cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7294/14736411
title: Data: Horsenettle (Solanum carolinense) fruit bacterial communities are not variable across fine spatial scales

description: Associated mapping file for S. carolinense fruit microbiome data.<br><br>biogeosamplesscaro06092021.tar.gz : Raw data needed to complete the analysis of data associated with the manuscript<br>AH_BioGeo_Mapping_file_NAupper_neworder_newpop_site.xlsx : Mapping file that contains information about samples and site-specific details including sampling distances. This file can be used to process raw reads and reproduce analyses in the manuscript.<br>Heminger_solanum_code.Rmd: This file is an R markdown file for the generation that contains all of the analysis steps. Combined with the mapping file and raw reads, this script can reproduce all of the analyses presented in the manuscript.
DOI: 10.5281/zenodo.5562483
title: Racism in Medicine: Impact of Discussion Among Medical Students

description: Purpose This study evaluated the impact of the Racism in Medicine Summit on student perceptions of various topics related to racism in medicine. The Summit was organized at the Wayne State University School of Medicine (WSUSOM) to educate students, faculty and staff on how structural racism affects the residents of Detroit and the historical relationship between health care and vulnerable populations. The Summit aimed at providing context for what students in Detroit will encounter as physicians-in- training and the skills they will need to master while working within similar communities. Method Qualtrics surveys were created and distributed via email to attendees before and after the event. Responses were obtained via Likert scale and open-text questions. Results A total of 342 out of 445 participants (77%) completed both the pre- and post-survey. Quantitative analysis in post-survey responses revealed more familiarity among participants regarding specific instances of racism in the history of medicine, greater extent of thinking the history of racism impacts present-day Detroit residents, greater extent of thinking that racism influences medical care and/or medical outcomes, and belief that racism is reflected in medical research, compared to pre-survey responses (p &lt; 0.001). Participants also reported more often considering racial or societal influences when learning medicine and more knowledge of what they can do to combat racism as a student and physician (p &lt; 0.001). Qualitative analysis revealed seven themes among participants: the history of racism in Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation medicine, personal reflection, racism in research, bias and microaggression, actions to take against racism, resources for anti-racist education, and racism in medical education. Conclusion Demonstrable changes in medical student attitude and awareness surrounding topics of racism and health care were achieved after the Racism in Medicine Summit. This can serve as a model for other medical schools to raise awareness about racism in medicine.
DOI: 10.5281/zenodo.5558029
title: Racism in Medicine: Impact of Discussion Among Medical Students

description: Purpose This study evaluated the impact of the Racism in Medicine Summit on student perceptions of various topics related to racism in medicine. The Summit was organized at the Wayne State University School of Medicine (WSUSOM) to educate students, faculty and staff on how structural racism affects the residents of Detroit and the historical relationship between health care and vulnerable populations. The Summit aimed at providing context for what students in Detroit will encounter as physicians-in- training and the skills they will need to master while working within similar communities. Method Qualtrics surveys were created and distributed via email to attendees before and after the event. Responses were obtained via Likert scale and open-text questions. Results A total of 342 out of 445 participants (77%) completed both the pre- and post-survey. Quantitative analysis in post-survey responses revealed more familiarity among participants regarding specific instances of racism in the history of medicine, greater extent of thinking the history of racism impacts present-day Detroit residents, greater extent of thinking that racism influences medical care and/or medical outcomes, and belief that racism is reflected in medical research, compared to pre-survey responses (p &lt; 0.001). Participants also reported more often considering racial or societal influences when learning medicine and more knowledge of what they can do to combat racism as a student and physician (p &lt; 0.001). Qualitative analysis revealed seven themes among participants: the history of racism in Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation medicine, personal reflection, racism in research, bias and microaggression, actions to take against racism, resources for anti-racist education, and racism in medical education. Conclusion Demonstrable changes in medical student attitude and awareness surrounding topics of racism and health care were achieved after the Racism in Medicine Summit. This can serve as a model for other medical schools to raise awareness about racism in medicine.
DOI: 10.5281/zenodo.5516552
title: Inland-Offshore Wind Farm Dataset1

