cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.13573811
title: Supplementary Information Files for Covid-19 vaccine hesitancy in the UK: The Oxford Coronavirus explanations, attitudes, and narratives survey (OCEANS) II

description: Supplementary Information Files for Covid-19 vaccine hesitancy in the UK: The Oxford Coronavirus explanations, attitudes, and narratives survey (OCEANS) IIBackground: Our aim was to estimate provisional willingness to receive a COVID-19 vaccine, identify predictive socio-demographic factors, and, principally, determine potential causes in order to guide information provision. Methods: A non-probability online survey was conducted (24th September-17th October 2020) with 5,114 UK adults, quota sampled to match the population for age, gender, ethnicity, income, and region. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to take an approved vaccine. Structural equation modelling estimated explanatory factor relationships. Results: 71.7% (n=3,667) were willing to be vaccinated, 16.6% (n=849) were very unsure, and 11.7% (n=598) were strongly hesitant. An excellent model fit (RMSEA=0.05/CFI=0.97/TLI=0.97), explaining 86% of variance in hesitancy, was provided by beliefs about the collective importance, efficacy, side effects, and speed of development of a COVID-19 vaccine. A second model, with reasonable fit (RMSEA=0.03/CFI=0.93/TLI=0.92), explaining 32% of variance, highlighted two higher-order explanatory factors: ‘excessive mistrust’ (r=0.51), including conspiracy beliefs, negative views of doctors, and need for chaos, and ‘positive healthcare experiences’ (r=-0.48), including supportive doctor interactions and good NHS care. Hesitancy was associated with younger age, female gender, lower income, and ethnicity, but socio-demographic information explained little variance (9.8%). Hesitancy was associated with lower adherence to social distancing guidelines. Conclusions: COVID-19 vaccine hesitancy is relatively evenly spread across the population. Willingness to take a vaccine is closely bound to recognition of the collective importance. Vaccine public information that highlights prosocial benefits may be especially effective. Factors such as conspiracy beliefs that foster mistrust and erode social cohesion will lower vaccine up-take.
DOI: 10.17028/rd.lboro.13573811.v1
title: Supplementary Information Files for Covid-19 vaccine hesitancy in the UK: The Oxford Coronavirus explanations, attitudes, and narratives survey (OCEANS) II

description: Supplementary Information Files for Covid-19 vaccine hesitancy in the UK: The Oxford Coronavirus explanations, attitudes, and narratives survey (OCEANS) IIBackground: Our aim was to estimate provisional willingness to receive a COVID-19 vaccine, identify predictive socio-demographic factors, and, principally, determine potential causes in order to guide information provision. Methods: A non-probability online survey was conducted (24th September-17th October 2020) with 5,114 UK adults, quota sampled to match the population for age, gender, ethnicity, income, and region. The Oxford COVID-19 Vaccine Hesitancy Scale assessed intent to take an approved vaccine. Structural equation modelling estimated explanatory factor relationships. Results: 71.7% (n=3,667) were willing to be vaccinated, 16.6% (n=849) were very unsure, and 11.7% (n=598) were strongly hesitant. An excellent model fit (RMSEA=0.05/CFI=0.97/TLI=0.97), explaining 86% of variance in hesitancy, was provided by beliefs about the collective importance, efficacy, side effects, and speed of development of a COVID-19 vaccine. A second model, with reasonable fit (RMSEA=0.03/CFI=0.93/TLI=0.92), explaining 32% of variance, highlighted two higher-order explanatory factors: ‘excessive mistrust’ (r=0.51), including conspiracy beliefs, negative views of doctors, and need for chaos, and ‘positive healthcare experiences’ (r=-0.48), including supportive doctor interactions and good NHS care. Hesitancy was associated with younger age, female gender, lower income, and ethnicity, but socio-demographic information explained little variance (9.8%). Hesitancy was associated with lower adherence to social distancing guidelines. Conclusions: COVID-19 vaccine hesitancy is relatively evenly spread across the population. Willingness to take a vaccine is closely bound to recognition of the collective importance. Vaccine public information that highlights prosocial benefits may be especially effective. Factors such as conspiracy beliefs that foster mistrust and erode social cohesion will lower vaccine up-take.
DOI: 10.5517/ccdc.csd.cc20swj7
title: CCDC 1870959: Experimental Crystal Structure Determination

