cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6174983.v1
title: Additional file 2: of Partial-arm translocations in evolution of malaria mosquitoes revealed by high-coverage physical mapping of the Anopheles atroparvus genome

description: Table S2. Scaffold-paired adjacencies in the An. atroparvus genome assembly. (XLSX 14Â kb)
DOI: 10.6084/m9.figshare.6174968
title: Additional file 1: of Partial-arm translocations in evolution of malaria mosquitoes revealed by high-coverage physical mapping of the Anopheles atroparvus genome

description: Table S1. Sizes, coordinates, and chromosomal positions of mapped An. atroparvus scaffolds. (XLSX 52Â kb)
DOI: 10.6084/m9.figshare.6174968.v1
title: Additional file 1: of Partial-arm translocations in evolution of malaria mosquitoes revealed by high-coverage physical mapping of the Anopheles atroparvus genome

description: Table S1. Sizes, coordinates, and chromosomal positions of mapped An. atroparvus scaffolds. (XLSX 52Â kb)
DOI: 10.6084/m9.figshare.6174308
title: fwt_historic

description: Dataset of past FWT winners 1996-2018<br>
DOI: 10.6084/m9.figshare.6174308.v1
title: fwt_historic

description: Dataset of past FWT winners 1996-2018<br>
DOI: 10.6084/m9.figshare.6174248
title: Tower_flashes_Electric_field_waveforms

description: The measured electric field waveform for two tower flashes 1593 and 1594.
DOI: 10.6084/m9.figshare.6174248.v1
title: Tower_flashes_Electric_field_waveforms

description: The measured electric field waveform for two tower flashes 1593 and 1594.
DOI: 10.6084/m9.figshare.6174125
title: NSF SI2 SSE: Improving Scikit-learn usability and automation

description: Single slight lightning-talk<br>Machine learning is a central component in many data-driven research areas, but it's adoption is limited by the often complex choice of data processing, model, and hyper-parameter settings.The goal of this project is to create software tools that enable automatic machine learning, that is solving predictive analytics tasks without requiring the user to explicitlyspecify the algorithm or model hyper-parameters used for prediction.The software developed in this will enable a wider use of machine learning, by providing tools to apply machine learning without requiring knowledge of the details of the algorithms involved.<br>The project extends the existing scikit-learn project, a machine learning library for Python, which is widely used in academic research across disciplines.The project will add features to this library to lower the amount of expert knowledge required to apply models to a new problem, and to facilitate the interaction with automated machine learning systems.The project will also create a separate software package that includes models for automatic supervised learning, with a very simple interface, requiring minimal user interaction. In contrast to existing research projects, this project focuses on creating easy-to-use tools that can be used by researchers without extensive training in machine learning or computer science.
DOI: 10.6084/m9.figshare.6174125.v1
title: NSF SI2 SSE: Improving Scikit-learn usability and automation

description: Single slight lightning-talk<br>Machine learning is a central component in many data-driven research areas, but it's adoption is limited by the often complex choice of data processing, model, and hyper-parameter settings.The goal of this project is to create software tools that enable automatic machine learning, that is solving predictive analytics tasks without requiring the user to explicitlyspecify the algorithm or model hyper-parameters used for prediction.The software developed in this will enable a wider use of machine learning, by providing tools to apply machine learning without requiring knowledge of the details of the algorithms involved.<br>The project extends the existing scikit-learn project, a machine learning library for Python, which is widely used in academic research across disciplines.The project will add features to this library to lower the amount of expert knowledge required to apply models to a new problem, and to facilitate the interaction with automated machine learning systems.The project will also create a separate software package that includes models for automatic supervised learning, with a very simple interface, requiring minimal user interaction. In contrast to existing research projects, this project focuses on creating easy-to-use tools that can be used by researchers without extensive training in machine learning or computer science.
DOI: 10.6084/m9.figshare.6174062
title: fwt_roster_2018

description: Dataset of the FWT 2018 roster.<br>
DOI: 10.6084/m9.figshare.6174062.v1
title: fwt_roster_2018

description: Dataset of the FWT 2018 roster.<br>
DOI: 10.6084/m9.figshare.6174056
title: fwt_standings.csv

description: Dataset of all FWT winners from the 23 tours between 1996 and 2018 and added the age of the riders from either the FWT website or a brief web-search (some missing).<br>
DOI: 10.6084/m9.figshare.6174056.v1
title: fwt_standings.csv

