cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23654175.v1
title: Supplementary Material for: Prognostic Implication of Pulmonary Arterial Pressure in Surgical Repair of Predominantly Congenital Mitral Value Regurgitation-based Intracardiac Abnormalities

description: Background Knowledge is limited regarding the significance of pulmonary arterial pressure (PAP) in predominantly congenital mitral value regurgitation (MR)-based intracardiac abnormalities. Methods From a prospective cohort, we included 200 patients with congenital mitral value regurgitation regardless of other associated intracardiac abnormalities (mean age 60.4 months, 67% female, systolic PAP [sPAP] 54.2 mm Hg) surgically repaired in 2012-2019 and followed up to 2020 (median 30.0 months). Significant pulmonary hypertension (PH) was defined as sPAP &gt; 50 mm Hg at rest or mean PAP &gt; 25 mmHg on right heart catheterization. By perioperative sPAP changes, patients were stratified as Group I (pre-normotension-to-post-normotension), Group II (pre-hypertension-to- post-normotension), or Group III (pre-hypertension-to- post-hypertension). Primary outcomes were the recurrence of MR (defined as the regurgitation grade of moderate or greater) and the progression of MR (defined as any increase in magnitude of regurgitation grade after surgery). Cox proportional hazard and Kaplan-Meier curve was performed. Results There was no association between preoperative PH and the recurrent MR (adjusted hazard ratios [aHR] 1.146 [95% CI 0.453-2.899]) and progressive MR (aHR 1.753 [95% CI 0.807-3.804]), respectively. There were no significant differences among Group I, Group II and Group III in the recurrent MR but in the progressive MR. A dose dependency was identified for preoperative sPAP with the recurrent MR (aHR 1.050 [95% CI 1.029-1.071]) and progressive MR risks (aHR 1.037 [95% CI 1.019-1.055]), respectively. Conclusions Preoperative higher sPAP are associated with worse outcomes, warranting heightened attention to the identification of perioperative sPAP.
DOI: 10.6084/m9.figshare.23261939
title: primary ATRT DNA methylation microarray

description: Infinium methylationEPIC array data from 54 human ATRT samples were processed using RnBeads (version 1.6.1) Bioconductor package. Probe intensities were normalized with the “illumina” method. Probes outside CpG context, targeting single nucleotide polymorphisms or targeting X and Y chromosomes were all filtered out. Matrix of beta-value were generated and annotated using the RnBeads.hg19 (version 1.26.0) Bioconductor package.
DOI: 10.6084/m9.figshare.23261939.v2
title: primary ATRT DNA methylation microarray

description: Infinium methylationEPIC array data from 54 human ATRT samples were processed using RnBeads (version 1.6.1) Bioconductor package. Probe intensities were normalized with the “illumina” method. Probes outside CpG context, targeting single nucleotide polymorphisms or targeting X and Y chromosomes were all filtered out. Matrix of beta-value were generated and annotated using the RnBeads.hg19 (version 1.26.0) Bioconductor package.
DOI: 10.6084/m9.figshare.23261939.v1
title: primary ATRT DNA methylation microarray

description: Infinium methylationEPIC array data from 54 human ATRT samples were processed using RnBeads (version 1.6.1) Bioconductor package. Probe intensities were normalized with the “illumina” method. Probes outside CpG context, targeting single nucleotide polymorphisms or targeting X and Y chromosomes were all filtered out. Matrix of beta-value were generated and annotated using the RnBeads.hg19 (version 1.26.0) Bioconductor package.
DOI: 10.21227/g4bx-h206
title: Segmentation and Classification of Interstitial Lung Diseases Based On Hybrid Deep Learning Network Model

description: Interstitial lung diseases (ILD) are a diverse group of diseases that share pathological, radiological, and clinical traits and involve interstitial fibrosis and inflammation. These have a significant impact on lung disease morbidity and mortality. The region of interest (ROI) from the lung High-Resolution Computed Tomography (HRCT) picture had to be manually identified for most of the early ILD classification investigations, which was time-consuming. Additionally, the clinical signs of various disorders are identical, which makes precise detection difficult. Outstanding results were achieved in categorizing medical photos using deep learning techniques in recent studies. For ILD classification, a hybrid deep learning network model has been developed in this research. The lung portion of the HRCT images was initially segmented using an improved U-Net++ model. For effective lung segmentation with lung anomalies, the multi-scale improved U-Net++ module has been applied. The segmented lung image’s features were extracted for categorization in the second stage using a Refined Attention Pyramid Network (RAPNet). Then, for the classification of five ILD classes, we developed an Improved MobileUNetV3. The ILD database is used to test the proposed approach. Due to the stage-by-stage improvement in DL method performance, the proposed hybrid deep learning network model's performance has improved dramatically.
DOI: 10.5281/zenodo.8272668
title: Simulations for impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle

description: Four experiments were conducted to investigate the impact of a large scale Saharan solar farm on the global carbon cycle. Here a fully coupled Earth System model (ESM) EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of Photovoltaic (PV) solar panels over the Sahara desert. These experiments considered solar panel coverage at 5, 20 or 50% of a region of the Sahara Desert. The dataset contains selected output data from the simulations. Simulations were run for 200 years. The last 60 years were used for analysis after the model reached an equilibrium. The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).
DOI: 10.5281/zenodo.8272667
title: Simulations for impacts of large-scale Saharan solar farms on the global terrestrial carbon cycle

description: Four experiments were conducted to investigate the impact of a large scale Saharan solar farm on the global carbon cycle. Here a fully coupled Earth System model (ESM) EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of Photovoltaic (PV) solar panels over the Sahara desert. These experiments considered solar panel coverage at 5, 20 or 50% of a region of the Sahara Desert. The dataset contains selected output data from the simulations. Simulations were run for 200 years. The last 60 years were used for analysis after the model reached an equilibrium. The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).
DOI: 10.5281/zenodo.8272777
title: COSMIC-1_level_1b_scnLv1_2007_2018_s4max

description: COSMIC-1_level_1b_scnLv1_2007_2018_s4max.h5 The dataset is collected from the COSMIC-1 product from 2007 to 2018, and its format is scnLv1. More information can be found in https://cdaac-www.cosmic.ucar.edu/cdaac/cgi_bin/fileFormats.cgi?type=scnLv1
DOI: 10.5281/zenodo.8272776
title: COSMIC-1_level_1b_scnLv1_2007_2018_s4max

description: COSMIC-1_level_1b_scnLv1_2007_2018_s4max.h5 The dataset is collected from the COSMIC-1 product from 2007 to 2018, and its format is scnLv1. More information can be found in https://cdaac-www.cosmic.ucar.edu/cdaac/cgi_bin/fileFormats.cgi?type=scnLv1
DOI: 10.26050/wdcc/ar6.c6pmnccnes
title: IPCC DDC: NCC NorESM1-F model output prepared for CMIP6 PMIP

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.PMIP.NCC.NorESM1-F' 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 NorESM1-F (a fast version of NorESM that is designed for paleo and multi-ensemble simulations) climate model, released in 2018, includes the following components: atmos: CAM4 (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), land: CLM4, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1, seaIce: CICE4. The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: atmos: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6pmnccnl
title: IPCC DDC: NCC NorESM2-LM model output prepared for CMIP6 PMIP

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.PMIP.NCC.NorESM2-LM' 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 NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven) climate model, released in 2017, includes the following components: aerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.25625/evx6vk/uyouvl
title: 2022-264_otu_Table_Wildermuth.xlsx

description: The description of the overview table as provided by biome-id: The results of the taxonomic assignments is provided in an additional file (2022- 264_otu_Table.xlsx) including the OTU number (OTU_ID), the total number of reads per OTU (size), the BOLD reference number of the best matching sequence (Match Ref.), the E-Value generated by the blastn algorithm (E-Value, the lower the better), the percentage of identity between OTU sequence and best match (Identity [%], the higher the better), the length in bases of the matching sequence (Ref. Length), the alignment length between OTU sequence and best match (Overlap, the higher the better), the accession number of the BOLD cluster ID, where the best match is available in BOLD (BOLD BIN ID), taxonomic information for the bast matching sequence (Phylum-Species), country of origin for the best matching sequence (Country) and the number of reads per sample in each OTU (columns P to BH). All OTUs with an identity value lower than 97% cannot be assigned with high confidence to a species, but to a lower taxonomic level. Missing OTUs could not be assigned to any taxonomic group for the absence of a matching reference. Additional explanation for columns P-BH: The sample number consists of the sampling site (2-4), the stand type (1-5) and the trap replicate (1-3). Stand types: 1 = Douglas fir, 2 = Beech-Douglas, 3 = Beech, 4 = Beech-Spruce, 5 = Spruce.
DOI: 10.25625/evx6vk/uvbpoj
title: Metabarcoding_fastq_files_Wildermuth.zip

