cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5362454
title: Conditions for electronic hybridization between transition-metal dichalcogenide monolayers and physisorbed carbon-conjugated molecules

description: We provide the numerical data for the projected unit cell band structures of the four TMDCs; for the hybrid systems, we include in- and output files of the geometry optimizations as well as the band structure calculations, together with the post-processed final band energies and spectral weights; for the isolated molecules, we provide the data files of relevant orbitals mapped to the Brillouin zone in conjunction with a python script for visualization
DOI: 10.5281/zenodo.5362453
title: Conditions for electronic hybridization between transition-metal dichalcogenide monolayers and physisorbed carbon-conjugated molecules

description: We provide the numerical data for the projected unit cell band structures of the four TMDCs; for the hybrid systems, we include in- and output files of the geometry optimizations as well as the band structure calculations, together with the post-processed final band energies and spectral weights; for the isolated molecules, we provide the data files of relevant orbitals mapped to the Brillouin zone in conjunction with a python script for visualization
DOI: 10.5519/qd.odhf4drx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4632 records)
DOI: 10.6084/m9.figshare.16595078
title: Supplement to Joining the club: explaining alliance preferences in the South Caucasus

description: <b>APPENDIX A: MATHEMATICAL MODEL</b><b>APPENDIX B: MULTINOMIAL REGRESSION TABLES</b><b><br></b><b>APPENDIX C: FIRST DIFFERENCES PLOTS</b><b><br></b><b>APPENDIX D: CLUSTERING IN THE SAMPLING</b><b><br></b><b><br></b>
DOI: 10.6084/m9.figshare.16595078.v1
title: Supplement to Joining the club: explaining alliance preferences in the South Caucasus

description: <b>APPENDIX A: MATHEMATICAL MODEL</b><b>APPENDIX B: MULTINOMIAL REGRESSION TABLES</b><b><br></b><b>APPENDIX C: FIRST DIFFERENCES PLOTS</b><b><br></b><b>APPENDIX D: CLUSTERING IN THE SAMPLING</b><b><br></b><b><br></b>
DOI: 10.5519/qd.akkygw3a
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4632 records)
DOI: 10.25405/data.ncl.16514172
title: L-arginine in Mitochondrial Encephalopathy, Lactic acidosis, and Stroke-like episodes: A Systematic Review

description: <b>eSupplementary files:</b>e1. Search strategyeFigure 1. Number of patients evaluated for safety and efficacy in Koga et al. 2018eReferenceseTable 2. Excluded studies after full-text revieweTable3. Summary characteristics of included case reports and cohort study (N=34)eTable4. Summary characteristics of included open-label trials (N=3)eTable 5. Quality appraisal of case reports and case series eTable 6. Quality appraisal for all other non-randomised controlled trialseTable 7. PRISMA 2002 Checklist eTable 8. PRISMA 2020 for Abstracts Checklist eTable 9. PRISMA-S Checklist <b>Raw data files:</b>Summary dataClinical features Acute response
DOI: 10.25405/data.ncl.16514172.v1
title: L-arginine in Mitochondrial Encephalopathy, Lactic acidosis, and Stroke-like episodes: A Systematic Review

description: <b>eSupplementary files:</b>e1. Search strategyeFigure 1. Number of patients evaluated for safety and efficacy in Koga et al. 2018eReferenceseTable 2. Excluded studies after full-text revieweTable3. Summary characteristics of included case reports and cohort study (N=34)eTable4. Summary characteristics of included open-label trials (N=3)eTable 5. Quality appraisal of case reports and case series eTable 6. Quality appraisal for all other non-randomised controlled trialseTable 7. PRISMA 2002 Checklist eTable 8. PRISMA 2020 for Abstracts Checklist eTable 9. PRISMA-S Checklist <b>Raw data files:</b>Summary dataClinical features Acute response
DOI: 10.5517/ccdc.csd.cc2695mb
title: CCDC 2034611: Experimental Crystal Structure Determination

