cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8241176.v2
title: OCIM1.1

description: This file contains the circulation output and grid information from the Ocean Circulation Inverse Model (OCIM) version 1.1 [Devries and Primeau, 2011; DeVries, 2014].<br>The data was converted for use by the Algebraic Implicit Biogeochemical Cycling System (AIBECS).<br>References: <br>- DeVries, T. ( 2014), The oceanic anthropogenic CO<sub>2</sub> sink: Storage, air‐sea fluxes, and transports over the industrial era, <i>Global Biogeochem. Cycles</i>, 28, 631– 647, doi:10.1002/2013GB004739. <br>- DeVries, T. and F. Primeau, 2011: Dynamically and Observationally Constrained Estimates of Water-Mass Distributions and Ages in the Global Ocean. <i>J. Phys. Oceanogr.,</i> <b>41</b>, 2381–2401, https://doi.org/10.1175/JPO-D-10-05011.1<br>- Benoit Pasquier. (2019, May 23). AIBECS.jl: The ideal tool for exploring global marine biogeochemical cycles (Version v0.1.5). Zenodo. http://doi.org/10.5281/zenodo.3192180<br><br>
DOI: 10.6084/m9.figshare.8240795.v1
title: Additional file 1: of Population and colony structure of an ant with territorial males, Cardiocondyla venustula

description: Raw data of the population genetic analysis of the ant Cardiocondyla venustula from South Africa. (XLSX 40 kb)
DOI: 10.6084/m9.figshare.8240789
title: MOESM2 of A community-informed list of key speciation concepts for undergraduate education

description: Additional file 2. Written comments provided by surveyed experts and anonymous reviewers regarding each key concept in final list.
DOI: 10.6084/m9.figshare.8241176.v3
title: AIBECS-OCIM1

description: This file contains the circulation output and grid information from the Ocean Circulation Inverse Model (OCIM) version 1.1 [Devries and Primeau, 2011; DeVries, 2014].<br>The data was converted for use by the Algebraic Implicit Biogeochemical Cycling System (AIBECS).<br>References: <br>- DeVries, T. ( 2014), The oceanic anthropogenic CO<sub>2</sub> sink: Storage, air‐sea fluxes, and transports over the industrial era, <i>Global Biogeochem. Cycles</i>, 28, 631– 647, doi:10.1002/2013GB004739. <br>- DeVries, T. and F. Primeau, 2011: Dynamically and Observationally Constrained Estimates of Water-Mass Distributions and Ages in the Global Ocean. <i>J. Phys. Oceanogr.,</i> <b>41</b>, 2381–2401, https://doi.org/10.1175/JPO-D-10-05011.1<br>- Benoit Pasquier. (2019, May 23). AIBECS.jl: The ideal tool for exploring global marine biogeochemical cycles (Version v0.1.5). Zenodo. http://doi.org/10.5281/zenodo.3192180<br><br>
DOI: 10.6084/m9.figshare.8240789.v1
title: MOESM2 of A community-informed list of key speciation concepts for undergraduate education

description: Additional file 2. Written comments provided by surveyed experts and anonymous reviewers regarding each key concept in final list.
DOI: 10.6084/m9.figshare.8241176.v4
title: AIBECS-OCIM1

description: This file contains the circulation output and grid information from the Ocean Circulation Inverse Model (OCIM) version 1 [Devries and Primeau, 2011; DeVries, 2014].<br>The data was converted for use by the Algebraic Implicit Biogeochemical Element Cycling System (AIBECS).<br>References: <br>- DeVries, T. ( 2014), The oceanic anthropogenic CO<sub>2</sub> sink: Storage, air‐sea fluxes, and transports over the industrial era, <i>Global Biogeochem. Cycles</i>, 28, 631– 647, doi:10.1002/2013GB004739. <br>- DeVries, T. and F. Primeau, 2011: Dynamically and Observationally Constrained Estimates of Water-Mass Distributions and Ages in the Global Ocean. <i>J. Phys. Oceanogr.,</i> <b>41</b>, 2381–2401, https://doi.org/10.1175/JPO-D-10-05011.1<br>- Benoit Pasquier. (2019, May 23). AIBECS.jl: The ideal tool for exploring global marine biogeochemical cycles (v0.2.0). Zenodo. http://doi.org/10.5281/zenodo.3192180<br><br>
DOI: 10.6084/m9.figshare.8241176.v5
title: AIBECS-OCIM1

