cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6560484
title: Neural network dataset, script and model

description: This data was collected from several sources and compiled into a single text file called NN.txt Ten quiet and five disturbed days in each month from 2009 to 2019 were used to develop the dataset. These days were got from the list of International Q and D days accessed on the website of the World Data Centre of Geomagnetism, Kyoto (<em>https://wdc.kugi.kyoto-u.ac.jp/qddays/index.html). </em>The dataset has seventeen columns defined as Date, Year (Y), Day of the year (DOY), hour of the day (HH), cosine and sine components of the day of the year for annual variation (DAC and DAS), cosine and sine components of the day of the year for semi-annual variation (DSC and DSS), cosine and sine components of the hour for daily variation (HRC and HRS), geographical coordinates (Lat, Lon), geomagnetic coordinates (Glat, Glon), Dst Index, F10.7 Index, TEC and sunspot number (SSN). The geographical coordinates were converted to geomagnetic coordinates using quasi dipole coordinates (Emmert et al., 2010; Richmond, 1995). Dst index, F10.7 index, and SSN were downloaded from the OmniWeb database (<em>https://omniweb.gsfc.nasa.gov/form/dx1.html</em>). Jason TEC data was downloaded from the FTP access of the CEDAR Madrigal database (<em>http://cedar.openmadrigal.org/ftp/</em>). The data resolution was an 18-second interval. It should be noted that any day that had a missing value was eliminated from the database. The data downloaded from Omni web was on a resolution of 1 hour and it was put at an 18-second interval by repeating the same value. The TEC data from the CEDAR Madrigal database is in a second interval and therefore it was averaged at an 18-second interval. The dataset was trained with MATLAB software, the MATLAB script (NN script) is attached. The output was a Neural network model also attached
description: It should be noted that any day that had a missing value was eliminated from the database. The data downloaded from Omni web was on a resolution of 1 hour and it was put at an 18-second interval by repeating the same value. The TEC data from the CEDAR Madrigal database is in a second interval and therefore it was averaged at an 18-second interval. The dataset was trained with MATLAB software, the MATLAB script (NN script) is attached. The output was a Neural network model also attached
DOI: 10.5281/zenodo.6560483
title: Neural network dataset, script and model

description: This data was collected from several sources and compiled into a single text file called NN.txt Ten quiet and five disturbed days in each month from 2009 to 2019 were used to develop the dataset. These days were got from the list of International Q and D days accessed on the website of the World Data Centre of Geomagnetism, Kyoto (<em>https://wdc.kugi.kyoto-u.ac.jp/qddays/index.html). </em>The dataset has seventeen columns defined as Date, Year (Y), Day of the year (DOY), hour of the day (HH), cosine and sine components of the day of the year for annual variation (DAC and DAS), cosine and sine components of the day of the year for semi-annual variation (DSC and DSS), cosine and sine components of the hour for daily variation (HRC and HRS), geographical coordinates (Lat, Lon), geomagnetic coordinates (Glat, Glon), Dst Index, F10.7 Index, TEC and sunspot number (SSN). The geographical coordinates were converted to geomagnetic coordinates using quasi dipole coordinates (Emmert et al., 2010; Richmond, 1995). Dst index, F10.7 index, and SSN were downloaded from the OmniWeb database (<em>https://omniweb.gsfc.nasa.gov/form/dx1.html</em>). Jason TEC data was downloaded from the FTP access of the CEDAR Madrigal database (<em>http://cedar.openmadrigal.org/ftp/</em>). The data resolution was an 18-second interval. It should be noted that any day that had a missing value was eliminated from the database. The data downloaded from Omni web was on a resolution of 1 hour and it was put at an 18-second interval by repeating the same value. The TEC data from the CEDAR Madrigal database is in a second interval and therefore it was averaged at an 18-second interval. The dataset was trained with MATLAB software, the MATLAB script (NN script) is attached. The output was a Neural network model also attached
description: It should be noted that any day that had a missing value was eliminated from the database. The data downloaded from Omni web was on a resolution of 1 hour and it was put at an 18-second interval by repeating the same value. The TEC data from the CEDAR Madrigal database is in a second interval and therefore it was averaged at an 18-second interval. The dataset was trained with MATLAB software, the MATLAB script (NN script) is attached. The output was a Neural network model also attached
DOI: 10.6084/m9.figshare.19780084
title: Supplementary Material.zip

