cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14390783.v1
title: Pitta sundering data file.xlsx

description: At each nest, we installed a dark-colored camouflaged tent 20 m away from the nest. From inside of the tent, we filmed and photographed visiting parents using digital cameras. From the observations and from the videos, we extracted the data.
DOI: 10.6084/m9.figshare.14390783.v2
title: Pitta sundering data file

description: At each nest, we installed a dark-colored camouflaged tent 20 m away from the nest. From inside of the tent, we filmed and photographed visiting parents using digital cameras. From the observations and from the videos, we extracted the data.
DOI: 10.5281/zenodo.3958835
title: Data of tangential wind speed of Typhoon Trami (2018) derived by Tsujino et al. (2021)

description: Time series data of tangential wind speed in the eye and eyewall of Typhoon Trami (2018) from 0000 UTC 25 to 0600 UTC 27 September 2018, derived by Tsujino et al. (2021) using the data observed by Himawari-8 satellite. More information is also available at http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html.
DOI: 10.5281/zenodo.3958834
title: Data of tangential wind speed of Typhoon Trami (2018) derived by Tsujino et al. (2021)

description: Time series data of tangential wind speed in the eye and eyewall of Typhoon Trami (2018) from 0000 UTC 25 to 0600 UTC 27 September 2018, derived by Tsujino et al. (2021) using the data observed by Himawari-8 satellite. More information is also available at http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html.
DOI: 10.6084/m9.figshare.14390783
title: noname

description: aaaaaaaaa
DOI: 10.6084/m9.figshare.14390783.v4
title: noname

description: aaaaaaaaa
DOI: 10.6084/m9.figshare.14390783.v3
title: Pitta sundering data file

description: aaaaaaaaa
DOI: 10.17632/kzjt49m86t.1
title: Data for: Core-based sedimentological and sequence stratigraphic analysis of shale-dominated gas plays: an example of the Roseneath Epsilon-Murteree strata in the Cooper Basin, Australia

description: File 1 provides core logs of additional nine wells in this study; File 2 provides mineralogical composition of studied wells from XRD analysis;
DOI: 10.3886/e137001
title: Revised Reproduction Data for Asher and Novosad (2020)

description: <b></b>Revised Reproduction Data for Asher and Novosad (2020). The original data comes from Asher and Novosad (2020). I do not own it in anyway. I have uploaded this data here for a reproduction exercise in the SocialScienceReproduction.org <br> <br><br>
DOI: 10.3886/e137001v2
title: Revised Reproduction Data for Asher and Novosad (2020)

description: <b></b>Revised Reproduction Data for Asher and Novosad (2020). The original data comes from Asher and Novosad (2020). I do not own it in anyway. I have uploaded this data here for a reproduction exercise in the SocialScienceReproduction.org <br> <br><br>
DOI: 10.6084/m9.figshare.14390909
title: The result of AM RIL of each line was genotyped using the wheat 55K SNP array.

description: The result of AM RIL of each line was genotyped using the wheat 55K SNP array.
DOI: 10.6084/m9.figshare.14390909.v1
title: The result of AM RIL of each line was genotyped using the wheat 55K SNP array.

description: The result of AM RIL of each line was genotyped using the wheat 55K SNP array.
DOI: 10.6084/m9.figshare.14390921
title: The genetic map information in this study.

description: The genetic map information in this study.
DOI: 10.6084/m9.figshare.14390921.v1
title: The genetic map information in this study.

description: The genetic map information in this study.
DOI: 10.5281/zenodo.4673699
title: Data from "Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional "

description: Input files to reproduce the results of the article "Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional". This is a summary of the contents of each folder: CoexistenceSimulations: LAMMPS input files to run coexistence simulations of liquid water-ice Ih and liquid water-ice Ic, using the neural network potential (NNP) and TIP4P/Ice. Crystallization: LAMMPS + PLUMED files to run crystallization simulations of ice Ih and Ic in the isothermal and multithermal ensembles. EnthalpyDifferenceIces0K: LAMMPS input files to calculate enthalpies at 0 K of ice Ih and ice Ic at 0 K using the NNP and TIP4P/Ice. QuantumEspresso input files to perform SCAN calculations. Initial configurations of 30 different proton configurations are included. MultithermalSimulations: LAMMPS + PLUMED input files to run multithermal simulations of liquid water, ice Ih, and ice Ic. NNP-DeePMD-model: DeePMD NNP frozen model. The file inside this folder is needed to run all calculations using the NNP. Reweight: Example of input files to perform reweighting using SCAN energies of configurations obtained from a trajectory driven by the NNP.
DOI: 10.5281/zenodo.4673698
title: Data from "Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional "

description: Input files to reproduce the results of the article "Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional". This is a summary of the contents of each folder: CoexistenceSimulations: LAMMPS input files to run coexistence simulations of liquid water-ice Ih and liquid water-ice Ic, using the neural network potential (NNP) and TIP4P/Ice. Crystallization: LAMMPS + PLUMED files to run crystallization simulations of ice Ih and Ic in the isothermal and multithermal ensembles. EnthalpyDifferenceIces0K: LAMMPS input files to calculate enthalpies at 0 K of ice Ih and ice Ic at 0 K using the NNP and TIP4P/Ice. QuantumEspresso input files to perform SCAN calculations. Initial configurations of 30 different proton configurations are included. MultithermalSimulations: LAMMPS + PLUMED input files to run multithermal simulations of liquid water, ice Ih, and ice Ic. NNP-DeePMD-model: DeePMD NNP frozen model. The file inside this folder is needed to run all calculations using the NNP. Reweight: Example of input files to perform reweighting using SCAN energies of configurations obtained from a trajectory driven by the NNP.
DOI: 10.5517/ccdc.csd.cc20p70z
title: CCDC 1867440: Experimental Crystal Structure Determination

description: Related Article: Lu Huang, Hongyuan Cheng, Luxi Wang, Xiaojing Wu, Bo Qin|2021|Cryst.Growth Des.|21|1905|doi:10.1021/acs.cgd.1c00010
DOI: 10.5517/ccdc.csd.cc25wq60
title: CCDC 2022632: Experimental Crystal Structure Determination

description: Related Article: Philippe Wagner, Frank Rominger, Wen-Shan Zhang, Jürgen H. Gross, Sven M. Elbert, Rasmus R. Schröder, Michael Mastalerz|2021|Angew.Chem.,Int.Ed.|60|8896|doi:10.1002/anie.202016592
DOI: 10.5517/ccdc.csd.cc25wq93
title: CCDC 2022635: Experimental Crystal Structure Determination

description: Related Article: Philippe Wagner, Frank Rominger, Wen-Shan Zhang, Jürgen H. Gross, Sven M. Elbert, Rasmus R. Schröder, Michael Mastalerz|2021|Angew.Chem.,Int.Ed.|60|8896|doi:10.1002/anie.202016592
DOI: 10.6084/m9.figshare.14390927
title: Data of KL and KW of AM RIL in six environments.

description: Data of KL and KW of AM RIL in six environments.
DOI: 10.6084/m9.figshare.14390927.v1
title: Data of KL and KW of AM RIL in six environments.

description: Data of KL and KW of AM RIL in six environments.
DOI: 10.6084/m9.figshare.14390906
title: Tender offer

description: This dataset includes information of Chinese tender offer from 2003 to 2018
DOI: 10.6084/m9.figshare.14390906.v1
title: Tender offer

description: This dataset includes information of Chinese tender offer from 2003 to 2018
DOI: 10.6084/m9.figshare.14390816
title: King et al. CSV dataset

description: All data files for DCMA paper<br>
DOI: 10.5281/zenodo.4673703
title: Data from "Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations"

description: This repository contains the input files to reproduce the results of the paper mentioned above. Brief summary of the contents of each folder: Environments : This folder has the reference environments that will be used for the definition of the CV Equilibration : Input and output files for the equilibration of liquid water and ice Ih. Distributions of the kernel and equilibrium box sizes for production runs. BiasedSimulations : Input and output files for the biased simulations (production runs). Most of the analysis of the simulations is described in a Jupyter Notebook in BiasedSimulations/Analysis.ipynb.
DOI: 10.5281/zenodo.4673702
title: Data from "Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations"

description: This repository contains the input files to reproduce the results of the paper mentioned above. Brief summary of the contents of each folder: Environments : This folder has the reference environments that will be used for the definition of the CV Equilibration : Input and output files for the equilibration of liquid water and ice Ih. Distributions of the kernel and equilibrium box sizes for production runs. BiasedSimulations : Input and output files for the biased simulations (production runs). Most of the analysis of the simulations is described in a Jupyter Notebook in BiasedSimulations/Analysis.ipynb.
DOI: 10.6084/m9.figshare.14390912
title: An updatable dataset revealing changes in land cover types in Mongolia

