cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6584944
title: Supplemental data files: XY sex determination in a cnidarian

description: <strong>Project Abstract:</strong> Sex determination occurs across animal species, but most of our knowledge about the mechanisms of sex determination comes from only a handful of bilaterian taxa. This limits our ability to infer the evolutionary history of sex determination within animals. In this study, we generated a linkage map of the genome of the colonial cnidarian <em>Hydractinia symbiolongicarpus</em> and used this map to determine that this species has an XX/XY sex determination system. We delineate the pseudoautosomal and non-recombining regions of the Y chromosome and show that the latter encodes a number of genes with male gonad-specific expression. These findings establish Hydractinia as a tractable non-bilaterian model system for the study of sex determination. This archive contains supplemental data files for "XY sex determination in a cnidarian" by Chen et. al. The manuscript will be submitted to bioRxiv. <strong>Hsym_primary_v1.0.fa.gz: </strong><em>Hydractinia </em>reference genome assembly <strong>160429_CRATOS_HKM5MBCXX.lane1.12268798.read1.DOWNSAMPLE_0.2.fq.gz: </strong>Illumina reads for paternal genome downsampled with seqtk. <strong>160429_CRATOS_HKM5MBCXX.lane1.12268798.read2.DOWNSAMPLE_0.2.fq.gz: </strong>Illumina reads for paternal genome downsampled with seqtk. <strong>rawvariants.90f1.vcf.gz: </strong>Raw genotypes for all 90 offspring and the two parents, called by GATK HaplotypeCaller <strong>GATKBP-passed.vcf.gz: </strong>Genotypes quality filtered according to GATK best practices and custom critera. <strong>GATKBP-passed.femaleHet.abxabRemoved.vcf.gz: </strong>Genotypes suitable for mapping the maternal genome with a pseudo-testcross strategy. <strong>GATKBP-passed.maleHet.abxabRemoved.vcf.gz: </strong>Genotypes suitable for mapping the paternal genome with a pseudo-testcross strategy.
DOI: 10.5281/zenodo.6368104
title: Supplemental data files: XY sex determination in a cnidarian

description: <strong>Project Abstract:</strong> Sex determination occurs across animal species, but most of our knowledge about the mechanisms of sex determination comes from only a handful of bilaterian taxa. This limits our ability to infer the evolutionary history of sex determination within animals. In this study, we generated a linkage map of the genome of the colonial cnidarian <em>Hydractinia symbiolongicarpus</em> and used this map to determine that this species has an XX/XY sex determination system. We delineate the pseudoautosomal and non-recombining regions of the Y chromosome and show that the latter encodes a number of genes with male gonad-specific expression. These findings establish Hydractinia as a tractable non-bilaterian model system for the study of sex determination. This archive contains supplemental data files for "XY sex determination in a cnidarian" by Chen et. al. The manuscript will be submitted to bioRxiv. <strong>Hsym_primary_v1.0.fa.gz: </strong><em>Hydractinia </em>reference genome assembly <strong>160429_CRATOS_HKM5MBCXX.lane1.12268798.read1.DOWNSAMPLE_0.2.fq.gz: </strong>Illumina reads for paternal genome downsampled with seqtk. <strong>160429_CRATOS_HKM5MBCXX.lane1.12268798.read2.DOWNSAMPLE_0.2.fq.gz: </strong>Illumina reads for paternal genome downsampled with seqtk. <strong>rawvariants.90f1.vcf.gz: </strong>Raw genotypes for all 90 offspring and the two parents, called by GATK HaplotypeCaller <strong>GATKBP-passed.vcf.gz: </strong>Genotypes quality filtered according to GATK best practices and custom critera. <strong>GATKBP-passed.femaleHet.abxabRemoved.vcf.gz: </strong>Genotypes suitable for mapping the maternal genome with a pseudo-testcross strategy. <strong>GATKBP-passed.maleHet.abxabRemoved.vcf.gz: </strong>Genotypes suitable for mapping the paternal genome with a pseudo-testcross strategy.
DOI: 10.6084/m9.figshare.19539004.v1
title: Joint interactions with humans pose a greater risk of zooanthroponotic disease outbreaks than social interactions among (peri)urban wildlife populations

