cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1j6gkr
title: CCDC 1406394: Experimental Crystal Structure Determination

description: Related Article: Sundaramurthy Santha Lakshmi, Kannappan Geetha|2016|J.Cryst.|2016|6078543|doi:10.1155/2016/6078543
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.cc1p25fc
title: CCDC 1551222: Experimental Crystal Structure Determination

description: Related Article: Mani Kaushal, Tarlok S. Lobana, Lovedeep Nim, Jaskamal Kaur, Ritu Bala, Geeta Hundal, Daljit S. Arora, Isabel Garcia-Santos, Courtney E. Duff, Jerry P. Jasinski|2018|New J.Chem.|42|15879|doi:10.1039/C8NJ03619E
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.cc1p25gd
title: CCDC 1551223: Experimental Crystal Structure Determination

description: Related Article: Mani Kaushal, Tarlok S. Lobana, Lovedeep Nim, Jaskamal Kaur, Ritu Bala, Geeta Hundal, Daljit S. Arora, Isabel Garcia-Santos, Courtney E. Duff, Jerry P. Jasinski|2018|New J.Chem.|42|15879|doi:10.1039/C8NJ03619E
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.cc1p0y8x
title: CCDC 1550039: Experimental Crystal Structure Determination

description: Related Article: Mani Kaushal, Tarlok S. Lobana, Lovedeep Nim, Jaskamal Kaur, Ritu Bala, Geeta Hundal, Daljit S. Arora, Isabel Garcia-Santos, Courtney E. Duff, Jerry P. Jasinski|2018|New J.Chem.|42|15879|doi:10.1039/C8NJ03619E
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.cc1p0y9y
title: CCDC 1550040: Experimental Crystal Structure Determination

description: Related Article: Mani Kaushal, Tarlok S. Lobana, Lovedeep Nim, Jaskamal Kaur, Ritu Bala, Geeta Hundal, Daljit S. Arora, Isabel Garcia-Santos, Courtney E. Duff, Jerry P. Jasinski|2018|New J.Chem.|42|15879|doi:10.1039/C8NJ03619E
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.cc247q6b
title: CCDC 1973621: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247q7c
title: CCDC 1973622: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247q8d
title: CCDC 1973623: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247q9f
title: CCDC 1973624: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247qbg
title: CCDC 1973625: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247qhm
title: CCDC 1973630: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247qch
title: CCDC 1973626: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247qdj
title: CCDC 1973627: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247qfk
title: CCDC 1973628: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc247qgl
title: CCDC 1973629: Experimental Crystal Structure Determination

description: Related Article: Xiao‐Lei Wang,  Jin‐Peng Xue,  Xiao‐Peng Sun,  Yan‐Xin Zhao,  Shu‐Qi Wu,  Zi‐Shuo Yao,  Jun Tao|2020|Chem.-Eur.J.|26|6778|doi:10.1002/chem.202000845
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.cc24m8hk
title: CCDC 1984728: Experimental Crystal Structure Determination

description: Related Article: Maya Marinova, Antoine Bonnefont, Thierry Achard, Aline Maisse-François, Stéphane Bellemin-Laponnaz|2020|Chem.Commun.|56|8703|doi:10.1039/D0CC01716G
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.cc24y0hm
title: CCDC 1994090: Experimental Crystal Structure Determination

description: Related Article: Kevin Huse, Hanns Weinert, Christoph Wölper, Stephan Schulz|2020|Dalton Trans.|49|9773|doi:10.1039/D0DT01943G
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: 10.6084/m9.figshare.20529427
title: <i>In silico</i> QSAR modeling to predict the safe use of antibiotics during pregnancy

