cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8137736
title: Docking Result.mdb

description: The data upload in this repository is the data get from in silico experiment.
DOI: 10.6084/m9.figshare.8137736.v2
title: Docking Result.mdb

description: The data upload in this repository is the data get from in silico experiment.
DOI: 10.6084/m9.figshare.8137736.v1
title: Data Hasil Docking.mdb

description: The data upload in this repository is the data get from in silico experiment.
DOI: 10.6084/m9.figshare.8137715.v1
title: input_data.zip

description: Input data for the reconstructions and required datasets and code to generate the figures in the paper.<br>Proxy data used for the reconstructions:<br> proxy_ama_2.0.0_PAGES-crit-regional+FDR.txt: PAGES2k v2.0.0 proxy records, R-FDR screened subset (See PAGES2k Consortium, 2017, Scientific Data, doi: 10.1038/sdata.2017.88). Data are tab separated, the first column is the "paleoData_TSid" to identify each record in the metadata file. First row is year CE.<br> metadata_2.0.0_PAGES-crit-regional+FDR.txt: According metadatam tab separated. Contains a selection of PAGES2k v.2.0.0 database fields in each row: dataSetName, geo_latitude, geo_longitude, archiveType, resMed (Median resolution in years), paleoData_TSid (the column header in the data files).<br> proxy_ama_2.0.0_HR-0.67_infilled_DINEOF_1850-2000_PAGES-crit-regional+FDR.txt: high-resolution (annual and higher) and infilled (calibration period) subset of the proxy data used for the PCR and CPS methods (210 records), see Methods section.<br> <br> Instrumental target:<br> had4_krig_ama_v2_0_0.txt: April to March aggregated target used for the reconstructions. Space separated, no column headers. Newest updated data available at: http://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_v2_0_0.txt<br> <br> Climate model data used for Figs. 2-4:<br> Models_fullforced_Past1000_GMST_AprMAr.RData: R Workspace containing the model data as time series over the April-March seasonal window, to be consistent with the reconstructions. colnames(models.ama.fullforced) identifies the name of each simulation. Details see Methods section.<br> Models_ctrl_GMST_AprMar.RData: Model control runs. Same format as model data described above.<br> <br> Forcing data:<br> forcing2.csv: Global mean forcing datasets, semicolon separated. Columns: Year CE, CO2, Volcanic, Solar<br> README_forcing.md: Further information about the forcing datasets (sources etc).<br> <br> Further results used to generate the Figures:<br> ebm_results_volc_vs_all_forcing300.RData: Results from the Energy Balance Model for Fig. 4a<br> DandA_CESM_ens_30-200.RData: Results from the D&A analysis for Fig. 3<br> recons.ARnoise.RData: Noise-proxy reconstructions for Figure 4b<br><br> Figs_gmst.R: R script to generate the Figures<br> R-functions_gmst.R Functions required to run Figs_gmst.R<br><br>
DOI: 10.6084/m9.figshare.8137715.v2
title: input_data.zip

description: Input data for the reconstructions and required datasets and code to generate the figures in the paper.<br>Proxy data used for the reconstructions:<br> proxy_ama_2.0.0_PAGES-crit-regional+FDR.txt: PAGES2k v2.0.0 proxy records, R-FDR screened subset (See PAGES2k Consortium, 2017, Scientific Data, doi: 10.1038/sdata.2017.88). Data are tab separated, the first column is the "paleoData_TSid" to identify each record in the metadata file. First row is year CE.<br> metadata_2.0.0_PAGES-crit-regional+FDR.txt: According metadatam tab separated. Contains a selection of PAGES2k v.2.0.0 database fields in each row: dataSetName, geo_latitude, geo_longitude, archiveType, resMed (Median resolution in years), paleoData_TSid (the column header in the data files).<br> proxy_ama_2.0.0_HR-0.67_infilled_DINEOF_1850-2000_PAGES-crit-regional+FDR.txt: high-resolution (annual and higher) and infilled (calibration period) subset of the proxy data used for the PCR and CPS methods (210 records), see Methods section.<br> <br> Instrumental target:<br> had4_krig_ama_v2_0_0.txt: April to March aggregated target used for the reconstructions. Space separated, no column headers. Newest updated data available at: http://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_v2_0_0.txt<br> <br> Climate model data used for Figs. 2-4:<br> Models_fullforced_Past1000_GMST_AprMAr.RData: R Workspace containing the model data as time series over the April-March seasonal window, to be consistent with the reconstructions. colnames(models.ama.fullforced) identifies the name of each simulation. Details see Methods section.<br> Models_ctrl_GMST_AprMar.RData: Model control runs. Same format as model data described above.<br> <br> Forcing data:<br> forcing2.csv: Global mean forcing datasets, semicolon separated. Columns: Year CE, CO2, Volcanic, Solar<br> README_forcing.md: Further information about the forcing datasets (sources etc).<br> <br> Further results used to generate the Figures:<br> ebm_results_volc_vs_all_forcing300.RData: Results from the Energy Balance Model for Fig. 4a<br> DandA_CESM_ens_30-200.RData: Results from the D&A analysis for Fig. 3<br> recons.ARnoise.RData: Noise-proxy reconstructions for Figure 4b<br><br> Figs_gmst.R: R script to generate the Figures<br> R-functions_gmst.R Functions required to run Figs_gmst.R<br><br>
DOI: 10.6084/m9.figshare.8137706.v1
title: Reconstruction ensembles