description: The Mongolian Plateau (MP) is located in the interior of Eurasia, far from the sea, and is extremely vulnerable to climate change and the deleterious effects of human activity. Land degradation is one of the indicators in the United Nations Sustainable Development Goals and combating it is a major challenge in Mongolia that is located in the core part of MP. Using object-oriented remote sensing interpretation, we obtained the refined land cover data of Mongolia from 1990, 2000, 2010, and 2020 with 30-m resolution. The land cover classification of Mongolia comprises 11 categories, which are forest, meadow steppe, real steppe, desert steppe, barren, sand, desert, ice, water, cropland, and built area. Their overall classification accuracies, corresponding to the year, were 82.26%, 82.77%, 92.34%, and 81.84%. The dataset of this study fills the gaps in high-resolution land cover data in Mongolia.
DOI: 10.14457/cu.the.1982.17
title: การศึกษาเพื่อวางแผนพัฒนาการท่องเที่ยวในกรุงรัตนโกสินทร์
DOI: 10.14457/cu.the.1982.18
title: ศักยภาพของอำเภอพื้นที่เป้าหมายในภาคเหนือตอนบนต่อการพัฒนาชนบท
DOI: 10.14457/cu.the.1983.4
title: การสร้างครอส-แอสเซมเบลอร์ สำหรับไมโครโปรเซสเซอร์เบออร์ 6800 ด้วยภาษาเบสิค
DOI: 10.14457/cu.the.1983.5
title: การพัฒนาตัวชี้บ่งประสิทธิภาพของการดำเนินงานในการพัฒนา และติดตั้งระบบออนไลน์ ของสำนักทะเบียนและประมวลผล จุฬาลงกรณ์มหาวิทยาลัย
DOI: 10.3886/e137181v1
title: The Economic Origins of Conflict in Africa -- Replication

description: This is a repository containing replication files for "The Economic Origins of Conflict in Africa", (Eoin McGurk and Marshall Burke, JPE, 2020).
DOI: 10.6084/m9.figshare.14391057
title: Additional file 1 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 1. Table S1: List of human LCRs similar to SARS-CoV-2 LCRs obtained from GBSC.
DOI: 10.6084/m9.figshare.14391060
title: Additional file 2 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 2. Table S2: List of human LCRs similar to SARS-CoV-2 LCRs obtained with MotifLCR—&gt; LCR-BLAST.
DOI: 10.6084/m9.figshare.14391060.v1
title: Additional file 2 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 2. Table S2: List of human LCRs similar to SARS-CoV-2 LCRs obtained with MotifLCR—&gt; LCR-BLAST.
DOI: 10.6084/m9.figshare.14391063
title: Additional file 3 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 3. Table S3: List of human LCRs similar to SARS-CoV-2 LCRs obtained with LCR-BLAST.
DOI: 10.6084/m9.figshare.14391063.v1
title: Additional file 3 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 3. Table S3: List of human LCRs similar to SARS-CoV-2 LCRs obtained with LCR-BLAST.
DOI: 10.6084/m9.figshare.14391066
title: Additional file 4 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 4. Table S4: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 protein nsp3 LCR fragment 108–124.
DOI: 10.6084/m9.figshare.14391066.v1
title: Additional file 4 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 4. Table S4: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 protein nsp3 LCR fragment 108–124.
DOI: 10.6084/m9.figshare.14391069
title: Additional file 5 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 5. Table S5: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 protein nsp3 LCR fragment 152–167.
DOI: 10.6084/m9.figshare.14391069.v1
title: Additional file 5 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 5. Table S5: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 protein nsp3 LCR fragment 152–167.
DOI: 10.6084/m9.figshare.14391072
title: Additional file 6 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 6. Table S6: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 spike glycoprotein (S) S LCR fragment 1233–1254.
DOI: 10.6084/m9.figshare.14391072.v1
title: Additional file 6 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 6. Table S6: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 spike glycoprotein (S) S LCR fragment 1233–1254.
DOI: 10.6084/m9.figshare.14391075
title: Additional file 7 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 7. Table S7: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 nucleocapsid protein (N) LCR fragment 175–207.
DOI: 10.6084/m9.figshare.14391057.v1
title: Additional file 1 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 1. Table S1: List of human LCRs similar to SARS-CoV-2 LCRs obtained from GBSC.
DOI: 10.6084/m9.figshare.14391075.v1
title: Additional file 7 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 7. Table S7: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 nucleocapsid protein (N) LCR fragment 175–207.
DOI: 10.6084/m9.figshare.14391081
title: Additional file 9 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 9. Table S9:Percentage of the sequences with mutations from the COVIDep and the Cov-GLUE databases for each residue of detected SARS-CoV-2 LCRs.
DOI: 10.6084/m9.figshare.14391078.v1
title: Additional file 8 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 8. Table S8: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 protein N LCR fragment 361–378.
DOI: 10.6084/m9.figshare.14390930
title: Patterns of spatially refined urban built environment stocks across Chinese cities

