cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12958373
title: Road Traffic Injuries in Nepal during COVID-19 Lockdown_ Media reporting and Police record (24 March to 14 June, 2020).csv

description: The data file include secondary data from multiple sources i.e. media reports, published, and unpublished police records. Data collection was done in two ways; online search of daily media reports for vehicle crash incidents and through obtaining police records. According to the nature of data sources, a limited number of variables - total number of crashes, crash location, vehicles that instigate the crashes, i.e. the vehicle and the counterparts (animal, people, or objects etc.) involved in the crash, and number of deaths & injuries were recorded. This paper includes the road crashes information for 82 days of the lockdown (24 March to 14 June 2020) from media reports and police records. The exact location and types of vehicles involved in fatal crashes were not available from the Police data, therefore the exact detail of the vehicles and the location of crashes were extracted from the media reporting. With the available information of location, <i>Palika level –</i> (local government unit) for fatal crashes, the cases were nationally mapped. Similarly, comparisons have been made between the data for the same period for both the sources, where needed.
DOI: 10.6084/m9.figshare.12958373.v2
title: Road Traffic Injuries in Nepal during COVID-19 Lockdown Media reporting and Police record (24 March to 14 June, 2020).csv

description: The data file include secondary data from multiple sources i.e. media reports, published, and unpublished police records. Data collection was done in two ways; online search of daily media reports for vehicle crash incidents and through obtaining police records. According to the nature of data sources, a limited number of variables - total number of crashes, crash location, vehicles that instigate the crashes, i.e. the vehicle and the counterparts (animal, people, or objects etc.) involved in the crash, and number of deaths & injuries were recorded. This paper includes the road crashes information for 82 days of the lockdown (24 March to 14 June 2020) from media reports and police records. The exact location and types of vehicles involved in fatal crashes were not available from the Police data, therefore the exact detail of the vehicles and the location of crashes were extracted from the media reporting. With the available information of location, <i>Palika level –</i> (local government unit) for fatal crashes, the cases were nationally mapped. Similarly, comparisons have been made between the data for the same period for both the sources, where needed.
DOI: 10.6084/m9.figshare.12815903
title: Allosteric hotspots in the main protease of SARS-CoV-2 dataset

description: On the top level the zip file contains two folders which are described in detail below: <br>Main results:contains two folders named after the PDB ID of the viral main protease apo structure. 1. SARS-CoV Mpro structure 2DUC2. SARS-CoV-2 Mpro structure 6Y2EEach directory contains the protonated structure, a .csv file with all residues in the dimer interface and a subfolder for each run performed on it. The subfolders are named after the source residues. Each subfolder contains three files:1. source_residues.txt A file listing all source residues2. propensity_residue_results.csv This file contains the raw results of the Bond-to-bond propensity analysis. 3. transient_residue_results.csv This file contains the raw results of the Markov transient analysis.<br>Small fragments:Contains a folder for each small fragment structure in close proximity to the identified hotspots. Each subfolder contains the protonated structure as well as the three files described above.
DOI: 10.6084/m9.figshare.12815903.v2
title: Allosteric hotspots in the main protease of SARS-CoV-2 dataset

description: On the top level the zip file contains two folders which are described in detail below: <br>Main results:contains two folders named after the PDB ID of the viral main protease apo structure. 1. SARS-CoV Mpro structure 2DUC2. SARS-CoV-2 Mpro structure 6Y2EEach directory contains the protonated structure and a subfolder for each run performed on it. The subfolders are named after the source residues. Each subfolder contains three files:1. source_residues.txt A file listing all source residues2. propensity_residue_results.csv This file contains the raw results of the Bond-to-bond propensity analysis. 3. transient_residue_results.csv This file contains the raw results of the Markov transient analysis.<br>Small fragments:Contains a folder for each small fragment structure in close proximity to the identified hotspots. Each subfolder contains the protonated structure as well as the three files described above.
DOI: 10.5281/zenodo.4031442
title: RCMIP II Hector Submission Results

