cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28406321
title: Additional file 9 of Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

description: Supplementary Material 9. Table S1: Overview of cancer datasets used in this study. (A) Breast and pancreatic cancer datasets used in this study. (B) Citations of breast cancer GEO accessions used in this study. Table S2: Gene modules characterized in this study. (A) WGCNA-generated COL10A1 (ColX) modules from TCGA breast and pancreatic cancer datasets, as well as gender-specific pancreatic cancer datasets. (B) WGCNA module assignments for all expressed genes in each dataset. Modules are numbered in descending order of size, beginning with module 1. “-” indicates genes which were filtered out based on low expression; genes labeled with a 0 were not assigned to any module by WGCNA. Table S3: Gene ontology and Reactome pathway enrichment analysis of ColX modules. (A–E) Gene ontology pathway enrichment for (A) breast cancer, (B) pancreatic cancer, (C) overlap between breast and pancreatic cancer, (D) male pancreatic cancer, and (E) female pancreatic cancer ColX modules. (F–J) Reactome pathway enrichment for (F) breast cancer, (G) pancreatic cancer, (H) overlap between breast and pancreatic cancer, (I) male pancreatic cancer, and (J) female pancreatic cancer ColX modules. All significantly enriched Reactome pathways/GO terms (q &lt; 0.05) are shown. Table S4: Hallmark pathway enrichment analysis of WGCNA modules. (A–D) Hallmark pathway enrichment for (A) breast cancer, (B) pancreatic cancer, (C) male pancreatic cancer, and (D) female pancreatic cancer ColX modules. All p-values shown were BH-corrected across all 50 hallmark pathways for each module. ColX modules for each dataset (#8, #13, #7, and #23, respectively) are shown in the first column for clarity. Related to Figure S2A–D. Table S5: TFs implicated in ColX modules. (A) GTRD TFTs enriched in BRCA and PAAD ColX modules. Cancer-relevant TF functions and overlapping targets are shown where applicable. (Related to Figure 2E) (B) Selected QuSAGE-significant transcription factors whose targets are differentially activated/inactivated between tumors with high and low ColX module expression. Cancer/OA-relevant TF functions are shown where applicable. (Related to Figure 3C). Table S6: Differential expression of bone marrow and cartilage cell type-specific genes. List of all genes defined as specific to normal cartilage stromal cells/NCSCs, OA mesenchymal stromal cells/OA-MSCs, OA chondrocytes/OACs, and bone marrow stromal cells/BMSCs, based on OA RNA-Seq data. Differential expression results were computed across all cells and statistics were extracted for each pairwise comparison. See Methods section for definition of “cell type-specific” genes.
DOI: 10.6084/m9.figshare.28406321.v1
title: Additional file 9 of Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

description: Supplementary Material 9. Table S1: Overview of cancer datasets used in this study. (A) Breast and pancreatic cancer datasets used in this study. (B) Citations of breast cancer GEO accessions used in this study. Table S2: Gene modules characterized in this study. (A) WGCNA-generated COL10A1 (ColX) modules from TCGA breast and pancreatic cancer datasets, as well as gender-specific pancreatic cancer datasets. (B) WGCNA module assignments for all expressed genes in each dataset. Modules are numbered in descending order of size, beginning with module 1. “-” indicates genes which were filtered out based on low expression; genes labeled with a 0 were not assigned to any module by WGCNA. Table S3: Gene ontology and Reactome pathway enrichment analysis of ColX modules. (A–E) Gene ontology pathway enrichment for (A) breast cancer, (B) pancreatic cancer, (C) overlap between breast and pancreatic cancer, (D) male pancreatic cancer, and (E) female pancreatic cancer ColX modules. (F–J) Reactome pathway enrichment for (F) breast cancer, (G) pancreatic cancer, (H) overlap between breast and pancreatic cancer, (I) male pancreatic cancer, and (J) female pancreatic cancer ColX modules. All significantly enriched Reactome pathways/GO terms (q &lt; 0.05) are shown. Table S4: Hallmark pathway enrichment analysis of WGCNA modules. (A–D) Hallmark pathway enrichment for (A) breast cancer, (B) pancreatic cancer, (C) male pancreatic cancer, and (D) female pancreatic cancer ColX modules. All p-values shown were BH-corrected across all 50 hallmark pathways for each module. ColX modules for each dataset (#8, #13, #7, and #23, respectively) are shown in the first column for clarity. Related to Figure S2A–D. Table S5: TFs implicated in ColX modules. (A) GTRD TFTs enriched in BRCA and PAAD ColX modules. Cancer-relevant TF functions and overlapping targets are shown where applicable. (Related to Figure 2E) (B) Selected QuSAGE-significant transcription factors whose targets are differentially activated/inactivated between tumors with high and low ColX module expression. Cancer/OA-relevant TF functions are shown where applicable. (Related to Figure 3C). Table S6: Differential expression of bone marrow and cartilage cell type-specific genes. List of all genes defined as specific to normal cartilage stromal cells/NCSCs, OA mesenchymal stromal cells/OA-MSCs, OA chondrocytes/OACs, and bone marrow stromal cells/BMSCs, based on OA RNA-Seq data. Differential expression results were computed across all cells and statistics were extracted for each pairwise comparison. See Methods section for definition of “cell type-specific” genes.
DOI: 10.6084/m9.figshare.28406345
title: Additional file 1 of Causal roles of immune cells and metabolites in chronic pancreatitis: a mendelian randomization study