description: Global mean surface temperature reconstructions. Each file is the 1000 member ensemble from one method.File names are methods abbreviations as in the paper; tab separated, members in columns, first column years CE.<br>
DOI: 10.6084/m9.figshare.8137706.v2
title: Reconstruction ensembles

description: Global mean surface temperature reconstructions. Each file is the 1000 member ensemble from one method.File names are methods abbreviations as in the paper; tab separated, members in columns, first column years CE.<br>
DOI: 10.6084/m9.figshare.8136293
title: MOESM4 of AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

description: Additional file 4: PluriUp and PluriPlus gene list and raw interaction network. Table S7, Updated HUGO gene symbols for PluriUp and PluriPlus; Table S8, Edges of PluriPlus interaction network; Table S9, Nodes and annotation of PluriPlus interaction network. (XLS 672 KB)
DOI: 10.6084/m9.figshare.8136293.v1
title: MOESM4 of AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

description: Additional file 4: PluriUp and PluriPlus gene list and raw interaction network. Table S7, Updated HUGO gene symbols for PluriUp and PluriPlus; Table S8, Edges of PluriPlus interaction network; Table S9, Nodes and annotation of PluriPlus interaction network. (XLS 672 KB)
DOI: 10.6084/m9.figshare.8136281
title: MOESM2 of AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

description: Additional file 2: Table S2. F-measure and NMI for each benchmarking dataset an clustering method. (XLS 34 KB)
DOI: 10.6084/m9.figshare.8136281.v1
title: MOESM2 of AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

description: Additional file 2: Table S2. F-measure and NMI for each benchmarking dataset an clustering method. (XLS 34 KB)
DOI: 10.6084/m9.figshare.8136209
title: Additional file 1: of Genome-wide association study identifies novel loci for type 2 diabetes-attributed end-stage kidney disease in African Americans

description: Figure S1. QQ plot of GWAS results of T2D-ESKD vs. non-diabetic non-nephropathy controls under baseline model. Figure S2. P value comparisons between baseline, APOL1-negative, and APOL1-adjusted models. Table S1. Study description. Table S2. Genome-wide significant variants associated with T2D-ESKD at Stage 2 Meta-analysis. Table S3. Discrimination analysis in 2756 T2D-lacking nephropathy individuals and 6977 controls for genome-wide significant T2D-ESKD-associated variants in baseline mode. Table S4. Discrimination analysis in 2756 T2D-lacking nephropathy individuals and 6977 controls for genome-wide significant T2D-ESKD-associated variants in APOL1-negative model. Table S5. Results of APOL1-negative model for top associations identified in baseline model. Table S6. (Excel). Functional annotations of genome-wide significant T2D-ESKD variants and proxies in linkage disequilibrium (r2â &gt;â 0.7). (ZIP 138 kb)
DOI: 10.6084/m9.figshare.8136209.v1
title: Additional file 1: of Genome-wide association study identifies novel loci for type 2 diabetes-attributed end-stage kidney disease in African Americans