description: Related Article: Mohammed Boulhaoua, Tibor Pasinszki, Ana Torvisco, Rita Oláh-Szabó, Szilvia Bősze, Antal Csámpai|2021|RSC Advances|11|28685|doi:10.1039/D1RA05095H
DOI: 10.5517/ccdc.csd.cc2695nc
title: CCDC 2034612: Experimental Crystal Structure Determination

description: Related Article: Mohammed Boulhaoua, Tibor Pasinszki, Ana Torvisco, Rita Oláh-Szabó, Szilvia Bősze, Antal Csámpai|2021|RSC Advances|11|28685|doi:10.1039/D1RA05095H
DOI: 10.5517/ccdc.csd.cc2695pd
title: CCDC 2034613: Experimental Crystal Structure Determination

description: Related Article: Mohammed Boulhaoua, Tibor Pasinszki, Ana Torvisco, Rita Oláh-Szabó, Szilvia Bősze, Antal Csámpai|2021|RSC Advances|11|28685|doi:10.1039/D1RA05095H
DOI: 10.5517/ccdc.csd.cc2695qf
title: CCDC 2034614: Experimental Crystal Structure Determination

description: Related Article: Mohammed Boulhaoua, Tibor Pasinszki, Ana Torvisco, Rita Oláh-Szabó, Szilvia Bősze, Antal Csámpai|2021|RSC Advances|11|28685|doi:10.1039/D1RA05095H
DOI: 10.5517/ccdc.csd.cc2695rg
title: CCDC 2034615: Experimental Crystal Structure Determination

description: Related Article: Mohammed Boulhaoua, Tibor Pasinszki, Ana Torvisco, Rita Oláh-Szabó, Szilvia Bősze, Antal Csámpai|2021|RSC Advances|11|28685|doi:10.1039/D1RA05095H
DOI: 10.5517/ccdc.csd.cc2695sh
title: CCDC 2034616: Experimental Crystal Structure Determination

description: Related Article: Mohammed Boulhaoua, Tibor Pasinszki, Ana Torvisco, Rita Oláh-Szabó, Szilvia Bősze, Antal Csámpai|2021|RSC Advances|11|28685|doi:10.1039/D1RA05095H
DOI: 10.18743/data.00158
title: Subjection in the human:non-human encounter tweets and code

description: The data was collected as part of a dissertation project for the MA Psychoanalytic Studies 2020-2021. The project is entitled "Subjection in the human:non-human encounter". The purpose is to understand the emotional and psychological reactions of a human encountering a non-human, and how it affects their process of subjection. The Twitter Academia API was used to search for tweets with the phrase "you are a bot", and the surrounding "threads" of the tweets were collected for contextual conversation data. The tweets were transformed into semiotic tweets which accentuated the psycholinguistics markings of the text using a series of original techniques detailed in the java code named dissertation-code. The transformed tweets were then clustered using K-means algorithm to try and decipher the range of emotional reactions twitter users had when realising they were encountering a bot. The results of the clustering are in the data set, as well as the code used to perform the clustering.
description: The Twitter Academia API was used to search for tweets with the phrase "you are a bot"; 122299 tweets from June 2016 to June 2021 were collected, these are named the declaration set. The code to collect the data is available in the data under the "dissertation code" folder - java was used to pull data from twitter's academic API. The surrounding "threads" of the tweets were collected for contextual conversation data; 112294 tweets were collected, and they are named the control set. The results of the clustering are also enclosed, the process used to create the clusters are in the java code uploaded
DOI: 10.5281/zenodo.3372231
title: UTCI-to-DTS

