cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc22hfst
title: CCDC 1921466: Experimental Crystal Structure Determination

description: Related Article: Réka Mokrai, Anabella Mocanu, Matthew P. Duffy, Thomas Vives, Elsa Caytan, Vincent Dorcet, Thierry Roisnel, László Nyulászi, Zoltán Benkő, Pierre-Antoine Bouit, Muriel Hissler|2021|Chem.Commun.|57|7256|doi:10.1039/D1CC02293H
DOI: 10.5517/ccdc.csd.cc25pwrj
title: CCDC 2017038: Experimental Crystal Structure Determination

description: Related Article: Zarife Sibel Şahin, Mehmet Demir, Tuğrul Yıldırım, Ömer Yurdakul, Dursun Ali Köse|2021|Int.J.Hydro.Eneg|46|27631|doi:10.1016/j.ijhydene.2021.06.026
DOI: 10.5517/ccdc.csd.cc260npl
title: CCDC 2026429: Experimental Crystal Structure Determination

description: Related Article: Jinhu Xi, Xinjie Wu, Mengmeng Huang, Jung Keun Kim, Jianye Zhang, Yabo Li, Yangjie Wu|2021|Chem.Commun.|57|7308|doi:10.1039/D1CC02399C
DOI: 10.5517/ccdc.csd.cc26ckrx
title: CCDC 2036909: Experimental Crystal Structure Determination

description: Related Article: Jinhu Xi, Xinjie Wu, Mengmeng Huang, Jung Keun Kim, Jianye Zhang, Yabo Li, Yangjie Wu|2021|Chem.Commun.|57|7308|doi:10.1039/D1CC02399C
DOI: 10.5517/ccdc.csd.cc26cksy
title: CCDC 2036910: Experimental Crystal Structure Determination

description: Related Article: Jinhu Xi, Xinjie Wu, Mengmeng Huang, Jung Keun Kim, Jianye Zhang, Yabo Li, Yangjie Wu|2021|Chem.Commun.|57|7308|doi:10.1039/D1CC02399C
DOI: 10.5517/ccdc.csd.cc274q15
title: CCDC 2060106: Experimental Crystal Structure Determination

description: Related Article: Qian Xiong, Qiliang Luo, Tinghui Zhang, Shunxi Dong, Xiaohua Liu, Xiaoming Feng|2021|Chem.Commun.|57|7288|doi:10.1039/D1CC02939H
DOI: 10.5517/ccdc.csd.cc27pjr7
title: CCDC 2076279: Experimental Crystal Structure Determination

description: Related Article: Hanah Na, Michael B. Watson, Fengzhi Tang, Nigam P. Rath, Liviu M. Mirica|2021|Chem.Commun.|57|7264|doi:10.1039/D1CC02114A
DOI: 10.5517/ccdc.csd.cc27pjs8
title: CCDC 2076280: Experimental Crystal Structure Determination

description: Related Article: Hanah Na, Michael B. Watson, Fengzhi Tang, Nigam P. Rath, Liviu M. Mirica|2021|Chem.Commun.|57|7264|doi:10.1039/D1CC02114A
DOI: 10.5517/ccdc.csd.cc251zy5
title: CCDC 1997917: Experimental Crystal Structure Determination

description: Related Article: Qian Xiong, Qiliang Luo, Tinghui Zhang, Shunxi Dong, Xiaohua Liu, Xiaoming Feng|2021|Chem.Commun.|57|7288|doi:10.1039/D1CC02939H
DOI: 10.5517/ccdc.csd.cc27rq2t
title: CCDC 2078366: Experimental Crystal Structure Determination

description: Related Article: Xun-Hui Zhou, Ying Zeng, Shao-Bin Tang, Zi-Ru Yu, Li-Ming Cao, Zi-Yi Du, Chun-Ting He|2021|Chem.Commun.|57|7292|doi:10.1039/D1CC02061G
DOI: 10.5517/ccdc.csd.cc27rq3v
title: CCDC 2078367: Experimental Crystal Structure Determination