description: Dataset of all FWT winners from the 23 tours between 1996 and 2018 and added the age of the riders from either the FWT website or a brief web-search (some missing).<br>
DOI: 10.6084/m9.figshare.6174056.v2
title: fwt_standings.csv

description: Dataset of all FWT winners from the 23 tours between 1996 and 2018 and added the age of the riders from either the FWT website or a brief web-search (some missing).<br>
DOI: 10.6084/m9.figshare.6174056.v3
title: fwt_standings.csv

description: Dataset of all FWT winners from the 23 tours between 1996 and 2018 and added the age of the riders from either the FWT website or a brief web-search (some missing).<br>
DOI: 10.6084/m9.figshare.6174056.v4
title: fwt_standings.csv

description: Dataset of all FWT winners from the 23 tours between 1996 and 2018 and added the age of the riders from either the FWT website or a brief web-search (some missing).<br>
DOI: 10.6084/m9.figshare.6173975
title: Automatic detection of 3D recirculation zones (volume)

description: The Matlab function for automatically detecting 3D recirculation zones based on a 3D high-resolution velocity field
DOI: 10.6084/m9.figshare.6173957
title: Population allele frequency of 1000G five superpopulations with LD-based SNP pruning ( hg19 )

description: Population allele frequency of 1000G five superpopulations with LD-based SNP pruning ( hg19 ).<br>.gz was created by bgzip on the tab-delimited input text file
DOI: 10.6084/m9.figshare.6173903
title: POWER SERIES FOR NORMAL STRESS DIFFERENCES OF POLYMERIC LIQUID IN LARGE-AMPLITUDE OSCILLATORY SHEAR FLOW: SUPPLEMENTARY MATERIAL II

description: In this supplementary material, we provide the relevant Maple code to show how we arrive at our main result in [Poungthong, P., A.J. Giacomin, C. Saengow, C. Kolitawong, “Power Series for Normal Stress Differences of Polymeric Liquids in Large-Amplitude Oscillatory Shear Flow,” PRG Report No. 042, QUCHEE-PRG-TR--2018-42, Polymers Research Group, Chemical Engineering Department, Queen’s University, Kingston, CANADA (April, 2018) pp. 1–30].
DOI: 10.6084/m9.figshare.6173903.v1
title: POWER SERIES FOR NORMAL STRESS DIFFERENCES OF POLYMERIC LIQUID IN LARGE-AMPLITUDE OSCILLATORY SHEAR FLOW: SUPPLEMENTARY MATERIAL II

description: In this supplementary material, we provide the relevant Maple code to show how we arrive at our main result in [Poungthong, P., A.J. Giacomin, C. Saengow, C. Kolitawong, “Power Series for Normal Stress Differences of Polymeric Liquids in Large-Amplitude Oscillatory Shear Flow,” PRG Report No. 042, QUCHEE-PRG-TR--2018-42, Polymers Research Group, Chemical Engineering Department, Queen’s University, Kingston, CANADA (April, 2018) pp. 1–30].
DOI: 10.6084/m9.figshare.6173873
title: SUPPLEMENTARY MATERIAL I OF POWER SERIES FOR NORMAL STRESS DIFFERENCES OF POLYMERIC LIQUID IN LARGE-AMPLITUDE OSCILLATORY SHEAR FLOW

description: In this supplementary material, we report the expressions of <i>κmCn and σmSn</i> in Tables I and II [Poungthong, P., A.J. Giacomin, C. Saengow, C. Kolitawong, “Power Series for Normal Stress Differences of Polymeric Liquids in Large-Amplitude Oscillatory Shear Flow,” PRG Report No. 042, QUCHEE-PRG-TR--2018-42, Polymers Research Group, Chemical Engineering Department, Queen’s University, Kingston, CANADA (April, 2018) pp. 1–30].<br>
DOI: 10.6084/m9.figshare.6173873.v1
title: SUPPLEMENTARY MATERIAL I OF POWER SERIES FOR NORMAL STRESS DIFFERENCES OF POLYMERIC LIQUID IN LARGE-AMPLITUDE OSCILLATORY SHEAR FLOW

