cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30325800
title: Additional file 11 of Integrated fibre-specific methylome and proteome profiling of human skeletal muscle across males and females with fibre-type deconvolution

description: Additional file 11. Sex differences analysis results.
DOI: 10.6084/m9.figshare.30325800.v1
title: Additional file 11 of Integrated fibre-specific methylome and proteome profiling of human skeletal muscle across males and females with fibre-type deconvolution

description: Additional file 11. Sex differences analysis results.
DOI: 10.6084/m9.figshare.30325801
title: Additional file 1 of A genome-wide investigation of insidious uveitis in Appaloosa horses

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.30325801.v1
title: Additional file 1 of A genome-wide investigation of insidious uveitis in Appaloosa horses

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.30325504.v23
title: Prince Abubakar Audu University 2025/2026 Supplementary Form/Direct-Entry Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department (+2347076040542) now to apply and for mode of making school fees, Tuitions, any for more information +2347076040542. This is direct from the admin department

description: Prince Abubakar Audu University 2025/2026 Supplementary Form/Direct-Entry Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department (+2347076040542) now to apply and for mode of making school fees, Tuitions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325504.v22
title: Adekunle Ajasin University, Akungba (AAUA) 2025/2026 Supplementary Form/Direct-Entry Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department (+2347076040542) now to apply and for mode of making school fees, Tuitions, any for more information +2347076040542. This is direct from the admin department

description: Adekunle Ajasin University, Akungba (AAUA) 2025/2026 Supplementary Form/Direct-Entry Form Call on 07076040542 to apply and For Admission Process on how to be admitted and mode of payment of School Fee And Acceptance Fee Contact school Admin Department (+2347076040542) now to apply and for mode of making school fees, Tuitions, any for more information +2347076040542. This is direct from the admin department
DOI: 10.6084/m9.figshare.30325827
title: Additional file 6 of Single-cell RNA sequencing reveals that Danggui Buxue Tang decoction facilitates wound healing after anal fistula by promoting M2 macrophage polarization

description: Supplementary Material 6: Supplementary Table 1. Differential gene expression analysis between each cluster and all other clusters.
DOI: 10.6084/m9.figshare.30325827.v1
title: Additional file 6 of Single-cell RNA sequencing reveals that Danggui Buxue Tang decoction facilitates wound healing after anal fistula by promoting M2 macrophage polarization

description: Supplementary Material 6: Supplementary Table 1. Differential gene expression analysis between each cluster and all other clusters.
DOI: 10.6084/m9.figshare.30325830
title: Additional file 7 of Single-cell RNA sequencing reveals that Danggui Buxue Tang decoction facilitates wound healing after anal fistula by promoting M2 macrophage polarization

description: Supplementary Material 7: Supplementary Table 2. Functional enrichment analysis on DEGs between Macrophage_C1_Areg (M1-like (M1C1)) and other macrophages clusters.
DOI: 10.6084/m9.figshare.30325830.v1
title: Additional file 7 of Single-cell RNA sequencing reveals that Danggui Buxue Tang decoction facilitates wound healing after anal fistula by promoting M2 macrophage polarization

description: Supplementary Material 7: Supplementary Table 2. Functional enrichment analysis on DEGs between Macrophage_C1_Areg (M1-like (M1C1)) and other macrophages clusters.
DOI: 10.6084/m9.figshare.30325833
title: Additional file 8 of Single-cell RNA sequencing reveals that Danggui Buxue Tang decoction facilitates wound healing after anal fistula by promoting M2 macrophage polarization

description: Supplementary Material 8: Supplementary Table 3. Functional enrichment analysis on DEGs between Macrophage_C3_Cd163 (M2-like (M2C3)) and other macrophages clusters.
DOI: 10.6084/m9.figshare.30325833.v1
title: Additional file 8 of Single-cell RNA sequencing reveals that Danggui Buxue Tang decoction facilitates wound healing after anal fistula by promoting M2 macrophage polarization

