cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22015541
title: Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and multi-modality phenotype network in major depressive disorder

description: Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and becomes a powerful strategy to discovery the associations between genetic variants( e.g. single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of existing MDD imaging genetic researches proposed by clinicians usually utilize simple statistical analysis methods and only consider single modality brain imaging, which are limited in deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we develop a novel imaging genetic association framework to mine multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging(rs-fMRI). Then, an association model based on multi-task learning strategy is adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status is introduced to further mine the relation among multiple modalities of different subjects. A multi-modality brain imaging data and genotype data are collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method, but also identify some consistent and stable brain regions of interest(ROIs) biomarkers from node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD are discovered.
DOI: 10.5281/zenodo.7607281
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2008

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 2008. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2007</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2009</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2008 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.7607280
title: MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2008

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 2008. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2007</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2009</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2008 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.7611814
title: Long-read transcriptome data (Iso-Seq) of the human and mouse brain

description: Datasets from "<strong>Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing</strong>", SK.Leung, A.Jeffries. et al. (2021) Deposited files are generated from running Cupcake, SQANTI2 (v7.4) and filter. <br> Note, only the files generated from SQANTI2 filtering are deposited. <br> To re-run SQANTI, use the files in cupcake_collapse folder as input. Please refer to code (https://github.com/SziKayLeung/Whole_Transcriptome_Paper) for more information. Datasets: <br> - AdultCTX: Adult human prefrontal cortex tissue (n = 4) merged dataset<br> - FetalCTX: Fetal human prefrontal cortex tissue (n = 3) merged dataset<br> - FetalHIP: Fetal human hippocampus tissue (n = 2, subset of FetalCTX) merged dataset<br> - FetalSTR: Fetal human striatum tissue (n = 2, subset of FetalCTX) merged dataset<br> - MouseCTX: Mouse entorhinal cortex tissue (n = 12) merged dataset
DOI: 10.5281/zenodo.7611813
title: Long-read transcriptome data (Iso-Seq) of the human and mouse brain

description: Datasets from "<strong>Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing</strong>", SK.Leung, A.Jeffries. et al. (2021) Deposited files are generated from running Cupcake, SQANTI2 (v7.4) and filter. <br> Note, only the files generated from SQANTI2 filtering are deposited. <br> To re-run SQANTI, use the files in cupcake_collapse folder as input. Please refer to code (https://github.com/SziKayLeung/Whole_Transcriptome_Paper) for more information. Datasets: <br> - AdultCTX: Adult human prefrontal cortex tissue (n = 4) merged dataset<br> - FetalCTX: Fetal human prefrontal cortex tissue (n = 3) merged dataset<br> - FetalHIP: Fetal human hippocampus tissue (n = 2, subset of FetalCTX) merged dataset<br> - FetalSTR: Fetal human striatum tissue (n = 2, subset of FetalCTX) merged dataset<br> - MouseCTX: Mouse entorhinal cortex tissue (n = 12) merged dataset
DOI: 10.5517/ccdc.csd.cc1q31kf
title: CCDC 1581854: Experimental Crystal Structure Determination

description: Related Article: Federico A. Rabuffetti, Leopoldo Suescun, and Hashini N. Munasinghe|2022|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.cc2067bt
title: CCDC 1853035: Experimental Crystal Structure Determination

description: Related Article: Federico Rabuffetti, Leopoldo Suescun, Hashini N. Munasinghe|2022|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.cc2067gy
title: CCDC 1853039: Experimental Crystal Structure Determination

description: Related Article: Federico A. Rabuffetti, Leopoldo Suescun, Jacob Frazier|2022|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.cc21krr4
title: CCDC 1893906: Experimental Crystal Structure Determination

description: Related Article: Federico A. Rabuffetti, Leopoldo Suescun, Hashini N. Munasinghe|2022|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.cc23452j
title: CCDC 1940416: Experimental Crystal Structure Determination

description: Related Article: Federico A. Rabuffetti, Leopoldo Suescun, Regina G. Szlag|2022|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.cc237vrz
title: CCDC 1943971: Experimental Crystal Structure Determination

description: Related Article: Federico A. Rabuffetti, Leopoldo Suescun, Hashini N. Munasinghe|2022|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.cc270w16
title: CCDC 2056417: Experimental Crystal Structure Determination

description: Related Article: Daniel Kratzert|2022|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.cc27231h
title: CCDC 2057595: Experimental Crystal Structure Determination

description: Related Article: Florian Pachel, Markus Ströbele, Hans-Jürgen Meyer|2022|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.cc27g765
title: CCDC 2069256: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Fujita, Shin Takemoto, Hiroyuki Matsuzaka|2022|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.cc27g776
title: CCDC 2069257: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Fujita, Shin Takemoto, Hiroyuki Matsuzaka|2022|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.cc27g787
title: CCDC 2069258: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Fujita, Shin Takemoto, Hiroyuki Matsuzaka|2022|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.cc27g798
title: CCDC 2069259: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Fujita, Shin Takemoto, Hiroyuki Matsuzaka|2022|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.cc28073n
title: CCDC 2085590: Experimental Crystal Structure Determination