description: Related Article: Sheng Feng, Kang Wang, Yifan Ping, Jianbo Wang|2020|J.Am.Chem.Soc.|142|21032|doi:10.1021/jacs.0c08089
DOI: 10.5517/ccdc.csd.cc25yc0j
title: CCDC 2024207: Experimental Crystal Structure Determination

description: Related Article: Yan Xu, Jonathan J. Wong, Adrian E. Samkian, Jeong Hoon Ko, Shuming Chen, K. N. Houk, Robert H. Grubbs|2020|J.Am.Chem.Soc.|142|20987|doi:10.1021/jacs.0c11334
DOI: 10.5517/ccdc.csd.cc25ytml
title: CCDC 2024660: Experimental Crystal Structure Determination

description: Related Article: Boyang Li, Wenyao Zhang, Shuai Lu, Bo Zheng, Dan Zhang, Anyang Li, Xiaopeng Li, Xiao-Juan Yang, Biao Wu|2020|J.Am.Chem.Soc.|142|21160|doi:10.1021/jacs.0c10346
DOI: 10.6084/m9.figshare.13567409.v1
title: Data for the combined Cogcarsim studies 2017+2019

description: SQL databases cogcarsim2_2017.db & cogcarsim2_2019.db contain the CogCarSim log data of 18 subjects, 9 from 2017 and 9 from 2019.<br>background_2017.csv & background_2019.csv contain profile data on 18 subjects. background_info.csv combines them.<br>fss_data_2017.csv & fss_data_2019.csv contain Flow Short Scale self-report data on 18 subjects. <br>fss_learning.csv combines them and adds variables on learning derived from models fitted to data from the SQL database files. This file is generated by the accompanying R code cogcarsim_analyses.R
DOI: 10.5281/zenodo.4438619
title: Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq

description: <strong>Beyondcell </strong>is a methodology for the identification of drug vulnerabilities in single cell RNA-seq data. To this end, <strong>Beyondcell</strong> focuses on the analysis of drug-related commonalities between cells by classifying them into distinct therapeutic clusters. We have validated the tool in a population of MCF7-AA cells exposed to 500nM of bortezomib and collected at different time points: t0 (before treatment), t12, t48 and t96 (72h treatment followed by drug wash and 24h of recovery) obtained from <strong><em>Ben-David U, et al., Nature, 2018</em></strong>. Here, you can find the integrated Seurat object obtained from this analysis. This object is meant to help users follow <strong>Beyondcell's</strong> analysis workflow.
description: The original single-cell data can be accessed through the Gene Expression Omnibus (GEO), accession number GSE114462.
DOI: 10.15468/e4pffg
title: Cockscomb-shaped twighopper, Cladonotus bhaskari sp. n., a new and rare pygmy grasshopper species from Sri Lanka (Orthoptera: Tetrigidae: Cladonotinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tumbrinck, Josef, Deranja, Maks, Adžić, Karmela, Pavlović, Marko, Skejo, Josip (2020): Cockscomb-shaped twighopper, Cladonotus bhaskari sp. n., a new and rare pygmy grasshopper species from Sri Lanka (Orthoptera: Tetrigidae: Cladonotinae). Zootaxa 4821 (2): 333-342, DOI: https://doi.org/10.11646/zootaxa.4821.2.5
DOI: 10.5281/zenodo.4319919
title: 3D Models from Menez Dregan I site (Plouhinec, Finistère, France)

description: 64 3D models (.pdf) of the handaxes and cleavers from Menez Dregan I sequence (Plouhinec, Finistère, France), Layers 8 to 4. The models are unscaled. If you need metrical information, please contact the author. The .xlsx file contains the list of tools (including code and layer information).
DOI: 10.5281/zenodo.4319918
title: 3D Models from Menez Dregan I site (Plouhinec, Finistère, France)

description: 64 3D models (.pdf) of the handaxes and cleavers from Menez Dregan I sequence (Plouhinec, Finistère, France), Layers 8 to 4. The models are unscaled. If you need metrical information, please contact the author. The .xlsx file contains the list of tools (including code and layer information).
DOI: 10.15468/g8gswa
title: First zoeal stage of the snapping shrimps Alpheus formosus Gibbes, 1850 and Alpheus malleator Dana, 1852 (Caridea: Alpheidae), with new characters to the genus