description: This file contains the circulation output and grid information from the Ocean Circulation Inverse Model (OCIM) version 1 [Devries and Primeau, 2011; DeVries, 2014].<br>The data was converted for use by the Algebraic Implicit Biogeochemical Element Cycling System (AIBECS).<br>References: <br>- DeVries, T. ( 2014), The oceanic anthropogenic CO<sub>2</sub> sink: Storage, air‐sea fluxes, and transports over the industrial era, <i>Global Biogeochem. Cycles</i>, 28, 631– 647, doi:10.1002/2013GB004739. <br>- DeVries, T. and F. Primeau, 2011: Dynamically and Observationally Constrained Estimates of Water-Mass Distributions and Ages in the Global Ocean. <i>J. Phys. Oceanogr.,</i> <b>41</b>, 2381–2401, https://doi.org/10.1175/JPO-D-10-05011.1<br>- Benoit Pasquier. (2019, May 23). AIBECS.jl: The ideal tool for exploring global marine biogeochemical cycles (v0.2.0). Zenodo. http://doi.org/10.5281/zenodo.3192180<br><br>
DOI: 10.6084/m9.figshare.8241176.v6
title: AIBECS-OCIM1

description: This file contains the circulation output and grid information from the Ocean Circulation Inverse Model (OCIM) version 1 [Devries and Primeau, 2011; DeVries, 2014].<br>The data was converted for use by the Algebraic Implicit Biogeochemical Element Cycling System (AIBECS).<br>References: <br>- DeVries, T. ( 2014), The oceanic anthropogenic CO<sub>2</sub> sink: Storage, air‐sea fluxes, and transports over the industrial era, <i>Global Biogeochem. Cycles</i>, 28, 631– 647, doi:10.1002/2013GB004739. <br>- DeVries, T. and F. Primeau, 2011: Dynamically and Observationally Constrained Estimates of Water-Mass Distributions and Ages in the Global Ocean. <i>J. Phys. Oceanogr.,</i> <b>41</b>, 2381–2401, https://doi.org/10.1175/JPO-D-10-05011.1<br>- Benoit Pasquier. (2019, May 23). AIBECS.jl: The ideal tool for exploring global marine biogeochemical cycles (v0.2.0). Zenodo. http://doi.org/10.5281/zenodo.3192180<br><br>
DOI: 10.6084/m9.figshare.8240741
title: MOESM3 of An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

description: Additional file 3. File containing the R functions and scripts to create the integrative model.
DOI: 10.6084/m9.figshare.8240741.v1
title: MOESM3 of An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

description: Additional file 3. File containing the R functions and scripts to create the integrative model.
DOI: 10.6084/m9.figshare.8240735
title: MOESM1 of An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

description: Additional file 1. This file contains the following supplementary tables: Table S1: Dataset; Table S2: Parameters and criteria considered for goodness of fit, internal and external validation; Table S3: External dataset with predicted LogKhsa; Table S4: Molecular descriptors appearing in the models; Table S5: Genes appearing in the models; Table S6: Summary of the models parameters and evaluation metrics.
DOI: 10.6084/m9.figshare.8240735.v1
title: MOESM1 of An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding

description: Additional file 1. This file contains the following supplementary tables: Table S1: Dataset; Table S2: Parameters and criteria considered for goodness of fit, internal and external validation; Table S3: External dataset with predicted LogKhsa; Table S4: Molecular descriptors appearing in the models; Table S5: Genes appearing in the models; Table S6: Summary of the models parameters and evaluation metrics.
DOI: 10.6084/m9.figshare.8240678
title: Additional file 4: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S4. Logistic regression model for MetS using male-specific GRS. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.8240678.v1
title: Additional file 4: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S4. Logistic regression model for MetS using male-specific GRS. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.8240669
title: Additional file 3: of Development of palliative care attitude and knowledge (PCAK) questionnaire for physicians in Kuwait