description: Version controlled data of Hector RCMIP II results.
DOI: 10.6084/m9.figshare.12170403.v1
title: Correcting Biases in Historical Bathythermograph Data Using Artificial Neural Networks

description: Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system, and for tracking changes in the Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared to high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 meters. A global correction, as well as corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction, instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely-used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC. <br>
DOI: 10.6084/m9.figshare.9436514
title: Global time lags in socio-economic development

description: The dataset provides time lags (difference in years between achievement of a given value of a development indicator in a developing country and a benchmark country) for 156 countries of the world, annually between 1920 and 2020 for nine indicators (GDP per capita, employment in agriculture, male and female life expectancy, infant mortality, education attainment, passenger car and telephone usage, CO<sub>2</sub> emissions index). The data is provided in table format and as graph for individual countries and regions of the world.
DOI: 10.6084/m9.figshare.9436514.v2
title: Global time lags in socio-economic development

description: The dataset provides time lags (difference in years between achievement of a given value of a development indicator in a developing country and a benchmark country) for 156 countries of the world, annually between 1920 and 2020 for nine indicators (GDP per capita, employment in agriculture, male and female life expectancy, infant mortality, education attainment, passenger car and telephone usage, CO<sub>2</sub> emissions index). The data is provided in table format and as graph for individual countries and regions of the world.
DOI: 10.6084/m9.figshare.12958424
title: SURVEY DATA for SHARING EER .xlsx

description: To inform current debate around climate change education (CCE) in the school curriculum in England, we surveyed the views of primary and secondary teachers (N=626).
DOI: 10.6084/m9.figshare.12958424.v2
title: SURVEY DATA for SHARING EER .xlsx

description: To inform current debate around climate change education (CCE) in the school curriculum in England, we surveyed the views of primary and secondary teachers (N=626).
DOI: 10.6084/m9.figshare.12958424.v1
title: SURVEY DATA for SHARING EER .xlsx

description: To inform current debate around climate change education (CCE) in the school curriculum in England, we surveyed the views of primary and secondary teachers (N=626).
DOI: 10.6084/m9.figshare.12959576.v1
title: SURVEY DATA for SHARING EER .xlsx

description: To inform current debate around climate change education (CCE) in the school curriculum in England, we surveyed the views of primary and secondary teachers (N=626).
DOI: 10.6084/m9.figshare.12959576
title: SURVEY DATA for SHARING EER .xlsx

description: To inform current debate around climate change education (CCE) in the school curriculum in England, we surveyed the views of primary and secondary teachers (N=626).
DOI: 10.6084/m9.figshare.12170403
title: Correcting Biases in Historical Bathythermograph Data Using Artificial Neural Networks

description: Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system, and for tracking changes in the Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared to high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 meters. A global correction, as well as corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction, instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely-used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC. <br>
DOI: 10.6084/m9.figshare.12170403.v3
title: Correcting Biases in Historical Bathythermograph Data Using Artificial Neural Networks

description: Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system, and for tracking changes in the Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared to high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 meters. A global correction, as well as corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction, instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely-used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC. <br>
DOI: 10.6084/m9.figshare.12170403.v2
title: Correcting Biases in Historical Bathythermograph Data Using Artificial Neural Networks

description: Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system, and for tracking changes in the Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared to high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 meters. A global correction, as well as corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction, instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely-used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC. <br>
DOI: 10.6084/m9.figshare.12958487
title: Geochemical data of sediment core from the central Arabian Sea

description: The data set contains the radiocarbon dates, organic carbon, and redox proxies values of SK 312/16 sediment core of the central Arabian Sea collected from 12.99°N and 65.00°E (water depth of ~4300m).<br>Chronology was established by Accelerator Mass Spectrometer (AMS) radiocarbon dating on mixed planktonic foraminifera (&gt;250μm).<br>Trace element were measured by Q-ICP-MS and the analytical precision of measurement for all elements was found to be better than ±5%<br>Organic carbon (Corg) was measured using Thermofisher Flash 2000 Elemental Analyser after decarbonating samples with 0.6N HCl. The analytical precision for organic carbon were within ±2%.
DOI: 10.6084/m9.figshare.12958487.v1
title: Geochemical data of sediment core from the central Arabian Sea

