cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7007330
title: Regional Sea-level Budget from 1993-2016

description: This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/. **<em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>** This has no impact on the results of the manuscript, as the 'axis error' occurred only when organising the files to be published. ** Please cite the appropriate papers when using this data **<br> Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, <strong>most of the data heavily relies on previous work and data sets by many authors,</strong> so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques').<br> ** please check this carefully!** This repository contains the following files: <strong>budget_components_ENS.nc</strong><br> Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). <em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>If required the individual data sets used for the ensemble, please contact the author. <strong>masks.nc</strong><br> netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. <strong>dmaps_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl) <strong>som_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl) The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB <br> Corresponding author: carolina.camargo@nioz.nl
DOI: 10.5281/zenodo.7007331
title: Regional Sea-level Budget from 1993-2016

description: This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022, submited). <strong>Note: The manuscript (and dataset) has not been peer reviewed yet!!! Use this data with caution! </strong> PLEASE CITE THE APPROPRIATE PAPERS WHEN USING THIS DATA<br> Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, most of the data heavily relies on previous work and data sets by many authors, so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques').<br> PLEASE CHECK THIS CAREFULLY! This repository contains the following files: <strong>budget_components_ENS.nc</strong><br> Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). If required the individual data sets used for the ensemble, please contact the author. <strong>masks.nc</strong><br> netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. <strong>dmaps_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl) <strong>som_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl) The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB <br> Corresponding author: carolina.camargo@nioz.nl
DOI: 10.5281/zenodo.7385561
title: Regional Sea-level Budget from 1993-2016

description: This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/. **<em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>** This has no impact on the results of the manuscript, as the 'axis error' occurred only when organising the files to be published. ** Please cite the appropriate papers when using this data **<br> Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, <strong>most of the data heavily relies on previous work and data sets by many authors,</strong> so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques').<br> ** please check this carefully!** This repository contains the following files: <strong>budget_components_ENS.nc</strong><br> Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). <em><strong>Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. </strong></em>If required the individual data sets used for the ensemble, please contact the author. <strong>masks.nc</strong><br> netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. <strong>dmaps_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl) <strong>som_trend.pkl (and .xlsx)</strong><br> Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl) The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB <br> Corresponding author: carolina.camargo@nioz.nl
DOI: 10.5281/zenodo.7895658
title: A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing

description: This repository holds experimental data and figures of our paper "A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing" in 10th International Conference on Dependable Systems and Their Applications (DSA) 2023.
DOI: 10.5281/zenodo.7895657
title: A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing

description: This repository holds experimental data and figures of our paper "A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing" in 10th International Conference on Dependable Systems and Their Applications (DSA) 2023.
DOI: 10.5517/ccdc.csd.cc2bhdnx
title: CCDC 2159759: Experimental Crystal Structure Determination

description: Related Article: Xinjie Lv, Xiao-li Zhao, Qixin Zhao, Qi Zheng, Weimin Xuan|2022|Dalton Trans.|51|8949|doi:10.1039/D2DT00856D
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.cc2bhdpy
title: CCDC 2159760: Experimental Crystal Structure Determination

description: Related Article: Xinjie Lv, Xiao-li Zhao, Qixin Zhao, Qi Zheng, Weimin Xuan|2022|Dalton Trans.|51|8949|doi:10.1039/D2DT00856D
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.22759088
title: A national questionnaire survey was conducted on the application of virtual simulation training platform in medical laboratory teaching.

description: it is necessary to improve the understanding of virtual simulation technology among teachers and students and strengthen the implementation strategy of virtual simulation training platform for medical laboratory teaching.
DOI: 10.6084/m9.figshare.22759088.v1
title: A national questionnaire survey was conducted on the application of virtual simulation training platform in medical laboratory teaching.

description: it is necessary to improve the understanding of virtual simulation technology among teachers and students and strengthen the implementation strategy of virtual simulation training platform for medical laboratory teaching.
DOI: 10.5281/zenodo.7890630
title: Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect

description: Source data for: Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect
DOI: 10.5281/zenodo.7890629
title: Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect

description: Source data for: Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect
DOI: 10.20351/29pc20230301_ctd
title: CTD data, Patí Científic

description: Water column data obtained at 6 stations in the Catalan coast with a CTD profiler on board the Patí Científic since March 2023.
DOI: 10.25835/ehvx8szz
title: INDRUM2022_Khellaf&Peters_supplement_REM_(tables)

description: The tables are part of the supplementary material of Khellaf & Peters (2022). They comprise an REM that was created as a tool for analyzing student solutions of “the curve sketching exercise” found in Khellaf & Peters (2022: p. 72), Fig. 1. Literature reference: Khellaf, S., & Peters, J. (2022). Design and analysis of an unusual curve sketching exercise for first year teacher students. In M. Trigueros, B. Barquero, R. Hochmuth, J. Peters (Eds.), Proceedings of the Fourth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2022, 19-22 October 2022) (pp. 70-79). Leibniz University Hannover and INDRUM. https://indrum2022.sciencesconf.org/data/Proceedings_Indrum2022.pdf Alternative version: https://hal.science/hal-04026618/document
DOI: 10.34740/kaggle/dsv/5599989
title: YouTube Daily
DOI: 10.25835/o8h1u7dv
title: INDRUM2022_Khellaf&Peters_supplement_flowchart