description: The use of medicines during pregnancy is a growing public health concern due to the risk of developmental toxicity. Healthcare providers heavily rely on the FDA pregnancy risk categories (A, B, C, D, and X). Antibiotics are among the most prescribed drugs during pregnancy and are often listed under category B or C. However, the risk-benefit assessment may be lacking due to challenges in the clinical toxicology studies on pregnant women, such as ethical concerns. The primary focus of this study is to generate a model that predicts the safe use of antibiotics during pregnancy by using <i>in silico</i> approaches. Thus, a QSAR model was created to assess the FDA pregnancy category (B or C) of antibiotics. The dataset consisted of 97 antibiotics obtained from the FDA. A total of 6420 molecular descriptors were determined <i>via</i> multiple software and various machine learning algorithms were utilized. The performance of the models was measured using internal and external validation. The accuracy (ACC) values of the most successful model were 83.82% for the internal and 94.11% for the external validation. Sensitivity (SE), specificity (SP), MCC, and ROC values were 0.878, 0.778, 0.68, and 0.892 for the internal validation and 0.9, 1, 0.887, and 0.936 for the external validation, respectively. Kappa statistics also indicate that there was a substantial agreement for internal validation with 0.6765 and an almost perfect agreement for external validation with 0.8811. In conclusion, our model can be used as an initial step before pre-clinical and clinical studies to predict the safe use of antibiotics in pregnancy.
DOI: 10.6084/m9.figshare.23866962.v14
title: Untitled Item

description: Regridding Global CMIP6 GCMs dataset(0.5 degree)Historical dataset: 7 CMIP6 GCMs (1850-2014, ACCESS-CM2 to GFDL-ESM4)Future dataset: 7 CMIP6 GCMs and 4 SSP scenarios (2015-2100)Composition of dataset: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiationIf you are using our codes and dataset, please use the following references:Song, Y.H., Chung, E.S., Shahid, S., Kim, Y.J., Kim, D.K. 2023. Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration. Scientific Data.<br>
DOI: 10.26050/wdcc/ar6.c6acneu0
title: IPCC DDC: NERC UKESM1.0-LL model output prepared for CMIP6 AerChemMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.NERC.UKESM1-0-LL' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run by the Natural Environment Research Council, STFC-RAL, Harwell, Oxford, OX11 0QX, UK (NERC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.6084/m9.figshare.23805093
title: Untitled Item

description: The pregnancy outcomes following D3 embryo transfer after 3 or 4 days of progesterone administration.
DOI: 10.6084/m9.figshare.23805093.v1
title: Untitled Item

description: The pregnancy outcomes following D3 embryo transfer after 3 or 4 days of progesterone administration.
DOI: 10.34696/tzrm-1372
title: A Study in Heaviness
DOI: 10.6084/m9.figshare.23896635
title: A clever neural network in solving inverse problems of Schr\"{o}dinger equation

description: This project constructs a physic-preserving neural network combined with a library-search method to solve inverse problems of Schr\"{o}dinger equation. The complete code as well as the corresponding preprint is included in the uploaded files. Here is some necessary information about the code.It mainly contains two parts: forward and inverse part of the solver. In particular, to test the performance of the forward solver, run SSFM_potential_test_cos.py. For the inverse problem, run SSFM_potential_cos.py. It is similar for the other two examples. Specially, for the coupled equation, we also plot the landscape the loss function, which is implemented in the file ssfm_potential_couple_landscape.py. The generated data is under separate subfolders, for example, ./result_cos. Generally, to train the network, we use the proximal gradient descent and one can refer to the file SSFM_potential_cos.py for details. We remark that the current coefficient of regularization term is given in "reg_list" which is tested relatively suitable for the setting.
DOI: 10.6084/m9.figshare.23896635.v1
title: A clever neural network in solving inverse problems of Schr\"{o}dinger equation

description: This project constructs a physic-preserving neural network combined with a library-search method to solve inverse problems of Schr\"{o}dinger equation. The complete code as well as the corresponding preprint is included in the uploaded files. Here is some necessary information about the code.It mainly contains two parts: forward and inverse part of the solver. In particular, to test the performance of the forward solver, run SSFM_potential_test_cos.py. For the inverse problem, run SSFM_potential_cos.py. It is similar for the other two examples. Specially, for the coupled equation, we also plot the landscape the loss function, which is implemented in the file ssfm_potential_couple_landscape.py. The generated data is under separate subfolders, for example, ./result_cos. Generally, to train the network, we use the proximal gradient descent and one can refer to the file SSFM_potential_cos.py for details. We remark that the current coefficient of regularization term is given in "reg_list" which is tested relatively suitable for the setting.
DOI: 10.5281/zenodo.8009521
title: The secretome atlas of two mouse models of progeria

