cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3724588
title: Coupled monitoring of soil moisture and suction in pyroclastic air-fall silty soils

description: The dataset contains the experimental field data coming from the automatic monitoring of the annual hydrological response of a shallow deposit in loose pyroclastic soils located in a mountainous area of Campania, Southern Italy. The monitoring station is installed at about 650 m a.s.l. along the northern slope of mount Cornito, about 2 km east of the town of Cervinara, that in December 1999 was involved in a rainfall-induced flowslide. The collected field data consist in: i) precipitations (measured by a rain gauge), ii) soil moisture contents (measured by TDR probes) and iii) soil matric suctions (measured by “Jet-fill” tensiometers). Suction and moisture sensors have been installed at the same depths and connected to a Campbell Scientific Inc. CR-1000 Data Logger, that allows the automatic acquisition and storage of data. Furthermore, the results of some laboratory flume tests carried out on a small-scale slope, reconstituted with volcanic ashes from the same site and from a nearby slope, subjected to an artificial rainwater infiltration, are attached too. Soil matric suction and moisture content were respectively measured by a miniaturised tensiometer and a TDR probe installed close to each other.
DOI: 10.5517/ccdc.csd.cc22c95y
title: CCDC 1917479: Experimental Crystal Structure Determination

description: Related Article: Federica Mancini, Anna K. H. Hirsch, Volker Huch|2019|CSD Communication|||
description: Related Article: Federica Mancini, M. Yagiz Unver, Walid A. M. Elgaher, Varsha R. Jumde, Alaa Alhayek, Peer Lukat, Jennifer Herrmann, Martin D. Witte, Matthias Köck, Wulf Blankenfeldt, Rolf Müller, Anna K. H. Hirsch|2020|Chem.-Eur.J.|26|14585|doi:10.1002/chem.202002250
DOI: 10.5517/ccdc.csd.cc22c96z
title: CCDC 1917480: Experimental Crystal Structure Determination

description: Related Article: Federica Mancini, Anna K. H. Hirsch, Volker Huch|2019|CSD Communication|||
description: Related Article: Federica Mancini, M. Yagiz Unver, Walid A. M. Elgaher, Varsha R. Jumde, Alaa Alhayek, Peer Lukat, Jennifer Herrmann, Martin D. Witte, Matthias Köck, Wulf Blankenfeldt, Rolf Müller, Anna K. H. Hirsch|2020|Chem.-Eur.J.|26|14585|doi:10.1002/chem.202002250
DOI: 10.5517/ccdc.csd.cc22c970
title: CCDC 1917481: Experimental Crystal Structure Determination

description: Related Article: Federica Mancini, Anna K. H. Hirsch, Volker Huch|2019|CSD Communication|||
description: Related Article: Federica Mancini, M. Yagiz Unver, Walid A. M. Elgaher, Varsha R. Jumde, Alaa Alhayek, Peer Lukat, Jennifer Herrmann, Martin D. Witte, Matthias Köck, Wulf Blankenfeldt, Rolf Müller, Anna K. H. Hirsch|2020|Chem.-Eur.J.|26|14585|doi:10.1002/chem.202002250
DOI: 10.5517/ccdc.csd.cc24jk08
title: CCDC 1982109: Experimental Crystal Structure Determination

description: Related Article: Hao Liang, Laxmaiah Vasamsetty, Teng Li, Jijun Jiang, Xingying Pang, Jun Wang|2020|Chem.-Eur.J.|26|14546|doi:10.1002/chem.202001814
DOI: 10.5517/ccdc.csd.cc24jk2b
title: CCDC 1982111: Experimental Crystal Structure Determination

description: Related Article: Hao Liang, Laxmaiah Vasamsetty, Teng Li, Jijun Jiang, Xingying Pang, Jun Wang|2020|Chem.-Eur.J.|26|14546|doi:10.1002/chem.202001814
DOI: 10.5517/ccdc.csd.cc24jk3c
title: CCDC 1982112: Experimental Crystal Structure Determination

description: Related Article: Hao Liang, Laxmaiah Vasamsetty, Teng Li, Jijun Jiang, Xingying Pang, Jun Wang|2020|Chem.-Eur.J.|26|14546|doi:10.1002/chem.202001814
DOI: 10.5517/ccdc.csd.cc24mbx1
title: CCDC 1984803: Experimental Crystal Structure Determination

