cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc26ld18
title: CCDC 2043459: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld29
title: CCDC 2043460: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld3b
title: CCDC 2043461: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld4c
title: CCDC 2043462: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld5d
title: CCDC 2043463: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld6f
title: CCDC 2043464: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld7g
title: CCDC 2043465: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld8h
title: CCDC 2043466: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ld9j
title: CCDC 2043467: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26ldbk
title: CCDC 2043468: Experimental Crystal Structure Determination

description: Related Article: Yoshihiro Sekine, Masaki Nishio, Tomoka Shimada, Wataru Kosaka, Hitoshi Miyasaka|2021|Inorg.Chem.|60|3046|doi:10.1021/acs.inorgchem.0c03335
DOI: 10.5517/ccdc.csd.cc26w2jq
title: CCDC 2051813: Experimental Crystal Structure Determination

description: Related Article: Guanglei Li, Haifeng Huang, Jun Yang, Chao Yan, Wei Li, Hongzhen Duan|2021|Polyhedron|199|115098|doi:10.1016/j.poly.2021.115098
DOI: 10.5517/ccdc.csd.cc26w2kr
title: CCDC 2051814: Experimental Crystal Structure Determination

description: Related Article: Guanglei Li, Haifeng Huang, Jun Yang, Chao Yan, Wei Li, Hongzhen Duan|2021|Polyhedron|199|115098|doi:10.1016/j.poly.2021.115098
DOI: 10.5517/ccdc.csd.cc26w2ls
title: CCDC 2051815: Experimental Crystal Structure Determination

description: Related Article: Guanglei Li, Haifeng Huang, Jun Yang, Chao Yan, Wei Li, Hongzhen Duan|2021|Polyhedron|199|115098|doi:10.1016/j.poly.2021.115098
DOI: 10.6084/m9.figshare.14178539
title: Rock Surface Temperature and Air Temperature

description: The dataset provides rock surface temperature and air temperature recorded in the Hungerli Valley.<br>
DOI: 10.6084/m9.figshare.14178539.v1
title: Rock Surface Temperature and Air Temperature

description: The dataset provides rock surface temperature and air temperature recorded in the Hungerli Valley.<br>
DOI: 10.5281/zenodo.4573965
title: Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries

description: Summary-level coronary artery disease (CAD) and ischaemic stroke (IS) GWAS data generated by Genomics plc as presented in: Weale M. et al. Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries. American Journal of Cardiology (in press). If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “PRS training” in the Supplementary Materials of the published paper. “PRS training” included 187,150 randomly sampled individuals from the White British unrelated (WBU) UK Biobank subset. CAD and IS case phenotypes were defined as described in the “UK Biobank phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events. All analyses included age-at-assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION<br> ----------------------------- cpra: Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom: Chromosome<br> pos: Position in base pairs (b37, 1-based)<br> alt: Alternative allele (effect allele)<br> beta: Effect size (log odds ratio)<br> standard_error: Standard error of beta <br> minus_log10_p: Minus log(base 10) of P-value<br> ref: Reference allele (non-effect allele)<br> ncase: Number of cases<br> ncontrol: Number of controls
DOI: 10.5281/zenodo.4573964
title: Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries

description: Summary-level coronary artery disease (CAD) and ischaemic stroke (IS) GWAS data generated by Genomics plc as presented in: Weale M. et al. Validation of an integrated risk tool, including polygenic risk score, for atherosclerotic cardiovascular disease in multiple ethnicities and ancestries. American Journal of Cardiology (in press). If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “PRS training” in the Supplementary Materials of the published paper. “PRS training” included 187,150 randomly sampled individuals from the White British unrelated (WBU) UK Biobank subset. CAD and IS case phenotypes were defined as described in the “UK Biobank phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events. All analyses included age-at-assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION<br> ----------------------------- cpra: Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom: Chromosome<br> pos: Position in base pairs (b37, 1-based)<br> alt: Alternative allele (effect allele)<br> beta: Effect size (log odds ratio)<br> standard_error: Standard error of beta <br> minus_log10_p: Minus log(base 10) of P-value<br> ref: Reference allele (non-effect allele)<br> ncase: Number of cases<br> ncontrol: Number of controls
DOI: 10.5281/zenodo.4421038
title: An integrated polygenic tool substantially enhances coronary artery disease prediction

description: Summary-level CAD GWAS data generated by Genomics plc as presented in: Riveros-Mckay F. et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circulation: Genomics and Precision Medicine (in press). If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “Group I” in the published paper. Group I, “no PCE/QRISK3 available”, included 114,196 European-ancestry individuals with missing data that prevented PCE or QRISK3 calculation. CAD case phenotypes were defined as described in the “Phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events. All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> cpra Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom Chromosome<br> pos Position in base pairs (b37, 1-based)<br> alt Alternative allele (effect allele)<br> beta Effect size (log odds ratio)<br> standard_error Standard error of beta <br> minus_log10_p Minus log(base 10) of P-value<br> ref Reference allele (non-effect allele)<br> ncase Number of cases<br> ncontrol Number of controls
DOI: 10.5281/zenodo.4421037
title: An integrated polygenic tool substantially enhances coronary artery disease prediction