description: Figure S1. QQ plot of GWAS results of T2D-ESKD vs. non-diabetic non-nephropathy controls under baseline model. Figure S2. P value comparisons between baseline, APOL1-negative, and APOL1-adjusted models. Table S1. Study description. Table S2. Genome-wide significant variants associated with T2D-ESKD at Stage 2 Meta-analysis. Table S3. Discrimination analysis in 2756 T2D-lacking nephropathy individuals and 6977 controls for genome-wide significant T2D-ESKD-associated variants in baseline mode. Table S4. Discrimination analysis in 2756 T2D-lacking nephropathy individuals and 6977 controls for genome-wide significant T2D-ESKD-associated variants in APOL1-negative model. Table S5. Results of APOL1-negative model for top associations identified in baseline model. Table S6. (Excel). Functional annotations of genome-wide significant T2D-ESKD variants and proxies in linkage disequilibrium (r2â &gt;â 0.7). (ZIP 138 kb)
DOI: 10.6084/m9.figshare.8136173
title: MOESM5 of Accurate sequence variant genotyping in cattle using variation-aware genome graphs

description: Additional file 5. Twelve 1-Mb regions for which Graphtyper initially failed to genotype sequence variants because the algorithm either ran out of memory or exceeded the allocated runtime (12Â h). Graphtyper eventually produced genotypes for the sequence variants when these regions were re-run in 10-kb segments.
DOI: 10.6084/m9.figshare.8136173.v1
title: MOESM5 of Accurate sequence variant genotyping in cattle using variation-aware genome graphs

description: Additional file 5. Twelve 1-Mb regions for which Graphtyper initially failed to genotype sequence variants because the algorithm either ran out of memory or exceeded the allocated runtime (12Â h). Graphtyper eventually produced genotypes for the sequence variants when these regions were re-run in 10-kb segments.
DOI: 10.6084/m9.figshare.8136167
title: MOESM4 of Accurate sequence variant genotyping in cattle using variation-aware genome graphs

description: Additional file 4. Sequence variant genotyping quality for 18 and 31 animals that were sequenced at a lower and higher than 12-fold sequencing coverage, respectively. Asterisks denote significant differences (* P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001) with the best value (italic) for a respective parameter.
DOI: 10.6084/m9.figshare.8136167.v1
title: MOESM4 of Accurate sequence variant genotyping in cattle using variation-aware genome graphs

description: Additional file 4. Sequence variant genotyping quality for 18 and 31 animals that were sequenced at a lower and higher than 12-fold sequencing coverage, respectively. Asterisks denote significant differences (* P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001) with the best value (italic) for a respective parameter.
DOI: 10.6084/m9.figshare.8136062
title: Additional file 3: of Genetic diversity of avocado (Persea americana Mill.) germplasm using pooled sequencing

description: Table S3. A list of single nucleotide polymorphisms (SNPs) in avocado transcriptome. Header descriptions: Contig – mRNA contig ID; Position – SNP position; Ref allele – Allele call on the reference contig; Alt allele – Allele call which is different from the reference allele; Guatemalan coverage – Reads’ depth of the Guatemalan pool sequencing; Guatemalan alt prop – proportion of the alternative allele; Mexican coverage – Reads’ depth of the Mexican pool sequencing; Mexican coverage – Reads’ depth of the Mexican pool sequencing; WestIndian coverage – Reads’ depth of the WestIndian pool sequencing; WestIndian alt prop – proportion of the alternative allele; Guatemalan – Guatemalan polymorphism call; Mexican – Mexican polymorphism call; WestIndian – WestIndian polymorphism call; Contig sequence – Sequence of the contig; Polymorphism call description: AA – homozygous to reference allele; BB – homozygous to alternative allele; AB - heterozygous (XLSX 50424 kb)
DOI: 10.6084/m9.figshare.8136062.v1
title: Additional file 3: of Genetic diversity of avocado (Persea americana Mill.) germplasm using pooled sequencing

description: Table S3. A list of single nucleotide polymorphisms (SNPs) in avocado transcriptome. Header descriptions: Contig – mRNA contig ID; Position – SNP position; Ref allele – Allele call on the reference contig; Alt allele – Allele call which is different from the reference allele; Guatemalan coverage – Reads’ depth of the Guatemalan pool sequencing; Guatemalan alt prop – proportion of the alternative allele; Mexican coverage – Reads’ depth of the Mexican pool sequencing; Mexican coverage – Reads’ depth of the Mexican pool sequencing; WestIndian coverage – Reads’ depth of the WestIndian pool sequencing; WestIndian alt prop – proportion of the alternative allele; Guatemalan – Guatemalan polymorphism call; Mexican – Mexican polymorphism call; WestIndian – WestIndian polymorphism call; Contig sequence – Sequence of the contig; Polymorphism call description: AA – homozygous to reference allele; BB – homozygous to alternative allele; AB - heterozygous (XLSX 50424 kb)
DOI: 10.26180/5f44732b3dabf
title: Data_all_traits_meta-analysis_Figshare