description: In this study, we combined social network analysis with epidemiological Susceptible Infected Recovered (SIR) models, to investigate the effects of macaques' (<i>Macaca</i> spp) co-occurrence and joint interactions with humans, and their social grooming of conspecifics, on simulated disease outbreaks. On each of 10 macaque groups representing three different species living in anthropogenic (peri)urban environments in India and Malaysia, we collected behavioral data on macaques' tendencies to co-occur and jointly engage in interactions (e.g. aggression, provisioning, snatching food) with humans within the same time and space, and their social grooming (cleaning and manipulating the fur of another individual) of their conspecifics (see our previous publications: Balasubramaniam et al., 2020, 2021). The data were used to construct both 'human co-interaction networks' and 'social grooming networks'. For each group and network type, we then ran 5000 SIR model simulations. These were run for pathogens of varying transmissibility, by randomly selecting a macaque to be infected first. The model simulation then proceeds until the pathogen reaches saturation or extinction. It does so by allowing animals to move through animals moving across disease states based on dynamic probabilities that are functions of both their network connectedness and pathogen transmissibility.<br>The dataset uploaded is the outcome of these simulations. For each "first infected macaque", we have included the average outbreak size from across the simulations in which it was the first infected individual. This information has been paired with various sociodemographic (e.g. group size, macaque species, sex of the individual) and behavioral characteristics (e.g. their direct and indirect connectedness or centrality within their human co-interaction networks and grooming networks, dominance ranks, and anthropogenic interactions indicated by their frequencies of interactions with humans and proportions of time spent foraging on human foods). The dataset was used to run GLMMs aimed at examining the effects of the above sociodemographic attributes and behavioral traits of first-infected individuals on the outcome of model-predicted disease outbreak sizes. Thus, we have also provided a second file that contains R code for the GLMMs.<br>The datasets provided were used to write a manuscript that is currently under review in the journal <i>Scientific Reports. </i>They have not been published elsewhere, or made available elsewhere.
DOI: 10.13012/b2idb-0883774_v1
title: Data for Long-lifetime spin excitations near domain walls in 1T-TaS2

description: The data files are for the paper entitled: Long-lifetime spin excitations near domain walls in 1T-TaS2 to be published in PNAS. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.
DOI: 10.6084/m9.figshare.19895638
title: LiDAR4Posture

description: The dataset contains a total of 444 2D LiDAR scans outlining the legs of office workers when sitting at a desk with correct and incorrect positions. <br> The dataset is balanced, i.e., the two classes have the same number of samples. <br> Samples of correct position are in the 'correct' folder; samples of incorrect positions are in the 'anomaly' folder. <br> <br> <br>
DOI: 10.6084/m9.figshare.17121464
title: Datasets and R scripts for "Reduced physiological plasticity in a fish adapted to stable temperatures"

description: All scripts and datasets (including raw data) for the article "Reduced physiological plasticity in a fish adapted to stable temperatures"
DOI: 10.5281/zenodo.6585071
title: Supporting data set for: Three-dimensional Configuration of Induced Magnetic Fields around Mars

description: Supplementary data to reproduce figures for: Three-dimensional Configuration of Induced Magnetic Fields around Mars
DOI: 10.5281/zenodo.6585084
title: ranked

description: ranked
DOI: 10.5281/zenodo.6585083
title: ranked

description: ranked
DOI: 10.5517/ccdc.csd.cc274dg8
title: CCDC 2059809: Experimental Crystal Structure Determination

description: Related Article: Kun Wu, Yong-Liang Huang, Ji Zheng, Dong Luo, Mo Xie, Yan Yan Li, Weigang Lu, Dan Li|2021|Mater. Chem. Front.|5|4300|doi:10.1039/D1QM00211B
DOI: 10.5517/ccdc.csd.cc274djb
title: CCDC 2059811: Experimental Crystal Structure Determination