description: Data included in these two datasets are 10-minute data generated from six wind turbines and three met masts and are arranged in six files, each of which is associated with a turbine. The six turbines are named WT1 through WT6, respectively. The layout of the turbines and the met masts is shown in Fig. 5.6 of the Data Science for Wind Energy book. On the offshore wind farm, all seven environmental variables as mentioned above are available, namely x =(V, D, rho, H, I, Sa, Sb), whereas on the inland wind farm, the humidity measurements are not available, nor is the above-hub wind shear, meaning that x =(V, D, rho, I, Sb). Variables in x were measured by sensors on the met mast, whereas y was measured at the wind turbines. Each met mast has two wind turbines associated with it, meaning that the x's measured at a met mast are paired with the y's of two associated turbines. For WT1 and WT2, the data were collected from July 30, 2010 through July 31, 2011 and for WT3 and WT4, the data were collected from April 29, 2010 through April 30, 2011. For WT5 and WT6, the data were collected from January 1, 2009 through December 31, 2009. Meaning of variables; V: wind speed; D: wind direction; rho: air density; H: humidity; I: turbulence intensity; S: vertical wind shear; Sa: above-hub height wind shear, Sb: below-hub height wind shear.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516551
title: Inland-Offshore Wind Farm Dataset1

description: Data included in these two datasets are 10-minute data generated from six wind turbines and three met masts and are arranged in six files, each of which is associated with a turbine. The six turbines are named WT1 through WT6, respectively. The layout of the turbines and the met masts is shown in Fig. 5.6 of the Data Science for Wind Energy book. On the offshore wind farm, all seven environmental variables as mentioned above are available, namely x =(V, D, rho, H, I, Sa, Sb), whereas on the inland wind farm, the humidity measurements are not available, nor is the above-hub wind shear, meaning that x =(V, D, rho, I, Sb). Variables in x were measured by sensors on the met mast, whereas y was measured at the wind turbines. Each met mast has two wind turbines associated with it, meaning that the x's measured at a met mast are paired with the y's of two associated turbines. For WT1 and WT2, the data were collected from July 30, 2010 through July 31, 2011 and for WT3 and WT4, the data were collected from April 29, 2010 through April 30, 2011. For WT5 and WT6, the data were collected from January 1, 2009 through December 31, 2009. Meaning of variables; V: wind speed; D: wind direction; rho: air density; H: humidity; I: turbulence intensity; S: vertical wind shear; Sa: above-hub height wind shear, Sb: below-hub height wind shear.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.6084/m9.figshare.16810891
title: Hc aligment - Sup File 2

description: Hc superfamily alignment inferred by MAFFT software with the accurate algorithm “E-INS-i” and trimmed with a with a 50% gap threshold.
DOI: 10.6084/m9.figshare.16810891.v1
title: Hc aligment - Sup File 2

description: Hc superfamily alignment inferred by MAFFT software with the accurate algorithm “E-INS-i” and trimmed with a with a 50% gap threshold.
DOI: 10.5281/zenodo.5516554
title: Inland-Offshore Wind Farm Dataset2

description: The wind turbine data in these two datasets include observations during the first four years of the turbines' operations. They are 10-minute data. The inland turbine data are from 2008 to 2011, whereas the offshore data are from 2007 to 2010. The measurements for the inland wind farm include the same x's as in the Inland Wind Farm Dataset1 and those for the offshore wind farm include the same x's as in the Offshore Wind Farm Dataset1. Most of the environmental measurements are taken from the met mast closest to the turbine, with the exception of wind speed and turbulence intensity which are measured on the wind turbine. The mast measurements are used either because some variables are only measured at the mast (such as air pressure and ambient temperature, which are used to calculate air density) or because the mast measurements are considered more reliable (such as wind direction).
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516553
title: Inland-Offshore Wind Farm Dataset2

description: The wind turbine data in these two datasets include observations during the first four years of the turbines' operations. They are 10-minute data. The inland turbine data are from 2008 to 2011, whereas the offshore data are from 2007 to 2010. The measurements for the inland wind farm include the same x's as in the Inland Wind Farm Dataset1 and those for the offshore wind farm include the same x's as in the Offshore Wind Farm Dataset1. Most of the environmental measurements are taken from the met mast closest to the turbine, with the exception of wind speed and turbulence intensity which are measured on the wind turbine. The mast measurements are used either because some variables are only measured at the mast (such as air pressure and ambient temperature, which are used to calculate air density) or because the mast measurements are considered more reliable (such as wind direction).
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.6084/m9.figshare.16810882
title: Hc alignment - Sup File 1