description: Supplementary Figure 1 | Genes that presented three types of AS events (CCDC51, PIK3CG, MCEMP1, STRADA, and ATP2A3). Supplementary Figure 2 | SNP annotation results from ensemble VEP. Supplementary Table 1 | Information of healthy cows and subclinical mastitis cows. Supplementary Table 2 | The list of DEGs and DELs. Supplementary Table 3 | Pathway of DEGs and DELs. Supplementary Table 4 | Target genes of lncRNAs. Supplementary Table 5 | Co-expression network of LncRNAs, target genes, and key pathways. Supplementary Table 6 | The list of DELs adjacent to SCC, SCS, and white blood cell count QTLs. Supplementary Table 7 | all AS events. Supplementary Table 8 | The list of 1,621个DSGs. AS SNPs from VEP annotator.
DOI: 10.6084/m9.figshare.19780084.v1
title: Supplementary Material.zip

description: Supplementary Figure 1 | Genes that presented three types of AS events (CCDC51, PIK3CG, MCEMP1, STRADA, and ATP2A3). Supplementary Figure 2 | SNP annotation results from ensemble VEP. Supplementary Table 1 | Information of healthy cows and subclinical mastitis cows. Supplementary Table 2 | The list of DEGs and DELs. Supplementary Table 3 | Pathway of DEGs and DELs. Supplementary Table 4 | Target genes of lncRNAs. Supplementary Table 5 | Co-expression network of LncRNAs, target genes, and key pathways. Supplementary Table 6 | The list of DELs adjacent to SCC, SCS, and white blood cell count QTLs. Supplementary Table 7 | all AS events. Supplementary Table 8 | The list of 1,621个DSGs. AS SNPs from VEP annotator.
DOI: 10.5517/ccdc.csd.cc25t82c
title: CCDC 2020272: Experimental Crystal Structure Determination

description: Related Article: Emmanuel Cadot, Clément Falaise, Soumaya Khlifi, Pierre Bauduin, Philipp Schmidt, william Shepard, Anton A. Ivanov, Maxim N. Sokolov, Pavel A. Abramov, Michael A. Shestopalov, Stéphane Cordier, Jérôme Marrot, Mohamed Haouas|2021|Angew.Chem.,Int.Ed.|60|14146|doi:10.1002/anie.202102507
DOI: 10.6084/m9.figshare.19787013
title: Data matrix in .nex file from First definitive record of abelisauridae (theropoda: ceratosauria) from the cretaceous Bahariya formation, Bahariya oasis, western desert of Egypt

description: We conducted a phylogenetic analysis using the data matrix of Smyth et al. (2020; which was itself modified from that of Tortosa et al. [2014]), with the addition of MUVP 477
DOI: 10.6084/m9.figshare.19787016
title: Data matrix in .tnt file from First definitive record of abelisauridae (theropoda: ceratosauria) from the cretaceous Bahariya formation, Bahariya oasis, western desert of Egypt

description: We conducted a phylogenetic analysis using the data matrix of Smyth et al. (2020; which was itself modified from that of Tortosa et al. [2014]), with the addition of MUVP 477
DOI: 10.6084/m9.figshare.19787016.v1
title: Data matrix in .tnt file from First definitive record of abelisauridae (theropoda: ceratosauria) from the cretaceous Bahariya formation, Bahariya oasis, western desert of Egypt

description: We conducted a phylogenetic analysis using the data matrix of Smyth et al. (2020; which was itself modified from that of Tortosa et al. [2014]), with the addition of MUVP 477
DOI: 10.6084/m9.figshare.19787013.v1
title: Data matrix in .nex file from First definitive record of abelisauridae (theropoda: ceratosauria) from the cretaceous Bahariya formation, Bahariya oasis, western desert of Egypt

description: We conducted a phylogenetic analysis using the data matrix of Smyth et al. (2020; which was itself modified from that of Tortosa et al. [2014]), with the addition of MUVP 477
DOI: 10.6084/m9.figshare.19581644
title: Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

description: HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.
DOI: 10.6084/m9.figshare.19657069.v1
title: An improved opposition-based crow search algorithm for biodegradable material classification

description: The development of a reliable quantitative structure–activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure–biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.
DOI: 10.6084/m9.figshare.19519276
title: Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

description: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion – toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.
DOI: 10.6084/m9.figshare.19581644.v1
title: Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

description: HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.
DOI: 10.6084/m9.figshare.19657069
title: An improved opposition-based crow search algorithm for biodegradable material classification