description: Related Article: Ying-Jie Cao, Lin Zhou, Lei He, Ping-Ping Shi, Qiong Ye, Da-Wei Fu|2020|Chem.-Eur.J.|26|14124|doi:10.1002/chem.202001266
DOI: 10.5517/ccdc.csd.cc24mbz3
title: CCDC 1984805: Experimental Crystal Structure Determination

description: Related Article: Ying-Jie Cao, Lin Zhou, Lei He, Ping-Ping Shi, Qiong Ye, Da-Wei Fu|2020|Chem.-Eur.J.|26|14124|doi:10.1002/chem.202001266
DOI: 10.5517/ccdc.csd.cc24w86j
title: CCDC 1992407: Experimental Crystal Structure Determination

description: Related Article: José A. González-Delgado, Miguel A. Romero, Francisco Boscá, Jesús F. Arteaga, Uwe Pischel|2020|Chem.-Eur.J.|26|14229|doi:10.1002/chem.202001893
DOI: 10.5517/ccdc.csd.cc24mdls
title: CCDC 1984855: Experimental Crystal Structure Determination

description: Related Article: Ying-Jie Cao, Lin Zhou, Lei He, Ping-Ping Shi, Qiong Ye, Da-Wei Fu|2020|Chem.-Eur.J.|26|14124|doi:10.1002/chem.202001266
DOI: 10.5517/ccdc.csd.cc24mc05
title: CCDC 1984806: Experimental Crystal Structure Determination

description: Related Article: Ying-Jie Cao, Lin Zhou, Lei He, Ping-Ping Shi, Qiong Ye, Da-Wei Fu|2020|Chem.-Eur.J.|26|14124|doi:10.1002/chem.202001266
DOI: 10.5517/ccdc.csd.cc24mdmt
title: CCDC 1984856: Experimental Crystal Structure Determination

description: Related Article: Ying-Jie Cao, Lin Zhou, Lei He, Ping-Ping Shi, Qiong Ye, Da-Wei Fu|2020|Chem.-Eur.J.|26|14124|doi:10.1002/chem.202001266
DOI: 10.5517/ccdc.csd.cc25kdfl
title: CCDC 2012719: Experimental Crystal Structure Determination

description: Related Article: Tsubasa Nishimura, Takahisa Ikeue, Osami Shoji, Hiroshi Shinokubo, Yoshihiro Miyake|2020|Inorg.Chem.|59|15751|doi:10.1021/acs.inorgchem.0c02166
DOI: 10.5517/ccdc.csd.cc25q59d
title: CCDC 2017303: Experimental Crystal Structure Determination

description: Related Article: Mei Zhang, Jingjing Guo, Tingting Liu, Zhanyu He, Majeed Irfan, Zujin Zhao, Zhuo Zeng|2020|J.Mater.Chem.C|8|14919|doi:10.1039/D0TC03797D
DOI: 10.5517/ccdc.csd.cc25q5bf
title: CCDC 2017304: Experimental Crystal Structure Determination

description: Related Article: Mei Zhang, Jingjing Guo, Tingting Liu, Zhanyu He, Majeed Irfan, Zujin Zhao, Zhuo Zeng|2020|J.Mater.Chem.C|8|14919|doi:10.1039/D0TC03797D
DOI: 10.5517/ccdc.csd.cc25q5cg
title: CCDC 2017305: Experimental Crystal Structure Determination

description: Related Article: Mei Zhang, Jingjing Guo, Tingting Liu, Zhanyu He, Majeed Irfan, Zujin Zhao, Zhuo Zeng|2020|J.Mater.Chem.C|8|14919|doi:10.1039/D0TC03797D
DOI: 10.5517/ccdc.csd.cc25q5dh
title: CCDC 2017306: Experimental Crystal Structure Determination

description: Related Article: Mei Zhang, Jingjing Guo, Tingting Liu, Zhanyu He, Majeed Irfan, Zujin Zhao, Zhuo Zeng|2020|J.Mater.Chem.C|8|14919|doi:10.1039/D0TC03797D
DOI: 10.5517/ccdc.csd.cc25wf4n
title: CCDC 2022351: Experimental Crystal Structure Determination