description: Related Article: Xun-Hui Zhou, Ying Zeng, Shao-Bin Tang, Zi-Ru Yu, Li-Ming Cao, Zi-Yi Du, Chun-Ting He|2021|Chem.Commun.|57|7292|doi:10.1039/D1CC02061G
DOI: 10.5517/ccdc.csd.cc27t9v7
title: CCDC 2079909: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27t9w8
title: CCDC 2079910: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27t9x9
title: CCDC 2079911: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27t9yb
title: CCDC 2079912: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27v5fq
title: CCDC 2080733: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27v5gr
title: CCDC 2080734: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27v5hs
title: CCDC 2080735: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc27vcbt
title: CCDC 2080916: Experimental Crystal Structure Determination

description: Related Article: Binsen Rong, Gaochen Xu, Huan Yan, Sai Zhang, Qinghuan Wu, Ning Zhu, Jindian Duan, Kai Guo|2021|Chem.Commun.|57|7272|doi:10.1039/D1CC02422A
DOI: 10.5517/ccdc.csd.cc2763rb
title: CCDC 2061461: Experimental Crystal Structure Determination

description: Related Article: Kun Liu, Kaiwei Huang, Anqi Lv, Wenpeng Ye, Yijun Yang, Kang Shen, Jiahuan Zhi, He Wang, Renxiang Zhang, Jiamin Wang, Huili Ma, Huifang Shi, Wei Yao, Zhongfu An, Wei Huang|2021|Chem.Commun.|57|7276|doi:10.1039/D1CC01563J
DOI: 10.5517/ccdc.csd.cc2763sc
title: CCDC 2061462: Experimental Crystal Structure Determination

description: Related Article: Kun Liu, Kaiwei Huang, Anqi Lv, Wenpeng Ye, Yijun Yang, Kang Shen, Jiahuan Zhi, He Wang, Renxiang Zhang, Jiamin Wang, Huili Ma, Huifang Shi, Wei Yao, Zhongfu An, Wei Huang|2021|Chem.Commun.|57|7276|doi:10.1039/D1CC01563J
DOI: 10.5517/ccdc.csd.cc2763td
title: CCDC 2061463: Experimental Crystal Structure Determination

description: Related Article: Kun Liu, Kaiwei Huang, Anqi Lv, Wenpeng Ye, Yijun Yang, Kang Shen, Jiahuan Zhi, He Wang, Renxiang Zhang, Jiamin Wang, Huili Ma, Huifang Shi, Wei Yao, Zhongfu An, Wei Huang|2021|Chem.Commun.|57|7276|doi:10.1039/D1CC01563J
DOI: 10.5517/ccdc.csd.cc27zxbh
title: CCDC 2085287: Experimental Crystal Structure Determination

description: Related Article: Emanuele Priola, Ghodrat Mahmoudi, Jacopo Andreo, Antonio Frontera|2021|Chem.Commun.|57|7268|doi:10.1039/D1CC02709C
DOI: 10.5517/ccdc.csd.cc27v5jt
title: CCDC 2080736: Experimental Crystal Structure Determination

description: Related Article: Ian A. Pocock, Alya M. Alotaibi, Kesar Jagdev, Connor Prior, Gregory R. Burgess, Louise Male, Richard S. Grainger|2021|Chem.Commun.|57|7252|doi:10.1039/D1CC02892H
DOI: 10.5517/ccdc.csd.cc26kxtj
title: CCDC 2043018: Experimental Crystal Structure Determination

description: Related Article: Yanan Ma, Xiaoping Yang, Zhiyin Xiao, Xiaoming Liu, Dongliang Shi, Mengyu Niu, Desmond Schipper|2021|Chem.Commun.|57|7316|doi:10.1039/D1CC01706C
DOI: 10.5517/ccdc.csd.cc2802yb
title: CCDC 2085461: Experimental Crystal Structure Determination

description: Related Article: Nathan Jackson, Irma Rocio Vazquez, Ying-Pin Chen, Yu-Sheng Chen, Wen-Yang Gao|2021|Chem.Commun.|57|7248|doi:10.1039/D1CC02806E
DOI: 10.34810/data72/1064
title: 03CO_1.MR.0004.0044.2016.02.04.15.48.15.906250.67232803.IMA
DOI: 10.34808/893y-x553
title: Dataset for a research study on scientific productivity of Polish technical universities (Silesian University Of Technology 2016-2020).

