cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4694523
title: simCENSE-18k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes Contact : mathieu.lagrange@cnrs.fr If used for research, please refer to: <pre>@article{gontier2021training, title={Polyphonic training set synthesis improves self-supervised urban sound classification}, author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier}, journal={The Journal of the Acoustical Society of America}, year={2021}, publisher={Acoustical Society of America} } </pre> simCENSE-18k contains 400 acoustic scenes of duration equal to 45 seconds.<br> We synthesized these polyphonic scenes via the simScene software, based on monophonic audio clips from the CENSE-2k dataset.<br> The total duration of the dataset is equal to 18k seconds, i.e., five hours.The audio is made available as third octave spectral data, see demoTob.zip for an implementation of its computation from audio in Python. &gt;&gt; import numpy as np &gt;&gt; s=np.load('simCENSE-18k_train_spectralData.npy') &gt;&gt; print(s.shape) (280, 351, 29) The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency). &gt;&gt; a=np.load('simCENSE-18k_train_annotations.npy') &gt;&gt; print(a.shape) (280, 344, 3) &gt;&gt; s=np.load('simCENSE-18k_validation_spectralData.npy') &gt;&gt; print(s.shape) (120, 344, 29) &gt;&gt; a=np.load('simCENSE-18k_validation_annotations.npy') &gt;&gt; print(a.shape) (120, 344, 3) The 3 dimensions corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds). Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame.
DOI: 10.5281/zenodo.4778742
title: Sites of High Richness for Ectomycorrhizal Fungi within Meise Botanic Garden

description: A number of sites within Meise Botanic Garden were monitored for Ectomycorrhizal Fungi between 2006 and 2012 by Omer Van de Kerckhove (Meise Botanic Garden). The sites were selected based on casual observations starting from 1996, see also: Van De Kerckhove, O. (2011). Red List ectomycorrhizal fungi and their dependence on park management. Scripta Botanica Belgica, 47(47), 181–191. The sites are presented as a dataset in ESRI shapefile format with a comma separated value text file with Well Known Text (WKT) representation of geometry, and an Extensable Markup Language (XML) file with Keyhole Markup Language (KML) geographic annotation as alternatives. For the larger files, compressed versions are included. These data are related to the occurrence dataset Mapping the wild ectomycorrhizal fungi of Meise Botanic Garden from 2006 to 2012 (Submitted) and contain the site_name field referenced on the original hand drawn maps. Also provided is a universally unique identifier (uuid) and a site label used in the PDF/PNG visualization. <br>
DOI: 10.5281/zenodo.4778741
title: Sites of High Richness for Ectomycorrhizal Fungi within Meise Botanic Garden

description: A number of sites within Meise Botanic Garden were monitored for Ectomycorrhizal Fungi between 2006 and 2012 by Omer Van de Kerckhove (Meise Botanic Garden). The sites were selected based on casual observations starting from 1996, see also: Van De Kerckhove, O. (2011). Red List ectomycorrhizal fungi and their dependence on park management. Scripta Botanica Belgica, 47(47), 181–191. The sites are presented as a dataset in ESRI shapefile format with a comma separated value text file with Well Known Text (WKT) representation of geometry, and an Extensable Markup Language (XML) file with Keyhole Markup Language (KML) geographic annotation as alternatives. For the larger files, compressed versions are included. These data are related to the occurrence dataset Mapping the wild ectomycorrhizal fungi of Meise Botanic Garden from 2006 to 2012 (Submitted) and contain the site_name field referenced on the original hand drawn maps. Also provided is a universally unique identifier (uuid) and a site label used in the PDF/PNG visualization. <br>
DOI: 10.5281/zenodo.4890276
title: Lya_zelda data

description: zELDA, a code to understand Lyman-alpha emission. https://github.com/sidgurun/Lya_zelda https://zelda.readthedocs.io/en/latest/
DOI: 10.5281/zenodo.4733517
title: Lya_zelda data