description: In this supplementary material, we report the expressions of <i>κmCn and σmSn</i> in Tables I and II [Poungthong, P., A.J. Giacomin, C. Saengow, C. Kolitawong, “Power Series for Normal Stress Differences of Polymeric Liquids in Large-Amplitude Oscillatory Shear Flow,” PRG Report No. 042, QUCHEE-PRG-TR--2018-42, Polymers Research Group, Chemical Engineering Department, Queen’s University, Kingston, CANADA (April, 2018) pp. 1–30].<br>
DOI: 10.6084/m9.figshare.6173834
title: Gene cluster conservation identifies melanin and perylenequinone biosynthesis pathways in multiple plant pathogenic fungi

description: Genome sequences and annotations for Elsinoë fawcettii and Cladosporium phlei in EMBL format for peer review public accessibility.
DOI: 10.6084/m9.figshare.6173834.v1
title: Gene cluster conservation identifies melanin and perylenequinone biosynthesis pathways in multiple plant pathogenic fungi

description: Genome sequences and annotations for Elsinoë fawcettii and Cladosporium phlei in EMBL format for peer review public accessibility.
DOI: 10.6084/m9.figshare.6171623
title: Landscape predictors of rodent dynamics in fragmented rainforests

description: Data on rodent species abundance (<i>Oryzomys</i> sp., <i>Heteromys desmarestianus</i>, <i>Sigmodon hispidus</i> and <i>Peromyscus mexicanus</i>) was obtained in 12 forest sites in the fragmented Lacandona rainforest. We obtained the correlation coefficients (Spearman), i.e., how population abundance relates to year (2011, 2012, 2014 and 2016) and related this with differences across time (2016-2010) in landscape structure (i.e., percentage of forest cover, matrix contrast, number of forest patches, forest edge density, and mean inter-patch distance) surrounding each site. Landscape metrics were measured across different landscape sizes (range of radii = 200 to 1400 m, at 200-m intervals). <br><br>
DOI: 10.6084/m9.figshare.6171623.v1
title: Landscape predictors of rodent dynamics in fragmented rainforests

description: Data on rodent species abundance (<i>Oryzomys</i> sp., <i>Heteromys desmarestianus</i>, <i>Sigmodon hispidus</i> and <i>Peromyscus mexicanus</i>) was obtained in 12 forest sites in the fragmented Lacandona rainforest. We obtained the correlation coefficients (Spearman), i.e., how population abundance relates to year (2011, 2012, 2014 and 2016) and related this with differences across time (2016-2010) in landscape structure (i.e., percentage of forest cover, matrix contrast, number of forest patches, forest edge density, and mean inter-patch distance) surrounding each site. Landscape metrics were measured across different landscape sizes (range of radii = 200 to 1400 m, at 200-m intervals). <br><br>
DOI: 10.6084/m9.figshare.6171572.v2
title: NSF_SI2_PI_Meeting2018.pdf

description: Interactive Parallelization Tool (IPT) assists domain-experts and students in efficiently parallelizing their existing C/C++ applications using any of the following parallel programming models: Message Passing Interface (MPI), OpenMP, and CUDA. For assisting in parallelization, IPT uses its knowledgebase of parallel programming expertise (encapsulated as design templates and rules), and specifications (i.e., what to parallelize and where) as provided by users. It parses the user-specifications and accordingly analyses the input source code to reengineer it for generating a parallel version of the code.IPT can be used for self-­paced learning of different parallel programming paradigms. It helps in understanding the differences in the structure and performance of the parallel code generated for different specifications while using the same serial application.
DOI: 10.6084/m9.figshare.6171563
title: Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation

description: In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker–Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.
DOI: 10.5517/ccdc.csd.cc216cd1
title: CCDC 1882952: Experimental Crystal Structure Determination

description: Related Article: Chun-Yu Liu,  Hui-Fang Wang,  Zhi-Gang Ren,  Pierre Braunstein,  Jian-Ping Lang|2019|Inorg.Chem.|58|8533|doi:10.1021/acs.inorgchem.9b00845
DOI: 10.5517/ccdc.csd.cc216cf2
title: CCDC 1882953: Experimental Crystal Structure Determination

description: Related Article: Chun-Yu Liu,  Hui-Fang Wang,  Zhi-Gang Ren,  Pierre Braunstein,  Jian-Ping Lang|2019|Inorg.Chem.|58|8533|doi:10.1021/acs.inorgchem.9b00845
DOI: 10.5517/ccdc.csd.cc21857r
title: CCDC 1884683: Experimental Crystal Structure Determination

description: Related Article: Jacqueline Ramler, Ivo Krummenacher, and Crispin Lichtenberg|2019|Angew.Chem.,Int.Ed.|58|12924|doi:10.1002/anie.201904365
DOI: 10.5517/ccdc.csd.cc21858s
title: CCDC 1884684: Experimental Crystal Structure Determination