description: Related Article: Kun Wu, Yong-Liang Huang, Ji Zheng, Dong Luo, Mo Xie, Yan Yan Li, Weigang Lu, Dan Li|2021|Mater. Chem. Front.|5|4300|doi:10.1039/D1QM00211B
DOI: 10.5517/ccdc.csd.cc274dkc
title: CCDC 2059812: Experimental Crystal Structure Determination

description: Related Article: Kun Wu, Yong-Liang Huang, Ji Zheng, Dong Luo, Mo Xie, Yan Yan Li, Weigang Lu, Dan Li|2021|Mater. Chem. Front.|5|4300|doi:10.1039/D1QM00211B
DOI: 10.5517/ccdc.csd.cc274dld
title: CCDC 2059813: Experimental Crystal Structure Determination

description: Related Article: Kun Wu, Yong-Liang Huang, Ji Zheng, Dong Luo, Mo Xie, Yan Yan Li, Weigang Lu, Dan Li|2021|Mater. Chem. Front.|5|4300|doi:10.1039/D1QM00211B
DOI: 10.5517/ccdc.csd.cc274dmf
title: CCDC 2059814: Experimental Crystal Structure Determination

description: Related Article: Kun Wu, Yong-Liang Huang, Ji Zheng, Dong Luo, Mo Xie, Yan Yan Li, Weigang Lu, Dan Li|2021|Mater. Chem. Front.|5|4300|doi:10.1039/D1QM00211B
DOI: 10.5517/ccdc.csd.cc28btgx
title: CCDC 2095769: Experimental Crystal Structure Determination

description: Related Article: Zhanfang Chen, Hongbin Fang, Xuewen Hua, Wenrui Liu, Yi Liu, Chenmeng Xue, Bingxiang Wang, Dzmitry Bazhanau, Xiaohe Zhu, Man Yuan, Jing Ru, Pengfei Chu|2021|J.Chem.|2021|1|doi:10.1155/2021/1036199
DOI: 10.5517/ccdc.csd.cc2bw5dt
title: CCDC 2171066: Experimental Crystal Structure Determination

description: Related Article: J.P.J. Bruekers, J.A.A.W. Elemans, R.J.M. Nolte, P. Tinnemans|2022|IUCrData|7|x220489|doi:10.1107/S2414314622004898
DOI: 10.5517/ccdc.csd.cc2bx9k4
title: CCDC 2172156: Experimental Crystal Structure Determination

description: Related Article: D.J. Angevine, J.B. Benedict|2022|IUCrData|7|x220510|doi:10.1107/S2414314622005107
DOI: 10.7281/t1/p69kyx/livdgo
title: census-regions-divisions.tab

description: CSV file identifies the states and territories that belong to each region (5 regions plus territories) and divisions (9 divisions plus territories).
DOI: 10.7281/t1/p69kyx/etrnee
title: 2017-data-axle-division.zip

description: The complete Data Axle business database for 2017, split into subsets by geographic divisions and territories.
DOI: 10.7281/t1/p69kyx/wr0i4s
title: 2017-data-axle-complete.zip

description: The complete Data Axle business database for 2017. Contains 1 CSV file, approximately 5 GB when un-compressed.
DOI: 10.7281/t1/p69kyx/vxkqtr
title: 2017-data-axle-region.zip

description: The complete Data Axle business database for 2017, split into subsets by geographic region and territories.
DOI: 10.7281/t1/p69kyx/8bm3hj
title: 2018-data-axle-division.zip

description: The complete Data Axle business database for 2018, split into subsets by geographic divisions and territories.
DOI: 10.7281/t1/p69kyx/wjm51e
title: 2018-data-axle-complete.zip

description: The complete Data Axle business database for 2018. Contains 1 CSV file, approximately 5 GB when un-compressed.
DOI: 10.7281/t1/p69kyx/j4fa6j
title: 2018-data-axle-region.zip

description: The complete Data Axle business database for 2018, split into subsets by geographic region and territories.
DOI: 10.7281/t1/p69kyx/i4ytez
title: 2019-data-axle-division.zip

