cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2clblx
title: CCDC 2192369: Experimental Crystal Structure Determination

description: Related Article: Toshiki Fujii, Yuichi Kitagawa, Yasuchika Hasegawa, Hiroaki Imoto, Kensuke Naka|2022|Inorg.Chem.|61|17662|doi:10.1021/acs.inorgchem.2c02757
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.cc2clbmy
title: CCDC 2192370: Experimental Crystal Structure Determination

description: Related Article: Toshiki Fujii, Yuichi Kitagawa, Yasuchika Hasegawa, Hiroaki Imoto, Kensuke Naka|2022|Inorg.Chem.|61|17662|doi:10.1021/acs.inorgchem.2c02757
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.cc2clbnz
title: CCDC 2192371: Experimental Crystal Structure Determination

description: Related Article: Toshiki Fujii, Yuichi Kitagawa, Yasuchika Hasegawa, Hiroaki Imoto, Kensuke Naka|2022|Inorg.Chem.|61|17662|doi:10.1021/acs.inorgchem.2c02757
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.21547196
title: Additional file 1 of MGIDI: a powerful tool to analyze plant multivariate data

description: Additional file 1. A website with the data, script, and results is available at https://tiagoolivoto.github.io/paper_mgidi_pm/ . The source code used to produce the static website and the results in this manuscript have been archived at 10.5281/zenodo.7155173 as manuscript v2.
DOI: 10.6084/m9.figshare.21547196.v1
title: Additional file 1 of MGIDI: a powerful tool to analyze plant multivariate data

description: Additional file 1. A website with the data, script, and results is available at https://tiagoolivoto.github.io/paper_mgidi_pm/ . The source code used to produce the static website and the results in this manuscript have been archived at 10.5281/zenodo.7155173 as manuscript v2.
DOI: 10.6084/m9.figshare.21547210
title: Additional file 1 of Functional brain imaging interventions for radiation therapy planning in patients with glioblastoma: a systematic review

description: Additional file 1. A Database Search Strategy.
DOI: 10.6084/m9.figshare.21547210.v1
title: Additional file 1 of Functional brain imaging interventions for radiation therapy planning in patients with glioblastoma: a systematic review

description: Additional file 1. A Database Search Strategy.
DOI: 10.6084/m9.figshare.21547273
title: Additional file 2 of Nutritional and metabolic process of the dung beetle Phelotrupes auratus depends on the plant ingredients that the herbivores eat

description: Additional file 2: Supplementary Table 3. Fluctuated transcript list in the midgut between Cape Toi and Nara Park.
DOI: 10.6084/m9.figshare.21547273.v1
title: Additional file 2 of Nutritional and metabolic process of the dung beetle Phelotrupes auratus depends on the plant ingredients that the herbivores eat

description: Additional file 2: Supplementary Table 3. Fluctuated transcript list in the midgut between Cape Toi and Nara Park.
DOI: 10.6084/m9.figshare.21547276
title: Additional file 3 of Nutritional and metabolic process of the dung beetle Phelotrupes auratus depends on the plant ingredients that the herbivores eat

description: Additional file 3: Supplementary Table 4. Fluctuated transcript list in the fat body between Cape Toi and Nara Park.
DOI: 10.6084/m9.figshare.21547276.v1
title: Additional file 3 of Nutritional and metabolic process of the dung beetle Phelotrupes auratus depends on the plant ingredients that the herbivores eat

