cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14173803
title: Additional file 2 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 2. Table of grid search parameters. Parameters are relevant to a scikit-learn implementation.
DOI: 10.6084/m9.figshare.14173806.v1
title: Additional file 3 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 3. Table of tuned models used by seqQscorer. Dataset: species-subset-layout, generic: all data. Feature sets: RAW (raw data), MAP (genome mapping), LOC (genomic localization), TSS (transcription start sites profile). Feature Selection: method-percentage (percentage of retained features), chi-square (chi2), recursive feature elimination (RFE). Algorithm Parameters: relevant to a scikit-learn implementation.
DOI: 10.6084/m9.figshare.14173812
title: Additional file 5 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 5. Table of models tuned using the grid search. Dataset: species-subset-layout, generic: all data. Feature sets: RAW (raw data), MAP (genome mapping), LOC (genomic localization), TSS (transcription start sites profile). Feature Selection: method-percentage (percentage of retained features), chi-square (chi2), recursive feature elimination (RFE). Algorithm Parameters: relevant to a scikit-learn implementation.
DOI: 10.6084/m9.figshare.14173812.v1
title: Additional file 5 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 5. Table of models tuned using the grid search. Dataset: species-subset-layout, generic: all data. Feature sets: RAW (raw data), MAP (genome mapping), LOC (genomic localization), TSS (transcription start sites profile). Feature Selection: method-percentage (percentage of retained features), chi-square (chi2), recursive feature elimination (RFE). Algorithm Parameters: relevant to a scikit-learn implementation.
DOI: 10.6084/m9.figshare.14173809
title: Additional file 4 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 4. Table of optimal models used by seqQscorer. Optimal models are the best models for each data subset (Dataset column). Dataset: species-subset-layout, generic: all data. Feature sets: RAW (raw data), MAP (genome mapping), LOC (genomic localization), TSS (transcription start sites profile). Feature Selection: method-percentage (percentage of retained features), chi-square (chi2), recursive feature elimination (RFE). Algorithm Parameters: relevant to a scikit-learn implementation.
DOI: 10.6084/m9.figshare.14173815
title: Additional file 6 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 6. Table of Cistrome datasets. The table details all Cistrome datasets used for independent validation. Dataset GSE60731 is listed 2 times to represent either mouse DNase-seq samples (GSE60731) or ChIP-seq samples (GSE60731_2). GEO_Series: identifier from the GEO database.
DOI: 10.6084/m9.figshare.14173815.v1
title: Additional file 6 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 6. Table of Cistrome datasets. The table details all Cistrome datasets used for independent validation. Dataset GSE60731 is listed 2 times to represent either mouse DNase-seq samples (GSE60731) or ChIP-seq samples (GSE60731_2). GEO_Series: identifier from the GEO database.
DOI: 10.6084/m9.figshare.14173818.v1
title: Additional file 7 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 7. Table of ENCODE datasets. The table details all ENCODE datasets used for training the model). Quality label is 0 for released files or 1 for revoked files.
DOI: 10.6084/m9.figshare.14173818
title: Additional file 7 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 7. Table of ENCODE datasets. The table details all ENCODE datasets used for training the model). Quality label is 0 for released files or 1 for revoked files.
DOI: 10.6084/m9.figshare.14173809.v1
title: Additional file 4 of seqQscorer: automated quality control of next-generation sequencing data using machine learning

description: Additional file 4. Table of optimal models used by seqQscorer. Optimal models are the best models for each data subset (Dataset column). Dataset: species-subset-layout, generic: all data. Feature sets: RAW (raw data), MAP (genome mapping), LOC (genomic localization), TSS (transcription start sites profile). Feature Selection: method-percentage (percentage of retained features), chi-square (chi2), recursive feature elimination (RFE). Algorithm Parameters: relevant to a scikit-learn implementation.
DOI: 10.6084/m9.figshare.14173877
title: Additional file 3 of Conditional power of antidepressant network meta-analysis

description: Additional file 3 Supplementary file 3.
DOI: 10.6084/m9.figshare.14173877.v1
title: Additional file 3 of Conditional power of antidepressant network meta-analysis

description: Additional file 3 Supplementary file 3.
DOI: 10.6084/m9.figshare.14173959
title: Additional file 1 of Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients

description: Additional file 1. Genetic Testing Panels Ordered for Study Patients.
DOI: 10.6084/m9.figshare.14173959.v1
title: Additional file 1 of Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients

description: Additional file 1. Genetic Testing Panels Ordered for Study Patients.
DOI: 10.6084/m9.figshare.14173962
title: Additional file 2 of Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients

description: Additional file 2. Likely Pathogenic and Pathogenic Variants Identified in the Study Patients.
DOI: 10.6084/m9.figshare.14173962.v1
title: Additional file 2 of Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients

description: Additional file 2. Likely Pathogenic and Pathogenic Variants Identified in the Study Patients.
DOI: 10.6084/m9.figshare.14173979
title: Additional file 5 of Midazolam for sedation before procedures in adults and children: a systematic review update

description: Additional file 5. Data and analyses: All data and results of meta-analyses
DOI: 10.6084/m9.figshare.14173979.v1
title: Additional file 5 of Midazolam for sedation before procedures in adults and children: a systematic review update

description: Additional file 5. Data and analyses: All data and results of meta-analyses
DOI: 10.17632/6dw574bwp6.1
title: Data for: Co-transport of U(VI) and bentonite colloids: Influence of colloidal gibbsite
DOI: 10.6084/m9.figshare.14174054.v1
title: plastid phylogenetic reconstruction of liverworts

description: Liverwort plastid phylogenetic analysis data are included in data_in_brief.zip, and substitution rate estimation data are included in the package of substitution_rates_analysis.zip.
DOI: 10.6084/m9.figshare.14174054
title: plastid phylogenetic reconstruction of liverworts

description: Liverwort plastid phylogenetic analysis data are included in data_in_brief.zip, and substitution rate estimation data are included in the package of substitution_rates_analysis.zip.
DOI: 10.5281/zenodo.4475577
title: Dataset for the Paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study"

description: This is the dataset for the paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study". It contains the 3084 Self-Claimed Assumptions (SCAs) extracted from nine deep learning frameworks (i.e., TensorFlow, Theano, PyTorch, Caffe, MXNet, Keras, CNTK, DL4J, and PaddlePaddle), which includes 2278 unique SCAs that have different content and 806 duplicated SCAs that have the same content as one of the 2278 SCAs.
DOI: 10.5281/zenodo.4475576
title: Dataset for the Paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study"

description: This is the dataset for the paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study". It contains the 3084 Self-Claimed Assumptions (SCAs) extracted from nine deep learning frameworks (i.e., TensorFlow, Theano, PyTorch, Caffe, MXNet, Keras, CNTK, DL4J, and PaddlePaddle), which includes 2278 unique SCAs that have different content and 806 duplicated SCAs that have the same content as one of the 2278 SCAs.
DOI: 10.14469/hpc/6487
title: SS-I

description: Gaussian Calculation
DOI: 10.14469/hpc/6488
title: SS-II'

description: Gaussian Calculation
DOI: 10.14469/hpc/6489
title: SS-II

description: Gaussian Calculation
DOI: 10.14469/hpc/6490
title: rac-II

description: Gaussian Calculation
DOI: 10.6084/m9.figshare.14174099
title: Raw Data of Locomotion and Metabolomics (CON-LSA).zip

description: We evaluated the effect of an L-type SALMFamide neuropeptide (LSA) on locomotory performance of <i>Apostichopus japonicus</i>. We also investigated the metabolites of longitudinal muscle tissue using ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to assess the potential physiological mechanisms underlying the effect of LSA.
DOI: 10.6084/m9.figshare.14174105
title: Supplementary Data.docx

description: Supplementary data for the <i>Journal of Clinical Endocrinology and Metabolism</i>
DOI: 10.6084/m9.figshare.14174105.v1
title: Supplementary Data.docx

description: Supplementary data for the <i>Journal of Clinical Endocrinology and Metabolism</i>
DOI: 10.6084/m9.figshare.14174099.v1
title: Raw Data of Locomotion and Metabolomics (CON-LSA).zip