description: The flowchart is part of the supplementary material of Khellaf & Peters (2022). It presents a number of solutions for “the curve sketching exercise” found in Khellaf & Peters (2022: p. 72), Fig. 1 Literature reference: Khellaf, S., & Peters, J. (2022). Design and analysis of an unusual curve sketching exercise for first year teacher students. In M. Trigueros, B. Barquero, R. Hochmuth, J. Peters (Eds.), Proceedings of the Fourth Conference of the International Network for Didactic Research in University Mathematics (INDRUM 2022, 19-22 October 2022) (pp. 70-79). Leibniz University Hannover and INDRUM. https://indrum2022.sciencesconf.org/data/Proceedings_Indrum2022.pdf Alternative version: https://hal.science/hal-04026618/document
DOI: 10.25403/upresearchdata.22100027
title: Confirmed 2020-2021 COVID-19 cases at Manzini, Eswatini

description: This is secondary data that was routinely collected by the Patient Management Information System (PMIS), Health Management Information System (HMIS), Laboratory Management Information System (LMIS), and the Immediate Disease Notification System (IDNS). The PMIS and HMIS systems are mostly found in facilities while the LMIS is found in the laboratories from the facility level to the national laboratory level. The IDNS is utilized by anyone who comes into contact with a patient from rural health motivators to facility level and also the emergency preparedness and response (EPR) teams/case management. The IDNS data is then shared using google sheets and the EPR team and the deputy director of clinical services have editor’s rights for these google sheets. The surveillance officers are responsible for retrieving data and data cleaning activities such as identifying duplications and outliers also correcting incorrect records. A nasopharyngeal swab was preferred over the oral swab as it was suggested to have a high yield of the virus policymakers. A rapid antigen test was used and a polymerase chain reaction (PCR) was used for confirmation or with asymptomatic clients. The data was available as an excel document which was changed and all string variables were converted to numerical variables. The variables were also catergorised accordingly and then converted to a stata dataset.
DOI: 10.25403/upresearchdata.22100027.v1
title: Confirmed 2020-2021 COVID-19 cases at Manzini, Eswatini

description: This is secondary data that was routinely collected by the Patient Management Information System (PMIS), Health Management Information System (HMIS), Laboratory Management Information System (LMIS), and the Immediate Disease Notification System (IDNS). The PMIS and HMIS systems are mostly found in facilities while the LMIS is found in the laboratories from the facility level to the national laboratory level. The IDNS is utilized by anyone who comes into contact with a patient from rural health motivators to facility level and also the emergency preparedness and response (EPR) teams/case management. The IDNS data is then shared using google sheets and the EPR team and the deputy director of clinical services have editor’s rights for these google sheets. The surveillance officers are responsible for retrieving data and data cleaning activities such as identifying duplications and outliers also correcting incorrect records. A nasopharyngeal swab was preferred over the oral swab as it was suggested to have a high yield of the virus policymakers. A rapid antigen test was used and a polymerase chain reaction (PCR) was used for confirmation or with asymptomatic clients. The data was available as an excel document which was changed and all string variables were converted to numerical variables. The variables were also catergorised accordingly and then converted to a stata dataset.
DOI: 10.5283/epub.53924
title: Data archive of 'Build-up and dephasing of Floquet–Bloch bands on subcycle timescales'
DOI: 10.5281/zenodo.7885901
title: SAMI3/WACCM-X Modeling of the Development of an Equatorial Plasma Bubble during a Midnight Temperature Maximum

description: Data set for figures in the paper ' Modeling the Development of an Equatorial Plasma Bubble during a Midnight Temperature Maximum with SAMI3/WACCM-X'
DOI: 10.5281/zenodo.7885900
title: SAMI3/WACCM-X Modeling of the Development of an Equatorial Plasma Bubble during a Midnight Temperature Maximum

description: Data set for figures in the paper ' Modeling the Development of an Equatorial Plasma Bubble during a Midnight Temperature Maximum with SAMI3/WACCM-X'
DOI: 10.5281/zenodo.7895795
title: Effects of hydrogen jet fires on the erosion of tunnel road materials and lining materials

description: This HSE test programme investigated erosive effects of an ignited high pressure hydrogen jet impinging onto concrete and tarmac structural materials. The chosen test conditions mimicked the scenario where a high-pressure release (700bar) occurs from a fuel cell hydrogen (FCH) car as a result of activation of the thermal pressure relief device (TPRD) on the fuel tank. Two nozzle sizes were used for the releases; the first had a diameter of 2.1mm (mimicking existing TPRD) and the second had a diameter of 0.57mm (mimicking a proposed alternative TPRD diameter. The reduced diameter is suggested as a strategy to reduce release hazard safety distances).
DOI: 10.5281/zenodo.7895794
title: Effects of hydrogen jet fires on the erosion of tunnel road materials and lining materials