description: Mapping values of the Universal Thermal Climate Index (UTCI) to Dynamic Thermal Sensation (DTS), cf. Fig. 10 in https://doi.org/10.1007/s00484-011-0454-1<br> Tab-delimited ASCII file with the two columns UTCI (in °C) and DTS representing values from the 7-point thermal sensation scale (-3:cold; -2:cool:; -1:slightly cool; 0:neutral; +1:slightly warm; +2:warm; +3:hot) Headers in line 3, first data row in line 4
description: {"references": ["Br\u00f6de, P., Fiala, D., Blazejczyk, K., Holm\u00e9r, I., Jendritzky, G., Kampmann, B., Tinz, B., Havenith, G., 2012. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). International Journal of Biometeorology 56, 481-494, https://doi.org/10.1007/s00484-011-0454-1"]}
DOI: 10.5281/zenodo.3372230
title: UTCI-to-DTS

description: Mapping values of the Universal Thermal Climate Index (UTCI) to Dynamic Thermal Sensation (DTS), cf. Fig. 10 in https://doi.org/10.1007/s00484-011-0454-1<br> Tab-delimited ASCII file with the two columns UTCI (in °C) and DTS representing values from the 7-point thermal sensation scale (-3:cold; -2:cool:; -1:slightly cool; 0:neutral; +1:slightly warm; +2:warm; +3:hot) Headers in line 3, first data row in line 4
description: {"references": ["Br\u00f6de, P., Fiala, D., Blazejczyk, K., Holm\u00e9r, I., Jendritzky, G., Kampmann, B., Tinz, B., Havenith, G., 2012. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). International Journal of Biometeorology 56, 481-494, https://doi.org/10.1007/s00484-011-0454-1"]}
DOI: 10.5281/zenodo.5497806
title: Storage experiment for the analysis of dissolved organic matter in surface water and groundwater

description: This dataset contains the data resulting from a storage experiment, whereby samples from surface water (<em>n</em>=3) and groundwater (<em>n</em>=1) were stored cooled (4°C) or frozen (-20°C) and analysed after 1, 2, 3, 7, 14, 30 and 90 days using fluorescence spectroscopy (Aqualog, Horiba Scientific, Edison, NJ, USA). For each sample, the absorbance spectrum (240–500 nm) and a fluorescence excitation-emission matrix (EEM) were obtained. EEMs were obtained for excitation wavelengths 240–500 nm (3 nm interval) and emission wavelengths 248–825 nm (~4.6 nm interval) with an integration time of 1 second. Five absorbance indicators and eight fluorescence indicators were calculated. The DOC concentration was estimated using the two-wavelength approach of Cook et al. (2017). The dataset consists of four files: Aqualog data processing and index calculation.R: R script with the code to process the Aqualog data and to calculate the indices. Aqualog.zip: Raw data obtained from the Aqualog, divided into subfolders corresponding to the day of measurement. For each sample, the absorbance spectrum (*ABS.dat) and EEMs of the sample (*SEM.dat) and corresponding blank measurement (*BEM.dat) are provided. Indices.xlsx: Table with index values for all samples. Samples.xlsx: Table with information about all samples (e.g. sampling site, storage duration, storage method) The geolocations of the sampling sites are: Site 102 - 50° 29' 6.09''N - 8° 33' 33.57''E Site 134 - 50° 28' 36.55''N - 8° 32' 39.56''E Site 175 - 50° 28' 34.05''N - 8° 32' 42.89''E Site 187 - 50° 28' 49.98''N - 8° 32' 35.12''E
description: {"references": ["Cook, S., Peacock, M., Evans, C.D., Page, S.E., Whelan, M.J., Gauci, V., Kho, L.K., 2017. Quantifying tropical peatland dissolved organic carbon (DOC) using UV-visible spectroscopy. Water Research 115, 229\u2013235. https://doi.org/10.1016/j.watres.2017.02.059"]}
DOI: 10.5281/zenodo.5497805
title: Storage experiment for the analysis of dissolved organic matter in surface water and groundwater