description: Related Article: Hong-Rui Tian, Zhong Zhang, Shu-Mei Liu, Tian-Yi Dang, Zhuo Li, Ying Lu, Shu-Xia Liu|2020|Green Chemistry|22|7513|doi:10.1039/D0GC02812F
DOI: 10.5517/ccdc.csd.cc25xm6y
title: CCDC 2023500: Experimental Crystal Structure Determination

description: Related Article: Kyu Hyung Lee, Sun Joo Kim, Hee Sun Park, Byung Wook Lim, Byeongno Lee, Young Jun Park, Wonwoo Nam, Nam Hwi Hur|2020|RSC Advances|10|38055|doi:10.1039/D0RA07814J
DOI: 10.5517/ccdc.csd.cc2607vb
title: CCDC 2026031: Experimental Crystal Structure Determination

description: Related Article: Wesley S. Farrell, Christine Greene, Pokhraj Ghosh, Timothy H. Warren, Peter Y. Zavalij|2020|Organometallics|39|3906|doi:10.1021/acs.organomet.0c00610
DOI: 10.5517/ccdc.csd.cc2607wc
title: CCDC 2026032: Experimental Crystal Structure Determination

description: Related Article: Wesley S. Farrell, Christine Greene, Pokhraj Ghosh, Timothy H. Warren, Peter Y. Zavalij|2020|Organometallics|39|3906|doi:10.1021/acs.organomet.0c00610
DOI: 10.5517/ccdc.csd.cc260sst
title: CCDC 2026556: Experimental Crystal Structure Determination

description: Related Article: Nicholas G. W. Cowper, Matthew J. Hesse, Katie M. Chan, Sarah E. Reisman|2020|Chemical Science|11|11897|doi:10.1039/D0SC04802J
DOI: 10.5517/ccdc.csd.cc260stv
title: CCDC 2026557: Experimental Crystal Structure Determination

description: Related Article: Nicholas G. W. Cowper, Matthew J. Hesse, Katie M. Chan, Sarah E. Reisman|2020|Chemical Science|11|11897|doi:10.1039/D0SC04802J
DOI: 10.5517/ccdc.csd.cc260mpk
title: CCDC 2026398: Experimental Crystal Structure Determination

description: Related Article: Jason A. McNulty, Alexandra M. Z. Slawin, Philip Lightfoot|2020|Dalton Trans.|49|15171|doi:10.1039/D0DT03449E
DOI: 10.25505/fiz.icsd.cc260mql
title: CCDC 2026399: Experimental Crystal Structure Determination

description: Related Article: Jason A. McNulty, Alexandra M. Z. Slawin, Philip Lightfoot|2020|Dalton Trans.|49|15171|doi:10.1039/D0DT03449E
DOI: 10.5517/ccdc.csd.cc2677rg
title: CCDC 2032755: Experimental Crystal Structure Determination

description: Related Article: Zandria Lamprecht, Frederick P. Malan, Israel Fernández, Simon Lotz, Daniela I. Bezuidenhout|2020|Dalton Trans.|49|15339|doi:10.1039/D0DT03298K
DOI: 10.5517/ccdc.csd.cc2677qf
title: CCDC 2032754: Experimental Crystal Structure Determination

description: Related Article: Zandria Lamprecht, Frederick P. Malan, Israel Fernández, Simon Lotz, Daniela I. Bezuidenhout|2020|Dalton Trans.|49|15339|doi:10.1039/D0DT03298K
DOI: 10.5517/ccdc.csd.cc2677sh
title: CCDC 2032756: Experimental Crystal Structure Determination

description: Related Article: Zandria Lamprecht, Frederick P. Malan, Israel Fernández, Simon Lotz, Daniela I. Bezuidenhout|2020|Dalton Trans.|49|15339|doi:10.1039/D0DT03298K
DOI: 10.5517/ccdc.csd.cc2677tj
title: CCDC 2032757: Experimental Crystal Structure Determination

description: Related Article: Zandria Lamprecht, Frederick P. Malan, Israel Fernández, Simon Lotz, Daniela I. Bezuidenhout|2020|Dalton Trans.|49|15339|doi:10.1039/D0DT03298K
DOI: 10.5517/ccdc.csd.cc2677vk
title: CCDC 2032758: Experimental Crystal Structure Determination