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in June 2021 by the SciVal benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles.The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of Silesian University of Technology consists records from 2016-2020:
1) Summary for Silesian University of Technology
2) Collaboration
3) Citation Count
4) Scholarly Output
5) Topic Clusters.
DOI: 10.5281/zenodo.4495941
title: Data Set of A Spatial and Dynamic Solution for how to Allocate the COVID-19 Vaccine in the Context of Limited Supply

description: All are about the original data or result data in the article of "A Spatial and Dynamic Solution for how to Allocate the COVID-19 Vaccine in the Context of Limited Supply"
DOI: 10.34810/data72/1065
title: 03AC_1.MR.0004.0215.2013.03.24.12.28.52.968750.171715511.IMA
DOI: 10.34740/kaggle/dsv/2236600
title: VVAD-LRS3
DOI: 10.17028/rd.lboro.15035118.v1
title: Supplementary information files for Maternal height-standardized prevalence of stunting in 67 low- and middle-income countries

description: Supplementary files for article Maternal height-standardized prevalence of stunting in 67 low- and middle-income countries.Background: Prevalence of stunting is frequently used as a marker of population-level child undernutrition. Parental height varies widely in low- and middle-income countries (LMIC) and is also a major determinant of stunting. While stunting is a useful measure of child health, with multiple causal components, removing the component attributable to parental height may in some cases be helpful to identify shortcoming in current environments.<br>Methods: We estimated maternal height-standardized prevalence of stunting (SPS) in 67 LMICs and parental height-SPS in 20 LMICs and compared with crude prevalence of stunting (CPS) using data on 575,767 children under-five from 67 Demographic and Health Surveys (DHS). We supplemented the DHS with population-level measures of other child health outcomes from the World Health Organization’s (WHO) Global Health Observatory and the United Nations’ Inter-Agency Group for Child Mortality Estimation. Prevalence of stunting was defined as percentage of children with height-for-age falling below −2 z-scores from the median of the 2006 WHO growth standard.<br>Results: The average CPS across countries was 27.8% (95% confidence interval [CI], 27.5–28.1%) and the average SPS was 23.3% (95% CI, 23.0–23.6%). The rank of countries according to SPS differed substantially from the rank according to CPS. Guatemala, Bangladesh, and Nepal had the biggest improvement in ranking according to SPS compared to CPS, while Gambia, Mali, and Senegal had the biggest decline in ranking. Guatemala had the largest difference between CPS and SPS with a CPS of 45.2 (95% CI, 43.7–46.9%) and SPS of 14.1 (95% CI, 12.6–15.8%). Senegal had the largest increase in the prevalence after standardizing maternal height, with a CPS of 28.0% (95% CI, 25.8–30.2%) and SPS of 31.6% (95% CI, 29.5–33.8%). SPS correlated better than CPS with other population-level measures of child health.<br>Conclusions: Our study suggests that CPS is sensitive to adjustment for maternal height. Maternal height, while a strong predictor of child stunting, is not amenable to policy interventions. We showed the plausibility of SPS in capturing current exposures to undernutrition and infections in children.
DOI: 10.17028/rd.lboro.15035118
title: Supplementary information files for Maternal height-standardized prevalence of stunting in 67 low- and middle-income countries

