cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7631283
title: mesocentrotus_franciscanus_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7628028
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2017

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2017. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2018</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2017 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7628027
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2017

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2017. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2018</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2017 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7627209
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7627208
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2016. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7488190
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2018

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2018. Please <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7488189
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2018

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2018. Please <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2017</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7631292
title: mopalia_muscosa_decontaminates_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7631291
title: mopalia_muscosa_decontaminates_FSCR.fasta.gz

description: .
DOI: 10.7303/syn50897018.2
title: Cross-population, cell-type informed atlas of gene, isoform, and splicing regulation in the developing human brain
DOI: 10.5517/ccdc.csd.cc2cjx2x
title: CCDC 2190989: Experimental Crystal Structure Determination

description: Related Article: Miguel Mellado-Hidalgo, Elias A. Romero-Cavagnaro, Sajanthanaa Nageswaran, Sabrina Puddu, Stuart C. D. Kennington, Anna M. Costa, Pedro Romea, Fèlix Urpí, Gabriel Aullón, Mercè Font-Bardia|2023|Org.Lett.|25|659|doi:10.1021/acs.orglett.2c04254
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjx3y
title: CCDC 2190990: Experimental Crystal Structure Determination

description: Related Article: Miguel Mellado-Hidalgo, Elias A. Romero-Cavagnaro, Sajanthanaa Nageswaran, Sabrina Puddu, Stuart C. D. Kennington, Anna M. Costa, Pedro Romea, Fèlix Urpí, Gabriel Aullón, Mercè Font-Bardia|2023|Org.Lett.|25|659|doi:10.1021/acs.orglett.2c04254
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cxp88
title: CCDC 2202310: Experimental Crystal Structure Determination

description: Related Article: Ting Ou-Yang, Yi Zhang, Heng-Zhen Luo, Yue Liu, Shuang-Cheng Ma|2023|J.Ethnopharmacol.|306|116148|doi:10.1016/j.jep.2023.116148
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.7303/syn50897018.3
title: Cross-population, cell-type informed atlas of gene, isoform, and splicing regulation in the developing human brain
DOI: 10.5517/ccdc.csd.cc2d713t
title: CCDC 2211295: Experimental Crystal Structure Determination

description: Related Article: Tian-Zhen Liu, Shuo Wang, Jia-Rui Xu, Jun-Ying Miao, Bao-Xiang Zhao, Zhao-Min Lin|2023|Talanta|256|124302|doi:10.1016/j.talanta.2023.124302
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7lfp
title: CCDC 2211832: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7lgq
title: CCDC 2211833: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7lhr
title: CCDC 2211834: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7ljs
title: CCDC 2211835: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7lmw
title: CCDC 2211838: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7ldn
title: CCDC 2211831: Experimental Crystal Structure Determination

description: Related Article: Kamil A. Ivshin, Kirill Metlushka, Anton Fedonin, Shamil K. Latypov, Vera V. Khrizanforova, Yulia H. Budnikova, Alexander E. Vandyukov, Airat G. Kiiamov, Artem Laskin, Stanislav M. Avdoshenko, Martin Knupfer, Olga Kataeva|2022|Cryst.Growth Des.|23|954|doi:10.1021/acs.cgd.2c01146
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.