description: Related Article: Zandria Lamprecht, Frederick P. Malan, Israel Fernández, Simon Lotz, Daniela I. Bezuidenhout|2020|Dalton Trans.|49|15339|doi:10.1039/D0DT03298K
DOI: 10.5517/ccdc.csd.cc2677wl
title: CCDC 2032759: Experimental Crystal Structure Determination

description: Related Article: Zandria Lamprecht, Frederick P. Malan, Israel Fernández, Simon Lotz, Daniela I. Bezuidenhout|2020|Dalton Trans.|49|15339|doi:10.1039/D0DT03298K
DOI: 10.5281/zenodo.4281154
title: Radiomag Interlaboratory Comparison of Magnetic Hyperthermia Characterisation Measurements: Data

description: These files contain the raw measurement data reported for the Radiomag interlaboratory comparison of magnetic hyperthermia characterisation measurements. Each heating curve for samples containing magnetic nanoparticles is presented in a separate excel file detailing the corrected-slope technique (CST) recalculation of the specific loss power and intrinsic loss power for that measurement as described in the manuscript. The measurements are organised into separate folders for Rounds 1 and 2 of the interlaboratory study, with further subfolders dividing the results for samples 1 and 2. Round 2 also includes the results for the “blank” pure water sample which was included in this Round. For each laboratory there is 3 repeated measurements of each sample during each round the study.
description: This data is based upon work supported by the COST action (RADIOMAG TD1402), supported by COST (European Cooperation in Science and Technology). The financial support of the DFG research grants "quantMPI: Establishment of quantitative Magnetic Particle Imaging (MPI) application oriented phantoms for preclinical investigations" (TR 408/9-1) and "Matrix in Vision" (SFB 1340/1 2018, no 372486779, projects A02 and B02) are also gratefully acknowledged. Additional financial support was received from the Spanish Ministry of Economy and Competitiveness through the grants RED2018-102626-T, MAT2017-85617-R, MAT2017-86826-R and the "Severo Ochoa" Program for Centers of Excellence in R&D (SEV-2016-0686). Additional financial support was received from the Portuguese Foundation for Science and Technology (FCT) under UID/MULTI/04046/2013 (BioISI).
DOI: 10.5281/zenodo.4281153
title: Radiomag Interlaboratory Comparison of Magnetic Hyperthermia Characterisation Measurements: Data

description: These files contain the raw measurement data reported for the Radiomag interlaboratory comparison of magnetic hyperthermia characterisation measurements. Each heating curve for samples containing magnetic nanoparticles is presented in a separate excel file detailing the corrected-slope technique (CST) recalculation of the specific loss power and intrinsic loss power for that measurement as described in the manuscript. The measurements are organised into separate folders for Rounds 1 and 2 of the interlaboratory study, with further subfolders dividing the results for samples 1 and 2. Round 2 also includes the results for the “blank” pure water sample which was included in this Round. For each laboratory there is 3 repeated measurements of each sample during each round the study.
description: This data is based upon work supported by the COST action (RADIOMAG TD1402), supported by COST (European Cooperation in Science and Technology). The financial support of the DFG research grants "quantMPI: Establishment of quantitative Magnetic Particle Imaging (MPI) application oriented phantoms for preclinical investigations" (TR 408/9-1) and "Matrix in Vision" (SFB 1340/1 2018, no 372486779, projects A02 and B02) are also gratefully acknowledged. Additional financial support was received from the Spanish Ministry of Economy and Competitiveness through the grants RED2018-102626-T, MAT2017-85617-R, MAT2017-86826-R and the "Severo Ochoa" Program for Centers of Excellence in R&D (SEV-2016-0686). Additional financial support was received from the Portuguese Foundation for Science and Technology (FCT) under UID/MULTI/04046/2013 (BioISI).
DOI: 10.5519/qd.vrpf9v3f
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3 records)
DOI: 10.5281/zenodo.4281172
title: Processed SpectrumDocuments from AllPositive (case study for MS2Query)

description: Processed SpectrumDocuments of the "AllPositive" dataset created from GNPS positive ionmode mass spectra. They are used in a case study for MS2Query: https://github.com/iomega/ms2query. Reading this pickle file in python will give a tuple of two lists of spec2vec.SpectrumDocument. The first list contains 2,000 spectra and the second list contains 93,320 spectra that are used as query and library spectra in the case study.
DOI: 10.5281/zenodo.4281171
title: Processed SpectrumDocuments from AllPositive (case study for MS2Query)