description: We evaluated the effect of an L-type SALMFamide neuropeptide (LSA) on locomotory performance of <i>Apostichopus japonicus</i>. We also investigated the metabolites of longitudinal muscle tissue using ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to assess the potential physiological mechanisms underlying the effect of LSA.
DOI: 10.17632/mmt9j3bvn7.1
title: Data for: Experimental Evaluation of Vacuum Drying for Removal of Residual Water from Spent Nuclear Fuel Dry Storage System Canister

description: Vacuum drying is a practical technology that has been successfully used for many years in various industries. The aim of the present study was to evaluate whether the drying of residual water inside the canister was adequate, as well as whether ice formed inside the canister during vacuum drying. To do so, a vacuum drying system and a small canister were designed, fabricated, and used to conduct vacuum drying tests.
DOI: 10.6084/m9.figshare.13465757
title: LCMS of the muscle of apostichopus japonicus

description: o obtain the role of neuropeptide in the locomotor activity of sea cucumbers, here we have evaluated the effect of a neuropeptide on locomotory performance in A. japonicus. Meanwhile, metabolism of longitudinal muscle tissue was detected by ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to investigate the potential physiological mechanisms underlying the effect.
DOI: 10.6084/m9.figshare.13465757.v2
title: LCMS of the muscle of apostichopus japonicus

description: o obtain the role of neuropeptide in the locomotor activity of sea cucumbers, here we have evaluated the effect of a neuropeptide on locomotory performance in A. japonicus. Meanwhile, metabolism of longitudinal muscle tissue was detected by ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to investigate the potential physiological mechanisms underlying the effect.
DOI: 10.6084/m9.figshare.13465757.v1
title: LM2018M0899H-青岛欧易生物科技有限公司-海参肌肉-LCMS（项目报告CON-LSA）

description: Neuropeptides in the family of SALMFamides serve as muscle relaxants in echinoderms, while the motor behavior in sea cucumbers involves the alternating contraction and extension of body wall, which is obviously under the control of longitudinal muscle. To obtain the role of SALMFamides in the locomotor activity of sea cucumbers, here we have evaluated the effect of a L-type SALMFamide neuropeptide (LSA) on locomotory performance in A. japonicus. Meanwhile, metabolism of longitudinal muscle tissue was detected by ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) to investigate the potential physiological mechanisms underlying the effect. The results indicated that LSA increased the hourly distance moved and cumulative duration of moving per hour significantly at the 4th h after injection, and enhanced the mean and maximum velocity by 10.56% and 19.83% in this species. In addition, a total of 27 metabolites in longitudinal muscle were changed after LSA administration, and the increased pantothenic acid, lysophosphatidylethanolamine (LysoPEs), arachidonic acid (ARA), and the altered value of phosphatidylethanolamine (PE) / phosphatidylcholine (PC), would be the potential physiological mechanisms that underlying the regulation of LSA on locomotor behavior in A. japonicus.
DOI: 10.6084/m9.figshare.14174129
title: BaselineDemographicSurveyaroundProposedJaitapurNuclearPowerPlantMaharashtraIndia.3.pdf

description: The present study deals with demographic survey around Jaitapur Nuclear Power Plant proposed site which comes in Ratnagiri and part of Sindhudurg District. Demography study is important for creating base line data. The study area includes 121 villages which come into 30 km radial range around JNPP. The present study focuses on the demographic characteristics and socio-economic conditions of this region. The household survey was carried out around the proposed project site
DOI: 10.6084/m9.figshare.14174129.v2
title: BaselineDemographicSurveyaroundProposedJaitapurNuclearPowerPlantMaharashtraIndia.3.pdf

description: The present study deals with demographic survey around Jaitapur Nuclear Power Plant proposed site which comes in Ratnagiri and part of Sindhudurg District. Demography study is important for creating base line data. The study area includes 121 villages which come into 30 km radial range around JNPP. The present study focuses on the demographic characteristics and socio-economic conditions of this region. The household survey was carried out around the proposed project site
DOI: 10.6084/m9.figshare.14174129.v1
title: BaselineDemographicSurveyaroundProposedJaitapurNuclearPowerPlantMaharashtraIndia.3.pdf