description: Related Article: Tao He, Xiang-Jing Kong, Zhen-Xing Bian, Yong-Zheng Zhang, Guang-Rui Si, Lin-Hua Xie, Xue-Qian Wu, Hongliang Huang, Ze Chang, Xian-He Bu, Michael J. Zaworotko, Zuo-Ren Nie, Jian-Rong Li|2022|Nat.Mater|21|689|doi:10.1038/s41563-022-01237-x
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.cc28074p
title: CCDC 2085591: Experimental Crystal Structure Determination

description: Related Article: Tao He, Xiang-Jing Kong, Zhen-Xing Bian, Yong-Zheng Zhang, Guang-Rui Si, Lin-Hua Xie, Xue-Qian Wu, Hongliang Huang, Ze Chang, Xian-He Bu, Michael J. Zaworotko, Zuo-Ren Nie, Jian-Rong Li|2022|Nat.Mater|21|689|doi:10.1038/s41563-022-01237-x
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.cc28075q
title: CCDC 2085592: Experimental Crystal Structure Determination

description: Related Article: Tao He, Xiang-Jing Kong, Zhen-Xing Bian, Yong-Zheng Zhang, Guang-Rui Si, Lin-Hua Xie, Xue-Qian Wu, Hongliang Huang, Ze Chang, Xian-He Bu, Michael J. Zaworotko, Zuo-Ren Nie, Jian-Rong Li|2022|Nat.Mater|21|689|doi:10.1038/s41563-022-01237-x
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.cc28076r
title: CCDC 2085593: Experimental Crystal Structure Determination

description: Related Article: Tao He, Xiang-Jing Kong, Zhen-Xing Bian, Yong-Zheng Zhang, Guang-Rui Si, Lin-Hua Xie, Xue-Qian Wu, Hongliang Huang, Ze Chang, Xian-He Bu, Michael J. Zaworotko, Zuo-Ren Nie, Jian-Rong Li|2022|Nat.Mater|21|689|doi:10.1038/s41563-022-01237-x
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.cc295g43
title: CCDC 2120404: Experimental Crystal Structure Determination

description: Related Article: Natalia Gogoleva, Maksim Shmelev, Alexey Sidorov|2022|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.cc28072m
title: CCDC 2085589: Experimental Crystal Structure Determination

description: Related Article: Tao He, Xiang-Jing Kong, Zhen-Xing Bian, Yong-Zheng Zhang, Guang-Rui Si, Lin-Hua Xie, Xue-Qian Wu, Hongliang Huang, Ze Chang, Xian-He Bu, Michael J. Zaworotko, Zuo-Ren Nie, Jian-Rong Li|2022|Nat.Mater|21|689|doi:10.1038/s41563-022-01237-x
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: 10.25405/data.ncl.21968501
title: GE2015 costs data No NI.sav

description: Dataset which combines and integrates official Cabinet Office data on the funding of election administration in the 2015 general election (https://www.gov.uk/government/publications/the-costs-of-the-2015-uk-parliamentary-general-election) with Census 2011 and Constituency level data from the British Election Study (https://www.britishelectionstudy.com/data-object/2015-bes-constituency-results-with-census-and-candidate-data/)
DOI: 10.25405/data.ncl.21968501.v1
title: GE2015 costs data No NI.sav

description: Dataset which combines and integrates official Cabinet Office data on the funding of election administration in the 2015 general election (https://www.gov.uk/government/publications/the-costs-of-the-2015-uk-parliamentary-general-election) with Census 2011 and Constituency level data from the British Election Study (https://www.britishelectionstudy.com/data-object/2015-bes-constituency-results-with-census-and-candidate-data/)
DOI: 10.5281/zenodo.7608444
title: Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"

description: Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform". The R code containsboth code for simulation and code for estimation.
DOI: 10.5281/zenodo.7608443
title: Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"

description: Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform". The R code containsboth code for simulation and code for estimation.
DOI: 10.5281/zenodo.7611971
title: BUKU DASAR DASAR STATISTIK PENELITIAN UNIVERSITAS GRESIK _JAM 20.00-21.30

description: Gambaran Umum Buku Dasar dasar Statistik Penelitian SEJARAH STATISTIKA<br> Ilmu statistika mempunyai sejarah yang sangat panjang seiring peradaban manusia. Pada zaman sebelum Masehi, bangsa-bangsa di Mesopotamia (Babilonia), Mesir, dan Cina telah mengumpulkan data statistic untuk memperoleh informasi tentang berapa besar pajak yang harus dibayar oleh setiap penduduk, beberapa banyak hasil pertanian yang mampu diproduksi, dan lain sebagainya. Pada abad pertengahan, lembaga gereja menggunakan statistika untuk mencatat jumlah kelahiran, kematian, dan pernikahan.<br> Statistika pertama kali di temukan oleh Aristoteles dalam bukunya yang berjudul “politea”, dalam buku tersebut ia menjelaskan data tentang keadaan 158 negara yang di sebut sebagai statistika. Pada abad ke-17 di Inggris, statistika di sebut sebagai political aritmatic. Pada abad ke-18, istilah statistika dipopulerkan oleh Sir John Sinclair dalam bukunya berjudul “statistical account of Scotland (17911799)”, setelah terlebih dahulu dikemukakan oleh seorang ahli hitung asal Jerman yang bernama Gottfried Achenwell (1719-1772).
description: BUKU DASAR DASAR STATISTIK PENELITIAN UNIVERSITAS GRESIK _JAM 20.00-21.30