description: Processed SpectrumDocuments of the "AllPositive" dataset created from GNPS positive ionmode mass spectra. They are used in a case study for MS2Query: https://github.com/iomega/ms2query. Reading this pickle file in python will give a tuple of two lists of spec2vec.SpectrumDocument. The first list contains 2,000 spectra and the second list contains 93,320 spectra that are used as query and library spectra in the case study.
DOI: 10.5281/zenodo.4281199
title: mtDNA from consensus motif paper

description: Mitochondrial DNA (mtDNA) dataset from https://sites.google.com/site/consensusmotifs/ as backup for tutorial on consensus motifs
DOI: 10.5281/zenodo.4281198
title: mtDNA from consensus motif paper

description: Mitochondrial DNA (mtDNA) dataset from https://sites.google.com/site/consensusmotifs/ as backup for tutorial on consensus motifs
DOI: 10.5521/forestplots.net/2020_7
title: Data package of Nordeste Plot Metadata and Species list from Brazilian Caatinga Long-term Inventory plots
DOI: 10.5521/forestplots.net/2019_1
title: Asynchronous Saturation of the Carbon Sink in African and Amazonian tropical forests

description: Data and R-code from Hubau W et al. 2020.  'Asynchronous Saturation of the Carbon Sink in African and Amazonian tropical forests'. Nature 579, 80-87. 2020.  DOI: 10.1038/s41586-020-2035-0.
ABSTRACT: Structurally intact tropical forests sequestered ~50% of global terrestrial carbon uptake over the 1990s and early 2000s, offsetting ~15% of anthropogenic CO2 emissions1-3. Climate-driven vegetation models typically predict that this tropical forest ‘carbon sink’ will continue for decades4,5. However, recent inventories of intact Amazonian forests show declining carbon sequestration6. Here, we assess the trends in African forests and compare with Amazonia. Records from 244 multi-census plots spanning 11 countries reveal that the carbon sink in aboveground live biomass in intact African tropical forests has been stable for the three decades to 2015, at 0.66 Mg C ha-1 yr-1 (95% CI: 0.53-0.79). Thus, the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged. As both continents show increasing tree growth (consistent with the expected net effect of rising atmospheric CO2 and air temperature on photosynthesis and respiration7), the divergence arises from differences in carbon losses from tree mortality (no detectable multi-decadal trend in Africa; monotonic increase in Amazonia). Despite the past stability of the African carbon sink, our data suggest a very recent (c. 2010) increase in carbon losses, delayed compared to Amazonia, indicating asynchronous carbon sink saturation on the two continents. A statistical model including CO2, temperature, drought, and forest dynamics can account for the observed trends. Extrapolating these predictor variables indicates a long-term decline in the African sink, being 18% smaller in 2030, while the Amazonian sink continues to rapidly weaken, reaching zero in the 2030s. Overall, the uptake of carbon into Earth’s intact tropical forests peaked in the 1990s. Furthermore, this tropical sink is set to end decades sooner than even the most pessimistic vegetation models predict4,5. Observations indicating greater recent carbon uptake into the Northern hemisphere landmass8 reinforce our conclusion that the intact tropical forest carbon sink has already saturated.
DOI: 10.5521/forestplots.net/2020_6
title: Data and R-code from 'Mode of death and mortality risk factors in Amazon trees'. Nature communications. 2020
DOI: 10.5521/forestplots.net/2020_2
title: Data from Sullivan et al. (2020) Long-term thermal sensitivity of Earth’s tropical forests. Science. DOI: 10.1126/science.aaw7578.  

description: ABSTRACT: The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater rate of decline in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate.
DOI: 10.5521/forestplots.net/2020_5
title: Data from Reis et al (2020) Causes and consequences of liana infestation in Southern Amazonia. Journal of Ecology DOI: 10.1111/1365-2745.13470
DOI: 10.5281/zenodo.4116503
title: Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets

description: The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (https://github.com/swagnercarena/ovejero).
DOI: 10.5281/zenodo.4116502
title: Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets

description: The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (https://github.com/swagnercarena/ovejero).
DOI: 10.5521/forestplots.net/2013_1
title: Plot Data from: African forest structure and above-ground biomass measured in inventory plots. Plot data from "Above-ground biomass and structure of 260 African tropical forests."