description: Related Article: Jacqueline Ramler, Ivo Krummenacher, and Crispin Lichtenberg|2019|Angew.Chem.,Int.Ed.|58|12924|doi:10.1002/anie.201904365
DOI: 10.5517/ccdc.csd.cc21tkwb
title: CCDC 1901412: Experimental Crystal Structure Determination

description: Related Article: Guo-Cheng Liu, Yan Li, Zhen-Jie Lu, Xiu-Li Wang, Xiang Wang, Xing-Xing Chen|2019|Polyhedron|170|294|doi:10.1016/j.poly.2019.05.031
DOI: 10.5517/ccdc.csd.cc21vd1c
title: CCDC 1902192: Experimental Crystal Structure Determination

description: Related Article: Tianju Shao, Yajuan Li, Nana Ma, Chunyang Li, Guobi Chai, Xiaowei Zhao, Baokun Qiao, Zhiyong Jiang|2019|iScience|16|410|doi:10.1016/j.isci.2019.06.007
DOI: 10.5517/ccdc.csd.cc21zm7x
title: CCDC 1906259: Experimental Crystal Structure Determination

description: Related Article: Vitalii Yu. Kotov, Elnara S. Safiullina, Andrey B. Ilyukhin, Petr A. Buikin, Kirill P. Birin, Khursand E. Yorov|2019|J.Mol.Struct.|1195|944|doi:10.1016/j.molstruc.2019.06.057
DOI: 10.5517/ccdc.csd.cc21zm8y
title: CCDC 1906260: Experimental Crystal Structure Determination

description: Related Article: Vitalii Yu. Kotov, Elnara S. Safiullina, Andrey B. Ilyukhin, Petr A. Buikin, Kirill P. Birin, Khursand E. Yorov|2019|J.Mol.Struct.|1195|944|doi:10.1016/j.molstruc.2019.06.057
DOI: 10.5517/ccdc.csd.cc21zm9z
title: CCDC 1906261: Experimental Crystal Structure Determination

description: Related Article: Vitalii Yu. Kotov, Elnara S. Safiullina, Andrey B. Ilyukhin, Petr A. Buikin, Kirill P. Birin, Khursand E. Yorov|2019|J.Mol.Struct.|1195|944|doi:10.1016/j.molstruc.2019.06.057
DOI: 10.5517/ccdc.csd.cc21zmb0
title: CCDC 1906262: Experimental Crystal Structure Determination

description: Related Article: Vitalii Yu. Kotov, Elnara S. Safiullina, Andrey B. Ilyukhin, Petr A. Buikin, Kirill P. Birin, Khursand E. Yorov|2019|J.Mol.Struct.|1195|944|doi:10.1016/j.molstruc.2019.06.057
DOI: 10.5517/ccdc.csd.cc21zsmg
title: CCDC 1906426: Experimental Crystal Structure Determination

description: Related Article: Alex J. Kosanovich, Wei-Chun Shih, Oleg V. Ozerov|2019|J.Organomet.Chem.|897|1|doi:10.1016/j.jorganchem.2019.06.020
DOI: 10.5517/ccdc.csd.cc2209hx
title: CCDC 1906918: Experimental Crystal Structure Determination

description: Related Article: Akihito Konishi,  Koki Horii,  Daisuke Shiomi,  Kazunobu Sato,  Takeji Takui,  Makoto Yasuda|2019|J.Am.Chem.Soc.|141|10165|doi:10.1021/jacs.9b04080
DOI: 10.5517/ccdc.csd.cc2209jy
title: CCDC 1906919: Experimental Crystal Structure Determination

description: Related Article: Akihito Konishi,  Koki Horii,  Daisuke Shiomi,  Kazunobu Sato,  Takeji Takui,  Makoto Yasuda|2019|J.Am.Chem.Soc.|141|10165|doi:10.1021/jacs.9b04080
DOI: 10.5517/ccdc.csd.cc2209kz
title: CCDC 1906920: Experimental Crystal Structure Determination

description: Related Article: Akihito Konishi,  Koki Horii,  Daisuke Shiomi,  Kazunobu Sato,  Takeji Takui,  Makoto Yasuda|2019|J.Am.Chem.Soc.|141|10165|doi:10.1021/jacs.9b04080
DOI: 10.5517/ccdc.csd.cc22109g
title: CCDC 1907594: Experimental Crystal Structure Determination