description: Hc superfamily untrimmed alignment inferred by MAFFT software with the accurate algorithm “E-INS-i”.
DOI: 10.6084/m9.figshare.16810882.v1
title: Hc alignment - Sup File 1

description: Hc superfamily untrimmed alignment inferred by MAFFT software with the accurate algorithm “E-INS-i”.
DOI: 10.6084/m9.figshare.16810894
title: Hc tree - Sup File 3

description: ML tree with branch lengths for the Hc gene superfamily rooted with two amoebozoan Hc sequences.
DOI: 10.6084/m9.figshare.16810894.v1
title: Hc tree - Sup File 3

description: ML tree with branch lengths for the Hc gene superfamily rooted with two amoebozoan Hc sequences.
DOI: 10.6084/m9.figshare.16810900.v1
title: Hc chronogram - Sup File 4

description: Hc superfamily chronogram.
DOI: 10.6084/m9.figshare.16810900
title: Hc chronogram - Sup File 4

description: Hc superfamily chronogram.
DOI: 10.6084/m9.figshare.16810909
title: Correlated vs uncorrelated PhyloBayes estimates

description: Correlated vs uncorrelated PhyloBayes estimates
DOI: 10.6084/m9.figshare.16810909.v1
title: Correlated vs uncorrelated PhyloBayes estimates

description: Correlated vs uncorrelated PhyloBayes estimates
DOI: 10.34740/kaggle/dsv/2704995
title: COVID Vaccination in World (updated daily)
DOI: 10.5517/cc13tsnc
title: CCDC 1037683: Experimental Crystal Structure Determination

description: Related Article: Ma Guifeng, Wang Yongheng, Zhou Yulan, Sutie Zhu, Gong Yanan, Huxia Ohui, Wanghong Tao|2014|CSD Communication|||
DOI: 10.5517/cc1jw2s8
title: CCDC 1426210: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger|2015|CSD Communication|||
DOI: 10.5517/cc1jxbkb
title: CCDC 1427412: Experimental Crystal Structure Determination

description: Related Article: Philipp Thiele, Martin Köckerling|2015|CSD Communication|||
DOI: 10.5517/cc1k4m0b
title: CCDC 1434401: Experimental Crystal Structure Determination

description: Related Article: Philipp Guetlich, Dieter Schollmeyer|2015|CSD Communication|||
DOI: 10.5517/cc13vfs5
title: CCDC 1038307: Experimental Crystal Structure Determination

description: Related Article: Jindou Yang|2014|CSD Communication|||
DOI: 10.5517/cc1knck4
title: CCDC 1449546: Experimental Crystal Structure Determination

description: Related Article: Nemat Bozorgi|2016|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1n8jwb
title: CCDC 1527552: Experimental Crystal Structure Determination

description: Related Article: Zhenhua Sun, Qi Huang, Jing Han, Weimin He, Jie Chen, Yuchen Xu, Hongmei Deng, Min Shao, Hui Zhang, Weiguo Cao|2018|Tetrahedron|74|2073|doi:10.1016/j.tet.2018.03.012
description: Related Article: Zhenhua Sun|2017|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1phpxs
title: CCDC 1564226: Experimental Crystal Structure Determination

description: Related Article:  M.A.R Oliveira|2017|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1q0r5n
title: CCDC 1579641: Experimental Crystal Structure Determination

description: Related Article: Raphaela Schäper, Dieter Bläser, Christoph Wölper, Stephan Schulz|2017|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1qd1tz
title: CCDC 1590511: Experimental Crystal Structure Determination

description: Related Article: Yue Wei Sun|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z437g
title: CCDC 1821195: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Sven Buschbeck, Kalle Nättinen|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z43t1
title: CCDC 1821213: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Patrik Dragut|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z44cm
title: CCDC 1821230: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Uwe Hahn|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z468l
title: CCDC 1821289: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Gudrun Mieden|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z48q3
title: CCDC 1821365: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Fritz Vögtle, Gudrun Mieden |2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z4tjg
title: CCDC 1821886: Experimental Crystal Structure Determination

