et al. (2022): "Synoptic variability in satellite retrievals of Arctic sea ice freeboard"</strong> Please refer to this publication for a full description of the data and cite this publication when using these data. Variables: Laser / Radar Freeboard (m), Uncertainty (m), Longitude, Latitude, Day Data is available for winter seasons only, with each winter season running from 1 October - 30 April of the specified years Days where no data is available are filled with NaNs
DOI: 10.6084/m9.figshare.21856830.v1
title: List of all abbreviations

description: List of all abbreviations.
DOI: 10.5281/zenodo.7520374
title: Temperature data of the 2022 MHW and underwater pictures of impacts on bath sponge populations

description: July-to-August seawater temperature recorded at 18 m depth at the “3PP” station, La Ciotat, near Marseille, South of France (43.163°N; 5.599°E) using <em>in situ</em> autonomous sensors during the 2022 MHW, in the two previous years (grey dotted lines), and during the reference 2003 MHW (orange dotted line). Only one record per day is shown; IMG 1467: Two living sponge species, <em>Ircinia oros</em> and the bath sponge <em>Spongia officinalis</em>, the latter with necrotic spots on its surface (September 7, 2022) - Author Thierry Pérez IMG 1688: The same two sponges after five days (September 12, 2022), <em>S. officinalis</em> is dying, covered by a white bacterial veil. Author Thierry Pérez P9216598: A once rich and healthy population of <em>S. officinalis</em> at 10 m depth, September 21, 2022; Arrows point to <em>S. officinalis</em> dead skeletons left attached to the rocky wall; Author Pierre Chevaldonné
description: Note published in Global Change Biology
DOI: 10.5281/zenodo.7520373
title: Temperature data of the 2022 MHW and underwater pictures of impacts on bath sponge populations

description: July-to-August seawater temperature recorded at 18 m depth at the “3PP” station, La Ciotat, near Marseille, South of France (43.163°N; 5.599°E) using <em>in situ</em> autonomous sensors during the 2022 MHW, in the two previous years (grey dotted lines), and during the reference 2003 MHW (orange dotted line). Only one record per day is shown; IMG 1467: Two living sponge species, <em>Ircinia oros</em> and the bath sponge <em>Spongia officinalis</em>, the latter with necrotic spots on its surface (September 7, 2022) - Author Thierry Pérez IMG 1688: The same two sponges after five days (September 12, 2022), <em>S. officinalis</em> is dying, covered by a white bacterial veil. Author Thierry Pérez P9216598: A once rich and healthy population of <em>S. officinalis</em> at 10 m depth, September 21, 2022; Arrows point to <em>S. officinalis</em> dead skeletons left attached to the rocky wall; Author Pierre Chevaldonné
description: Note published in Global Change Biology
DOI: 10.5281/zenodo.7515393
title: X-ray scattering datasets and simulations associated with the publication "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine"

description: This dataset contains the processed and analysed small-angle X-ray scattering data associated with all samples from the publications "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine" (https://doi.org/10.1039/D2CP05053F) Files associated with McSAS3 analyses are included, alongside the relevant SAXS data, with datasets labelled in accordance to the protein (G5P), its concentration (1, 2 or 4 mg/mL), and if Ectoine is present (Ect) or absent (Pure). PEPSIsaxs simulations of the GVP monomer (PDB structure: 1GV5 ) and dimer are also included.[1] TOPAS-bioSAXS-dosimetry extension for TOPAS-nBio based particle scattering simulations can be obtained from https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry which is further described in https://doi.org/10.26272/opus4-55751. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number 442240902 (HA 8528/2-1 and SE 2999/2-1). We acknowledge Diamond Light Source for time on Beamline B21 under Proposal SM29806. This work has been supported by iNEXT-Discovery, grant number 871037, funded by the Horizon 2020 program of the European Commission. [1] S. Su, Y.-G. Gao, H. Zhang, T. C. Terwilliger and A. H.-J. Wang, Protein Science, 1997, 6, 771–780. <br>
DOI: 10.5281/zenodo.7515394
title: X-ray scattering datasets and simulations associated with the publication "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine"

description: This dataset contains the processed and analysed small-angle X-ray scattering data associated with all samples from the publications "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine" (https://doi.org/10.1039/D2CP05053F) Files associated with McSAS3 analyses are included, alongside the relevant SAXS data, with datasets labelled in accordance to the protein (G5P), its concentration (1, 2 or 4 mg/mL), and if Ectoine is present (Ect) or absent (Pure). PEPSIsaxs simulations of the GVP monomer (PDB structure: 1GV5 ) and dimer are also included.[1] TOPAS-bioSAXS-dosimetry extension for TOPAS-nBio based particle scattering simulations can be obtained from https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry which is further described in https://doi.org/10.26272/opus4-55751. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number 442240902 (HA 8528/2-1 and SE 2999/2-1). We acknowledge Diamond Light Source for time on Beamline B21 under Proposal SM29806. This work has been supported by iNEXT-Discovery, grant number 871037, funded by the Horizon 2020 program of the European Commission. [1] S. Su, Y.-G. Gao, H. Zhang, T. C. Terwilliger and A. H.-J. Wang, Protein Science, 1997, 6, 771–780. <br>
DOI: 10.34740/kaggle/dsv/4833638
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.6606290
title: Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"

