cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13146842
title: SARS-CoV-2 not found in pressure injury exudates from COVID-19 patients

description: SARS-CoV-2 not found in pressure injury exudates from COVID-19 patients
DOI: 10.6084/m9.figshare.13147172
title: Additional file 1 of Mixture toxicity analysis in zebrafish embryo: a time and concentration resolved study on mixture effect predictivity

description: Additional file 1: TableS12_chemical_information.xlsx.
DOI: 10.6084/m9.figshare.13146842.v1
title: SARS-CoV-2 not found in pressure injury exudates from COVID-19 patients

description: SARS-CoV-2 not found in pressure injury exudates from COVID-19 patients
DOI: 10.6084/m9.figshare.13147178
title: Additional file 3 of Mixture toxicity analysis in zebrafish embryo: a time and concentration resolved study on mixture effect predictivity

description: Additional file 3: TableS13_toxicity_raw_data.xlsx.
DOI: 10.6084/m9.figshare.13147172.v1
title: Additional file 1 of Mixture toxicity analysis in zebrafish embryo: a time and concentration resolved study on mixture effect predictivity

description: Additional file 1: TableS12_chemical_information.xlsx.
DOI: 10.6084/m9.figshare.13147178.v1
title: Additional file 3 of Mixture toxicity analysis in zebrafish embryo: a time and concentration resolved study on mixture effect predictivity

description: Additional file 3: TableS13_toxicity_raw_data.xlsx.
DOI: 10.6084/m9.figshare.13115192
title: Additional file 1 of Association of TERT and DSP variants with microscopic polyangiitis and myeloperoxidase-ANCA positive vasculitis in a Japanese population: a genetic association study

description: Additional file 1: Supplementary Table S1. Power to detect associations under the sample size in this study.
DOI: 10.6084/m9.figshare.13147138.v1
title: Additional file 2 of How important are parents in the development of child anxiety and depression? A genomic analysis of parent-offspring trios in the Norwegian Mother Father and Child Cohort Study (MoBa)

description: Additional file 2. Code for running the Relatedness Disequilibrium Regression model.
DOI: 10.6084/m9.figshare.13115192.v1
title: Additional file 1 of Association of TERT and DSP variants with microscopic polyangiitis and myeloperoxidase-ANCA positive vasculitis in a Japanese population: a genetic association study

description: Additional file 1: Supplementary Table S1. Power to detect associations under the sample size in this study.
DOI: 10.6084/m9.figshare.13147193.v1
title: Additional file 2 of A feasibility pragmatic clinical trial of a primary care network exercise and education program for people with COPD

description: Additional file 2. PCN vs. Breathe Easy Data Set
DOI: 10.6084/m9.figshare.13147193
title: Additional file 2 of A feasibility pragmatic clinical trial of a primary care network exercise and education program for people with COPD

description: Additional file 2. PCN vs. Breathe Easy Data Set
DOI: 10.6084/m9.figshare.13147187.v1
title: Additional file 1 of A feasibility pragmatic clinical trial of a primary care network exercise and education program for people with COPD

description: Additional file 1. PCN data set
DOI: 10.6084/m9.figshare.13147204
title: Additional file 1 of RNA editing in cancer impacts mRNA abundance in immune response pathways

description: Additional file 1: Table S1. Primary tumor samples used in this study. Cancer types and the corresponding numbers of categorized E and M tumor samples analyzed in this study. Table S2. List of editing sites predicted to regulate host gene mRNA abundance. Editing-expression associations were supported by consistent expression changes upon ADAR KD in at least one cell line. editlevel_est represents editing level regression coefficient; adj_edit_pvalue is the adjusted p-value of the coefficient. Table S3. List of primers and siRNAs used in this study.
DOI: 10.6084/m9.figshare.13147211
title: Additional file 2 of Treatment monitoring of colorectal cancer by integrated analysis of plasma concentration and sequencing of circulating tumor DNA

description: Additional file 2: Supplementary Table S1. Genes included in the cfDNA assay. Supplementary Table S2. Clinical characteristics of CRC patients in the disease negative and positive group. Supplementary Table S3. The performance of the cfDNA test when applying different cutoff levels of allele frequency (AF). Supplementary Table S4. Variants detected in 1st cfDNA analysis. Supplementary Table S5. Performance of the cfDNA assay in 2nd and 3rd cfDNA analysis. Supplementary Table S6. Variants detected in 2nd cfDNA analysis. Supplementary Table S7. Variants detected in 3rd cfDNA analysis.
DOI: 10.6084/m9.figshare.13147187
title: Additional file 1 of A feasibility pragmatic clinical trial of a primary care network exercise and education program for people with COPD