description: Additional file 3: Supplementary Table 4. Fluctuated transcript list in the fat body between Cape Toi and Nara Park.
DOI: 10.6084/m9.figshare.21547282
title: Additional file 10 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 10: Table S10. Comparison of differentially expressed genes for each comparable cluster between scRNAseq and snRNAseq data.
DOI: 10.6084/m9.figshare.21547282.v1
title: Additional file 10 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 10: Table S10. Comparison of differentially expressed genes for each comparable cluster between scRNAseq and snRNAseq data.
DOI: 10.6084/m9.figshare.21547288
title: Additional file 2 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 2: Table S2. Score Matrix used to annotate cell types and list of references for individual marker genes used for cluster/cell type annotation.
DOI: 10.6084/m9.figshare.21547288.v1
title: Additional file 2 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 2: Table S2. Score Matrix used to annotate cell types and list of references for individual marker genes used for cluster/cell type annotation.
DOI: 10.6084/m9.figshare.21547291
title: Additional file 3 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 3: Table S3. Results of automatic cluster annotation and assignment to combined clusters.
DOI: 10.6084/m9.figshare.21547291.v1
title: Additional file 3 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 3: Table S3. Results of automatic cluster annotation and assignment to combined clusters.
DOI: 10.6084/m9.figshare.21547294
title: Additional file 4 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 4: Table S4. List of marker genes for each cluster in scRNAseq analysis.
DOI: 10.6084/m9.figshare.21547294.v1
title: Additional file 4 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 4: Table S4. List of marker genes for each cluster in scRNAseq analysis.
DOI: 10.6084/m9.figshare.21547297
title: Additional file 5 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 5: Table S5. GO analysis of cell clusters identified in scRNAseq.
DOI: 10.6084/m9.figshare.21547297.v1
title: Additional file 5 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 5: Table S5. GO analysis of cell clusters identified in scRNAseq.
DOI: 10.6084/m9.figshare.21547300
title: Additional file 6 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 6: Table S6. List of marker genes for each cluster in snRNAseq analysis.
DOI: 10.6084/m9.figshare.21547303
title: Additional file 7 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 7: Table S7. GO analysis of cell clusters identified in snRNAseq.
DOI: 10.6084/m9.figshare.21547303.v1
title: Additional file 7 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 7: Table S7. GO analysis of cell clusters identified in snRNAseq.
DOI: 10.6084/m9.figshare.21547306
title: Additional file 8 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 8: Table S8. List of top 3000 variable genes for scRNAseq and snRNAseq data, respectively.
DOI: 10.6084/m9.figshare.21547306.v1
title: Additional file 8 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 8: Table S8. List of top 3000 variable genes for scRNAseq and snRNAseq data, respectively.
DOI: 10.6084/m9.figshare.21547309
title: Additional file 9 of Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material

description: Additional file 9: Table S9. Top 3000 variable genes and GO enrichment results for scRNAseq, snRNAseq and shared.
DOI: 10.6084/m9.figshare.21547327
title: Additional file 4 of Lithological structure of western Pacific lithosphere reconstructed from mantle xenoliths in a petit-spot volcano

description: Additional file 4. Supplementary tables. Table S1. Major element compositions of olivine. Table S2. Major element compositions of clinopyroxene. Table S3. Major element compositions of orthopyroxene. Table S4. Major element compositions of spinel. Table S5. Trace element compositions of clinopyroxene. Table S6. Trace element compositions of orthopyroxene. Table S7. Data of Raman spectroscopy. Table S8. Average compositions of pyroxenes using the two pyroxene geothermobarometry.
DOI: 10.6084/m9.figshare.21547327.v1
title: Additional file 4 of Lithological structure of western Pacific lithosphere reconstructed from mantle xenoliths in a petit-spot volcano

description: Additional file 4. Supplementary tables. Table S1. Major element compositions of olivine. Table S2. Major element compositions of clinopyroxene. Table S3. Major element compositions of orthopyroxene. Table S4. Major element compositions of spinel. Table S5. Trace element compositions of clinopyroxene. Table S6. Trace element compositions of orthopyroxene. Table S7. Data of Raman spectroscopy. Table S8. Average compositions of pyroxenes using the two pyroxene geothermobarometry.
DOI: 10.6084/m9.figshare.21547343
title: Additional file 1 of Histological and biochemical evaluation of plasma rich in growth factors treatment for grade II muscle injuries in sheep

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.21547343.v1
title: Additional file 1 of Histological and biochemical evaluation of plasma rich in growth factors treatment for grade II muscle injuries in sheep

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.21547369
title: Additional file 2 of Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer

description: Additional file 2: It includes supplementary tables 1–4. Table S1. Individual data for the correlation between PRELP mRNA expression and somatic mutations. Table S2. Individual data for the correlation between PRELP mRNA expression and copy number aberrations. Table S3. Individual data for the correlation between PRELP mRNA expression and DNA methylation. Table S4. Information about the certified cell lines. STR method was used for certification.
DOI: 10.6084/m9.figshare.21547369.v1
title: Additional file 2 of Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer

description: Additional file 2: It includes supplementary tables 1–4. Table S1. Individual data for the correlation between PRELP mRNA expression and somatic mutations. Table S2. Individual data for the correlation between PRELP mRNA expression and copy number aberrations. Table S3. Individual data for the correlation between PRELP mRNA expression and DNA methylation. Table S4. Information about the certified cell lines. STR method was used for certification.
DOI: 10.6084/m9.figshare.21547402
title: Additional file 1 of Epidemiologic features, clinical characteristics, and predictors of mortality in patients with candidemia in Alameda County, California; a 2017–2020 retrospective analysis