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.28406345.v1
title: Additional file 1 of Causal roles of immune cells and metabolites in chronic pancreatitis: a mendelian randomization study

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.28406348
title: Additional file 2 of Causal roles of immune cells and metabolites in chronic pancreatitis: a mendelian randomization study

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.28406348.v1
title: Additional file 2 of Causal roles of immune cells and metabolites in chronic pancreatitis: a mendelian randomization study

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.28406311
title: Additional file 2 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 2: Table S2: Many Diagnoses are Significantly and Robustly Associated with Indicated PTBs vs. Term Births. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406311.v1
title: Additional file 2 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 2: Table S2: Many Diagnoses are Significantly and Robustly Associated with Indicated PTBs vs. Term Births. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406314
title: Additional file 3 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 3: Table S3: Many Diagnoses are Significantly and Robustly Associated with All PTBs vs. Term Births. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406314.v1
title: Additional file 3 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 3: Table S3: Many Diagnoses are Significantly and Robustly Associated with All PTBs vs. Term Births. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406317
title: Additional file 4 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 4: Table S4: No Diagnoses are Significantly Associated with Spontaneous PTBs vs. Term Births. The most significant (adjusted p-value &lt; 0.5) diagnoses' odds ratios, p-values, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406317.v1
title: Additional file 4 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 4: Table S4: No Diagnoses are Significantly Associated with Spontaneous PTBs vs. Term Births. The most significant (adjusted p-value &lt; 0.5) diagnoses' odds ratios, p-values, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406320.v1
title: Additional file 5 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 5: Table S5: Many Diagnoses are Significantly and Robustly Associated with All PTBs vs. Term Births, After Including Individuals with No Pre-Conception Diagnoses. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406320
title: Additional file 5 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 5: Table S5: Many Diagnoses are Significantly and Robustly Associated with All PTBs vs. Term Births, After Including Individuals with No Pre-Conception Diagnoses. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406323
title: Additional file 6 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 6: Table S6: Many Diagnoses are Significantly and Robustly Associated with Indicated PTBs vs. Term Births, After Including Individuals with No Pre-Conception Diagnoses. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406323.v1
title: Additional file 6 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 6: Table S6: Many Diagnoses are Significantly and Robustly Associated with Indicated PTBs vs. Term Births, After Including Individuals with No Pre-Conception Diagnoses. The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406326
title: Additional file 7 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 7: Table S7: No Diagnoses are Significantly Associated with Spontaneous PTBs vs. Term Births, After Including Individuals with No Pre-Conception Diagnoses. The most significant (adjusted p-value &lt; 0.5) diagnoses' odds ratios, p-values, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406326.v1
title: Additional file 7 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 7: Table S7: No Diagnoses are Significantly Associated with Spontaneous PTBs vs. Term Births, After Including Individuals with No Pre-Conception Diagnoses. The most significant (adjusted p-value &lt; 0.5) diagnoses' odds ratios, p-values, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406329
title: Additional file 8 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 8: Table S8: Removing Covariates from our Analysis Results in More Associations between Diagnoses and Indicated PTBs. vs. Term Births The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406329.v1
title: Additional file 8 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 8: Table S8: Removing Covariates from our Analysis Results in More Associations between Diagnoses and Indicated PTBs. vs. Term Births The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406332
title: Additional file 9 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 9: Table S9: No Diagnoses are Significantly Associated with Spontaneous PTBs vs. Term Births, After Removing Covariates. The most significant (adjusted p-value &lt; 0.5) diagnoses' odds ratios, p-values, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406332.v1
title: Additional file 9 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 9: Table S9: No Diagnoses are Significantly Associated with Spontaneous PTBs vs. Term Births, After Removing Covariates. The most significant (adjusted p-value &lt; 0.5) diagnoses' odds ratios, p-values, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406335
title: Additional file 10 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 10: Table S10: Removing Covariates from our Analysis Results in More Associations between Diagnoses and All PTBs vs. Term Births.The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406335.v1
title: Additional file 10 of Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort

description: Supplementary Material 10: Table S10: Removing Covariates from our Analysis Results in More Associations between Diagnoses and All PTBs vs. Term Births.The most significant diagnoses' odds ratios, p-values, robustness percentages, and number of individuals are listed.
DOI: 10.6084/m9.figshare.28406363
title: Additional file 1 of Health inequality and improvement gap in the prevalence of gynecological cancers among perimenopausal women globally, 1990–2019

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.28406363.v1
title: Additional file 1 of Health inequality and improvement gap in the prevalence of gynecological cancers among perimenopausal women globally, 1990–2019

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.28406383
title: Additional file 2 of Haematology dimension reduction, a large scale application to regular care haematology data

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.28406383.v1
title: Additional file 2 of Haematology dimension reduction, a large scale application to regular care haematology data

description: Supplementary Material 2.
DOI: 10.34740/kaggle/dsv/10737530
title: Layline Shareholder activism dataset
DOI: 10.6084/m9.figshare.26770774
title: Predictive Approach for DSPL under Multi-Source Uncertainty

description: This paper proposes a predictive approach to consider the impact of feature uncertainty in the feature model based on multi-sources uncertainty in order to perform decision making for dynamic re-configurations in Dynamic Software Product Line (DSPL)
DOI: 10.6084/m9.figshare.26770774.v9
title: Predictive Approach for DSPL under Multi-Source Uncertainty

description: This paper proposes a predictive approach to consider the impact of feature uncertainty in the feature model based on multi-sources uncertainty in order to perform decision making for dynamic re-configurations in Dynamic Software Product Line (DSPL)
DOI: 10.6084/m9.figshare.26770774.v8
title: Predictive Approach for DSPL under Multi-Source Uncertainty

description: This paper proposes a predictive approach to consider the impact of feature uncertainty in the feature model based on multi-sources uncertainty in order to perform decision making for dynamic re-configurations in Dynamic Software Product Line (DSPL)
DOI: 10.5281/zenodo.14862625
title: Global Synthetic Biology Market 2025 To 2034

description: Synthetic Biology Market Size, Trends and Insights By Tool (Oligonucleotides and Synthetic DNA, Enzymes, Cloning Technologies Kits, Synthetic Cells, Chassis Organisms, Xeno-Nucleic Acids), By Application (Medical Application, Pharmaceuticals, Drug Discovery and Therapeutics, Artificial Tissue and Tissue Regeneration, Industrial Applications, Food & Agriculture, Environmental Applications), By Technology (Gene Synthesis, Genome Engineering, Sequencing, Bioinformatics, Site-directed Mutagenesis, Cloning, Measurement and Modelling, Microfluidics, Nanotechnology), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

Global Synthetic Biology Market size is calculated at USD 28.23 Billion in 2025 and is expected to reach around USD 150.93 Billion by 2034, growing at a CAGR of 25% over the forecast period 2025 – 2034.

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DOI: 10.5281/zenodo.14862626
title: Global Synthetic Biology Market 2025 To 2034

description: Synthetic Biology Market Size, Trends and Insights By Tool (Oligonucleotides and Synthetic DNA, Enzymes, Cloning Technologies Kits, Synthetic Cells, Chassis Organisms, Xeno-Nucleic Acids), By Application (Medical Application, Pharmaceuticals, Drug Discovery and Therapeutics, Artificial Tissue and Tissue Regeneration, Industrial Applications, Food & Agriculture, Environmental Applications), By Technology (Gene Synthesis, Genome Engineering, Sequencing, Bioinformatics, Site-directed Mutagenesis, Cloning, Measurement and Modelling, Microfluidics, Nanotechnology), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

Global Synthetic Biology Market size is calculated at USD 28.23 Billion in 2025 and is expected to reach around USD 150.93 Billion by 2034, growing at a CAGR of 25% over the forecast period 2025 – 2034.