description: The complete Data Axle business database for 2019, split into subsets by geographic divisions and territories.
DOI: 10.7281/t1/p69kyx/bdtf4h
title: 2019-data-axle-region.zip

description: The complete Data Axle business database for 2019, split into subsets by geographic region and territories.
DOI: 10.7281/t1/p69kyx/lk8igb
title: 2019-data-axle-complete.zip

description: The complete Data Axle business database for 2019. Contains 1 CSV file, approximately 5 GB when un-compressed.
DOI: 10.7281/t1/p69kyx/g83tvz
title: us-reg-div.pdf

description: A US census-provided map of United States geographic regions and divisions for reference. Available online at: https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf
DOI: 10.7281/t1/p69kyx/wvu3ct
title: data-axle-data-dictionary.tab

description: Data dictionary describing the data variables included in the Data Axle business data.
DOI: 10.7281/t1/p69kyx/9c0fah
title: README.md

description: Readme file for this dataset.
DOI: 10.7281/t1/p69kyx/2l4eot
title: 2020-data-axle-complete.zip

description: The complete Data Axle business database for 2020. Contains 1 CSV file, approximately 5 GB when un-compressed.
DOI: 10.7281/t1/p69kyx/xs2zqv
title: 2020-data-axle-division.zip

description: The complete Data Axle business database for 2020, split into subsets by geographic divisions and territories.
DOI: 10.7281/t1/p69kyx/flltr7
title: 2020-data-axle-region.zip

description: The complete Data Axle business database for 2020, split into subsets by geographic region and territories.
DOI: 10.7281/t1/p69kyx/ebcqtn
title: README.md

description: Readme file for this dataset. Updated with 2020 data, NAICS/SIC code data dictionaries, and information about 2022 NAICS reclassification.
DOI: 10.7281/t1/p69kyx/ctmdsj
title: 2017-naics-sic-definitions.tab

description: NAICS/SIC code definitions for 2017 files.
DOI: 10.7281/t1/p69kyx/vz8j5t
title: 2018-2019-2020-naics-sic-definitions.tab

description: NAICS/SIC code definitions for 2018, 2019, and 2020 files.
DOI: 10.6084/m9.figshare.19651397
title: Bayesian Distance Weighted Discrimination

description: Distance weighted discrimination (DWD) is a linear discrimination method that is particularly well-suited for classification tasks with high-dimensional data. The DWD coefficients minimize an intuitive objective function, which can solved efficiently using state-of-the-art optimization techniques. However, DWD has not yet been cast into a model-based framework for statistical inference. In this article we show that DWD identifies the mode of a proper Bayesian posterior distribution, that results from a particular link function for the class probabilities and a shrinkage-inducing proper prior distribution on the coefficients. We describe a relatively efficient Markov chain Monte Carlo (MCMC) algorithm to simulate from the true posterior under this Bayesian framework. We show that the posterior is asymptotically normal and derive the mean and covariance matrix of its limiting distribution. Through several simulation studies and an application to breast cancer genomics we demonstrate how the Bayesian approach to DWD can be used to (a) compute well-calibrated posterior class probabilities, (b) assess uncertainty in the DWD coefficients and resulting sample scores, (c) improve power via semisupervised analysis when not all class labels are available, and (d) automatically determine a penalty tuning parameter within the model-based framework. R code to perform Bayesian DWD is available at https://github.com/lockEF/BayesianDWD. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651397.v1
title: Bayesian Distance Weighted Discrimination