description: Supplementary Material 8: Supplementary Table 3. Functional enrichment analysis on DEGs between Macrophage_C3_Cd163 (M2-like (M2C3)) and other macrophages clusters.
DOI: 10.6084/m9.figshare.30325852
title: Additional file 2 of Multi-omics insights into microbiome-rumen epithelium interaction mechanisms underlying subacute rumen acidosis tolerance in dairy goats

description: Additional file 2: Table S1 the contribution analysis of functions and microbiota. Table S2 The KEGG pathways enriched by differential genes from HGW-SARA vs HGW-Health goats and HGW-SARA vs HGC-Health goats. Table S3 The number of cells of each type in rumen epithelium in each group of dairy goats. Table S4 The quantity and ratio of basal spinous and granule cells. Table S5 The differential compounds in the rumen between HGW-SARA and LGW-CON goats. Table S6 The correlation between microbiome upregulated in HGW-Health and metabolites related with metabolism. TableS7 The differential compounds participated in Tryptophan metabolism in the rumen of HGW-SARA vs H + S and HGW-SARA vs S + H goats. Table S8 Ingredients and nutrient composition of the low and high-concentrate diet on a dry matter (DM) basis. Table S9 The ingredient list of Reinforced Clostridium Medium (RCM). Table S10 The primers used for relative real-time PCR.
DOI: 10.6084/m9.figshare.30325852.v1
title: Additional file 2 of Multi-omics insights into microbiome-rumen epithelium interaction mechanisms underlying subacute rumen acidosis tolerance in dairy goats

description: Additional file 2: Table S1 the contribution analysis of functions and microbiota. Table S2 The KEGG pathways enriched by differential genes from HGW-SARA vs HGW-Health goats and HGW-SARA vs HGC-Health goats. Table S3 The number of cells of each type in rumen epithelium in each group of dairy goats. Table S4 The quantity and ratio of basal spinous and granule cells. Table S5 The differential compounds in the rumen between HGW-SARA and LGW-CON goats. Table S6 The correlation between microbiome upregulated in HGW-Health and metabolites related with metabolism. TableS7 The differential compounds participated in Tryptophan metabolism in the rumen of HGW-SARA vs H + S and HGW-SARA vs S + H goats. Table S8 Ingredients and nutrient composition of the low and high-concentrate diet on a dry matter (DM) basis. Table S9 The ingredient list of Reinforced Clostridium Medium (RCM). Table S10 The primers used for relative real-time PCR.
DOI: 10.6084/m9.figshare.29999194
title: Additional file 1 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.29999194.v1
title: Additional file 1 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.29999197
title: Additional file 2 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.29999197.v1
title: Additional file 2 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.30325861
title: Additional file 3 of Complex structural variant visualization with SVTopo

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.30325861.v1
title: Additional file 3 of Complex structural variant visualization with SVTopo

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.29999200
title: Additional file 3 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.29999200.v1
title: Additional file 3 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.29999203
title: Additional file 4 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.29999203.v1
title: Additional file 4 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.29999206
title: Additional file 5 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.29999206.v1
title: Additional file 5 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.29999209
title: Additional file 6 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.29999209.v1
title: Additional file 6 of Large-scale CSF proteome profiling identifies biomarkers for accurate diagnosis of frontotemporal dementia

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.30325866
title: Additional file 1 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.30325866.v1
title: Additional file 1 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.30325869
title: Additional file 2 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.30325869.v1
title: Additional file 2 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.30325872
title: Additional file 3 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.30325872.v1
title: Additional file 3 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.30325875
title: Additional file 4 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 4.
DOI: 10.6084/m9.figshare.30325875.v1
title: Additional file 4 of Identification and characterization of the gene OvANS associated with purple flower in Orychophragmus violaceus

description: Supplementary Material 4.
DOI: 10.57760/sciencedb.29581
title: Synthesis and characterization of divalent ion-modified ZnF2–AlF3-based glass for mid-infrared fiber applications
title: Synthesis and characterization of divalent ion-modified ZnF2–AlF3-based glass for mid-infrared fiber applications