description: This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh. We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution). Matlab code Description: Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis; Untitled 2- the code for comparison between the experimental data and the proposed model; Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity; Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.
DOI: 10.5281/zenodo.6606291
title: Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"

description: This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh. We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution).
DOI: 10.5281/zenodo.7248822
title: Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"

description: This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh. We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution). Matlab code Description: Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis; Untitled 2- the code for comparison between the experimental data and the proposed model; Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity; Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.
DOI: 10.5281/zenodo.7520411
title: Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"

description: This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh. We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution). Matlab code Description: Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis; Untitled 2- the code for comparison between the experimental data and the proposed model; Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity; Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.
DOI: 10.5281/zenodo.7333213
title: Supporting Information (software and data) for: Client-side energy and GHGs assessment of advertising and tracking in the news websites

description: This is the open data and free/libre and open source software repository for the article "Client-side energy and GHGs assessment of advertising and tracking in the news websites" by Fabio Pesari, Giovanni Lagioia, Annarita Paiano.
description: Used software: Ruby, Rust (+Enigo), Python (+Matplotlib), LibreOffice Calc, Gretl, Chromium (+uBlock Origin)
DOI: 10.5281/zenodo.7333212
title: Supporting Information (software and data) for: Client-side energy and GHGs assessment of advertising and tracking in the news websites

description: This is the open data and free/libre and open source software repository for the article "Client-side energy and GHGs assessment of advertising and tracking in the news websites" by Fabio Pesari, Giovanni Lagioia, Annarita Paiano.
description: Used software: Ruby, Rust (+Enigo), Python (+Matplotlib), LibreOffice Calc, Gretl, Chromium (+uBlock Origin)
DOI: 10.5258/soton/d2201
title: Dataset supporting the University of Southampton Doctoral thesis: Shining lights, even in death: Modelling the optical and ultraviolet emission from tidal disruption events

description: This dataset contains: Provided in the data set are the parameter file required to run the simulations yourself, as well as the raw data that was output from the simulation software. This includes the synthetic spectra (.spec, .log_spec, .spec_tot, etc) and tables of wind variables in ASCII format. In some cases scripts have been provided to re-generate figures created for the thesis using the data provided in this repository. Additional software will be required in some cases to use these script, which can be found at https://github.com/saultyevil/pypython and https://github.com/agnwinds/python.
DOI: 10.5281/zenodo.7495801
title: CSRCZ

description: CSRCZ (Corporate Social Responsibility in CZechia) is a dataset containing public information about 1000 companies operating in Czech Republic, retrieved from their websites. Each record is a JSON line containing the following fields: "Company Name", "URL", "Size of Company", "Number of employees", "Sector", and "Initiatives". The last one contains the corporate social responsibility initiatives that each company is committed to. The data can be used not only to study corporate social responsibility in the Czech Republic landscape, but also to derive insights in a general context.
DOI: 10.5281/zenodo.7495802
title: CSRCZ

description: CSRCZ (Corporate Social Responsibility in CZechia) is a dataset containing public information about 1000 companies operating in Czech Republic, retrieved from their websites. Each record is a JSON line containing the following fields: "Company Name", "URL", "Size of Company", "Number of employees", "Sector", and "Initiatives". The last one contains the corporate social responsibility initiatives that each company is committed to. The data can be used not only to study corporate social responsibility in the Czech Republic landscape, but also to derive insights in a general context.
DOI: 10.5281/zenodo.6972394
title: Model code and output data for Tang et al. 2022 (doi:10.3389/fenvs.2022.1013875)

description: flex_extract.tar: The code used to prepare inputdata for flexpart and flexdust. FLEXDUST_default_20210301-20210331.nc: The output of default FLEXDUST. FLEXDUST_updated_20210311-20210320.nc: The output of updated FLEXDUST. flexpart_1h_default.nc: The output of forward FLEXPART simulation using default FLEXDUST. flexpart_1h_erode_topoTc_true_moreveg0.1_76FVThresh_noblock_KOK_AN05.nc: The output of forward FLEXPART simulation using updated FLEXDUST.
DOI: 10.5281/zenodo.6972393
title: Model code and output data for Tang et al. 2022 (doi:10.3389/fenvs.2022.1013875)

description: flex_extract.tar: The code used to prepare inputdata for flexpart and flexdust. FLEXDUST_default_20210301-20210331.nc: The output of default FLEXDUST. FLEXDUST_updated_20210311-20210320.nc: The output of updated FLEXDUST. flexpart_1h_default.nc: The output of forward FLEXPART simulation using default FLEXDUST. flexpart_1h_erode_topoTc_true_moreveg0.1_76FVThresh_noblock_KOK_AN05.nc: The output of forward FLEXPART simulation using updated FLEXDUST.
DOI: 10.20351/29he20221213
title: ANTARLOG_20221213 Cruise, RV Hespérides