description: Additional file 1. Candidemia dataset csv.
DOI: 10.6084/m9.figshare.21547402.v1
title: Additional file 1 of Epidemiologic features, clinical characteristics, and predictors of mortality in patients with candidemia in Alameda County, California; a 2017–2020 retrospective analysis

description: Additional file 1. Candidemia dataset csv.
DOI: 10.6084/m9.figshare.21547454
title: Additional file 1 of Vikrahraun—the 1961 basaltic lava flow eruption at Askja, Iceland: morphology, geochemistry, and planetary analogs

description: Additional file 1: Contains sample locations, an analytical summary containing a table of contents for Additional files 1-3, topographic profile data, and flow volume calculations, crystallinity, vesicularity, density, whole-rock chemistry, mineral chemistry, and additional calculations
DOI: 10.6084/m9.figshare.21547454.v1
title: Additional file 1 of Vikrahraun—the 1961 basaltic lava flow eruption at Askja, Iceland: morphology, geochemistry, and planetary analogs

description: Additional file 1: Contains sample locations, an analytical summary containing a table of contents for Additional files 1-3, topographic profile data, and flow volume calculations, crystallinity, vesicularity, density, whole-rock chemistry, mineral chemistry, and additional calculations
DOI: 10.6084/m9.figshare.21547457
title: Additional file 2 of Vikrahraun—the 1961 basaltic lava flow eruption at Askja, Iceland: morphology, geochemistry, and planetary analogs

description: Additional file 2: Textural data presented in this paper, including crystallinity, vesicularity, density, and viscosity calculations
DOI: 10.6084/m9.figshare.21547457.v1
title: Additional file 2 of Vikrahraun—the 1961 basaltic lava flow eruption at Askja, Iceland: morphology, geochemistry, and planetary analogs

description: Additional file 2: Textural data presented in this paper, including crystallinity, vesicularity, density, and viscosity calculations
DOI: 10.6084/m9.figshare.21547460
title: Additional file 3 of Vikrahraun—the 1961 basaltic lava flow eruption at Askja, Iceland: morphology, geochemistry, and planetary analogs

description: Additional file 3: Cumulative chemistry data presented in this paper, including whole-rock chemistry, mineral chemistry, thermometry, and compiled geochemistry of Vikrahraun from the literature
DOI: 10.6084/m9.figshare.21547460.v1
title: Additional file 3 of Vikrahraun—the 1961 basaltic lava flow eruption at Askja, Iceland: morphology, geochemistry, and planetary analogs