description: This dataset contains the data resulting from a storage experiment, whereby samples from surface water (<em>n</em>=3) and groundwater (<em>n</em>=1) were stored cooled (4°C) or frozen (-20°C) and analysed after 1, 2, 3, 7, 14, 30 and 90 days using fluorescence spectroscopy (Aqualog, Horiba Scientific, Edison, NJ, USA). For each sample, the absorbance spectrum (240–500 nm) and a fluorescence excitation-emission matrix (EEM) were obtained. EEMs were obtained for excitation wavelengths 240–500 nm (3 nm interval) and emission wavelengths 248–825 nm (~4.6 nm interval) with an integration time of 1 second. Five absorbance indicators and eight fluorescence indicators were calculated. The DOC concentration was estimated using the two-wavelength approach of Cook et al. (2017). The dataset consists of four files: Aqualog data processing and index calculation.R: R script with the code to process the Aqualog data and to calculate the indices. Aqualog.zip: Raw data obtained from the Aqualog, divided into subfolders corresponding to the day of measurement. For each sample, the absorbance spectrum (*ABS.dat) and EEMs of the sample (*SEM.dat) and corresponding blank measurement (*BEM.dat) are provided. Indices.xlsx: Table with index values for all samples. Samples.xlsx: Table with information about all samples (e.g. sampling site, storage duration, storage method) The geolocations of the sampling sites are: Site 102 - 50° 29' 6.09''N - 8° 33' 33.57''E Site 134 - 50° 28' 36.55''N - 8° 32' 39.56''E Site 175 - 50° 28' 34.05''N - 8° 32' 42.89''E Site 187 - 50° 28' 49.98''N - 8° 32' 35.12''E
description: {"references": ["Cook, S., Peacock, M., Evans, C.D., Page, S.E., Whelan, M.J., Gauci, V., Kho, L.K., 2017. Quantifying tropical peatland dissolved organic carbon (DOC) using UV-visible spectroscopy. Water Research 115, 229\u2013235. https://doi.org/10.1016/j.watres.2017.02.059"]}
DOI: 10.15131/shef.data.15060942
title: Data for paper "Modeling Musculoskeletal Dynamics During Gait: Evaluating the Best Personalization Strategy Through Model Anatomical Consistency"

description: This repository contains the data and models used to generate the results reported in the paper "Modeling Musculoskeletal Dynamics During Gait: Evaluating the Best Personalization Strategy Through Model Anatomical Consistency".Ethical approval for the studies was obtained from the local ethical comittees.<br>The 'Data_for_paper' folder contains 10 folders, one for each participant of the study (Subject1, ..., Subject10). Please, note that for some participants bi-lateral data are available, while for others only one body side was collected. Body sides are denoted by "_R/L" in the name of folders and models.Within each of these folders, two sets of models are available: Model1 contains the models generated under the scenario referred to as M1 in the paper; Model2 contains the models where ligament and cartilage forces were accounted for while simulating gait. Input data are available as well in folders "Data_L/R".<br>A .doc file containing all the additional results mentioned in the paper is made available as well. <br>
DOI: 10.15131/shef.data.15060942.v1
title: Data for paper "Modeling Musculoskeletal Dynamics During Gait: Evaluating the Best Personalization Strategy Through Model Anatomical Consistency"

description: This repository contains the data and models used to generate the results reported in the paper "Modeling Musculoskeletal Dynamics During Gait: Evaluating the Best Personalization Strategy Through Model Anatomical Consistency".Ethical approval for the studies was obtained from the local ethical comittees.<br>The 'Data_for_paper' folder contains 10 folders, one for each participant of the study (Subject1, ..., Subject10). Please, note that for some participants bi-lateral data are available, while for others only one body side was collected. Body sides are denoted by "_R/L" in the name of folders and models.Within each of these folders, two sets of models are available: Model1 contains the models generated under the scenario referred to as M1 in the paper; Model2 contains the models where ligament and cartilage forces were accounted for while simulating gait. Input data are available as well in folders "Data_L/R".<br>A .doc file containing all the additional results mentioned in the paper is made available as well. <br>
DOI: 10.5281/zenodo.4300253
title: Measurements of the solar wind propagation delay for L1 to Earth based on ACE and ground-based magnetometer data