description: Palliative Care Attitude and Knowledge Questionnaire (PCAK) row data to be opened by Statistical Package for Social Science (SPSS) program (SAV 19 kb)
DOI: 10.6084/m9.figshare.8240666
title: Additional file 3: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S3. Metabolic syndrome associated loci in males. Association results of these loci were shown in the discovery stage (KARE), replication stage (Rural1816, Rural3667, and HEXA), and overall meta-analysis, respectively. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8240669.v1
title: Additional file 3: of Development of palliative care attitude and knowledge (PCAK) questionnaire for physicians in Kuwait

description: Palliative Care Attitude and Knowledge Questionnaire (PCAK) row data to be opened by Statistical Package for Social Science (SPSS) program (SAV 19 kb)
DOI: 10.6084/m9.figshare.8240666.v1
title: Additional file 3: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S3. Metabolic syndrome associated loci in males. Association results of these loci were shown in the discovery stage (KARE), replication stage (Rural1816, Rural3667, and HEXA), and overall meta-analysis, respectively. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8240657
title: Additional file 2: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S2. Genetic loci associated with MetS component traits in females. Association results of these loci were shown in the discovery stage (KARE), replication stage (Rural1816, Rural3667, and HEXA), and overall meta-analysis, respectively. (XLSX 18 kb)
DOI: 10.6084/m9.figshare.8240657.v1
title: Additional file 2: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S2. Genetic loci associated with MetS component traits in females. Association results of these loci were shown in the discovery stage (KARE), replication stage (Rural1816, Rural3667, and HEXA), and overall meta-analysis, respectively. (XLSX 18 kb)
DOI: 10.6084/m9.figshare.8240645
title: Additional file 1: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S1. Metabolic syndrome associated loci in females. Association results of these loci were shown in the discovery stage (KARE), replication stage (Rural1816, Rural3667, and HEXA), and overall meta-analysis, respectively. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.8240645.v1
title: Additional file 1: of Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females

description: Table S1. Metabolic syndrome associated loci in females. Association results of these loci were shown in the discovery stage (KARE), replication stage (Rural1816, Rural3667, and HEXA), and overall meta-analysis, respectively. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.8240627
title: Additional file 4: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S4. Gene Set Enrichment Analysis (GSEA) results of RNA sequencing data. GSEA results of RNA sequencing data, containing Biocarta, KEGG, Reactome, and Biological Pathway results. (XLSX 835 kb)
DOI: 10.6084/m9.figshare.8240627.v1
title: Additional file 4: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S4. Gene Set Enrichment Analysis (GSEA) results of RNA sequencing data. GSEA results of RNA sequencing data, containing Biocarta, KEGG, Reactome, and Biological Pathway results. (XLSX 835 kb)
DOI: 10.6084/m9.figshare.8240618
title: Additional file 3: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S3. Protein expression data. Full list of all proteins identified with label-free mass spectrometry by Scaffold. (XLSX 82 kb)
DOI: 10.6084/m9.figshare.8240618.v1
title: Additional file 3: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S3. Protein expression data. Full list of all proteins identified with label-free mass spectrometry by Scaffold. (XLSX 82 kb)
DOI: 10.6084/m9.figshare.8240612
title: Additional file 2: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S2. Gene ontology enrichment analysis data of differentially expressed genes. Full list of enriched biological pathway gene ontologies in upregulated and downregulated gene datasets in brain, heart, skeletal muscle, and common genes as outputted fromx GOrilla. (XLSX 140 kb)
DOI: 10.6084/m9.figshare.8240612.v1
title: Additional file 2: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S2. Gene ontology enrichment analysis data of differentially expressed genes. Full list of enriched biological pathway gene ontologies in upregulated and downregulated gene datasets in brain, heart, skeletal muscle, and common genes as outputted fromx GOrilla. (XLSX 140 kb)
DOI: 10.6084/m9.figshare.8240603
title: Additional file 1: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S1. Differential gene expression of RNA sequencing. Full list of differentially expressed genes with FDR 1 Log2FC is higher expression during hibernation). (XLSX 1302 kb)
DOI: 10.6084/m9.figshare.8240603.v1
title: Additional file 1: of Waking the sleeping dragon: gene expression profiling reveals adaptive strategies of the hibernating reptile Pogona vitticeps