description: Additional file 3: Cumulative chemistry data presented in this paper, including whole-rock chemistry, mineral chemistry, thermometry, and compiled geochemistry of Vikrahraun from the literature
DOI: 10.6084/m9.figshare.21547477
title: Additional file 2 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 2: Table S2. Summary of different models tested for estimating differences between controls and COVID-19 patients for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p.value for each tested model for each cell type are shown. The following models were tested, mod1; Age and ethnicity as covariates, mod2; age as a covariate, mod3; ethnicity as a covariate, mod4; no covariates.
DOI: 10.6084/m9.figshare.21547477.v1
title: Additional file 2 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 2: Table S2. Summary of different models tested for estimating differences between controls and COVID-19 patients for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p.value for each tested model for each cell type are shown. The following models were tested, mod1; Age and ethnicity as covariates, mod2; age as a covariate, mod3; ethnicity as a covariate, mod4; no covariates.
DOI: 10.6084/m9.figshare.21547480
title: Additional file 3 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 3: Table S3. Summary of identified differentially methylated CpGs between COVID-19 patients and controls. A. All significant CpGs B. Variable description from Table S2A. C. Significant CpGs from genes previously described as COVID-19 important [1]. D. Functional annotation of genes from Supplemental table 3C.
DOI: 10.6084/m9.figshare.21547480.v1
title: Additional file 3 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 3: Table S3. Summary of identified differentially methylated CpGs between COVID-19 patients and controls. A. All significant CpGs B. Variable description from Table S2A. C. Significant CpGs from genes previously described as COVID-19 important [1]. D. Functional annotation of genes from Supplemental table 3C.
DOI: 10.6084/m9.figshare.21547486
title: Additional file 5 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 5: Table S5. Summary of differentially methylated CpGs in recovered and died COVID-19 patients. A. Immune cell comparison between baseline and recovered pairs, B. Significant CpGs between baseline and recovered pairs, C. CpG pathways between baseline and recovered pairs, D. Immune cell comparison between baseline and died pairs, E. Significant CpGs between baseline and died pairs, F. CpG pathways between baseline and died pairs.
DOI: 10.6084/m9.figshare.21547486.v1
title: Additional file 5 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 5: Table S5. Summary of differentially methylated CpGs in recovered and died COVID-19 patients. A. Immune cell comparison between baseline and recovered pairs, B. Significant CpGs between baseline and recovered pairs, C. CpG pathways between baseline and recovered pairs, D. Immune cell comparison between baseline and died pairs, E. Significant CpGs between baseline and died pairs, F. CpG pathways between baseline and died pairs.
DOI: 10.6084/m9.figshare.21547489
title: Additional file 6 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 6: Table S6. Analysis of dead and recovered+A3 COVID-19 patients for immune cell proportions. A. Summary of different models tested for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p-value for each tested model for each cell type are shown. The following models were tested, mod1; Age + MV days + Gender + ICU LoS + ECMO + Nosocomial infections, mod2; Age + Gender + ICU LoS + ECMO + Nosocomial infections, mod3; Age + MV days + ICU LoS + ECMO + Nosocomial infections, mod4; Age + ECMO + Nosocomial infections, mod5; Age + Nosocomial infections, mod6; Age, mod7; no covariates. B. Immune cell differences between dead and recovered at baseline, C. Immune cell differences between dead and recovered at last time point.
DOI: 10.6084/m9.figshare.21547489.v1
title: Additional file 6 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 6: Table S6. Analysis of dead and recovered+A3 COVID-19 patients for immune cell proportions. A. Summary of different models tested for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p-value for each tested model for each cell type are shown. The following models were tested, mod1; Age + MV days + Gender + ICU LoS + ECMO + Nosocomial infections, mod2; Age + Gender + ICU LoS + ECMO + Nosocomial infections, mod3; Age + MV days + ICU LoS + ECMO + Nosocomial infections, mod4; Age + ECMO + Nosocomial infections, mod5; Age + Nosocomial infections, mod6; Age, mod7; no covariates. B. Immune cell differences between dead and recovered at baseline, C. Immune cell differences between dead and recovered at last time point.
DOI: 10.6084/m9.figshare.21547492
title: Additional file 7 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 7: Table S7. Summary of immune cell changes and differentially methylated CpGs between recovered and dead patients over four time points 7A. Immune cell changes between recovered and dead patients over four time points, 7B. Differential methylation of CpGs between recovered and died patients over four time points. The b_0, b_1, b_2, and b_3 coefficients correspond to the reference model parameters, where survival phenotype is used as a reference. The d_0, d_1, d_2, d_3 coefficients represent the differences between the reference model and the model parameters in the compared group (died). AveExprs refers to the average log2-expression for an individual immune cell proportion or CpG. The F column contains moderate F-statistics, P-value -raw p-value, and adj.P.Valu- Benjamini-Hochberg adjusted p-value. Other column variables are described in Supplemental table 2B.
DOI: 10.6084/m9.figshare.21547492.v1
title: Additional file 7 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 7: Table S7. Summary of immune cell changes and differentially methylated CpGs between recovered and dead patients over four time points 7A. Immune cell changes between recovered and dead patients over four time points, 7B. Differential methylation of CpGs between recovered and died patients over four time points. The b_0, b_1, b_2, and b_3 coefficients correspond to the reference model parameters, where survival phenotype is used as a reference. The d_0, d_1, d_2, d_3 coefficients represent the differences between the reference model and the model parameters in the compared group (died). AveExprs refers to the average log2-expression for an individual immune cell proportion or CpG. The F column contains moderate F-statistics, P-value -raw p-value, and adj.P.Valu- Benjamini-Hochberg adjusted p-value. Other column variables are described in Supplemental table 2B.
DOI: 10.6084/m9.figshare.21547498
title: Additional file 9 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

description: Additional file 9: Table S9. Summary of univariate Cox proportional hazard analysis of the previously identified CpGs. Column variables represent Name; chr; chromosome number, pos; position, CpG name, relation_to_Island; where is CpG located in relationship to island, UCSC RefGene Name; UCSC gene name, UCSC RefGene Accession; UCSC gene accession, UCSC RefGene Group; where in respect to gene is CpG located, Beta; estimated coefficient beta from the model, StandardError; standard error, Z; z-score, LRT; likelihood ratio test, Wald; Wald test, LogRank; log-rank test, HR; Hazardous ratio, HR lower; Hazardous ratio lower 95% bound, HR upper; Hazardous ratio upper 95% bound.