description: This database is the basis for the analysis described in the manuscript 'Timing of the solar wind propagation delay between L1 and Earth based on machine learning' published in Journal of Space Weather and Space Climate. https://doi.org/10.1051/swsc/2021026 The database contains the times of 380 interplanetary shocks detected at ACE (T_ACE) which also caused a sudden impulse (T_SI) in the magnetosphere based on ground-based magnetometer data. This information can be found in 'measurement_SW_propagation.txt'. final_learningset_SWdelay.pickle contains the corresponding ACE data (solar wind speed, ACE position) at time T_ACE for each of the 380 cases. The datafile can be loaded with Python as follows: import pickle<br> with open('final_learningset_SWdelay.pickle', 'rb') as f:<br> [learnvector_o,learnvector_m,learnvector_s,learnvector,timevector]=pickle.load(f) The content is described as follows: learnvector_o - contains a list of ACE data in its original form, ordering ['rx','ry','rz','vx','vy','vz'] learnvector_m - median of each feature learnvector_s - standard deviation of each feature learnvector - contains an array of the standardized data, which have been used to train the ML models. timevector - contains an array with the vector delay in seconds(first column), flat delay in seconds (second column), and measured solar wind propagation delay in seconds (third column)
DOI: 10.5281/zenodo.4300252
title: Measurements of the solar wind propagation delay for L1 to Earth based on ACE and ground-based magnetometer data

description: This database is the basis for the analysis described in the manuscript 'Timing of the solar wind propagation delay between L1 and Earth based on machine learning' published in Journal of Space Weather and Space Climate. https://doi.org/10.1051/swsc/2021026 The database contains the times of 380 interplanetary shocks detected at ACE (T_ACE) which also caused a sudden impulse (T_SI) in the magnetosphere based on ground-based magnetometer data. This information can be found in 'measurement_SW_propagation.txt'. final_learningset_SWdelay.pickle contains the corresponding ACE data (solar wind speed, ACE position) at time T_ACE for each of the 380 cases. The datafile can be loaded with Python as follows: import pickle<br> with open('final_learningset_SWdelay.pickle', 'rb') as f:<br> [learnvector_o,learnvector_m,learnvector_s,learnvector,timevector]=pickle.load(f) The content is described as follows: learnvector_o - contains a list of ACE data in its original form, ordering ['rx','ry','rz','vx','vy','vz'] learnvector_m - median of each feature learnvector_s - standard deviation of each feature learnvector - contains an array of the standardized data, which have been used to train the ML models. timevector - contains an array with the vector delay in seconds(first column), flat delay in seconds (second column), and measured solar wind propagation delay in seconds (third column)
DOI: 10.5281/zenodo.5497867
title: Table S1

description: Radiomics features extracted from each VOI by using PyRadiomics software.
DOI: 10.5281/zenodo.5497866
title: Table S1

description: Radiomics features extracted from each VOI by using PyRadiomics software.
DOI: 10.17608/k6.auckland.13619297
title: Physicochemical and corresponding elastic properties of hydrothermally altered rocks from Whakaari (White Island) volcano, New Zealand

description: This data set accompanies the following manuscript:<i><br></i>Kanakiya, S., Adam, L., Rowe, M. C., Lindsay, J. M., & Esteban, L. (2021). The role of tuffs in sealing volcanic conduits. Manuscript accepted for publication in <i>Geophysical Research Letters</i>.<i><br></i><i><b>File descriptions:</b></i><i><br></i><i>Sample_Location_Type_Dimensions.txt </i>contains sampling locations and core dimensions of samples used in this study.<br><i>Raw_XRD_Data.txt</i> contains raw X-ray diffraction data of samples used in this study.<br><i>Phase_Composition_and_Hydrothermal_Alteration_Intensity.txt</i> contains semi-quantitative phase composition of samples used in this study.<br><i>Backscatter_Electron_Images.pdf</i> contains backscattered-electron images of samples used in this study. <br><i>Porosity_and_Permeability.txt</i> contains measurements of porosity, grain density, and permeability of samples used in this study.<br><i>Elastic_Properties.txt</i> contains measurements of P-wave velocity, S-wave velocity, and dynamic young's modulus of samples used in this study.
DOI: 10.17608/k6.auckland.13619297.v1
title: Physicochemical and corresponding elastic properties of hydrothermally altered rocks from Whakaari (White Island) volcano, New Zealand