description: This HSE test programme investigated erosive effects of an ignited high pressure hydrogen jet impinging onto concrete and tarmac structural materials. The chosen test conditions mimicked the scenario where a high-pressure release (700bar) occurs from a fuel cell hydrogen (FCH) car as a result of activation of the thermal pressure relief device (TPRD) on the fuel tank. Two nozzle sizes were used for the releases; the first had a diameter of 2.1mm (mimicking existing TPRD) and the second had a diameter of 0.57mm (mimicking a proposed alternative TPRD diameter. The reduced diameter is suggested as a strategy to reduce release hazard safety distances).
DOI: 10.6084/m9.figshare.22760087
title: Accuracy for 3 materials (words, faces and houses) x 2 tasks (detection vs. localization of change) x 2 orientation (upright vs. upside)

description: Accuracy of change detection vs. change localization for faces, words and houses.
DOI: 10.6084/m9.figshare.22760087.v1
title: Accuracy for 3 materials (words, faces and houses) x 2 tasks (detection vs. localization of change) x 2 orientation (upright vs. upside)

description: Accuracy of change detection vs. change localization for faces, words and houses.
DOI: 10.6084/m9.figshare.22759148
title: Evidence-based comparative severity assessment in young and adult mice

description: In animal-based research, welfare assessments are essential for ethical and legal reasons. Accurate assessment of suffering in laboratory animals is often complicated by the multidimensional character of distress and pain and the associated affective states. The present study aimed to design and validate multidimensional composite measure schemes comprising behavioral and biochemical parameters based on a bioinformatics approach. Published data sets from induced and genetic mouse models of neurological and psychiatric disorders were subjected to a bioinformatics workflow for cross-model analyses. An online tool (R package) is provided, allowing the application of the bioinformatics approach to severity assessment data sets regardless of the parameters or models used. This tool is available at: https://github.com/mytalbot/cms.The tool can also be used to validate refinement measures. <br> Raw data sets: Data-set_Fig2a corresponding to Figure 2a Data-set_Fig2b corresponding to Figure 2b Data-set_Fig2c corresponding to Figure 2c Data-set_Fig2d corresponding to Figure 2d Data-set_Fig2e corresponding to Figure 2e Data-set_Fig2f corresponding to Figure 2f Data-set_Fig2g corresponding to Figure 2g Data-set_Fig3a corresponding to Figure 3a Data-set_Fig3b corresponding to Figure 3b Data-set_Fig3c corresponding to Figure 3c Data-set_Fig3d corresponding to Figure 3d Data-set_Fig4a corresponding to Figure 4a Data-set_Fig4b corresponding to Figure 4b Data-set_Fig4c corresponding to Figure 4c Data-set_FigS1 corresponding to Supplementary Figure S1 Data-set_FigS2 corresponding to Supplementary Figure S2 Data-set_FigS3 corresponding to Supplementary Figure S3 Data-set_FigS4 corresponding to Supplementary Figure S4 Data-set_FigS5 corresponding to Supplementary Figure S5 Data-set_FigS6 corresponding to Supplementary Figure S6 Data-set_FigS7 corresponding to Supplementary Figure S7 Data-set_FigS8 corresponding to Supplementary Figure S8 Data-set_FigS9 corresponding to Supplementary Figure S9
DOI: 10.25403/upresearchdata.22664938
title: Solution options categorisation for the selection of unmanned aerial vehicle (UAV) or service providing solution for the crop monitoring purposes

description: This dataset contains a number of unmanned aerial vehicles (UAV) and service-providing companies within South Africa to be used for crop health monitoring and precision agriculture applications. The solution options are grouped based on common similarities between them. The categories are intended to be used as a quick reference guide for a potential system user/designer.
DOI: 10.25403/upresearchdata.22664938.v1
title: Solution options categorisation for the selection of unmanned aerial vehicle (UAV) or service providing solution for the crop monitoring purposes

description: This dataset contains a number of unmanned aerial vehicles (UAV) and service-providing companies within South Africa to be used for crop health monitoring and precision agriculture applications. The solution options are grouped based on common similarities between them. The categories are intended to be used as a quick reference guide for a potential system user/designer.
DOI: 10.5517/ccdc.csd.cc2799hc
title: CCDC 2064522: Experimental Crystal Structure Determination

description: Related Article: Baohua Cai, Yuan Cui, Jian Zhou, Yong‐Bin Wang, Limin Yang, Bin Tan, Jun (Joelle) Wang|2023|Angew.Chem.,Int.Ed.|62|e202215820|doi:10.1002/anie.202215820
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.cc2bymdb
title: CCDC 2173422: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Christian Boelsen, Edgar Niecke|2022|CSD Communication|||
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.cc2cb7cb
title: CCDC 2184581: Experimental Crystal Structure Determination

description: Related Article: Xihao Tang, Xiangji Liao, Xinting Cai, Jialin Wu, Xueying Wu, Qianni Zhang, Yilun Yan, Shengrun Zheng, Huawei Jiang, Jun Fan, Songliang Cai, Weiguang Zhang, Yi Liu|2023|Angew.Chem.,Int.Ed.|62|e202216310|doi:10.1002/anie.202216310
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.