description: Data associated with "Effects of immune challenge on expression of life-history and immune trait expression in sexually reproducing metazoans - a meta-analysis".
DOI: 10.6084/m9.figshare.11842185
title: Statistical analysis

description: Codes and outputs from the statistical analyses:correlation, discriminant analysis, and classification kmeans<br>
DOI: 10.6084/m9.figshare.11842185.v1
title: Statistical analysis

description: Codes and outputs from the statistical analyses:correlation, discriminant analysis, and classification kmeans<br>
DOI: 10.6084/m9.figshare.8135987
title: Additional file 1: of Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

description: Table S1. Bile acid deconjugation and transformation genes identified in the 693 analyzed genomes. Table S2. Description of the bile acid metabolism subsystem reconstructed for AGORA: a) metabolites, b) reactions. Table S3. Constraints implemented to simulate the Average European (AE) diet supplemented with taurocholate, glycocholate, taurochenodeoxycholate, and glycochenodeoxycholate. Table S4. Bile acid production potential for each of the 232 AGORA models carrying bile acid reactions. Table S5. Secondary bile acids produced in pairwise AGORA models that could not be produced individually by either model. Table S6. Bile acid production potential (mmol × person-1 × day-1) predicted for the community models representing 194 human microbiomes. Table S7. Features that significantly differed between 15 pediatric Crohn's Disease patients and 25 healthy controls. Table S9. Correlations between total community bile acid production (mmol × person-1 × day-1) and total community reaction abundance across all 194 community models. Table S10. Shadow prices in the flux balance solutions when optimizing for secondary bile acid production in all community models. Shown are metabolites that had a nonzero shadow price in at least one model. Table S11. Description of newly reconstructed strains in this study as an update to the AGORA resource. (XLSX 2194 kb)
DOI: 10.6084/m9.figshare.8135987.v1
title: Additional file 1: of Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

description: Table S1. Bile acid deconjugation and transformation genes identified in the 693 analyzed genomes. Table S2. Description of the bile acid metabolism subsystem reconstructed for AGORA: a) metabolites, b) reactions. Table S3. Constraints implemented to simulate the Average European (AE) diet supplemented with taurocholate, glycocholate, taurochenodeoxycholate, and glycochenodeoxycholate. Table S4. Bile acid production potential for each of the 232 AGORA models carrying bile acid reactions. Table S5. Secondary bile acids produced in pairwise AGORA models that could not be produced individually by either model. Table S6. Bile acid production potential (mmol × person-1 × day-1) predicted for the community models representing 194 human microbiomes. Table S7. Features that significantly differed between 15 pediatric Crohn's Disease patients and 25 healthy controls. Table S9. Correlations between total community bile acid production (mmol × person-1 × day-1) and total community reaction abundance across all 194 community models. Table S10. Shadow prices in the flux balance solutions when optimizing for secondary bile acid production in all community models. Shown are metabolites that had a nonzero shadow price in at least one model. Table S11. Description of newly reconstructed strains in this study as an update to the AGORA resource. (XLSX 2194 kb)
DOI: 10.6084/m9.figshare.12871460
title: Supplementary Material for: Glycosylation of Anti-Thyroglobulin IgG1 and IgG4 Subclasses in Thyroid Diseases