description: Summary-level CAD GWAS data generated by Genomics plc as presented in: Riveros-Mckay F. et al. An integrated polygenic tool substantially enhances coronary artery disease prediction. Circulation: Genomics and Precision Medicine (in press). If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES<br> -----------------------------<br> These analyses were carried out using the full UK Biobank imputation data release (v3b). Analyses were restricted to a subset of UK Biobank, described as “Group I” in the published paper. Group I, “no PCE/QRISK3 available”, included 114,196 European-ancestry individuals with missing data that prevented PCE or QRISK3 calculation. CAD case phenotypes were defined as described in the “Phenotype definitions” section of the paper’s Supplementary Materials, using both prevalent (pre-baseline) and incident (post-baseline) events. All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink2.0 logistic regression. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> cpra Variant ID in ‘CPRA’ format. Position reflects position in b37. <br> chrom Chromosome<br> pos Position in base pairs (b37, 1-based)<br> alt Alternative allele (effect allele)<br> beta Effect size (log odds ratio)<br> standard_error Standard error of beta <br> minus_log10_p Minus log(base 10) of P-value<br> ref Reference allele (non-effect allele)<br> ncase Number of cases<br> ncontrol Number of controls
DOI: 10.5281/zenodo.2671777
title: Phenome-wide association studies across large population cohorts support drug target validation

description: Summary-level data generated by Genomics plc as presented in:<br> Diogo, D. et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat. Commun. 9, 4285 (2018). https://doi.org/10.1038/s41467-018-06540-3 If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES<br> -----------------------------<br> These analyses were carried out using the interim UK Biobank imputation data release. Analyses were restricted to a subset of "white-British" unrelated samples with a maximum sample size of 112,337 individuals. Case control phenotypes were defined based on categorical datafields as listed in the accompanying file. <br> Quantitative phenotypes were either rank-normalised before analysis, or beta/se values were standardised after analysis using the variance of the phenotype. The normalisation value is indicated in the accompanying file.<br> <br> All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink1.9 linear/logistic regression as appropriate. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> CHR - Chromosome<br> SNP - Variant rsID<br> ALT - Alternative allele (effect allele)<br> REF - Reference Allele (non-effect allele)<br> BP - Position in base pairs (b37, 1-based)<br> NMISS - Number of samples with non-missing genotypes<br> BETA - Effect size (log odds ratio or standardised effect size)<br> SE - Standard error<br> P - P-value<br> F_MISS - genotype missing rate<br> P_hwe - Hardy-weinberg p-value<br> MAF - ALT allele frequency
DOI: 10.5281/zenodo.2671776
title: Phenome-wide association studies across large population cohorts support drug target validation

description: Summary-level data generated by Genomics plc as presented in:<br> Diogo, D. et al. Phenome-wide association studies across large population cohorts support drug target validation. Nat. Commun. 9, 4285 (2018). https://doi.org/10.1038/s41467-018-06540-3 If you have any questions or comments regarding these files, please contact Genomics plc at research@genomicsplc.com NOTES<br> -----------------------------<br> These analyses were carried out using the interim UK Biobank imputation data release. Analyses were restricted to a subset of "white-British" unrelated samples with a maximum sample size of 112,337 individuals. Case control phenotypes were defined based on categorical datafields as listed in the accompanying file. <br> Quantitative phenotypes were either rank-normalised before analysis, or beta/se values were standardised after analysis using the variance of the phenotype. The normalisation value is indicated in the accompanying file.<br> <br> All analyses included Age at assessment, sex, genotyping chip, and 10 principal components as covariates. We used plink1.9 linear/logistic regression as appropriate. For chromosome X variants males were treated as having 0 or 2 alternative alleles. The results are not adjusted for genomic control. DATA FILE CONTENT DESCRIPTION<br> -----------------------------<br> CHR - Chromosome<br> SNP - Variant rsID<br> ALT - Alternative allele (effect allele)<br> REF - Reference Allele (non-effect allele)<br> BP - Position in base pairs (b37, 1-based)<br> NMISS - Number of samples with non-missing genotypes<br> BETA - Effect size (log odds ratio or standardised effect size)<br> SE - Standard error<br> P - P-value<br> F_MISS - genotype missing rate<br> P_hwe - Hardy-weinberg p-value<br> MAF - ALT allele frequency
DOI: 10.5281/zenodo.4581206
title: Supplementary data for 'Comparison of sparse biclustering algorithms for gene expression datasets'