description: Supplementary files for article Maternal height-standardized prevalence of stunting in 67 low- and middle-income countries.Background: Prevalence of stunting is frequently used as a marker of population-level child undernutrition. Parental height varies widely in low- and middle-income countries (LMIC) and is also a major determinant of stunting. While stunting is a useful measure of child health, with multiple causal components, removing the component attributable to parental height may in some cases be helpful to identify shortcoming in current environments.<br>Methods: We estimated maternal height-standardized prevalence of stunting (SPS) in 67 LMICs and parental height-SPS in 20 LMICs and compared with crude prevalence of stunting (CPS) using data on 575,767 children under-five from 67 Demographic and Health Surveys (DHS). We supplemented the DHS with population-level measures of other child health outcomes from the World Health Organization’s (WHO) Global Health Observatory and the United Nations’ Inter-Agency Group for Child Mortality Estimation. Prevalence of stunting was defined as percentage of children with height-for-age falling below −2 z-scores from the median of the 2006 WHO growth standard.<br>Results: The average CPS across countries was 27.8% (95% confidence interval [CI], 27.5–28.1%) and the average SPS was 23.3% (95% CI, 23.0–23.6%). The rank of countries according to SPS differed substantially from the rank according to CPS. Guatemala, Bangladesh, and Nepal had the biggest improvement in ranking according to SPS compared to CPS, while Gambia, Mali, and Senegal had the biggest decline in ranking. Guatemala had the largest difference between CPS and SPS with a CPS of 45.2 (95% CI, 43.7–46.9%) and SPS of 14.1 (95% CI, 12.6–15.8%). Senegal had the largest increase in the prevalence after standardizing maternal height, with a CPS of 28.0% (95% CI, 25.8–30.2%) and SPS of 31.6% (95% CI, 29.5–33.8%). SPS correlated better than CPS with other population-level measures of child health.<br>Conclusions: Our study suggests that CPS is sensitive to adjustment for maternal height. Maternal height, while a strong predictor of child stunting, is not amenable to policy interventions. We showed the plausibility of SPS in capturing current exposures to undernutrition and infections in children.
DOI: 10.34810/data72/1066
title: 03AC_1.MR.0004.0020.2013.03.24.12.28.52.968750.171711994.IMA
DOI: 10.5255/ukda-sn-855084
title: Zoomshock: The Geography and Local Labour Market Consequences of Working from Home, 2020-2021

description: The increase in the extent of working-from-home determined by the COVID-19 health crisis has led to a substantial shift of economic activity across geographical areas; which we refer to as a Zoomshock. When a person works from home rather than at the office, their work-related consumption of goods and services provided by the locally consumed service industries will take place where they live, not where they work. Much of the clientèle of restaurants, coffee bars, pubs, hair stylists, health clubs, taxi providers and the like located near workplaces is transferred to establishment located near where people live. These data are our calculations of the Zoomshock at the MSOA level. They reflect estimats of the change in the number of people working in UK neighbourhoods due to home-working.
DOI: 10.34810/data72/1067
title: 03AC_1.MR.0004.0156.2013.03.24.12.28.52.968750.171714442.IMA
DOI: 10.5281/zenodo.5114771
title: MACS - Multi-Annotator Captioned Soundscapes

description: This is a dataset containing <strong>audio captions </strong>and corresponding<strong> audio tags</strong> for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool. Each file is annotated by multiple annotators that provided tags and a one-sentence description of the audio content. The data also includes<strong> annotator competence </strong>estimated using MACE (Multi-Annotator Competence Estimation). The annotation procedure, processing and analysis of the data are presented in the following papers: Irene Martin-Morato, Annamaria Mesaros. <em>What is the ground truth? Reliability of multi-annotator data for audio tagging, </em>29th European Signal Processing Conference, EUSIPCO 2021 Irene Martin-Morato, Annamaria Mesaros. <em>Diversity and bias in audio captioning datasets, </em>submitted to DCASE 2021 Workshop (to be updated with arxiv link) Data is provided as two files: <strong>MACS.yaml</strong> - containing the complete annotations in the following format: - filename: file1.wav <br> annotations:<br> - annotator_id: ann_1<br> sentence: caption text<br> tags:<br> - tag1<br> - tag2 <br> - annotator_id: ann_2<br> sentence: caption text<br> tags:<br> - tag1 <strong>MACS_competence.csv</strong> - containing the estimated annotator competence; for each annotator_id in the yaml file, competence is a number between 0 (considered as annotating at random) and 1 id [tab] competence The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.
DOI: 10.5281/zenodo.5114770
title: MACS - Multi-Annotator Captioned Soundscapes