description: Oceanographic data acquired during the ANTARLOG_20221213 Cruise (29HE20221213)  on board the Research Vessel Hespérides in 2022.
DOI: 10.6084/m9.figshare.24422308.v1
title: Game Data for the Paint Fever game

description: Game data collected through the "Paint Fever" game generated at the GRIST Lab for NSF Project 1901721
DOI: 10.6084/m9.figshare.24422323
title: SPSS Data File

description: Statistic anxiety is the feeling of worrying and tension that students experience when taking statistics courses, especially in social sciences programs. Studying statistic anxiety and the related variables is crucial because this anxiety negatively and significantly affects students’ achievement and learning.
DOI: 10.5517/ccdc.csd.cc29j376
title: CCDC 2130637: Experimental Crystal Structure Determination

description: Related Article: Shugao Zhu, Dan Wang, Shihan Liu, Yufeng Ma, Hong Wang, Yu Lan, Ruopeng Bai, Ruwei Shen|2023|ACS Catalysis|13|8402|doi:10.1021/acscatal.3c00857
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.cc29j387
title: CCDC 2130638: Experimental Crystal Structure Determination

description: Related Article: Shugao Zhu, Dan Wang, Shihan Liu, Yufeng Ma, Hong Wang, Yu Lan, Ruopeng Bai, Ruwei Shen|2023|ACS Catalysis|13|8402|doi:10.1021/acscatal.3c00857
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.cc29ywkq
title: CCDC 2143884: Experimental Crystal Structure Determination

description: Related Article: Yuan-He Li, Su-Lei Zhang, Yong Lu, Bo Xiao, Tian-Yu Sun, Qian-Qian Xu, Jia-Hua Chen, Zhen Yang|2023|Angew.Chem.,Int.Ed.|62|e202303075|doi:10.1002/anie.202303075
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.cc29ywy3
title: CCDC 2143896: Experimental Crystal Structure Determination

description: Related Article: Yuan-He Li, Su-Lei Zhang, Yong Lu, Bo Xiao, Tian-Yu Sun, Qian-Qian Xu, Jia-Hua Chen, Zhen Yang|2023|Angew.Chem.,Int.Ed.|62|e202303075|doi:10.1002/anie.202303075
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.24073156.v3
title: Multiple Imputation Through XGBoost

description: The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with complex data structures. Their imputation performances usually rely on the proper specification of imputation models, and this requires expert knowledge of the inherent relations among variables. Moreover, these standard approaches tend to be computationally inefficient for medium and large datasets. In this article, we propose a scalable MI framework mixgb, which is based on XGBoost, subsampling, and predictive mean matching. Our approach leverages the power of XGBoost, a fast implementation of gradient boosted trees, to automatically capture interactions and nonlinear relations while achieving high computational efficiency. In addition, we incorporate subsampling and predictive mean matching to reduce bias and to better account for appropriate imputation variability. The proposed framework is implemented in an R package mixgb. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24073156
title: Multiple Imputation Through XGBoost

description: The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with complex data structures. Their imputation performances usually rely on the proper specification of imputation models, and this requires expert knowledge of the inherent relations among variables. Moreover, these standard approaches tend to be computationally inefficient for medium and large datasets. In this article, we propose a scalable MI framework mixgb, which is based on XGBoost, subsampling, and predictive mean matching. Our approach leverages the power of XGBoost, a fast implementation of gradient boosted trees, to automatically capture interactions and nonlinear relations while achieving high computational efficiency. In addition, we incorporate subsampling and predictive mean matching to reduce bias and to better account for appropriate imputation variability. The proposed framework is implemented in an R package mixgb. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24073156.v2
title: Multiple Imputation Through XGBoost

description: The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with complex data structures. Their imputation performances usually rely on the proper specification of imputation models, and this requires expert knowledge of the inherent relations among variables. Moreover, these standard approaches tend to be computationally inefficient for medium and large datasets. In this paper, we propose a scalable MI framework mixgb, which is based on XGBoost, subsampling, and predictive mean matching. Our approach leverages the power of XGBoost, a fast implementation of gradient boosted trees, to automatically capture interactions and nonlinear relations while achieving high computational efficiency. In addition, we incorporate subsampling and predictive mean matching to reduce bias and to better account for appropriate imputation variability. The proposed framework is implemented in an R package mixgb. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.10034722
title: RTS Spring 2023 Route 11 Ride Check Data Set

description: This data set contains ride check data for Route 11 for March, 2023. The ride check data set contains fields for Stop ID, Stop Name, Stop Seq ID, Day of Week, Date, Arrive, Passenger On, Passenger Off, Passenger Load, Passenger Miles, Interstop Distance, Bus, Lat., Long. The data set was requested and given from the Regional Transit System of Gainesville, Florida.