description: zELDA, a code to understand Lyman-alpha emission. https://github.com/sidgurun/Lya_zelda https://zelda.readthedocs.io/en/latest/
DOI: 10.25345/c59f96
title: MassIVE MSV000087550 - Sucrose Gradient fractions replicate 2 C1 to C25 and R1 to R25
title: Sucrose gradient fractions analyzed by MudPIT biological replicate 2
DOI: 10.5281/zenodo.4890377
title: Cycle 2_PTW_sensor_data_Track

description: This is the sensor dataset for motorcycle drivers collected through Track study used in Simusafe cycle 2
DOI: 10.5281/zenodo.4890376
title: Cycle 2_PTW_sensor_data_Track

description: This is the sensor dataset for motorcycle drivers collected through Track study used in Simusafe cycle 2
DOI: 10.5281/zenodo.4036988
title: L4B - Maximum tree height map for the Brazilian Amazon

description: Maximum tree height distribution estimated by the Random Forest model based on the environmental variables. To explore the influence and importance of the environmental variables for development in tree height, we employed Random Forest modeling, which consists of generating a large number of regression trees, each constructed considering a random data subset. The regression trees are used to identify the best sequence to split the solution space to estimate the output. Were considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days &gt; 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months &lt; 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %).
description: Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvi­mento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); Instituto Nacional de Pesquisas Espaciais (INPE); São Paulo Research Foundation (#2018/21338-3 and #2019/14697-0); INCT-Madeiras da Amazônia and Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), as part of DOE's Terrestrial Ecosystem Science Program – Contract No. DE-AC02-05CH11231; National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Royal Society University Research Fellowship (URF\R\191014).
DOI: 10.5281/zenodo.4036987
title: L4B - Maximum tree height map for the Brazilian Amazon

description: Maximum tree height distribution estimated by the Random Forest model based on the environmental variables. To explore the influence and importance of the environmental variables for development in tree height, we employed Random Forest modeling, which consists of generating a large number of regression trees, each constructed considering a random data subset. The regression trees are used to identify the best sequence to split the solution space to estimate the output. Were considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days &gt; 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months &lt; 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %).
description: Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvi­mento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); Instituto Nacional de Pesquisas Espaciais (INPE); São Paulo Research Foundation (#2018/21338-3 and #2019/14697-0); INCT-Madeiras da Amazônia and Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), as part of DOE's Terrestrial Ecosystem Science Program – Contract No. DE-AC02-05CH11231; National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Royal Society University Research Fellowship (URF\R\191014).
DOI: 10.5281/zenodo.4037101
title: L4D - Probability map of giant trees occurrence (&gt; 70 m) in the Brazilian Amazon

description: The probability of giant trees occurrence (&gt; 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days &gt; 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months &lt; 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %).
description: Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvi­mento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1); National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); Instituto Nacional de Pesquisas Espaciais (INPE); São Paulo Research Foundation (#2018/21338-3 and #2019/14697-0); INCT-Madeiras da Amazônia and Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), as part of DOE's Terrestrial Ecosystem Science Program – Contract No. DE-AC02-05CH11231; UK Natural Environment Research Council grant NE/S010750/1; Academy of Finland (decision number 319905); Royal Society University Research Fellowship (URF\R\191014);
DOI: 10.5281/zenodo.4037100
title: L4D - Probability map of giant trees occurrence (&gt; 70 m) in the Brazilian Amazon