description: Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disease caused by nuclear envelope alterations that lead to accelerated aging and premature death. Several studies have linked health and longevity to cell-extrinsic mechanisms, highlighting the relevance of circulating factors in the aging process as well as in age related diseases. We performed a global plasma proteomic analysis in two preclinical progeroid models (<em>Lmna<sup>G609G/G609G</sup></em> and <em>Zmpste24<sup>-/-</sup></em> mice) using aptamer-based proteomic technology. Pathways related to the extracellular matrix, growth factor response and calcium ion binding were among the most enriched in the proteomic signature of progeroid samples compared to controls. Despite the global down-regulation trend found in the plasma proteome of progeroid mice, several proteins associated with cardiovascular disease, the main cause of death in HGPS, were up-regulated. We also developed a chronological age predictor using plasma proteome data from a cohort of healthy mice (aged 1-30 months), that reported an age acceleration when applied to progeroid mice, indicating that these mice exhibit an ‘old’ plasma proteomic signature. Furthermore, when compared to naturally-aged mice, a great proportion of differentially expressed circulating proteins in progeroid mice were specific to premature aging, highlighting secretome-associated differences between physiological and accelerated aging. This is the first large-scale profiling of the plasma proteome in progeroid mice, which provides an extensive list of candidate circulating plasma proteins as potential biomarkers and/or therapeutic targets for further exploration and hypothesis generation in the context of both physiological and premature aging.
DOI: 10.5281/zenodo.8009520
title: The secretome atlas of two mouse models of progeria

description: Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disease caused by nuclear envelope alterations that lead to accelerated aging and premature death. Several studies have linked health and longevity to cell-extrinsic mechanisms, highlighting the relevance of circulating factors in the aging process as well as in age related diseases. We performed a global plasma proteomic analysis in two preclinical progeroid models (<em>Lmna<sup>G609G/G609G</sup></em> and <em>Zmpste24<sup>-/-</sup></em> mice) using aptamer-based proteomic technology. Pathways related to the extracellular matrix, growth factor response and calcium ion binding were among the most enriched in the proteomic signature of progeroid samples compared to controls. Despite the global down-regulation trend found in the plasma proteome of progeroid mice, several proteins associated with cardiovascular disease, the main cause of death in HGPS, were up-regulated. We also developed a chronological age predictor using plasma proteome data from a cohort of healthy mice (aged 1-30 months), that reported an age acceleration when applied to progeroid mice, indicating that these mice exhibit an ‘old’ plasma proteomic signature. Furthermore, when compared to naturally-aged mice, a great proportion of differentially expressed circulating proteins in progeroid mice were specific to premature aging, highlighting secretome-associated differences between physiological and accelerated aging. This is the first large-scale profiling of the plasma proteome in progeroid mice, which provides an extensive list of candidate circulating plasma proteins as potential biomarkers and/or therapeutic targets for further exploration and hypothesis generation in the context of both physiological and premature aging.
DOI: 10.57702/2xl01lm1
title: CoyPu Project Knowledge Graph

description: This dataset presents all the datasets used to create the CoyPu project knowledge graph.
DOI: 10.5517/ccsy15x
title: CCDC 742889: Experimental Crystal Structure Determination

description: Related Article: Fa-Yun Chen, Shu-Hua Zhang, Guang-Zhao Li, Cheng-Min Ge|2009|Jiegou Huaxue|28|921|
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/ccxqbwt
title: CCDC 855627: Experimental Crystal Structure Determination

description: Related Article: Gang Xie, Zheng-Qiang Xia, San-Ping Chen, Sheng-Li Gao|2012|Wuji Huaxue Xuebao|28|367|
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.