description: Related Article: Keiko Komori-Orisaku, Olaf Stefańczyk, Saori Ohishi, Noriaki Ozaki, Yasuto Miyamoto, Kenta Imoto and Shin-ichi Ohkoshi|2019|Chem.-Eur.J.|25|11066|doi:10.1002/chem.201901790
DOI: 10.5517/ccdc.csd.cc221jb0
title: CCDC 1908091: Experimental Crystal Structure Determination

description: Related Article: Keiko Komori-Orisaku, Olaf Stefańczyk, Saori Ohishi, Noriaki Ozaki, Yasuto Miyamoto, Kenta Imoto and Shin-ichi Ohkoshi|2019|Chem.-Eur.J.|25|11066|doi:10.1002/chem.201901790
DOI: 10.5517/ccdc.csd.cc2245gw
title: CCDC 1910637: Experimental Crystal Structure Determination

description: Related Article: Sergey M. Ivanov, Artem O. Dmitrienko, Michael G. Medvedev, Lyudmila M. Mironovich|2019|J.Organomet.Chem.|896|168|doi:10.1016/j.jorganchem.2019.06.009
DOI: 10.25318/1410008801-eng
title:  Labour force characteristics of immigrants by country of birth, three-month moving average, unadjusted for seasonality

description: Number of immigrants in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by country of birth, immigrant status, sex, and age group, last 5 months.
DOI: 10.25318/1410008801-fra
title: Caractéristiques de la population active chez les immigrants selon le pays de naissance, moyennes mobiles de trois mois, données non désaisonnalisées

description: Nombre d'immigrants faisant partie de la population active (emploi et chômage) et de la population inactive, taux de chômage, taux d'activité et taux d'emploi, selon le pays de naissance, le statut d'immigrant, le sexe et le groupe d'âge, 5 derniers mois.
DOI: 10.25318/1410032701-fra
title: Caractéristiques de la population active selon le sexe et le groupe d'âge détaillé, données annuelles

description: Nombre de personnes faisant partie de la population active (emploi et chômage) et de la population inactive, taux de chômage, taux d'activité et taux d'emploi, selon le sexe et le groupe d'âge détaillé, 5 dernières années.
DOI: 10.5517/ccdc.csd.cc1yzxkd
title: CCDC 1817175: Experimental Crystal Structure Determination

description: Related Article: Ya-Yi Wang,  Jeffrey W. Bode|2019|J.Am.Chem.Soc.|141|9739|doi:10.1021/jacs.9b05074
DOI: 10.5517/ccdc.csd.cc2126v6
title: CCDC 1878967: Experimental Crystal Structure Determination

description: Related Article: Ester Manrique,  Ingrid Ferrer,  Changyong Lu,  Xavier Fontrodona,  Montserrat Rodríguez,  Isabel Romero|2019|Inorg.Chem.|58|8460|doi:10.1021/acs.inorgchem.9b00664
DOI: 10.5517/ccdc.csd.cc2126w7
title: CCDC 1878968: Experimental Crystal Structure Determination

description: Related Article: Ester Manrique,  Ingrid Ferrer,  Changyong Lu,  Xavier Fontrodona,  Montserrat Rodríguez,  Isabel Romero|2019|Inorg.Chem.|58|8460|doi:10.1021/acs.inorgchem.9b00664
DOI: 10.5517/ccdc.csd.cc216cg3
title: CCDC 1882954: Experimental Crystal Structure Determination

description: Related Article: Chun-Yu Liu,  Hui-Fang Wang,  Zhi-Gang Ren,  Pierre Braunstein,  Jian-Ping Lang|2019|Inorg.Chem.|58|8533|doi:10.1021/acs.inorgchem.9b00845
DOI: 10.5517/ccdc.csd.cc216ch4
title: CCDC 1882955: Experimental Crystal Structure Determination

description: Related Article: Chun-Yu Liu,  Hui-Fang Wang,  Zhi-Gang Ren,  Pierre Braunstein,  Jian-Ping Lang|2019|Inorg.Chem.|58|8533|doi:10.1021/acs.inorgchem.9b00845
DOI: 10.5517/ccdc.csd.cc216cj5
title: CCDC 1882956: Experimental Crystal Structure Determination