description: We developed an approach combining big data mining technology, bottom-up stock modeling and Chinese temperature-zone MCI database to calculate and predict the urban built environment stocks in 2020. We considered building, road, railway and metro, and aimed to characterize their quality, composition, and spatial distribution. By applying this approach to 50 cities, we found considerable diversities MS quality, composition and distribution, discussed the impact factors and growth patterns among cities, and highlighted the disparities behind economic conditions, population size, built-up area, development rate, land use, geographical location, and urban form.
DOI: 10.6084/m9.figshare.14391081.v1
title: Additional file 9 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 9. Table S9:Percentage of the sequences with mutations from the COVIDep and the Cov-GLUE databases for each residue of detected SARS-CoV-2 LCRs.
DOI: 10.6084/m9.figshare.14391078
title: Additional file 8 of Common low complexity regions for SARS-CoV-2 and human proteomes as potential multidirectional risk factor in vaccine development

description: Additional file 8. Table S8: GO terms enriched for human proteins from all clusters obtained for the SARS-Cov-2 protein N LCR fragment 361–378.
DOI: 10.6084/m9.figshare.14390930.v2
title: Patterns of spatially refined urban built environment stocks across Chinese cities

description: We developed an approach combining big data mining technology, bottom-up stock modeling and Chinese temperature-zone MCI database to calculate and predict the urban built environment stocks in 2020. We considered building, road, railway and metro, and aimed to characterize their quality, composition, and spatial distribution. By applying this approach to 50 cities, we found considerable diversities MS quality, composition and distribution, discussed the impact factors and growth patterns among cities, and highlighted the disparities behind economic conditions, population size, built-up area, development rate, land use, geographical location, and urban form.
DOI: 10.6084/m9.figshare.14390930.v1
title: Result and Code for "Patterns of spatially refined urban built environment stocks across Chinese cities".zip

description: We developed an approach combining big data mining technology, bottom-up stock modeling and Chinese temperature-zone MCI database to calculate and predict the urban built environment stocks in 2020. We considered building, road, railway and metro, and aimed to characterize their quality, composition, and spatial distribution. By applying this approach to 50 cities, we found considerable diversities MS quality, composition and distribution, discussed the impact factors and growth patterns among cities, and highlighted the disparities behind economic conditions, population size, built-up area, development rate, land use, geographical location, and urban form.
DOI: 10.6084/m9.figshare.14033665
title: Additional file 1 of Impact of tearing spermatic cords during castration in live and dead piglets and consequences on welfare

description: Additional file 1.
DOI: 10.6084/m9.figshare.14033665.v1
title: Additional file 1 of Impact of tearing spermatic cords during castration in live and dead piglets and consequences on welfare

description: Additional file 1.
DOI: 10.5517/ccdc.csd.cc26hl6j
title: CCDC 2040767: Experimental Crystal Structure Determination

description: Related Article: Javier Ceballos, Elija Grinhagena, Gontran Sangouard, Christian Heinis, Jerome Waser|2021|Angew.Chem.,Int.Ed.|60|9022|doi:10.1002/anie.202014511
DOI: 10.5517/ccdc.csd.cc26k431
title: CCDC 2042252: Experimental Crystal Structure Determination

description: Related Article: Andreas Eitzinger, Jan Otevrel, Victoria Haider, Antonio Macchia, Antonio Massa, Kirill Faust, Bernhard Spingler, Albrecht Berkessel, Mario Waser|2021|Adv.Synth.Catal.|363|1955|doi:10.1002/adsc.202100029
DOI: 10.5517/ccdc.csd.cc26lfdn
title: CCDC 2043501: Experimental Crystal Structure Determination