description: Distance weighted discrimination (DWD) is a linear discrimination method that is particularly well-suited for classification tasks with high-dimensional data. The DWD coefficients minimize an intuitive objective function, which can solved efficiently using state-of-the-art optimization techniques. However, DWD has not yet been cast into a model-based framework for statistical inference. In this article we show that DWD identifies the mode of a proper Bayesian posterior distribution, that results from a particular link function for the class probabilities and a shrinkage-inducing proper prior distribution on the coefficients. We describe a relatively efficient Markov chain Monte Carlo (MCMC) algorithm to simulate from the true posterior under this Bayesian framework. We show that the posterior is asymptotically normal and derive the mean and covariance matrix of its limiting distribution. Through several simulation studies and an application to breast cancer genomics we demonstrate how the Bayesian approach to DWD can be used to (a) compute well-calibrated posterior class probabilities, (b) assess uncertainty in the DWD coefficients and resulting sample scores, (c) improve power via semisupervised analysis when not all class labels are available, and (d) automatically determine a penalty tuning parameter within the model-based framework. R code to perform Bayesian DWD is available at https://github.com/lockEF/BayesianDWD. Supplementary materials for this article are available online.
DOI: 10.5517/ccdc.csd.cc294sdn
title: CCDC 2119761: Experimental Crystal Structure Determination

description: Related Article: leslie.hjw|2021|CSD Communication|||
description: Related Article: Jing-Wei Huang, Yu-Chieh Hsu, Xiugang Wu, Sai Wang, Xiang-Qin Gan, Wei-Qiong Zheng, Hu Zhang, Yin-Zhi Gong, Wen-Yi Hung, Pi-Tai Chou, Weiguo Zhu|2022|J.Mater.Chem.C|10|7866|doi:10.1039/D1TC06165H
DOI: 10.5517/ccdc.csd.cc294sfp
title: CCDC 2119762: Experimental Crystal Structure Determination

description: Related Article: leslie.hjw|2021|CSD Communication|||
description: Related Article: Jing-Wei Huang, Yu-Chieh Hsu, Xiugang Wu, Sai Wang, Xiang-Qin Gan, Wei-Qiong Zheng, Hu Zhang, Yin-Zhi Gong, Wen-Yi Hung, Pi-Tai Chou, Weiguo Zhu|2022|J.Mater.Chem.C|10|7866|doi:10.1039/D1TC06165H
DOI: 10.5517/ccdc.csd.cc294sgq
title: CCDC 2119763: Experimental Crystal Structure Determination

description: Related Article: leslie.hjw|2021|CSD Communication|||
description: Related Article: Jing-Wei Huang, Yu-Chieh Hsu, Xiugang Wu, Sai Wang, Xiang-Qin Gan, Wei-Qiong Zheng, Hu Zhang, Yin-Zhi Gong, Wen-Yi Hung, Pi-Tai Chou, Weiguo Zhu|2022|J.Mater.Chem.C|10|7866|doi:10.1039/D1TC06165H
DOI: 10.5517/ccdc.csd.cc294shr
title: CCDC 2119764: Experimental Crystal Structure Determination

description: Related Article: leslie.hjw|2021|CSD Communication|||
description: Related Article: Jing-Wei Huang, Yu-Chieh Hsu, Xiugang Wu, Sai Wang, Xiang-Qin Gan, Wei-Qiong Zheng, Hu Zhang, Yin-Zhi Gong, Wen-Yi Hung, Pi-Tai Chou, Weiguo Zhu|2022|J.Mater.Chem.C|10|7866|doi:10.1039/D1TC06165H
DOI: 10.5517/ccdc.csd.cc29b34x
title: CCDC 2124868: Experimental Crystal Structure Determination

description: Related Article: Wen-Tao Wang, Sen Zhang, Ling-Fei Tao, Zi-Qi Pan, Linghui Qian, Jia-Yu Liao|2022|Chem.Commun.|58|6292|doi:10.1039/D2CC01625G
DOI: 10.5517/ccdc.csd.cc29b35y
title: CCDC 2124869: Experimental Crystal Structure Determination

description: Related Article: Wen-Tao Wang, Sen Zhang, Ling-Fei Tao, Zi-Qi Pan, Linghui Qian, Jia-Yu Liao|2022|Chem.Commun.|58|6292|doi:10.1039/D2CC01625G
DOI: 10.5517/ccdc.csd.cc29f5qn
title: CCDC 2127831: Experimental Crystal Structure Determination

description: Related Article: Francesco Marin, Serena Tombolesi, Tommaso Salzillo, Omer Yaffe, Lucia Maini|2022|J.Mater.Chem.C|10|8089|doi:10.1039/D1TC06132A
DOI: 10.5517/ccdc.csd.cc29m1bb
title: CCDC 2133461: Experimental Crystal Structure Determination