description: This data set accompanies the following manuscript:<i><br></i>Kanakiya, S., Adam, L., Rowe, M. C., Lindsay, J. M., & Esteban, L. (2021). The role of tuffs in sealing volcanic conduits. Manuscript accepted for publication in <i>Geophysical Research Letters</i>.<i><br></i><i><b>File descriptions:</b></i><i><br></i><i>Sample_Location_Type_Dimensions.txt </i>contains sampling locations and core dimensions of samples used in this study.<br><i>Raw_XRD_Data.txt</i> contains raw X-ray diffraction data of samples used in this study.<br><i>Phase_Composition_and_Hydrothermal_Alteration_Intensity.txt</i> contains semi-quantitative phase composition of samples used in this study.<br><i>Backscatter_Electron_Images.pdf</i> contains backscattered-electron images of samples used in this study. <br><i>Porosity_and_Permeability.txt</i> contains measurements of porosity, grain density, and permeability of samples used in this study.<br><i>Elastic_Properties.txt</i> contains measurements of P-wave velocity, S-wave velocity, and dynamic young's modulus of samples used in this study.
DOI: 10.5281/zenodo.5497880
title: Cities

description: Test
DOI: 10.5281/zenodo.5497879
title: Cities

description: Test
DOI: 10.25318/3310036301-eng
title: Business or organization expectations over the next three months, third quarter of 2021

description: Expectations over the next three months by the business or organization, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2021.
DOI: 10.25318/3310036301-fra
title: Attentes des entreprises ou des organismes pour les trois prochains mois, troisième trimestre de 2021

description: Attentes des entreprises ou des organismes pour les trois prochains mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, troisième trimestre de 2021.
DOI: 10.5281/zenodo.5468947
title: Insect Vectors of Plant Disease

description: This is a database of Insect Vectors of Plant Disease worldwide. The database contains ~250 records of Auchenorrhyncha species (leafhoppers, planthoppers, treehoppers, froghoppers, spittlebugs) confirmed to be vectors of plant pathogens. Taxonomy, global distributions, species descriptions and plant pathogen relationships are included for each vector species. The website is available at https://Insectvectors.science. A RESTful Data APi is provided at https://Insectvectors.science/api
description: This work is funded from the BRIGIT project by UK Research and Innovation through the Strategic Priorities Fund, by a grant from BBSRC, with support from the Department for Environment, Food and Rural Affairs and the Scottish Government (BB/S016325/1)
DOI: 10.5281/zenodo.5468946
title: Insect Vectors of Plant Disease

description: This is a database of Insect Vectors of Plant Disease worldwide. The database contains ~250 records of Auchenorrhyncha species (leafhoppers, planthoppers, treehoppers, froghoppers, spittlebugs) confirmed to be vectors of plant pathogens. Taxonomy, global distributions, species descriptions and plant pathogen relationships are included for each vector species. The website is available at https://Insectvectors.science. A RESTful Data APi is provided at https://Insectvectors.science/api
description: This work is funded from the BRIGIT project by UK Research and Innovation through the Strategic Priorities Fund, by a grant from BBSRC, with support from the Department for Environment, Food and Rural Affairs and the Scottish Government (BB/S016325/1)
DOI: 10.25318/3310036401-eng
title: Business or organization obstacles over the next three months, third quarter of 2021

description: Obstacles faced by the business or organization over the next three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2021.