description: Related Article: Jian-an Li|2018|CSD Communication|||
description: Related Article: Ze-Kun Tao, Cheng-Kun Li, Jian-An Li, Adedamola Shoberu, Wei Zhang, Jian-Ping Zou|2021|Org.Lett.|23|4342|doi:10.1021/acs.orglett.1c01286
DOI: 10.5517/ccdc.csd.cc1z80mw
title: CCDC 1824958: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Wolfgang Malisch, Christa Abdelbaky|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z81w5
title: CCDC 1824997: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Wolfgang Malisch, Heinrich Jehle|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z829m
title: CCDC 1825010: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Wolfgang Malisch, Dirk Schumacher|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1zb5bt
title: CCDC 1827026: Experimental Crystal Structure Determination

description: Related Article: Hideaki Takashima, Yusuke Inagaki, Hiroyuki Momma, Eunsang Kwon, Kentaro Yamaguchi, Wataru Setaka|2018|Organometallics|37|1501|doi:10.1021/acs.organomet.8b00154
description: Related Article: Wataru Setaka|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1zb5cv
title: CCDC 1827027: Experimental Crystal Structure Determination

description: Related Article: Hideaki Takashima, Yusuke Inagaki, Hiroyuki Momma, Eunsang Kwon, Kentaro Yamaguchi, Wataru Setaka|2018|Organometallics|37|1501|doi:10.1021/acs.organomet.8b00154
description: Related Article: Wataru Setaka|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1zb66q
title: CCDC 1827053: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Edgar Niecke, Zoltan Török|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1q08dd
title: CCDC 1579183: Experimental Crystal Structure Determination

description: Related Article: Monique Tillard|2017|CSD Communication|||
description: Related Article: Tarik Harit, Houssam Abouloifa, Monique Tillard, Driss Eddike, Abdeslam Asehraou, Fouad Malek|2018|J.Mol.Struct.|1163|300|doi:10.1016/j.molstruc.2018.03.008
DOI: 10.5517/ccdc.csd.cc1zn3mc
title: CCDC 1836583: Experimental Crystal Structure Determination

description: Related Article: Tuğba Aycan, Filiz Öztürk, Hümeyra Paşaoğlu|2019|J.Mol.Struct.|1176|685|doi:10.1016/j.molstruc.2018.08.109
description: Related Article: Tuǧba Aycan, Filiz Öztürk, Hümeyra Paşaoǧlu|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc20360c
title: CCDC 1850111: Experimental Crystal Structure Determination

description: Related Article: Sen Yang, Dengfu Lu, Hengrui Huo, Fan Luo, Yuefa Gong|2018|Org.Lett.|20|6943|doi:10.1021/acs.orglett.8b03090
description: Related Article: Sen Yang|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc203syw
title: CCDC 1850698: Experimental Crystal Structure Determination

description: Related Article: Tuǧba Aycan, Hümeyra Paşaoǧlu, Can Kocabiyik|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc208p00
title: CCDC 1855381: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Wolfgang Malisch, Paul Dopf|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc208p44
title: CCDC 1855385: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger, Wolfgang Malisch, Paul Dopf|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc20dwfr
title: CCDC 1859424: Experimental Crystal Structure Determination

description: Related Article: Heiner Detert, Dieter Schollmeyer|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc20l6vp
title: CCDC 1864552: Experimental Crystal Structure Determination

description: Related Article: Tuǧba Aycan, Hümeyra Paşaoǧlu|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1nm4n2
title: CCDC 1537744: Experimental Crystal Structure Determination

description: Related Article: Makoto Kobayashi, Hiroki Sakabe, Arisa Takeda, Hideki Kato, Masato Kakihana|2018|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc224r44
title: CCDC 1911185: Experimental Crystal Structure Determination

description: Related Article: Manohar V. Kulkarni|2019|CSD Communication|||
description: Related Article: Netravati Khanapurmath, Manohar V. Kulkarni, Shrinivas D. Joshi, G.N. Anil Kumar|2019|Bioorg.Med.Chem.|27|115054|doi:10.1016/j.bmc.2019.115054
DOI: 10.5517/ccdc.csd.cc224vz2
title: CCDC 1911304: Experimental Crystal Structure Determination