description: This dataset contains the digitized treatments in Plazi based on the original journal article Pasinatto, Karmine, Mantelatto, Fernando L., Terossi, Mariana (2020): First zoeal stage of the snapping shrimps Alpheus formosus Gibbes, 1850 and Alpheus malleator Dana, 1852 (Caridea: Alpheidae), with new characters to the genus. Zootaxa 4820 (3): 540-550, DOI: https://doi.org/10.11646/zootaxa.4820.3.7
DOI: 10.5281/zenodo.4438663
title: Smart Building Model (SBuM) Data

description: This dataset is to be used in conjonction with the SBuM model available online at: https://github.com/jeanlouisnico/SBuM Download add the file into the path of your MatLab environment to allow MatLab to locate the files. v1.1: added the zip file that it is easier to download all the files at once.
DOI: 10.5281/zenodo.4290267
title: Smart Building Model (SBuM) Data

description: This dataset is to be used in conjonction with the SBuM model available online at: https://github.com/jeanlouisnico/SBuM Download add the file into the path of your MatLab environment to allow MatLab to locate the files. v1.1: added the zip file that it is easier to download all the files at once.
DOI: 10.5281/zenodo.4262543
title: Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"

description: Data from the paper: Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w Link: https://www.nature.com/articles/s41598-020-80809-w This repository contains: 1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL. 2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data. 3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif). If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).
description: {"references": ["Dalagnol, R.\u00a0et al.\u00a0Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates.\u00a0Sci Rep\u00a011,\u00a01388 (2021). https://doi.org/10.1038/s41598-020-80809-w"]}
DOI: 10.5281/zenodo.4262542
title: Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"

description: Data from the paper: Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w Link: https://www.nature.com/articles/s41598-020-80809-w This repository contains: 1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL. 2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data. 3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif). If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).
description: {"references": ["Dalagnol, R.\u00a0et al.\u00a0Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates.\u00a0Sci Rep\u00a011,\u00a01388 (2021). https://doi.org/10.1038/s41598-020-80809-w"]}
DOI: 10.15468/4dkcyc
title: Insights into the Urogymnid whiprays (Chondrichthyes: Batoidea) in the Persian Gulf and the Gulf of Oman, with an amendment of their diagnostic characteristics and dispersal range

description: This dataset contains the digitized treatments in Plazi based on the original journal article Golzarianpour, Kiavash, Malek, Masoumeh, Golestaninasab, Mehdi, Sarafrazi, Alimorad, Kochmann, Judith, Klimpel, Sven (2020): Insights into the Urogymnid whiprays (Chondrichthyes: Batoidea) in the Persian Gulf and the Gulf of Oman, with an amendment of their diagnostic characteristics and dispersal range. Zootaxa 4819 (2): 316-334, DOI: https://doi.org/10.11646/zootaxa.4819.2.5
DOI: 10.6084/m9.figshare.13573847
title: DefaultData

description: DefaultData dataset contains 30000 instances including 23364 non-default instances and 6636 default instances. Each instance contains 24 features (15 numeric features and 9 categorical features).<br><br>
DOI: 10.5281/zenodo.4438584
title: Dataset for Direct field evidence of autocatalytic iodine release from atmospheric aerosol

description: The datasets generated and/or analyzed for the publication of Tham et al., 'Direct field evidence of autocatalytic iodine release from atmospheric aerosol' in PNAS (https://doi.org/10.1073/pnas.2009951118). Data are also available from the corresponding authors upon request.
DOI: 10.5281/zenodo.4438583
title: Dataset for Direct field evidence of autocatalytic iodine release from atmospheric aerosol

description: The datasets generated and/or analyzed for the publication of Tham et al., 'Direct field evidence of autocatalytic iodine release from atmospheric aerosol' in PNAS (https://doi.org/10.1073/pnas.2009951118). Data are also available from the corresponding authors upon request.
DOI: 10.6084/m9.figshare.13573856
title: PPDaiData