description: The present study deals with demographic survey around Jaitapur Nuclear Power Plant proposed site which comes in Ratnagiri and part of Sindhudurg District. Demography study is important for creating base line data. The study area includes 121 villages which come into 30 km radial range around JNPP. The present study focuses on the demographic characteristics and socio-economic conditions of this region. The household survey was carried out around the proposed project site
DOI: 10.6084/m9.figshare.14173634.v2
title: Yangtze Delta Zircon U-Pb dataset_Shang etal_2021March.xlsx

description: Zircon U-Pb dataset for Shang et al.'s paper entitled "Yellow River's contribution to the building of Yangtze Delta during the last 500 years - evidence from detrital zircon U-Pb geochronology" submitted to Geophysical Research Letters.
DOI: 10.5517/ccdc.csd.cc1zzmbx
title: CCDC 1846680: Experimental Crystal Structure Determination

description: Related Article: Hongjun Jang, Soo Yeon Kwak, Dongjoo Lee, Juan V. Alegre-Requena, Hyoungsu Kim, Robert S. Paton, Deukjoon Kim|2021|Org.Lett.|23|1321|doi:10.1021/acs.orglett.0c04303
DOI: 10.5517/ccdc.csd.cc1zzmcy
title: CCDC 1846681: Experimental Crystal Structure Determination

description: Related Article: Hongjun Jang, Soo Yeon Kwak, Dongjoo Lee, Juan V. Alegre-Requena, Hyoungsu Kim, Robert S. Paton, Deukjoon Kim|2021|Org.Lett.|23|1321|doi:10.1021/acs.orglett.0c04303
DOI: 10.5517/ccdc.csd.cc1zzmdz
title: CCDC 1846682: Experimental Crystal Structure Determination

description: Related Article: Hongjun Jang, Soo Yeon Kwak, Dongjoo Lee, Juan V. Alegre-Requena, Hyoungsu Kim, Robert S. Paton, Deukjoon Kim|2021|Org.Lett.|23|1321|doi:10.1021/acs.orglett.0c04303
DOI: 10.5517/ccdc.csd.cc2325gv
title: CCDC 1938506: Experimental Crystal Structure Determination

description: Related Article: Shenghan Teng, Yonggui Robin Chi, Jianrong Steve Zhou|2021|Angew.Chem.,Int.Ed.|60|4491|doi:10.1002/anie.202014781
DOI: 10.5517/ccdc.csd.cc243v9f
title: CCDC 1969904: Experimental Crystal Structure Determination

description: Related Article: Qilin Xing, Xiang Wang, Xiaojing Yan, Xue Ren, Wen Wang, Xia Yan, Jiayu Zhang, Chunyu Cao, Haichuang Lan, Shuzhang Xiao|2021|Dyes Pigm.|188|109232|doi:10.1016/j.dyepig.2021.109232
DOI: 10.5517/ccdc.csd.cc243vbg
title: CCDC 1969905: Experimental Crystal Structure Determination

description: Related Article: Qilin Xing, Xiang Wang, Xiaojing Yan, Xue Ren, Wen Wang, Xia Yan, Jiayu Zhang, Chunyu Cao, Haichuang Lan, Shuzhang Xiao|2021|Dyes Pigm.|188|109232|doi:10.1016/j.dyepig.2021.109232
DOI: 10.5517/ccdc.csd.cc244gj9
title: CCDC 1970500: Experimental Crystal Structure Determination

description: Related Article: Zhili Liu, Lianfen Chen, Dong Zhu, Shifa Zhu|2021|Org.Lett.|23|1275|doi:10.1021/acs.orglett.0c04251
DOI: 10.5517/ccdc.csd.cc24gmbl
title: CCDC 1980259: Experimental Crystal Structure Determination

description: Related Article: Sudhir Kumar Keshri, Tomoya Ishizuka, Takahiko Kojima, Yoshitaka Matsushita, Masayuki Takeuchi|2021|J.Am.Chem.Soc.|143|3238|doi:10.1021/jacs.0c13389
DOI: 10.5517/ccdc.csd.cc25f2l9
title: CCDC 2008570: Experimental Crystal Structure Determination

description: Related Article: Animesh Biswas, Jörg-M. Neudörfl, Nils E. Schlörer, Albrecht Berkessel|2021|Angew.Chem.,Int.Ed.|60|4507|doi:10.1002/anie.202010348
DOI: 10.5517/ccdc.csd.cc25f2mb
title: CCDC 2008571: Experimental Crystal Structure Determination