description: The probability of giant trees occurrence (&gt; 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days &gt; 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months &lt; 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %).
description: Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvi­mento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1); National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); Instituto Nacional de Pesquisas Espaciais (INPE); São Paulo Research Foundation (#2018/21338-3 and #2019/14697-0); INCT-Madeiras da Amazônia and Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), as part of DOE's Terrestrial Ecosystem Science Program – Contract No. DE-AC02-05CH11231; UK Natural Environment Research Council grant NE/S010750/1; Academy of Finland (decision number 319905); Royal Society University Research Fellowship (URF\R\191014);
DOI: 10.15468/vjdx2j
title: Revision of the family Latreilliidae Stimpson, 1858 (Crustacea, Decapoda, Brachyura)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Castro, Peter, Williams, Austin B., Cooper, Lara L. (2003): Revision of the family Latreilliidae Stimpson, 1858 (Crustacea, Decapoda, Brachyura). Zoosystema 25 (4): 601-634, DOI: http://doi.org/10.5281/zenodo.4890280
DOI: 10.5281/zenodo.4889983
title: Cycle 2 PTW sensor data Simulator

description: This is the sensor dataset for motorcycle drivers collected through simulator study used in Simusafe Cycle 2
description: Data collector : Dariusz Cieslar
DOI: 10.5281/zenodo.4889982
title: Cycle 2 PTW sensor data Simulator

description: This is the sensor dataset for motorcycle drivers collected through simulator study used in Simusafe Cycle 2
description: Data collector : Dariusz Cieslar
DOI: 10.15142/t36307
title: Data from: How Uncertainty in the Neutral Wind Limits the Accuracy of Ionospheric Modeling and Forecasting

description: Output files from runs of the TDIM ionospheric model used for the figures and calculations in the article in JGR Space Physics.
DOI: 10.34740/kaggle/dsv/2293187
title: COVID Vaccination in World (updated daily)
DOI: 10.22024/unikent/01.01.174
title: Q sort data for PaN-20-08-211
DOI: 10.15468/6sgzwj
title: Reassignment and redescription of “ Eupagurus ” microps Balss, 1911 (Crustacea, Decapoda, Paguridae), with notes on Bathypaguropsis kuroshioensis (Miyake, 1978)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Mclaughlin, Patsy A. (2003): Reassignment and redescription of “ Eupagurus ” microps Balss, 1911 (Crustacea, Decapoda, Paguridae), with notes on Bathypaguropsis kuroshioensis (Miyake, 1978). Zoosystema 25 (4): 635-642, DOI: http://doi.org/10.5281/zenodo.4689535
DOI: 10.5517/ccdc.csd.cc26m3dc
title: CCDC 2044152: Experimental Crystal Structure Determination

description: Related Article: Yong Hu, Xiao-Jun Wang, Wen-Shuai Dong, Yu-Fan Bi, Zu-Jia Lu, Wen-Li Cao, Jian-Guo Zhang, Qi Zhang, Dong Chen|2021|Org.Chem.Front.|8|2420|doi:10.1039/D1QO00123J
DOI: 10.5517/ccdc.csd.cc26m3fd
title: CCDC 2044153: Experimental Crystal Structure Determination

description: Related Article: Yong Hu, Xiao-Jun Wang, Wen-Shuai Dong, Yu-Fan Bi, Zu-Jia Lu, Wen-Li Cao, Jian-Guo Zhang, Qi Zhang, Dong Chen|2021|Org.Chem.Front.|8|2420|doi:10.1039/D1QO00123J
DOI: 10.5517/ccdc.csd.cc26m3gf
title: CCDC 2044154: Experimental Crystal Structure Determination

description: Related Article: Yong Hu, Xiao-Jun Wang, Wen-Shuai Dong, Yu-Fan Bi, Zu-Jia Lu, Wen-Li Cao, Jian-Guo Zhang, Qi Zhang, Dong Chen|2021|Org.Chem.Front.|8|2420|doi:10.1039/D1QO00123J
DOI: 10.5517/ccdc.csd.cc26m3hg
title: CCDC 2044155: Experimental Crystal Structure Determination

description: Related Article: Yong Hu, Xiao-Jun Wang, Wen-Shuai Dong, Yu-Fan Bi, Zu-Jia Lu, Wen-Li Cao, Jian-Guo Zhang, Qi Zhang, Dong Chen|2021|Org.Chem.Front.|8|2420|doi:10.1039/D1QO00123J
DOI: 10.5517/ccdc.csd.cc26nbfn
title: CCDC 2045331: Experimental Crystal Structure Determination