description: PPDai dataset contains 55596 instances including 48413 non-default instances and 7183 default instances. Each instance contains 29 features (22 numeric features and 7 categorical features).
DOI: 10.5281/zenodo.4269715
title: Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity

description: Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity
description: {"references": ["Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity"]}
DOI: 10.5281/zenodo.4415762
title: Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity

description: Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity
description: {"references": ["Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity"]}
DOI: 10.15468/sq2eep
title: A new Himalayan species of Alfieriella (Coleoptera: Cryptophagidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Chen, Yan-Dong, Leschen, Richard A. B., Bai, Ming, Yang, Xing-Ke (2020): A new Himalayan species of Alfieriella (Coleoptera: Cryptophagidae). Zootaxa 4819 (3): 571-581, DOI: https://doi.org/10.11646/zootaxa.4819.3.8
DOI: 10.6084/m9.figshare.13573871
title: DefaultData and PPDaiData

description: DefaultData dataset is obtained from the UCI machine learning library (Asuncion & Newman, 2007). PPDai dataset is obtained from customer data from a Chinese P2P lending platform named PaiPaiDai .A brief description of these datasets is shown in Table 1. DefaultData dataset contains 30000 instances including 23364 non-default instances and 6636 default instances. Each instance contains 24 features (15 numeric features and 9 categorical features). PPDai dataset contains 55596 instances including 48413 non-default instances and 7183 default instances. Each instance contains 29 features (22 numeric features and 7 categorical features).
DOI: 10.25646/7845
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5281/zenodo.4438608
title: ManyTypes4Py: A benchmark Python Dataset for Machine Learning-Based Type Inference

description: The dataset is gathered on Sep. 17th 2020. It has more than 5.4K Python repositories that are hosted on GitHub. Check out the file <strong>ManyTypes4PyDataset.spec </strong>for repositories URL and their commit SHA. The dataset is also de-duplicated using the CD4Py tool. The list of duplicate files is provided in <strong>duplicate_files.txt </strong>file. All of its Python projects are processed in JSON-formatted files. They contain a seq2seq representation of each file, type-related hints, and information for machine learning models. The structure of JSON-formatted files is described in <strong>JSONOutput.md</strong> file. The dataset is split into train, validation and test sets by source code files. The list of files and their corresponding set is provided in <strong>dataset_split.csv</strong> file.
DOI: 10.5281/zenodo.4438474
title: Datasets associated with the publication of the "satuRn" R package

description: On this Zenodo link, we share the data that is required to reproduce all the analyses from our publication "satuRn: Scalable Analysis of differential Transcript Usage for bulk and single-cell RNA-sequencing applications". This repository includes input transcript-level expression matrices and metadata for all datasets, as well as intermediate results and final outputs of the respective DTU analyses. For a more elaborate description of the data, we refer to the companion GitHub for our publications; https://github.com/statOmics/satuRnPaper. Note that this is version 1.0.0 of the data (uploaded on 2021-01-14). If any changes were to be made to the datasets in the future, this will also be communicated on our companion GitHub page.
DOI: 10.11583/dtu.12933431
title: A guidance for navigating trade-offs to support sustainability-related decision-making

description: The purpose of this guidance is to support decision-making process when sustainability-related trade-offs arise. The guidance relies on a structured step-by-step procedure and support tools embedded in an Excel workbook.
DOI: 10.11583/dtu.12933431.v3
title: A guidance for navigating trade-offs to support sustainability-related decision-making

description: The purpose of this guidance is to support decision-making process when sustainability-related trade-offs arise. The guidance relies on a structured step-by-step procedure and support tools embedded in an Excel workbook.
DOI: 10.11583/dtu.12933431.v2
title: A guidance for navigating trade-offs to support sustainability-related decision-making

description: The purpose of this guidance is to support decision-making process when sustainability-related trade-offs arise. The guidance relies on a structured step-by-step procedure and support tools embedded in an Excel workbook.
DOI: 10.6084/m9.figshare.13573963
title: Supplementary Data from Touch inhibits touch: sanshool-induced paradoxical tingling reveals perceptual interaction between somatosensory submodalities

description: Data for all the experiments
DOI: 10.6084/m9.figshare.13573963.v1
title: Supplementary Data from Touch inhibits touch: sanshool-induced paradoxical tingling reveals perceptual interaction between somatosensory submodalities