description: Related Article: Animesh Biswas, Jörg-M. Neudörfl, Nils E. Schlörer, Albrecht Berkessel|2021|Angew.Chem.,Int.Ed.|60|4507|doi:10.1002/anie.202010348
DOI: 10.5517/ccdc.csd.cc25f2nc
title: CCDC 2008572: Experimental Crystal Structure Determination

description: Related Article: Animesh Biswas, Jörg-M. Neudörfl, Nils E. Schlörer, Albrecht Berkessel|2021|Angew.Chem.,Int.Ed.|60|4507|doi:10.1002/anie.202010348
DOI: 10.5517/ccdc.csd.cc25f2pd
title: CCDC 2008573: Experimental Crystal Structure Determination

description: Related Article: Animesh Biswas, Jörg-M. Neudörfl, Nils E. Schlörer, Albrecht Berkessel|2021|Angew.Chem.,Int.Ed.|60|4507|doi:10.1002/anie.202010348
DOI: 10.5517/ccdc.csd.cc25nvk8
title: CCDC 2016040: Experimental Crystal Structure Determination

description: Related Article: Guijie Li, Jianbing Zheng, Xiaoli Fang, Kewei Xu, Yun-Fang Yang, Jiang Wu, Linyu Cao, Jian Li, Yuanbin She|2021|Organometallics|40|472|doi:10.1021/acs.organomet.0c00648
DOI: 10.5517/ccdc.csd.cc25qc1b
title: CCDC 2017481: Experimental Crystal Structure Determination

description: Related Article: Bo Lin, Yu Liu, MengWei Wang, Yan Wang, Shichao Du, JunBo Gong, Songgu Wu|2021|Cryst.Growth Des.|21|1473|doi:10.1021/acs.cgd.0c01069
DOI: 10.5517/ccdc.csd.cc25qc2c
title: CCDC 2017482: Experimental Crystal Structure Determination

description: Related Article: Bo Lin, Yu Liu, MengWei Wang, Yan Wang, Shichao Du, JunBo Gong, Songgu Wu|2021|Cryst.Growth Des.|21|1473|doi:10.1021/acs.cgd.0c01069
DOI: 10.5517/ccdc.csd.cc25qc3d
title: CCDC 2017483: Experimental Crystal Structure Determination

description: Related Article: Bo Lin, Yu Liu, MengWei Wang, Yan Wang, Shichao Du, JunBo Gong, Songgu Wu|2021|Cryst.Growth Des.|21|1473|doi:10.1021/acs.cgd.0c01069
DOI: 10.5517/ccdc.csd.cc25vd9r
title: CCDC 2021364: Experimental Crystal Structure Determination

description: Related Article: José F. Quílez del Moral, Cristina Ruiz Martínez, Helena Pérez del Pulgar, Juan Eduardo Martín González, Ignacio Fernández, José Luis López-Pérez, Alejandro Fernández-Arteaga, Alejandro F. Barrero|2021|J.Org.Chem.|86|3344|doi:10.1021/acs.joc.0c02698
DOI: 10.5517/ccdc.csd.cc2699rl
title: CCDC 2034739: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Miyake, Eri Suganuma, Shun Kimura, Hirotoshi Mori, Jun Okabayashi, Tetsuro Kusamoto|2021|Angew.Chem.,Int.Ed.|60|5179|doi:10.1002/anie.202013373
DOI: 10.5517/ccdc.csd.cc2699sm
title: CCDC 2034740: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Miyake, Eri Suganuma, Shun Kimura, Hirotoshi Mori, Jun Okabayashi, Tetsuro Kusamoto|2021|Angew.Chem.,Int.Ed.|60|5179|doi:10.1002/anie.202013373
DOI: 10.5517/ccdc.csd.cc2699tn
title: CCDC 2034741: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Miyake, Eri Suganuma, Shun Kimura, Hirotoshi Mori, Jun Okabayashi, Tetsuro Kusamoto|2021|Angew.Chem.,Int.Ed.|60|5179|doi:10.1002/anie.202013373
DOI: 10.5517/ccdc.csd.cc26bb0x
title: CCDC 2035708: Experimental Crystal Structure Determination