description: Data of Synthesis and characterization of divalent ion-modified ZnF2–AlF3-based glass for mid-infrared fiber applications
description: Data of Synthesis and characterization of divalent ion-modified ZnF2–AlF3-based glass for mid-infrared fiber applications
DOI: 10.5281/zenodo.17309459
title: Nitrogen addition enhances phytolith-occluded carbon stabilization across particulate and mineral-associated organic matter fractions in a meadow steppe
title: Data
DOI: 10.5281/zenodo.17309458
title: Nitrogen addition enhances phytolith-occluded carbon stabilization across particulate and mineral-associated organic matter fractions in a meadow steppe
title: Data
DOI: 10.57760/sciencedb.xbyxb.00106
title: Microglial Polarization in Retinal Neovascularization: Friend or Foe?
title: Microglial Polarization in Retinal Neovascularization: Friend or Foe?

description: All images in this dataset were originally created by the author using Adobe Illustrator. The dataset was generated through a digital illustration process, where each figure was manually drawn and designed to visually represent key conceptual models, biological pathways, and schematic diagrams relevant to the research topic. The creation process involved iterative drafting and revision to ensure accuracy and clarity in scientific communication. No real-world spatial or temporal information is associated with these images, as they are conceptual representations rather than experimental or observational data. The dataset does not contain tabular data, numerical records, or measured values, and therefore issues such as data missingness, numerical inaccuracy，or resolution specifications are not applicable. All final illustrations are provided in high-resolution vector (.ai, .eps) and raster (.png, .tiff) formats, ensuring compatibility with common visualization software. Adobe Illustrator is the primary software used for both creation and editing; it is commercially available through the official Adobe website (https://www.adobe.com/).
description: All images in this dataset were originally created by the author using Adobe Illustrator. The dataset was generated through a digital illustration process, where each figure was manually drawn and designed to visually represent key conceptual models, biological pathways, and schematic diagrams relevant to the research topic. The creation process involved iterative drafting and revision to ensure accuracy and clarity in scientific communication. No real-world spatial or temporal information is associated with these images, as they are conceptual representations rather than experimental or observational data. The dataset does not contain tabular data, numerical records, or measured values, and therefore issues such as data missingness, numerical inaccuracy，or resolution specifications are not applicable. All final illustrations are provided in high-resolution vector (.ai, .eps) and raster (.png, .tiff) formats, ensuring compatibility with common visualization software. Adobe Illustrator is the primary software used for both creation and editing; it is commercially available through the official Adobe website (https://www.adobe.com/).
DOI: 10.6084/m9.figshare.30325887
title: Additional file 2 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 2: GWAS regions. This file contains the significant regions from the LF GWAS analysis, including the prioritised genes based on the different selection methods and the top SNP for each region.
DOI: 10.6084/m9.figshare.30325887.v1
title: Additional file 2 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 2: GWAS regions. This file contains the significant regions from the LF GWAS analysis, including the prioritised genes based on the different selection methods and the top SNP for each region.
DOI: 10.6084/m9.figshare.30325890
title: Additional file 3 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 3: Conditional SNPs. This file contains the conditionally independent genetic variants associated to the LFs; these were identified using a chromosome-wide stepwise conditional-joint analysis implemented in the GCTA software (as described in the manuscript).
DOI: 10.6084/m9.figshare.30325890.v1
title: Additional file 3 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 3: Conditional SNPs. This file contains the conditionally independent genetic variants associated to the LFs; these were identified using a chromosome-wide stepwise conditional-joint analysis implemented in the GCTA software (as described in the manuscript).
DOI: 10.6084/m9.figshare.30325893
title: Additional file 4 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 4: GWAS regions tECG missed by LF. This file contains the significant regions from the traditional ECG parameter GWAS which were not identified by the LFs, including the.
DOI: 10.6084/m9.figshare.30325893.v1
title: Additional file 4 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 4: GWAS regions tECG missed by LF. This file contains the significant regions from the traditional ECG parameter GWAS which were not identified by the LFs, including the.
DOI: 10.6084/m9.figshare.30325896
title: Additional file 5 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 5: GWAS catalog. This file describes the groupings of the GWAS catalog traits and diseases into categories and groups, which were used to generate Fig. 3 in the Manuscript.
DOI: 10.6084/m9.figshare.30325896.v1
title: Additional file 5 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 5: GWAS catalog. This file describes the groupings of the GWAS catalog traits and diseases into categories and groups, which were used to generate Fig. 3 in the Manuscript.
DOI: 10.6084/m9.figshare.30325899
title: Additional file 6 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 6: Lead SNPs. This file contains the lead SNPs for each of the LF GWAS.
DOI: 10.6084/m9.figshare.30325899.v1
title: Additional file 6 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 6: Lead SNPs. This file contains the lead SNPs for each of the LF GWAS.
DOI: 10.6084/m9.figshare.30325902
title: Additional file 7 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 7: Polyfun likely causal variants: this file contains the likely causal variants for each genomic region associated with the LFs in the GWAS analysis. These variants were used for downstream annotation with the nearest protein-coding gene and gene prioritisation.
DOI: 10.6084/m9.figshare.30325902.v1
title: Additional file 7 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 7: Polyfun likely causal variants: this file contains the likely causal variants for each genomic region associated with the LFs in the GWAS analysis. These variants were used for downstream annotation with the nearest protein-coding gene and gene prioritisation.
DOI: 10.6084/m9.figshare.30325905
title: Additional file 8 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 8: Polyfun likely causal variants FDR. This file contains the additional likely causal variants for the genomic regions selected at the relaxed significance threshold of 5% FDR.
DOI: 10.6084/m9.figshare.30325905.v1
title: Additional file 8 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 8: Polyfun likely causal variants FDR. This file contains the additional likely causal variants for the genomic regions selected at the relaxed significance threshold of 5% FDR.
DOI: 10.6084/m9.figshare.30325908
title: Additional file 9 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 9: GWAS regions FDR. This file contains the significant regions from the LF GWAS analysis at the relaxed significance threshold of % FDR, including the prioritised genes based on the different selection methods and the top SNP for each region.
DOI: 10.6084/m9.figshare.30325908.v1
title: Additional file 9 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 9: GWAS regions FDR. This file contains the significant regions from the LF GWAS analysis at the relaxed significance threshold of % FDR, including the prioritised genes based on the different selection methods and the top SNP for each region.
DOI: 10.6084/m9.figshare.30325911
title: Additional file 10 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 10: This file contains the code used to generate this paper.
DOI: 10.6084/m9.figshare.30325911.v1
title: Additional file 10 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