description: Related Article: Andreas Eitzinger, Jan Otevrel, Victoria Haider, Antonio Macchia, Antonio Massa, Kirill Faust, Bernhard Spingler, Albrecht Berkessel, Mario Waser|2021|Adv.Synth.Catal.|363|1955|doi:10.1002/adsc.202100029
DOI: 10.5517/ccdc.csd.cc26qfx9
title: CCDC 2047361: Experimental Crystal Structure Determination

description: Related Article: Zheng Zhang|2020|CSD Communication|||
description: Related Article: Zheng Zhang, Zhi-Hao Zhang, Feng Zhou, Jian Zhou|2021|Org.Lett.|23|2726|doi:10.1021/acs.orglett.1c00632
DOI: 10.5517/ccdc.csd.cc26s5y4
title: CCDC 2049036: Experimental Crystal Structure Determination

description: Related Article: Jie Ke, Wentan Liu, Xujiang Zhu, Xingfa Tan, Chuan He|2021|Angew.Chem.,Int.Ed.|60|8744|doi:10.1002/anie.202016620
DOI: 10.6084/m9.figshare.14391148
title: Additional file 1 of The rumen eukaryotome is a source of novel antimicrobial peptides with therapeutic potential

description: Additional file 1.
DOI: 10.5517/ccdc.csd.cc26tb3h
title: CCDC 2050126: Experimental Crystal Structure Determination

description: Related Article: Johann Bosson, Geraldine M. Labrador, Céline Besnard, Denis Jacquemin, Jérôme Lacour|2021|Angew.Chem.,Int.Ed.|60|8733|doi:10.1002/anie.202016643
DOI: 10.6084/m9.figshare.14391148.v1
title: Additional file 1 of The rumen eukaryotome is a source of novel antimicrobial peptides with therapeutic potential

description: Additional file 1.
DOI: 10.5517/ccdc.csd.cc26tb4j
title: CCDC 2050127: Experimental Crystal Structure Determination

description: Related Article: Johann Bosson, Geraldine M. Labrador, Céline Besnard, Denis Jacquemin, Jérôme Lacour|2021|Angew.Chem.,Int.Ed.|60|8733|doi:10.1002/anie.202016643
DOI: 10.5517/ccdc.csd.cc26tshb
title: CCDC 2050572: Experimental Crystal Structure Determination

description: Related Article: Lu-Wen Zhang, Xiao-Jun Deng, Dong-Xu Zhang, Qin-Qin Tian, Wei He|2021|J.Org.Chem.|86|5152|doi:10.1021/acs.joc.1c00074
DOI: 10.5517/ccdc.csd.cc26tsnh
title: CCDC 2050577: Experimental Crystal Structure Determination

description: Related Article: Lu-Wen Zhang, Xiao-Jun Deng, Dong-Xu Zhang, Qin-Qin Tian, Wei He|2021|J.Org.Chem.|86|5152|doi:10.1021/acs.joc.1c00074
DOI: 10.5517/ccdc.csd.cc26tsrl
title: CCDC 2050580: Experimental Crystal Structure Determination

description: Related Article: Lu-Wen Zhang, Xiao-Jun Deng, Dong-Xu Zhang, Qin-Qin Tian, Wei He|2021|J.Org.Chem.|86|5152|doi:10.1021/acs.joc.1c00074
DOI: 10.5517/ccdc.csd.cc26tssm
title: CCDC 2050581: Experimental Crystal Structure Determination

description: Related Article: Lu-Wen Zhang, Xiao-Jun Deng, Dong-Xu Zhang, Qin-Qin Tian, Wei He|2021|J.Org.Chem.|86|5152|doi:10.1021/acs.joc.1c00074
DOI: 10.5517/ccdc.csd.cc26txqp
title: CCDC 2050703: Experimental Crystal Structure Determination

description: Related Article: Luca Verardo, Klaus Bernauer, Antonia Neels and Helen Stoeckli-Evans|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26tydd
title: CCDC 2050724: Experimental Crystal Structure Determination

description: Related Article: F. Knoch, K. Hahn, M. Doehring, K. Brodersen|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26tz56
title: CCDC 2050748: Experimental Crystal Structure Determination

description: Related Article: Robin Rogers|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26tzhj
title: CCDC 2050758: Experimental Crystal Structure Determination

description: Related Article: Robin Rogers|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26tzjk
title: CCDC 2050759: Experimental Crystal Structure Determination

description: Related Article: Robin Rogers|2020|CSD Communication|||