description: Related Article: Sudhir Kumar Keshri, Tomoya Ishizuka, Takahiko Kojima, Yoshitaka Matsushita, Masayuki Takeuchi|2021|J.Am.Chem.Soc.|143|3238|doi:10.1021/jacs.0c13389
DOI: 10.5517/ccdc.csd.cc26bfzz
title: CCDC 2035831: Experimental Crystal Structure Determination

description: Related Article: Xuefeng Tan, Xiaoyan Hou, Torben Rogge, Lutz Ackermann|2021|Angew.Chem.,Int.Ed.|60|4619|doi:10.1002/anie.202014289
DOI: 10.5517/ccdc.csd.cc26bg01
title: CCDC 2035832: Experimental Crystal Structure Determination

description: Related Article: Xuefeng Tan, Xiaoyan Hou, Torben Rogge, Lutz Ackermann|2021|Angew.Chem.,Int.Ed.|60|4619|doi:10.1002/anie.202014289
DOI: 10.5517/ccdc.csd.cc26j13x
title: CCDC 2041198: Experimental Crystal Structure Determination

description: Related Article: Shouang Lan, Rui Liu, Xiangwen Kong, Jinggong Liu, Benlong Luo, Shuang Yang, Xinqiang Fang|2021|Org.Lett.|23|1504|doi:10.1021/acs.orglett.1c00291
DOI: 10.5517/ccdc.csd.cc26j8st
title: CCDC 2041436: Experimental Crystal Structure Determination

description: Related Article: Xiao-Di Nie, Xiao-Li Han, Jian-Ting Sun, Chang-Mei Si, Bang-Guo Wei, Guo-Qiang Lin|2021|J.Org.Chem.|86|3433|doi:10.1021/acs.joc.0c02807
DOI: 10.5517/ccdc.csd.cc26j8tv
title: CCDC 2041437: Experimental Crystal Structure Determination

description: Related Article: Xiao-Di Nie, Xiao-Li Han, Jian-Ting Sun, Chang-Mei Si, Bang-Guo Wei, Guo-Qiang Lin|2021|J.Org.Chem.|86|3433|doi:10.1021/acs.joc.0c02807
DOI: 10.5517/ccdc.csd.cc26l5bb
title: CCDC 2043251: Experimental Crystal Structure Determination

description: Related Article: Shenghan Teng, Yonggui Robin Chi, Jianrong Steve Zhou|2021|Angew.Chem.,Int.Ed.|60|4491|doi:10.1002/anie.202014781
DOI: 10.5517/ccdc.csd.cc26qmxh
title: CCDC 2047547: Experimental Crystal Structure Determination

description: Related Article: Zhili Liu, Lianfen Chen, Dong Zhu, Shifa Zhu|2021|Org.Lett.|23|1275|doi:10.1021/acs.orglett.0c04251
DOI: 10.5517/ccdc.csd.cc26qzxv
title: CCDC 2047888: Experimental Crystal Structure Determination

description: Related Article: Dzeneta Halilovic, Venkatachalam Rajeshkumar, Mihaiela C. Stuparu|2021|Org.Lett.|23|1468|doi:10.1021/acs.orglett.1c00146
DOI: 10.5517/ccdc.csd.cc26qzyw
title: CCDC 2047889: Experimental Crystal Structure Determination

description: Related Article: Dzeneta Halilovic, Venkatachalam Rajeshkumar, Mihaiela C. Stuparu|2021|Org.Lett.|23|1468|doi:10.1021/acs.orglett.1c00146
DOI: 10.5517/ccdc.csd.cc26rdft
title: CCDC 2048276: Experimental Crystal Structure Determination

description: Related Article: Xiaoping Hu, Xin Xie, Yi Gan, Gaonan Wang, Yuanhong Liu|2021|Org.Lett.|23|1296|doi:10.1021/acs.orglett.0c04278
DOI: 10.5517/ccdc.csd.cc26rdgv
title: CCDC 2048277: Experimental Crystal Structure Determination

description: Related Article: Xiaoping Hu, Xin Xie, Yi Gan, Gaonan Wang, Yuanhong Liu|2021|Org.Lett.|23|1296|doi:10.1021/acs.orglett.0c04278