description: Additional file 10: This file contains the code used to generate this paper.
DOI: 10.57760/sciencedb.29589
title: ATAC-seq and RNA-seq analyses of PEDV-infected porcine small intestine
title: ATAC-seq and RNA-seq analyses of PEDV-infected porcine small intestine

description: ATAC-seq and RNA-seq analyses of PEDV-infected porcine small intestine
description: ATAC-seq and RNA-seq analyses of PEDV-infected porcine small intestine
DOI: 10.57760/sciencedb.27917
title: Road data for China
title: Road data for China

description: The road datasets for China from the years 2000 and 2020 encompass roads at the county level and above, which include expressways, national highways, provincial roads, and county roads. Additionally, the 2035 road planning dataset for China includes county-level and higher roads in five provinces: Guangdong, Zhejiang, Gansu, Sichuan, and Jilin.
description: The road datasets for China from the years 2000 and 2020 encompass roads at the county level and above, which include expressways, national highways, provincial roads, and county roads. Additionally, the 2035 road planning dataset for China includes county-level and higher roads in five provinces: Guangdong, Zhejiang, Gansu, Sichuan, and Jilin.
DOI: 10.6084/m9.figshare.25530441
title: Additional file 2 of CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny

description: Additional file 2. Tukey Honestly Significant Difference Test of Detected Antibodies per Patient Donor.
DOI: 10.6084/m9.figshare.25530441.v1
title: Additional file 2 of CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny

description: Additional file 2. Tukey Honestly Significant Difference Test of Detected Antibodies per Patient Donor.
DOI: 10.6084/m9.figshare.30326008
title: Additional file 2 of A morphological and molecular study of phlebotomine sand flies of Taiwan reveals the record of six species

description: Additional file 2.