description: Table S1. Differential gene expression of RNA sequencing. Full list of differentially expressed genes with FDR 1 Log2FC is higher expression during hibernation). (XLSX 1302 kb)
DOI: 10.6084/m9.figshare.8240594
title: Additional file 8: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S7 Results of the multivariate Cox regression analysis for single genes separately for patients of ISUP grade groups 2â 5. (TXT 1940 kb)
DOI: 10.6084/m9.figshare.8240594.v1
title: Additional file 8: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S7 Results of the multivariate Cox regression analysis for single genes separately for patients of ISUP grade groups 2â 5. (TXT 1940 kb)
DOI: 10.6084/m9.figshare.8240579
title: Additional file 6: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S5 Full results of the multivariate Cox regression analysis for single genes. (TXT 1949 kb)
DOI: 10.6084/m9.figshare.8240579.v1
title: Additional file 6: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S5 Full results of the multivariate Cox regression analysis for single genes. (TXT 1949 kb)
DOI: 10.6084/m9.figshare.8240573
title: Additional file 5: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S4 Correlation between ISUP histological grade group of the tumor (dichotomized as high-grade, ≥4 + 4, vs low-grade tumors) and mRNA expression of single genes. Correlation analysis for ISUP grade groups (Groups 1–5) is provided in parallel for comparison. Delta of Pearson r correlation levels is calculated for these 2 different settings. (TXT 2104 kb)
DOI: 10.6084/m9.figshare.8240567
title: Additional file 4: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S3 Correlation between ISUP histological grade group of the tumor (Grade groups 1â 5) and mRNA expression of single genes, univariate Cox-regression analysis with biochemical recurrence as an endpoint. Full list of genes (nâ =â 17,681). (TXT 3078 kb)
DOI: 10.6084/m9.figshare.8240573.v1
title: Additional file 5: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S4 Correlation between ISUP histological grade group of the tumor (dichotomized as high-grade, ≥4 + 4, vs low-grade tumors) and mRNA expression of single genes. Correlation analysis for ISUP grade groups (Groups 1–5) is provided in parallel for comparison. Delta of Pearson r correlation levels is calculated for these 2 different settings. (TXT 2104 kb)
DOI: 10.6084/m9.figshare.8240564
title: Additional file 3: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S2 Full results of Gene Set Enrichment Analysis (GSEA) for tumor vs normal tissue using Gene Ontology Biological Processes database. (TXT 131 kb)
DOI: 10.6084/m9.figshare.8240567.v1
title: Additional file 4: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S3 Correlation between ISUP histological grade group of the tumor (Grade groups 1â 5) and mRNA expression of single genes, univariate Cox-regression analysis with biochemical recurrence as an endpoint. Full list of genes (nâ =â 17,681). (TXT 3078 kb)
DOI: 10.6084/m9.figshare.8240558
title: Additional file 2: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S1 Pairwise comparison of the gene expression of normal and tumor tissue using negative binomial generalized log-linear model and correction for false discovery rate (FDR). (TXT 2100 kb)
DOI: 10.6084/m9.figshare.8240564.v1
title: Additional file 3: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S2 Full results of Gene Set Enrichment Analysis (GSEA) for tumor vs normal tissue using Gene Ontology Biological Processes database. (TXT 131 kb)
DOI: 10.6084/m9.figshare.8240555
title: Additional file 2: of Mapping analysis to estimate EQ-5D utility values using the COPD assessment test in Korea

description: Mapping EQ-5D using the CAT. (R 23 kb)
DOI: 10.6084/m9.figshare.8240549
title: Additional file 1: of Mapping analysis to estimate EQ-5D utility values using the COPD assessment test in Korea