description: The data set contains the radiocarbon dates, organic carbon, and redox proxies values of SK 312/16 sediment core of the central Arabian Sea collected from 12.99°N and 65.00°E (water depth of ~4300m).<br>Chronology was established by Accelerator Mass Spectrometer (AMS) radiocarbon dating on mixed planktonic foraminifera (&gt;250μm).<br>Trace element were measured by Q-ICP-MS and the analytical precision of measurement for all elements was found to be better than ±5%<br>Organic carbon (Corg) was measured using Thermofisher Flash 2000 Elemental Analyser after decarbonating samples with 0.6N HCl. The analytical precision for organic carbon were within ±2%.
DOI: 10.5517/ccdc.csd.cc1mnymv
title: CCDC 1509688: Experimental Crystal Structure Determination

description: Related Article: Le-Yi Tao, Yin Wei, Min Shi|2020|Org.Chem.Front.|7|2634|doi:10.1039/D0QO00878H
DOI: 10.5517/ccdc.csd.cc247rsy
title: CCDC 1973670: Experimental Crystal Structure Determination

description: Related Article: Kosuke Kitase, Takafumi Kitazawa|2020|Dalton Trans.|49|12210|doi:10.1039/D0DT01681K
DOI: 10.5517/ccdc.csd.cc247rtz
title: CCDC 1973671: Experimental Crystal Structure Determination

description: Related Article: Kosuke Kitase, Takafumi Kitazawa|2020|Dalton Trans.|49|12210|doi:10.1039/D0DT01681K
DOI: 10.5517/ccdc.csd.cc247rv0
title: CCDC 1973672: Experimental Crystal Structure Determination

description: Related Article: Kosuke Kitase, Takafumi Kitazawa|2020|Dalton Trans.|49|12210|doi:10.1039/D0DT01681K
DOI: 10.5517/ccdc.csd.cc249jqq
title: CCDC 1975373: Experimental Crystal Structure Determination

description: Related Article: Xin-Xing Wu|2020|CSD Communication|||
description: Related Article: Xin-Xing Wu, Hao Ye, Hong Dai, Bing Yang, Yang Wang, Shufeng Chen, Lanping Hu|2020|Org.Chem.Front.|7|2731|doi:10.1039/D0QO00615G
DOI: 10.5517/ccdc.csd.cc24prby
title: CCDC 1987110: Experimental Crystal Structure Determination

description: Related Article: Siba Prasad Bera, Arpan Mondal, Sanjit Konar|2020|Inorg.Chem.Front.|7|3352|doi:10.1039/D0QI00558D
DOI: 10.5517/ccdc.csd.cc24s0vt
title: CCDC 1989296: Experimental Crystal Structure Determination

description: Related Article: Wenhao Dai, Chunpu Li, Yichu Liu, Xu Han, Xingjun Li, Kaixian Chen, Hong Liu|2020|CSD Communication|||
description: Related Article: Wenhao Dai, Chunpu Li, Yichu Liu, Xu Han, Xingjun Li, Kaixian Chen, Hong Liu|2020|Org.Chem.Front.|7|2612|doi:10.1039/D0QO00320D
DOI: 10.5517/ccdc.csd.cc24s0wv
title: CCDC 1989297: Experimental Crystal Structure Determination

description: Related Article: Wenhao Dai, Chunpu Li, Yichu Liu, Xu Han, Xingjun Li, Kaixian Chen, Hong Liu|2020|CSD Communication|||
description: Related Article: Wenhao Dai, Chunpu Li, Yichu Liu, Xu Han, Xingjun Li, Kaixian Chen, Hong Liu|2020|Org.Chem.Front.|7|2612|doi:10.1039/D0QO00320D
DOI: 10.5517/ccdc.csd.cc2504dr
title: CCDC 1996133: Experimental Crystal Structure Determination