description: Fastq files as provided by biome-id (https://www.biome-id.com/)
DOI: 10.5517/ccdc.csd.cc1m7zvp
title: CCDC 1497233: Experimental Crystal Structure Determination

description: Related Article: Bhaskar Sen, Sanjoy Kumar Sheet, Sumit Kumar Patra, Debaprasad Koner, Nirmalendu Saha, Snehadrinarayan Khatua|2019|Inorg.Chem.|58|9982|doi:10.1021/acs.inorgchem.9b01125
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.cc2117w7
title: CCDC 1878038: Experimental Crystal Structure Determination

description: Related Article: Chengkai Zhang, Yanqian Zhao, Dandan Li, Jiejie Liu, Heguo Han, Daoyu He, Xiaohe Tian, Shengli Li, Jieying Wu, Yupeng Tian|2019|Chem.Commun.|55|1450|doi:10.1039/C8CC09230C
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.cc23lxwj
title: CCDC 1954608: Experimental Crystal Structure Determination

description: Related Article: Zhen-Hua Wang, Tong Zhang, Qing-Fei Yan, Jian-Qiang Zhao, Yong You, Yan-Ping Zhang, Jun-Qing Yin, Wei-Cheng Yuan|2023|Org.Chem.Front.|10|4256|doi:10.1039/D3QO00784G
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.cc21vnq9
title: CCDC 1902461: Experimental Crystal Structure Determination

description: Related Article: Giulia Bonfant, Matteo Melegari, Davide Balestri, Francesco Mezzadri, Vittoria Marzaroli, Irene Bassanetti, Luciano Marchiò|2020|Inorg.Chem.|59|4140|doi:10.1021/acs.inorgchem.0c00256
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.cc25wd9s
title: CCDC 2022325: Experimental Crystal Structure Determination

description: Related Article: Michael J. Vela, Louis A. Carpino, Bruce M. Foxman|2020|CSD Communication|||
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.cc25xph9
title: CCDC 2023571: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
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.cc25brny
title: CCDC 2006340: Experimental Crystal Structure Determination

description: Related Article: Tomoya Ishizuka, Masaki Itogawa, Hinatsu Shimomura, Yoshihito Shiota, Hiroaki Kotani, Kazunari Yoshizawa, Takahiko Kojima|2020|Inorg.Chem.Commun.|120|108150|doi:10.1016/j.inoche.2020.108150
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.cc25mk1f
title: CCDC 2014784: Experimental Crystal Structure Determination

description: Related Article: Zi-Lu Wang, Feng-Lian Zhang, Jian-Lin Xu, Cui-Cui Shan, Meng Zhao, Yun-He Xu|2020|Org.Lett.|22|7735|doi:10.1021/acs.orglett.0c02952
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.cc25rfq3
title: CCDC 2018525: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
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.cc20jfww
title: CCDC 1862848: Experimental Crystal Structure Determination

description: Related Article: Jyoti Shukla, Rajith Illathvalappil, Sharvan Kumar, Sonam Chorol, Ajmal Pandikassala, Sreekumar Kurungot, Pritam Mukhopadhyay|2022|Org.Lett.|24|3038|doi:10.1021/acs.orglett.2c00993
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.cc25p6x0
title: CCDC 2016392: Experimental Crystal Structure Determination

description: Related Article: Cheok-Lam Wong, Yat-Hin Cheng, Chun-Ting Poon, Vivian Wing-Wah Yam|2020|Inorg.Chem.|59|14785|doi:10.1021/acs.inorgchem.0c02089
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.cc2c95vq
title: CCDC 2183573: Experimental Crystal Structure Determination

description: Related Article: Hai-Juan Shuai, Xiao-Gang Du, Xiao-Meng Jin, Chuan-Ming Jin, Juan Sun|2023|J.Mol.Struct.|1275|134668|doi:10.1016/j.molstruc.2022.134668
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.cc2ckt5y
title: CCDC 2191860: Experimental Crystal Structure Determination

description: Related Article: Hang-Fei Tu, Aliénor Jeandin, Marcos G. Suero|2022|J.Am.Chem.Soc.|144|16737|doi:10.1021/jacs.2c07769
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.