description: We report above-ground biomass (AGB), basal area, stem density and wood mass density estimates from 260 sample plots (mean size: 1.2 ha) in intact closed-canopy tropical forests across 12 African countries. Mean AGB is 395.7 Mg dry mass ha-1 (95% CI: 14.3), substantially higher than Amazonian values, with the Congo Basin and contiguous forest region attaining AGB values (429 Mg ha-1) similar to those of Bornean forests, and significantly greater than East or West African forests. AGB therefore appears generally higher in palaeo- compared with neo-tropical forests. However, mean stem density is low (426±11 stems ha-1 greater than or equal to 100 mm diameter) compared with both Amazonian and Bornean forests (cf. approx. 600) and is the signature structural feature of African tropical forests. While spatial autocorrelation complicates analyses, AGB shows a positive relationship with rainfall in the driest nine months of the year, and an opposite association with the wettest three months of the year; a negative relationship with temperature; positive relationship with clay-rich soils; and negative relationships with C:N ratio (suggesting a positive soil phosphorus–AGB relationship), and soil fertility computed as the sum of base cations. The results indicate that AGB is mediated by both climate and soils, and suggest that the AGB of African closed-canopy tropical forests may be particularly sensitive to future precipitation and temperature changes.
DOI: 10.5517/ccdc.csd.cc238mvw
title: CCDC 1944718: Experimental Crystal Structure Determination

description: Related Article: Shuxiao Feng, Kaiyan Qi, Yafei Guo, Junling Wang, Guangna Gu, Pu Liu, Junying Ma, Lingbo Qu, Shouren Zhang|2020|Tetrahedron Lett.|61|152561|doi:10.1016/j.tetlet.2020.152561
DOI: 10.5517/ccdc.csd.cc238myz
title: CCDC 1944721: Experimental Crystal Structure Determination

description: Related Article: Shuxiao Feng, Kaiyan Qi, Yafei Guo, Junling Wang, Guangna Gu, Pu Liu, Junying Ma, Lingbo Qu, Shouren Zhang|2020|Tetrahedron Lett.|61|152561|doi:10.1016/j.tetlet.2020.152561
DOI: 10.5517/ccdc.csd.cc238mz0
title: CCDC 1944722: Experimental Crystal Structure Determination

description: Related Article: Shuxiao Feng, Kaiyan Qi, Yafei Guo, Junling Wang, Guangna Gu, Pu Liu, Junying Ma, Lingbo Qu, Shouren Zhang|2020|Tetrahedron Lett.|61|152561|doi:10.1016/j.tetlet.2020.152561
DOI: 10.5517/ccdc.csd.cc238n02
title: CCDC 1944723: Experimental Crystal Structure Determination

description: Related Article: Shuxiao Feng, Kaiyan Qi, Yafei Guo, Junling Wang, Guangna Gu, Pu Liu, Junying Ma, Lingbo Qu, Shouren Zhang|2020|Tetrahedron Lett.|61|152561|doi:10.1016/j.tetlet.2020.152561
DOI: 10.5517/ccdc.csd.cc238n13
title: CCDC 1944724: Experimental Crystal Structure Determination

description: Related Article: Shuxiao Feng, Kaiyan Qi, Yafei Guo, Junling Wang, Guangna Gu, Pu Liu, Junying Ma, Lingbo Qu, Shouren Zhang|2020|Tetrahedron Lett.|61|152561|doi:10.1016/j.tetlet.2020.152561
DOI: 10.5517/ccdc.csd.cc238n35
title: CCDC 1944726: Experimental Crystal Structure Determination

description: Related Article: Shuxiao Feng, Kaiyan Qi, Yafei Guo, Junling Wang, Guangna Gu, Pu Liu, Junying Ma, Lingbo Qu, Shouren Zhang|2020|Tetrahedron Lett.|61|152561|doi:10.1016/j.tetlet.2020.152561
DOI: 10.5517/ccdc.csd.cc23dqt2
title: CCDC 1948654: Experimental Crystal Structure Determination

description: Related Article: Vladimir Iashin, Dénes Berta, Konstantin Chernichenko, Martin Nieger, Karina Moslova, Imre Pápai, Timo Repo|2020|Chem.-Eur.J.|26|13873|doi:10.1002/chem.202001436