description: Related Article: Runjiang Song, Yingguo Liu, Pankaj Kumar Majhi, Pei Rou Ng, Lin Hao, Jun Xu, Weiyi Tian, Long Zhang, Hongmei Liu, Xinglong Zhang, Yonggui Robin Chi|2021|Org.Chem.Front.|8|2413|doi:10.1039/D1QO00212K
DOI: 10.5517/ccdc.csd.cc26p2jj
title: CCDC 2046047: Experimental Crystal Structure Determination

description: Related Article: Peng Yang, Xia Wang, Liang Wang, Jiahua He, Qian Zhang, Dong Li|2021|Org.Chem.Front.|8|2556|doi:10.1039/D1QO00225B
DOI: 10.5517/ccdc.csd.cc26pl3m
title: CCDC 2046530: Experimental Crystal Structure Determination

description: Related Article: Dan Song, Changfeng Huang, Peishi Liang, Baofu Zhu, Xiang Liu, Hua Cao|2021|Org.Chem.Front.|8|2583|doi:10.1039/D1QO00224D
DOI: 10.5517/ccdc.csd.cc26rx63
title: CCDC 2048765: Experimental Crystal Structure Determination

description: Related Article: Xianghai Cai|2020|CSD Communication|||
description: Related Article: Bao-Bao Shi, Jing-Song Lu, Jing Wu, Mei-Fen Bao, Xiang-Hai Cai|2021|Org.Chem.Front.|8|2601|doi:10.1039/D0QO01565B
DOI: 10.5517/ccdc.csd.cc26s0xy
title: CCDC 2048880: Experimental Crystal Structure Determination

description: Related Article: Lin Huang, Jia-Hao Xie, Yue Cai, Chao Zheng, Xue-Long Hou, Li-Xin Dai, Shu-Li You|2021|Chem.Commun.|57|5390|doi:10.1039/D1CC01929E
DOI: 10.5517/ccdc.csd.cc26vtnk
title: CCDC 2051569: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vttq
title: CCDC 2051574: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vtvr
title: CCDC 2051575: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vtws
title: CCDC 2051576: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vtxt
title: CCDC 2051577: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vtzw
title: CCDC 2051579: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vv0y
title: CCDC 2051580: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vv1z
title: CCDC 2051581: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vv20
title: CCDC 2051582: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26vv31
title: CCDC 2051583: Experimental Crystal Structure Determination

description: Related Article: Wen‐Jie Shi, Dan Liu, Xin Li, Sha Bai, Yao‐Yu Wang, Ying‐Feng Han|2021|Chem.-Eur.J.|27|7853|doi:10.1002/chem.202100710
DOI: 10.5517/ccdc.csd.cc26w65h
title: CCDC 2051926: Experimental Crystal Structure Determination

description: Related Article: Yifan Ouyang, Kaifu Wu, Wei Zhou, Qian Cai|2021|Org.Chem.Front.|8|2456|doi:10.1039/D0QO01657H
DOI: 10.5517/ccdc.csd.cc26ybcw
title: CCDC 2053978: Experimental Crystal Structure Determination

description: Related Article: Andrés Felipe Sierra, Gemma Aragay, Guillem Peñuelas-Haro, Pablo Ballester|2021|Org.Chem.Front.|8|2402|doi:10.1039/D1QO00517K
DOI: 10.5517/ccdc.csd.cc26ybfy
title: CCDC 2053980: Experimental Crystal Structure Determination

description: Related Article: Andrés Felipe Sierra, Gemma Aragay, Guillem Peñuelas-Haro, Pablo Ballester|2021|Org.Chem.Front.|8|2402|doi:10.1039/D1QO00517K
DOI: 10.5517/ccdc.csd.cc26yn61
title: CCDC 2054283: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26zgk7
title: CCDC 2055069: Experimental Crystal Structure Determination

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