description: Related Article: Chun-Yu Liu,  Hui-Fang Wang,  Zhi-Gang Ren,  Pierre Braunstein,  Jian-Ping Lang|2019|Inorg.Chem.|58|8533|doi:10.1021/acs.inorgchem.9b00845
DOI: 10.5517/ccdc.csd.cc216cl7
title: CCDC 1882958: Experimental Crystal Structure Determination

description: Related Article: Sheng-Li Hou|2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc21856q
title: CCDC 1884682: Experimental Crystal Structure Determination

description: Related Article: Jacqueline Ramler, Ivo Krummenacher, and Crispin Lichtenberg|2019|Angew.Chem.,Int.Ed.|58|12924|doi:10.1002/anie.201904365
DOI: 10.5517/ccdc.csd.cc21859t
title: CCDC 1884685: Experimental Crystal Structure Determination

description: Related Article: Jacqueline Ramler, Ivo Krummenacher, and Crispin Lichtenberg|2019|Angew.Chem.,Int.Ed.|58|12924|doi:10.1002/anie.201904365
DOI: 10.5517/ccdc.csd.cc2229m3
title: CCDC 1908844: Experimental Crystal Structure Determination

description: Related Article: Wei Wang,  Haofu Zhu,  Shuya Liu,  Zhiguo Zhao,  Liang Zhang,  Jingcheng Hao,  Yao Wang|2019|J.Am.Chem.Soc.|141|9175|doi:10.1021/jacs.9b03806
DOI: 10.5517/ccdc.csd.cc222vdf
title: CCDC 1909364: Experimental Crystal Structure Determination

description: Related Article: Moises Ballesteros II, Emily Y. Tsui|2019|Inorg.Chem.|58|10501|doi:10.1021/acs.inorgchem.9b01074
DOI: 10.5517/ccdc.csd.cc222vfg
title: CCDC 1909365: Experimental Crystal Structure Determination

description: Related Article: Moises Ballesteros II, Emily Y. Tsui|2019|Inorg.Chem.|58|10501|doi:10.1021/acs.inorgchem.9b01074
DOI: 10.5517/ccdc.csd.cc222vgh
title: CCDC 1909366: Experimental Crystal Structure Determination

description: Related Article: Moises Ballesteros II, Emily Y. Tsui|2019|Inorg.Chem.|58|10501|doi:10.1021/acs.inorgchem.9b01074
DOI: 10.5517/ccdc.csd.cc222vhj
title: CCDC 1909367: Experimental Crystal Structure Determination

description: Related Article: Moises Ballesteros II, Emily Y. Tsui|2019|Inorg.Chem.|58|10501|doi:10.1021/acs.inorgchem.9b01074
DOI: 10.5517/ccdc.csd.cc2230mv
title: CCDC 1909526: Experimental Crystal Structure Determination

description: Related Article: Jun Okuda, Priyabrata Ghana, Franziska van Krüchten, Thomas Paul Spaniol|2019|Chem.-Eur.J.|25|10718|doi:10.1002/chem.201901796
DOI: 10.5517/ccdc.csd.cc22b21k
title: CCDC 1916297: Experimental Crystal Structure Determination

description: Related Article: Rui Li, Jinfeng Li, Yajing Sun, Xiaotao Zhang and Wenping Hu|2019|ChemPlusChem|84|1245|doi:10.1002/cplu.201900197
DOI: 10.5517/ccdc.csd.cc1zq6yv
title: CCDC 1838608: Experimental Crystal Structure Determination

description: Related Article: Huize Jing, Haonan Ge, Chenshuai Li, Yi Jin, Zikuan Wang, Cengceng Du, Xuefeng Fu, Huayi Fang|2019|Organometallics|38|2412|doi:10.1021/acs.organomet.9b00237
DOI: 10.5517/ccdc.csd.cc20cyv6
title: CCDC 1858538: Experimental Crystal Structure Determination

description: Related Article: Yi He, Zhen Liu, Danjun Wu, Zhenghua Li, Koen Robeyns, Luc Van Meervelt, Erik V. Van der Eycken
|2019|Org.Lett.|21|4469|doi:10.1021/acs.orglett.9b01296
DOI: 10.5517/ccdc.csd.cc20cyw7
title: CCDC 1858539: Experimental Crystal Structure Determination

description: Related Article: Yi He, Zhen Liu, Danjun Wu, Zhenghua Li, Koen Robeyns, Luc Van Meervelt, Erik V. Van der Eycken
|2019|Org.Lett.|21|4469|doi:10.1021/acs.orglett.9b01296