description: Related Article: Min Wang, Yang Xu, Donghui Wei, Rongqiang Tian, Zheng Duan, François Mathey|2019|Chem.-Eur.J.|25|15036|doi:10.1002/chem.201902948
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DOI: 10.5517/ccdc.csd.cc23rm8s
title: CCDC 1959115: Experimental Crystal Structure Determination

description: Related Article: T. N. Moshkina, E. V. Nosova, G. N. Lipunova, M. S. Valova, P. A. Slepukhin, V. N. Charushin |2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc23rx5z
title: CCDC 1959391: Experimental Crystal Structure Determination

description: Related Article: David S Pugh, Nicholas J Wood, Fabian Kommoss, Pascal Zimmer, Adrian C Whitwood, Rachel R Parker|2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc243q33
title: CCDC 1969774: Experimental Crystal Structure Determination

description: Related Article: Martin Nieger|2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc1z43v2
title: CCDC 1821214: Experimental Crystal Structure Determination

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DOI: 10.15468/ztras6
title: The taxonomy of spider crabs of the genera Eurynome, Choniognathus, Seiitaiodes and Kasagia (Crustacea: Brachyura: Majidae) from southwest Indian Ocean

description: This dataset contains the digitized treatments in Plazi based on the original journal article Forges, Bertrand Richer De, Lee, Bee Yan, Ng, Peter K. L. (2021): The taxonomy of spider crabs of the genera Eurynome, Choniognathus, Seiitaiodes and Kasagia (Crustacea: Brachyura: Majidae) from southwest Indian Ocean. Zootaxa 5048 (3): 301-333, DOI: https://doi.org/10.11646/zootaxa.5048.3.1
DOI: 10.5281/zenodo.5569402
title: Cathorops multiradiatus RB0728 SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5569401
title: Cathorops multiradiatus RB0728 SPAdes preassembly

description: .
DOI: 10.15468/ve6ptr
title: Solving an old dilemma: are the two sympatric species Lema apicalis and L. reticulosa (Coleoptera, Chrysomelidae, Criocerinae) morphotypes of a single species?

description: This dataset contains the digitized treatments in Plazi based on the original journal article Groll, Elisa Von (2021): Solving an old dilemma: are the two sympatric species Lema apicalis and L. reticulosa (Coleoptera, Chrysomelidae, Criocerinae) morphotypes of a single species? Zootaxa 5048 (3): 334-346, DOI: https://doi.org/10.11646/zootaxa.5048.3.2
DOI: 10.5281/zenodo.5569438
title: OC-782K: Knowledge Graph of "Scientometrics" modelled according to the OpenCitations Data Model

description: This dataset is a knowledge graph extracted from a triplestore covering information about the journal <em>Scientometrics</em> and modelled according to the OpenCitations Data Model. The original triplestore is available here. This KG was extracted for a research project on knowledge graph embeddings (KGEs) for author disambiguation. Structural triples of the knowledge graph are split into training, testing and validation for applying representation learning methods. Textual literals and numeric literals were stored separately in order to implement multimodal approaches for KGEs (see arXiv:1802.00934). For the same reason, textual literals and numeric literals are already stored into sentence embeddings and a numeric matrix respectively in the files <em>textual_literals.npy </em>and <em>numeric_literals.npy</em>. For the script used to gather this dataset see the GitHub repository: https://github.com/sntcristian/and-kge/tree/main/open-citations.
DOI: 10.15468/ksx26m
title: Taxonomy of the genus Autosticha Meyrick (Lepidoptera: Autostichidae) in China Descriptions of fifteen new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tao, Zhulin, Wang, Yuqi, Wang, Shuxia (2021): Taxonomy of the genus Autosticha Meyrick (Lepidoptera: Autostichidae) in China Descriptions of fifteen new species. Zootaxa 5048 (3): 347-370, DOI: https://doi.org/10.11646/zootaxa.5048.3.3
DOI: 10.5281/zenodo.5569359
title: Questões do Exame Nacional do Ensino Médio (ENEM)

description: Corpus estruturado do comando das questões do Exame Nacional do Ensino Médio (ENEM) do ano de 2016 e 2017.
DOI: 10.25721/kvmz-8c24
title: Brave marin s'en va