description: <strong>Motivation:</strong> Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, in heterogeneous samples (e.g. different tissues or disease states), genes may cluster differently. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. <br> <strong>Results:</strong> In this study we compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. In particular we use a knockout mouse RNA-seq dataset to evaluate each algorithm’s ability to simultaneously cluster genes and cluster samples. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and didn't require any post-processing, but were considerably slower than other algorithm classes. We assessed whether non-negative matrix factorisation algorithms can be repurposed for biclustering and found that, although the raw output was poor, after using a sparsity-inducing post-processing procedure we introduce, one such algorithm was one of the most highly ranked on real datasets. We also exhibit the limitations of biclustering algorithms by varying the complexity of simulated datasets. The algorithms generally struggled on simulated datasets with a large number of implanted factors, or with a large number of genes. In real datasets, the algorithms rarely returned clusters containing samples from multiple tissues, which highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. <br> Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing.
DOI: 10.5281/zenodo.4317285
title: Supplementary data for 'Comparison of sparse biclustering algorithms for gene expression datasets'

description: <strong>Motivation:</strong> Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, in heterogeneous samples (e.g. different tissues or disease states), genes may cluster differently. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. <br> <strong>Results:</strong> In this study we compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. In particular we use a knockout mouse RNA-seq dataset to evaluate each algorithm’s ability to simultaneously cluster genes and cluster samples. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and didn't require any post-processing, but were considerably slower than other algorithm classes. We assessed whether non-negative matrix factorisation algorithms can be repurposed for biclustering and found that, although the raw output was poor, after using a sparsity-inducing post-processing procedure we introduce, one such algorithm was one of the most highly ranked on real datasets. We also exhibit the limitations of biclustering algorithms by varying the complexity of simulated datasets. The algorithms generally struggled on simulated datasets with a large number of implanted factors, or with a large number of genes. In real datasets, the algorithms rarely returned clusters containing samples from multiple tissues, which highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. <br> Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing.
DOI: 10.14278/rodare.828
title: Data for: Experimental studies on bubble aspect ratio and corresponding correlations under bubble swarm condition

description: Zip-file that contains the raw images on a study on bubble aspect ratio under swarm condition. Further information can be found in the respective paper.
DOI: 10.14278/rodare.827
title: Data for: Experimental studies on bubble aspect ratio and corresponding correlations under bubble swarm condition

description: Zip-file that contains the raw images on a study on bubble aspect ratio under swarm condition. Further information can be found in the respective paper.
DOI: 10.6084/m9.figshare.12000255.v2
title: Raw data of gut microbiota in 1month, 3months, and 6months infants

description: This data was used in a study on effect of maternal antibiotics exposure on infants' gut microbiota.
DOI: 10.5281/zenodo.4588374
title: maudqueroue/MultispeciesIPM_SkuaPetrel: First realease mIPM SkuaPetrel

description: This is the first release.
DOI: 10.48329/wh2f-8168
title: CPRD GOLD March 2021

description: The CPRD GOLD database contains longitudinal routinely-collected electronic health records (EHR data) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval. Further information is available at https://www.cprd.com/data-access.
DOI: 10.5281/zenodo.4573819
title: Test

description: Test
DOI: 10.5281/zenodo.4573818
title: Test

description: Test
DOI: 10.6084/m9.figshare.14178623
title: Supplementary Material for: Metabolic Effects of Growth Hormone Treatment in Short Prepubertal Children: A Double-Blinded Randomized Clinical Trial

description: <b><i>Introduction:</i></b> Growth hormone (GH) is a central hormone for regulating linear growth during childhood and also highly involved in the metabolism of lipids, carbohydrates, and protein. However, few studies report on how treatment with GH during childhood influences metabolic parameters. Our aim was to investigate metabolic effects of different doses of GH in short children with GH peak levels in the low to normal range. <b><i>Design:</i></b> Thirty-five prepubertal short children (<i>Results:</i> At 24 months, the high-dose group (HD) had higher fasting insulin compared with the standard-dose (SD) and low-dose (LD) groups (HD: 111.7 vs. SD: 61.2 and LD: 46.0 pmol/L [<i>p</i> &lt; 0.001]) and showed signs of insulin resistance (HOMA-IR, HD: 4.20 vs. SD: 2.17 and LD: 1.71 (LD) [<i>p</i> &lt; 0.001]). The FSIVGTT also demonstrated higher acute insulin response (<i>p</i> &lt; 0.05). Few other metabolic differences were found at 24 months, but a decreased insulin sensitivity index (Si) could already be seen at 12 months for both SD and HD compared with the LD group (<i>p</i> &lt; 0.05). <b><i>Conclusion:</i></b> Treatment with GH resulted in a dose-dependent decrease in insulin sensitivity, demonstrated by higher levels of fasting insulin and signs of insulin resistance in both HOMA indices and FSIVGTT examinations.