description: <b><i>Objective:</i></b> Thyroglobulin antibodies (TgAb), principally comprising immunoglobulin G (IgG), are frequently found in healthy individuals. Previously, we showed that the glycosylation levels of TgAb IgG differed across various thyroid diseases, suggesting an important role of glycosylation on antibodies in the pathogenesis of thyroid diseases. Since IgG1 and IgG4 are the primary TgAb IgG subclasses, this study aimed to investigate the glycosylation of TgAb IgG1 and IgG4 subclasses in thyroid diseases. <b><i>Methods:</i></b> TgAb IgG was purified by affinity chromatography from the serum of patients with Hashimoto’s thyroiditis (HT) (<i>n</i> = 16), Graves’ disease (GD) (<i>n</i> = 8), papillary thyroid carcinoma (PTC) (<i>n</i> = 6), and PTC with histological lymphocytic thyroiditis (PTC-T) (<i>n</i> = 9) as well as healthy donors (<i>n</i> = 10). TgAb IgG1 and IgG4 concentrations were determined by enzyme-linked immunosorbent assay, and a lectin microassay was used to assess TgAb IgG1 and IgG4 glycosylation. <b><i>Results:</i></b> Significantly elevated mannose, sialic acid, and galactose levels on TgAb IgG1 were found in HT and PTC patients compared to GD patients and healthy controls (all <i>p</i> &lt; 0.05). The mannose, sialic acid, and core fucose levels on TgAb IgG1 in PTC-T patients were higher than in healthy controls (all <i>p</i> &lt; 0.05). Additionally, TgAb IgG1 from PTC-T patients exhibited lower sialylation than that from patients with PTC and higher fucosylation than that from patients with HT (both <i>p</i> &lt; 0.05). However, TgAb IgG4 glycosylation did not differ among the five groups (<i>p</i> &lt; 0.05). <b><i>Conclusion:</i></b> Our study describes different distributions of TgAb IgG1 glycosylation in various thyroid diseases. The aberrantly increased glycosylation levels of TgAb IgG1 observed in HT, PTC, and PTC-T might be indicative of immune disorders and participate in the pathogenesis of these diseases.
DOI: 10.6084/m9.figshare.8135960
title: Additional file 9: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S7. Lists of genes in each group shown in Table 2. (XLSX 321 kb)
DOI: 10.6084/m9.figshare.11842176
title: Spatio-temporal metrics

description: Spatio-temporal metrics<br>Outputs from IndiFrag software
DOI: 10.6084/m9.figshare.8135960.v1
title: Additional file 9: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S7. Lists of genes in each group shown in Table 2. (XLSX 321 kb)
DOI: 10.6084/m9.figshare.8135957
title: Additional file 8: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S6. Known virulence genes. (XLSX 208 kb)
DOI: 10.6084/m9.figshare.11842176.v1
title: Spatio-temporal metrics

description: Spatio-temporal metrics<br>Outputs from IndiFrag software
DOI: 10.6084/m9.figshare.8135957.v1
title: Additional file 8: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S6. Known virulence genes. (XLSX 208 kb)
DOI: 10.6084/m9.figshare.8135954
title: Additional file 7: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S5. Significantly regulated genes, capsule expression and all expression data. (XLSX 2279 kb)
DOI: 10.6084/m9.figshare.8135954.v1
title: Additional file 7: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S5. Significantly regulated genes, capsule expression and all expression data. (XLSX 2279 kb)
DOI: 10.6084/m9.figshare.8135951
title: Additional file 6: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S4. Proteins detected in vitro and in vivo. (XLSX 1095 kb)
DOI: 10.6084/m9.figshare.8135951.v1
title: Additional file 6: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S4. Proteins detected in vitro and in vivo. (XLSX 1095 kb)
DOI: 10.6084/m9.figshare.8135945
title: Additional file 5: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S3. Transcripts detected in vitro and in vivo. (XLSX 2144 kb)
DOI: 10.6084/m9.figshare.8135945.v1
title: Additional file 5: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S3. Transcripts detected in vitro and in vivo. (XLSX 2144 kb)
DOI: 10.6084/m9.figshare.8135942
title: Additional file 4: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S2. Gene expression results and details of the genes subjected to RT-qPCR analysis. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8135942.v1
title: Additional file 4: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S2. Gene expression results and details of the genes subjected to RT-qPCR analysis. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8135933
title: Additional file 3: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S1. Details of B. thailandensis strain E555 genes and proteins. (XLSX 6051 kb)
DOI: 10.6084/m9.figshare.8135933.v1
title: Additional file 3: of Burkholderia thailandensis strain E555 is a surrogate for the investigation of Burkholderia pseudomallei replication and survival in macrophages

description: Table S1. Details of B. thailandensis strain E555 genes and proteins. (XLSX 6051 kb)
DOI: 10.34740/kaggle/dsv/1445192
title: COVID-19 cases by Continent
DOI: 10.6084/m9.figshare.11841729
title: Land use change model outputs

description: These folders need to be unzipped in a common folder in order to create a Grassdata directory (GRASS GIS Software)