description: Supplement 1: Comparison of the models based on RMSE, MAE (full sample). Supplement 2. Comparison of the models based on the means of RMSEs, MAEs from 10,000 times iterated estimation. (R 22 kb)
DOI: 10.6084/m9.figshare.8240558.v1
title: Additional file 2: of Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers

description: Table S1 Pairwise comparison of the gene expression of normal and tumor tissue using negative binomial generalized log-linear model and correction for false discovery rate (FDR). (TXT 2100 kb)
DOI: 10.6084/m9.figshare.8240555.v1
title: Additional file 2: of Mapping analysis to estimate EQ-5D utility values using the COPD assessment test in Korea

description: Mapping EQ-5D using the CAT. (R 23 kb)
DOI: 10.6084/m9.figshare.8240549.v1
title: Additional file 1: of Mapping analysis to estimate EQ-5D utility values using the COPD assessment test in Korea

description: Supplement 1: Comparison of the models based on RMSE, MAE (full sample). Supplement 2. Comparison of the models based on the means of RMSEs, MAEs from 10,000 times iterated estimation. (R 22 kb)
DOI: 10.6084/m9.figshare.8240480
title: Additional file 5: of Airway host-microbiome interactions in chronic obstructive pulmonary disease

description: Spearman correlation of individual genes in the top two pathways. a. tPC2 and b. tPC4 with Haemophilus and Moraxella in both microbiome and qPCR datasets. (XLSX 32 kb)
DOI: 10.6084/m9.figshare.8240480.v1
title: Additional file 5: of Airway host-microbiome interactions in chronic obstructive pulmonary disease

description: Spearman correlation of individual genes in the top two pathways. a. tPC2 and b. tPC4 with Haemophilus and Moraxella in both microbiome and qPCR datasets. (XLSX 32 kb)
DOI: 10.6084/m9.figshare.8240471
title: Additional file 4: of Airway host-microbiome interactions in chronic obstructive pulmonary disease

description: a. Top 25 loadings by magnitude for each tPC. b. Top 25 loadings by magnitude for each pPC. c. Top 10 positively and negatively enriched pathways and their enrichment scores as shown in the heatmap in Fig. 4b. d. Top 25 significantly enriched positive and negative pathways (FDR PÂ
DOI: 10.6084/m9.figshare.8240462
title: Additional file 6: of A next generation approach to species delimitation reveals the role of hybridization in a cryptic species complex of corals

description: Concatenated nexus files of all phylogenies constructed using RAxML on different pyRAD RADSeq datasets. (TXT 141 kb)
DOI: 10.6084/m9.figshare.8240465
title: Additional file 3: of Airway host-microbiome interactions in chronic obstructive pulmonary disease

description: Cross associations among microbiome (9 microbiome genera and Shannon diversity), host transcriptome (9 tPCs) and proteome (8 pPCs) data both across all samples and within stable samples in the multivariate analysis. FDR P-values are indicated in the table. Significant positive correlations are highlighted in red. Significant negative correlations are highlighted in blue. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8240471.v1
title: Additional file 4: of Airway host-microbiome interactions in chronic obstructive pulmonary disease

description: a. Top 25 loadings by magnitude for each tPC. b. Top 25 loadings by magnitude for each pPC. c. Top 10 positively and negatively enriched pathways and their enrichment scores as shown in the heatmap in Fig. 4b. d. Top 25 significantly enriched positive and negative pathways (FDR PÂ
DOI: 10.6084/m9.figshare.8240462.v1
title: Additional file 6: of A next generation approach to species delimitation reveals the role of hybridization in a cryptic species complex of corals

description: Concatenated nexus files of all phylogenies constructed using RAxML on different pyRAD RADSeq datasets. (TXT 141 kb)
DOI: 10.6084/m9.figshare.8240456
title: Additional file 2: of Airway host-microbiome interactions in chronic obstructive pulmonary disease

description: a. MetaCore pathways significantly enriched for differential expressed genes (DEGs) between stable and exacerbations (FDR P ≤ 0.01). b. Significantly differentially expressed proteins between sputum samples of COPD ex-smokers and current smokers (Wilcoxon, FDR P ≤ 0.05). (XLSX 68 kb)