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DOI: 10.26180/28406183
title: Data for Wild Sorghum species exhibit greater drought tolerance but less plasticity than domesticated sorghum

description: Dataset associated with the manuscript "Wild Sorghum species exhibit greater drought tolerance but less plasticity than domesticated sorghum"Two accessions of wild Sorghum plumosum, Sorghum stipoideum and Sorghum timorense were grown alongside a domesticated Sorghum bicolor line under well-watered or drought conditions for four weeks. Biomass, root:shoot ratio, chlorophyll a:b ratio, and concentrations of chlorophyll, phenolics and cyanogenic glucosides, were measured. Soil volumetric water content (VWC) of each pot was measured twice weekly.
DOI: 10.26180/28406183.v1
title: Data for Wild Sorghum species exhibit greater drought tolerance but less plasticity than domesticated sorghum

description: Dataset associated with the manuscript "Wild Sorghum species exhibit greater drought tolerance but less plasticity than domesticated sorghum"Two accessions of wild Sorghum plumosum, Sorghum stipoideum and Sorghum timorense were grown alongside a domesticated Sorghum bicolor line under well-watered or drought conditions for four weeks. Biomass, root:shoot ratio, chlorophyll a:b ratio, and concentrations of chlorophyll, phenolics and cyanogenic glucosides, were measured. Soil volumetric water content (VWC) of each pot was measured twice weekly.
DOI: 10.34740/kaggle/dsv/10737577
title: Hinduphobic COVID-19 X (Twitter) Dataset
DOI: 10.6084/m9.figshare.28380497
title: 通过社交媒体和街景图像分析检查街景视觉效果和情感反应

description: 该数据集适用于题为“通过社交媒体和街景图像分析检查街景视觉和情感反应”的手稿，其中包括两个名为 Street View Images 和 Weibo 的文件夹。街景图像文件夹包括语义分割代码、原始街景图像文件夹和语义分割后的街景图像文件夹。Weibo 文件夹包括使用“北山街”作为关键字和位于北山街的历史建筑、餐馆和酒店名称作为关键字的微博搜索结果。
DOI: 10.6084/m9.figshare.28380497.v2
title: 通过社交媒体和街景图像分析检查街景视觉效果和情感反应

description: 该数据集适用于题为“通过社交媒体和街景图像分析检查街景视觉和情感反应”的手稿，其中包括两个名为 Street View Images 和 Weibo 的文件夹。街景图像文件夹包括语义分割代码、原始街景图像文件夹和语义分割后的街景图像文件夹。Weibo 文件夹包括使用“北山街”作为关键字和位于北山街的历史建筑、餐馆和酒店名称作为关键字的微博搜索结果。
DOI: 10.6084/m9.figshare.28380497.v1
title: Examining Streetscape Visuals and Emotional Responses Through Social Media and Street View Image Analysis

description: This dataset is for the manuscript titled “Examining Streetscape Visuals and Emotional Responses Through Social Media and Street View Image Analysis”, which includes two folders named Street View Images and Weibo. The Street View Image folder includes semantic segmentation code, the original Street View Image folder, and the Street View Image folder after semantic segmentation. The Weibo folder includes the results of Weibo searches using “Beishan Street” as the keyword and the names of historical buildings, restaurants, and hotels located on Beishan Street as the keyword.
DOI: 10.5281/zenodo.14862653
title: Global Lithium-Sulfur Battery Market 2025 To 2034

description: Lithium-Sulfur Battery Market Size, Trends and Insights By Component (Anode, Cathode, Electrolyte, Separator, Others), By Battery Capacity (Low Capacity, Medium Capacity, High Capacity), By End-Use Industry (Automotive, Consumer Electronics, Energy Storage Systems, Aerospace and Defense, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the global Lithium-Sulfur Battery Market is expected to record a CAGR of 38.2% from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD 90.1 Million. By 2033, the valuation is anticipated to reach USD 1,654.5 Million.

The Lithium-Sulfur Battery Market encompasses the global trade of advanced rechargeable battery systems utilizing lithium metal and sulfur as primary components. These batteries offer high energy density, lightweight construction, and potential for cost-effectiveness compared to traditional lithium-ion batteries.

Key applications include electric vehicles, portable electronics, grid energy storage, and aerospace. Market growth is fueled by increasing demand for sustainable energy solutions, advancements in battery technology, and government initiatives promoting clean transportation.

Key players in the market engage in research, development, and commercialization efforts to capitalize on emerging opportunities and address challenges related to performance, safety, and scalability.

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