description: Related Article: Hang Li, Paul M. Mirzayans, Mark S. Butler, Alastair E. Lacey, Daniel Vuong, Rachel Chen, John A. Kalaitzis, Stephen A. Moggach, Ernest Lacey, Andrew M. Piggott, Yit-Heng Chooi|2022|Chem.Commun.|58|6296|doi:10.1039/D2CC01679F
DOI: 10.5517/ccdc.csd.cc29ysjl
title: CCDC 2143790: Experimental Crystal Structure Determination

description: Related Article: Xiaozhong Liang, Bizheng Dong, Hua Wang, Zheng Zhang, Sijing Wang, Jie Li, Bo Zhao, Zhijun Li, Yifan Xing, Kunpeng Guo|2022|J.Mater.Chem.C|10|7857|doi:10.1039/D2TC00876A
DOI: 10.5517/ccdc.csd.cc2b9mny
title: CCDC 2154210: Experimental Crystal Structure Determination

description: Related Article: Yu Wu, Feng-Wei Wu, Kun Zhou, Yiming Li, Lei Chen, Shuang Wang, Zhen-Yuan Xu, Shao-Jie Lou, Dan-Qian Xu|2022|Chem.Commun.|58|6280|doi:10.1039/D2CC01355J
DOI: 10.5517/ccdc.csd.cc2b9mpz
title: CCDC 2154211: Experimental Crystal Structure Determination

description: Related Article: Yu Wu, Feng-Wei Wu, Kun Zhou, Yiming Li, Lei Chen, Shuang Wang, Zhen-Yuan Xu, Shao-Jie Lou, Dan-Qian Xu|2022|Chem.Commun.|58|6280|doi:10.1039/D2CC01355J
DOI: 10.5517/ccdc.csd.cc2bf510
title: CCDC 2157601: Experimental Crystal Structure Determination

description: Related Article: Wen-Tao Wang, Sen Zhang, Ling-Fei Tao, Zi-Qi Pan, Linghui Qian, Jia-Yu Liao|2022|Chem.Commun.|58|6292|doi:10.1039/D2CC01625G
DOI: 10.5517/ccdc.csd.cc2bfqh0
title: CCDC 2158142: Experimental Crystal Structure Determination

description: Related Article: Younghun Kim, Philjae Kang, Han Sol Oh, Hyeon Mo Cho, Moon-Gun Choi|2022|Chem.Commun.|58|6304|doi:10.1039/D2CC01544G
DOI: 10.5517/ccdc.csd.cc2bhkl0
title: CCDC 2159912: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkm1
title: CCDC 2159913: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkn2
title: CCDC 2159914: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkp3
title: CCDC 2159915: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkq4
title: CCDC 2159916: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkr5
title: CCDC 2159917: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhks6
title: CCDC 2159918: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkt7
title: CCDC 2159919: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkv8
title: CCDC 2159920: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc2bhkw9
title: CCDC 2159921: Experimental Crystal Structure Determination

description: Related Article: Marko Dunatov, Andreas Puškarić, Luka Pavić, Zoran Štefanić, Lidija Androš Dubraja|2022|J.Mater.Chem.C|10|8024|doi:10.1039/D2TC01103D
DOI: 10.5517/ccdc.csd.cc287q9k
title: CCDC 2092788: Experimental Crystal Structure Determination

description: Related Article: Shuai-Fang Cai, Li-Qi Qiu, Wen-Bin Huang, Hong-Ru Li, Liang-Nian He|2022|Chem.Commun.|58|6332|doi:10.1039/D2CC01635D
DOI: 10.5517/ccdc.csd.cc29f5nl
title: CCDC 2127829: Experimental Crystal Structure Determination

description: Related Article: Francesco Marin, Serena Tombolesi, Tommaso Salzillo, Omer Yaffe, Lucia Maini|2022|J.Mater.Chem.C|10|8089|doi:10.1039/D1TC06132A