description: The development of a reliable quantitative structure–activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure–biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.
DOI: 10.6084/m9.figshare.19519276.v1
title: Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

description: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion – toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.
DOI: 10.6084/m9.figshare.19697477.v1
title: Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules

description: The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.
DOI: 10.6084/m9.figshare.19697477
title: Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules

description: The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.
DOI: 10.6084/m9.figshare.19786810
title: TwitterBan: A Sentiment Analysis of the Impact on Nigerians

description: This initial move by the Nigerian government to suspend the Twitter operation in Nigeria, and the subsequent lifting of the ban after seven months in 2022 is the focus of this reseach. The study examines the impact of this ban on the citizens residing in Nigeria.
DOI: 10.6084/m9.figshare.19786810.v1
title: TwitterBan: A Sentiment Analysis of the Impact on Nigerians

description: This initial move by the Nigerian government to suspend the Twitter operation in Nigeria, and the subsequent lifting of the ban after seven months in 2022 is the focus of this reseach. The study examines the impact of this ban on the citizens residing in Nigeria.
DOI: 10.5281/zenodo.6560641
title: Duration of canine retraction with fixed appliances: A systematic review and meta-analysis

description: Dataset for all analyses
DOI: 10.5281/zenodo.6560640
title: Duration of canine retraction with fixed appliances: A systematic review and meta-analysis

description: Dataset for all analyses
DOI: 10.6084/m9.figshare.14222938.v1
title: Development of a Life Skills Self-Assessment Tool for Coaches

description: Researchers have been increasingly interested in exploring how coaches can be best supported to explicitly teach life skills through sport. The purpose of the present paper is to describe the process of developing the Life Skills Self-Assessment Tool for Coaches. The tool represents a knowledge product designed for practical use on the ground, anchored in theory and backed by empirical support. The tool is available in four languages and accompanied by a “how-to” video, thereby enabling more coaches from around the world to teach life skills through sport.
DOI: 10.6084/m9.figshare.14222938
title: Development of a Life Skills Self-Assessment Tool for Coaches

description: Researchers have been increasingly interested in exploring how coaches can be best supported to explicitly teach life skills through sport. The purpose of the present paper is to describe the process of developing the Life Skills Self-Assessment Tool for Coaches. The tool represents a knowledge product designed for practical use on the ground, anchored in theory and backed by empirical support. The tool is available in four languages and accompanied by a “how-to” video, thereby enabling more coaches from around the world to teach life skills through sport.
DOI: 10.18112/openneuro.ds003216.v2.1.2
title: Whole brain layer-fMRI connectome: An open dataset
DOI: 10.15129/6bb91792-4de1-405f-9cee-57228fc49713
title: Data for: "High-charge electron beams from a laser-wakefield accelerator driven by a CO2 laser"

description: This dataset contains simulation results obtained with the code FBPIC (https://github.com/fbpic/fbpic) on the production of high-charge electron beams from a laser-wakefield accelerator driven by a CO2 laser at the wavelength of 10.6 um. This is the basis of the paper by Brunetti et al. "High-charge electron beams from a laser-wakefield accelerator driven by a CO2 laser" accepted in Scientific Reports.
DOI: 10.5281/zenodo.6560630
title: Methner et al., "Triple Oxygen Isotope Paleoaltimetry of the Kettle Metamorphic Core Complex (WA, USA)"

description: Data set to Methner et al., "Triple Oxygen Isotope Paleoaltimetry of the Kettle Metamorphic Core Complex (WA, USA)" submitted to Tectonics
DOI: 10.5281/zenodo.6560629
title: Methner et al., "Triple Oxygen Isotope Paleoaltimetry of the Kettle Metamorphic Core Complex (WA, USA)"

description: Data set to Methner et al., "Triple Oxygen Isotope Paleoaltimetry of the Kettle Metamorphic Core Complex (WA, USA)" submitted to Tectonics
DOI: 10.5281/zenodo.4659589
title: Methner et al. "Warm High-elevation Mid-latitudes during the Miocene Climatic Optimum: Paleosol Clumped Isotope Temperatures from the Northern Rocky Mountains, USA"

description: Data set comprising clumped stable and isotope data that support the publication "Warm High-elevation Mid-latitudes during the Miocene Climatic Optimum: Paleosol Clumped Isotope Temperatures from the Northern Rocky Mountains, USA" by Methner et al.