description: This is a dataset containing <strong>audio captions </strong>and corresponding<strong> audio tags</strong> for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool. Each file is annotated by multiple annotators that provided tags and a one-sentence description of the audio content. The data also includes<strong> annotator competence </strong>estimated using MACE (Multi-Annotator Competence Estimation). The annotation procedure, processing and analysis of the data are presented in the following papers: Irene Martin-Morato, Annamaria Mesaros. <em>What is the ground truth? Reliability of multi-annotator data for audio tagging, </em>29th European Signal Processing Conference, EUSIPCO 2021 Irene Martin-Morato, Annamaria Mesaros. <em>Diversity and bias in audio captioning datasets, </em>submitted to DCASE 2021 Workshop (to be updated with arxiv link) Data is provided as two files: <strong>MACS.yaml</strong> - containing the complete annotations in the following format: - filename: file1.wav <br> annotations:<br> - annotator_id: ann_1<br> sentence: caption text<br> tags:<br> - tag1<br> - tag2 <br> - annotator_id: ann_2<br> sentence: caption text<br> tags:<br> - tag1 <strong>MACS_competence.csv</strong> - containing the estimated annotator competence; for each annotator_id in the yaml file, competence is a number between 0 (considered as annotating at random) and 1 id [tab] competence The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.
DOI: 10.34810/data72/1068
title: 03CO_1.MR.0004.0125.2016.02.04.15.48.15.906250.67236996.IMA
DOI: 10.6084/m9.figshare.14974002
title: Foulwind geochem data for appendix_revised.xlsxMajor and Trace element geochemical data for the Foulwind and Windy Point Granites, New Zealand.

description: Major and trace element data for granites and xenoliths from Foulwind Grante and Windy Point Granite, New Zealand
DOI: 10.6084/m9.figshare.14974002.v1
title: Foulwind geochem data for appendix_revised.xlsxMajor and Trace element geochemical data for the Foulwind and Windy Point Granites, New Zealand.

description: Major and trace element data for granites and xenoliths from Foulwind Grante and Windy Point Granite, New Zealand
DOI: 10.6084/m9.figshare.15035130
title: Formic acid modulates latency and accuracy of nestmate recognition in carpenter ants

description: Entire dataset
DOI: 10.6084/m9.figshare.15035130.v1
title: Formic acid modulates latency and accuracy of nestmate recognition in carpenter ants

description: Entire dataset
DOI: 10.5255/ukda-sn-855048
title: Phase 3 Staff and Detainee Survey Data, 2016-2017

description: In 2016 and 2017, survey data were collected from 473 staff and 371 detainees in 27 custody suites in 13 police forces in England and Wales using two separate closed-question questionnaires. Data were collected about the following themes: (i) perceptions of fairness and the use of authority staff; (ii) the conditions of custody; (iii) reactions to being detained in police custody (detainees); (iv) perceptions of governance and accountability; (v) views on the occupational culture within the custody suite; (vi) distinctions between different types of police custody workers; (vi) the purposes of police custody; (vii) and risk. All of these survey items entailed Likert scale responses of strongly agree, agree, neither agree nor disagree, disagree or strongly disagree. Demographic data, as well as information about how long people had worked in or been detained in police custody and what detainees had been arrested for were also collected.
DOI: 10.6084/m9.figshare.15035199
title: Supplementary Material for: Anatomic Distribution of Cherry Angiomas in the General Population

description: <b><i>Background:</i></b> Cherry angiomas are common benign vascular skin lesions of unknown aetiology, found largely on the trunk. However, their exact anatomic distribution besides their truncal predisposition, and how they manifest in the general population, has not been characterised. <b><i>Methods:</i></b> Three-dimensional (3D) total body imaging was obtained from 163 adult participants of a general population cohort study in Brisbane, Australia. Demographic, phenotypic, and sun behaviour characteristics were collected using a standard questionnaire along with history of melanoma and keratinocyte cancers. Cherry angiomas were identified using an automated classification algorithm with a sensitivity of 87% and a specificity of 99%, developed specifically for this study population. <b><i>Results:</i></b> The 3D total body images of 163 participants were analysed. Participants had a median age of 57 years and 61% were male. On average, males had more angiomas than females (median of 16 vs. 12) and the number and size of cherry angiomas increased with age. In addition to male sex and age, an increase in angiomas was associated with Caucasian ancestry other than British/Irish only, fair skin colour opposed to medium/olive, having green/hazel eyes compared to blue/grey, and personal history of melanoma. The most common site for cherry angiomas was the front trunk, followed by the back. Interestingly, although males had more angiomas overall, females had more angiomas on the legs. <b><i>Conclusion:</i></b> Describing the distribution of cherry angiomas by body site is an important step towards further understanding of the aetiology of angiomas. While personal history of melanoma is associated with an increased number of cherry angiomas, whether this association is prognostic, co-occurs with development of melanoma, or is merely fortuitous requires further investigation.
DOI: 10.34810/data72/1069
title: 03CO_1.MR.0004.0225.2016.02.04.15.48.15.906250.67245005.IMA