description: Data for all the experiments
DOI: 10.5517/ccdc.csd.cc2412z8
title: CCDC 1967259: Experimental Crystal Structure Determination

description: Related Article: Junjian Wang, Donghui Wei, Zheng Duan, François Mathey|2020|J.Am.Chem.Soc.|142|20973|doi:10.1021/jacs.0c11195
DOI: 10.5517/ccdc.csd.cc24135h
title: CCDC 1967265: Experimental Crystal Structure Determination

description: Related Article: Junjian Wang, Donghui Wei, Zheng Duan, François Mathey|2020|J.Am.Chem.Soc.|142|20973|doi:10.1021/jacs.0c11195
DOI: 10.5517/ccdc.csd.cc25nffq
title: CCDC 2015633: Experimental Crystal Structure Determination

description: Related Article: Arnab Bhattacharyya, Aida Jameei, Rupak Saha, Aditya Garai, Anjali A. Karande, Akhil R. Chakravarty|2021|Dalton Trans.|50|103|doi:10.1039/D0DT03342A
DOI: 10.5517/ccdc.csd.cc260jf6
title: CCDC 2026297: Experimental Crystal Structure Determination

description: Related Article: Junjian Wang, Donghui Wei, Zheng Duan, François Mathey|2020|J.Am.Chem.Soc.|142|20973|doi:10.1021/jacs.0c11195
DOI: 10.5517/ccdc.csd.cc260jg7
title: CCDC 2026298: Experimental Crystal Structure Determination

description: Related Article: Junjian Wang, Donghui Wei, Zheng Duan, François Mathey|2020|J.Am.Chem.Soc.|142|20973|doi:10.1021/jacs.0c11195
DOI: 10.5517/ccdc.csd.cc260vkn
title: CCDC 2026611: Experimental Crystal Structure Determination

description: Related Article: Yan Zhao, Lei Li, Bo Ding, Xiu-Guang Wang, Zheng-Yu Liu, En-Cui Yang, Xiao-Jun Zhao|2021|Dalton Trans.|50|197|doi:10.1039/D0DT03662E
DOI: 10.5517/ccdc.csd.cc2656p9
title: CCDC 2030800: Experimental Crystal Structure Determination

description: Related Article: Junjian Wang, Donghui Wei, Zheng Duan, François Mathey|2020|J.Am.Chem.Soc.|142|20973|doi:10.1021/jacs.0c11195
DOI: 10.5517/ccdc.csd.cc269rx6
title: CCDC 2035178: Experimental Crystal Structure Determination

description: Related Article: Yong Ho Lee, Elliott H. Denton, Bill Morandi|2020|J.Am.Chem.Soc.|142|20948|doi:10.1021/jacs.0c10832
DOI: 10.5517/ccdc.csd.cc269ry7
title: CCDC 2035179: Experimental Crystal Structure Determination

description: Related Article: Yong Ho Lee, Elliott H. Denton, Bill Morandi|2020|J.Am.Chem.Soc.|142|20948|doi:10.1021/jacs.0c10832
DOI: 10.5517/ccdc.csd.cc26l4sr
title: CCDC 2043234: Experimental Crystal Structure Determination

description: Related Article: Boyang Li, Wenyao Zhang, Shuai Lu, Bo Zheng, Dan Zhang, Anyang Li, Xiaopeng Li, Xiao-Juan Yang, Biao Wu|2020|J.Am.Chem.Soc.|142|21160|doi:10.1021/jacs.0c10346
DOI: 10.5517/ccdc.csd.cc26n7vz
title: CCDC 2045251: Experimental Crystal Structure Determination

description: Related Article: Yong Ho Lee, Elliott H. Denton, Bill Morandi|2020|J.Am.Chem.Soc.|142|20948|doi:10.1021/jacs.0c10832
DOI: 10.5517/ccdc.csd.cc26n7w0
title: CCDC 2045252: Experimental Crystal Structure Determination

description: Related Article: Yong Ho Lee, Elliott H. Denton, Bill Morandi|2020|J.Am.Chem.Soc.|142|20948|doi:10.1021/jacs.0c10832