description: Related Article: Jun Liu, Xueyu Wang, Baihua Chen, Lina Lv, Qiang Li, Xingliang Li, Songdong Ding, Yanqiu Yang|2020|Inorg.Chem.Front.|7|3412|doi:10.1039/D0QI00480D
DOI: 10.5517/ccdc.csd.cc2504fs
title: CCDC 1996134: Experimental Crystal Structure Determination

description: Related Article: Jun Liu, Xueyu Wang, Baihua Chen, Lina Lv, Qiang Li, Xingliang Li, Songdong Ding, Yanqiu Yang|2020|Inorg.Chem.Front.|7|3412|doi:10.1039/D0QI00480D
DOI: 10.5517/ccdc.csd.cc2504gt
title: CCDC 1996135: Experimental Crystal Structure Determination

description: Related Article: Jun Liu, Xueyu Wang, Baihua Chen, Lina Lv, Qiang Li, Xingliang Li, Songdong Ding, Yanqiu Yang|2020|Inorg.Chem.Front.|7|3412|doi:10.1039/D0QI00480D
DOI: 10.5517/ccdc.csd.cc2504hv
title: CCDC 1996136: Experimental Crystal Structure Determination

description: Related Article: Jun Liu, Xueyu Wang, Baihua Chen, Lina Lv, Qiang Li, Xingliang Li, Songdong Ding, Yanqiu Yang|2020|Inorg.Chem.Front.|7|3412|doi:10.1039/D0QI00480D
DOI: 10.5281/zenodo.4029653
title: Supplemental Materials

description: Supplemental Materials for Molecules - Special Issue "Inflammasome Inhibitors"
DOI: 10.5281/zenodo.4029652
title: Supplemental Materials

description: Supplemental Materials for Molecules - Special Issue "Inflammasome Inhibitors"
DOI: 10.6084/m9.figshare.12959758
title: Marked_AsympEigF_20rev.pdf from Functional difference equations and eigenfunctions of a Schrödinger operator with <i>δ</i>′ −interaction on a circular conical surface

description: The file with highlighted changes in the revised manuscript.
DOI: 10.6084/m9.figshare.12959758.v1
title: Marked_AsympEigF_20rev.pdf from Functional difference equations and eigenfunctions of a Schrödinger operator with <i>δ</i>′ −interaction on a circular conical surface

description: The file with highlighted changes in the revised manuscript.
DOI: 10.5281/zenodo.3829122
title: Dataset to "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations"

description: This record contains raw data of a 3-month monitoring period of the HeLLo project. The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal <em>energies</em>. Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute. Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643). Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.
DOI: 10.5281/zenodo.3829121
title: Dataset to "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations"

description: This record contains raw data of a 3-month monitoring period of the HeLLo project. The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal <em>energies</em>. Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute. Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643). Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.
DOI: 10.21227/9x9y-gx35
title: Exasens: a novel dataset for the classification of saliva samples of COPD patients

description: This repository introduces a novel dataset for the classification of Chronic Obstructive Pulmonary Disease (COPD) patients and Healthy Controls. The Exasens dataset includes demographic information on 4 groups of saliva samples (COPD-HC-Asthma-Infected) collected in the frame of a joint research project, Exasens (https://www.leibniz-healthtech.de/en/research/projects/bmbf-project-exasens/), at the Research Center Borstel, BioMaterialBank Nord (Borstel, Germany). The sampling procedure of the patient materials was approved by the local ethics committee of the University of Luebeck under the approval number AZ-16-167 and a written informed consent was obtained from all subjects. A permittivity biosensor, developed at IHP Microelectronics (Frankfurt Oder, Germany), was used for the dielectric characterization of the saliva samples for classification purposes (https://doi.org/10.3390/healthcare7010011).