description: Additional file 1. PCN data set
DOI: 10.6084/m9.figshare.13147211.v1
title: Additional file 2 of Treatment monitoring of colorectal cancer by integrated analysis of plasma concentration and sequencing of circulating tumor DNA

description: Additional file 2: Supplementary Table S1. Genes included in the cfDNA assay. Supplementary Table S2. Clinical characteristics of CRC patients in the disease negative and positive group. Supplementary Table S3. The performance of the cfDNA test when applying different cutoff levels of allele frequency (AF). Supplementary Table S4. Variants detected in 1st cfDNA analysis. Supplementary Table S5. Performance of the cfDNA assay in 2nd and 3rd cfDNA analysis. Supplementary Table S6. Variants detected in 2nd cfDNA analysis. Supplementary Table S7. Variants detected in 3rd cfDNA analysis.
DOI: 10.6084/m9.figshare.13147214
title: Additional file 3 of Treatment monitoring of colorectal cancer by integrated analysis of plasma concentration and sequencing of circulating tumor DNA

description: Additional file 3: Supplementary Figure S1. The number and timing of cfDNA analysis in 60 CRC patients. Supplementary Figure S2. Analytical pipeline of the cfDNA test.
DOI: 10.6084/m9.figshare.13147214.v1
title: Additional file 3 of Treatment monitoring of colorectal cancer by integrated analysis of plasma concentration and sequencing of circulating tumor DNA

description: Additional file 3: Supplementary Figure S1. The number and timing of cfDNA analysis in 60 CRC patients. Supplementary Figure S2. Analytical pipeline of the cfDNA test.
DOI: 10.6084/m9.figshare.13147204.v1
title: Additional file 1 of RNA editing in cancer impacts mRNA abundance in immune response pathways

description: Additional file 1: Table S1. Primary tumor samples used in this study. Cancer types and the corresponding numbers of categorized E and M tumor samples analyzed in this study. Table S2. List of editing sites predicted to regulate host gene mRNA abundance. Editing-expression associations were supported by consistent expression changes upon ADAR KD in at least one cell line. editlevel_est represents editing level regression coefficient; adj_edit_pvalue is the adjusted p-value of the coefficient. Table S3. List of primers and siRNAs used in this study.
DOI: 10.6084/m9.figshare.13147219
title: Additional file 1 of Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

description: Table S1. List of datasets used in this study (TCGA, external cancer genomic databases (ICGC, CPTAC, and MET500), and a cohort of patients treated with anti-PD1 from a published study). Table S2. List of cancer ID and description from TCGA included in this study. Table S3. List of the 160 genes from the T cell-inflamed expression signature. Table S4. List of mutated genes investigated in the individual cancer type analysis that are relatively enriched in non-T cell-inflamed relative to T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S5. List of mutated genes investigated in the individual cancer type analysis that are relatively enriched in T cell-inflamed relative to non-T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S6. List of mutated genes investigated in the pan-cancer analysis that are relatively enriched in non-T cell-inflamed relative to T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S7. List of mutated genes investigated in the pan-cancer analysis that are relatively enriched in T cell-inflamed relative to non-T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S8. Annotation of mutation effects (LOF, GOF, etc.) for NSSMs from genes associated with non-T cell-inflamed phenotype (n = 29, from Fig. 3a) and genes associated with the T cell-inflamed phenotype (n = 3, from Fig. 3b). Table S9. Distribution of tumor samples carrying NSSMs in genes associated with non-T cell-inflamed phenotype (n = 29, from Fig. 3a) or genes associated with the T cell-inflamed phenotype (n = 3, from Fig. 3b) by mutation effect categories (LOF, GOF, etc.). Table S10. List of 266 pathways predicted to be activated in non-T cell-inflamed relative to T cell-inflamed tumors. 1 = activated; 0 = not-activated. Table S11. RPPA validation of activated transcriptional programs at protein level. Table S12. List of genes carrying NSSMs enriched in non-T cell-inflamed tumors (n = 29, from Fig. 3a) or transcriptional programs activated in non-T cell-inflamed tumors across at least 4 cancer types (n = 31, from Fig. 4a) at per patient level. Table S13. Summary of samples carrying NSSMs enriched in non-T cell-inflamed tumors (n = 29, from Fig. 3a) or transcriptional programs activated in non-T cell-inflamed tumors (n = 266). Table S14. Heterogeneity analysis of the NSSM and pathway score distribution across different tumor types. Table S15. Linear regression model of the T cell-inflamed gene expression and NSSM or pathway scores. Table S16. List of drugs targeting relevant molecular mechanisms identified in our study from The Drug Gene Interaction Database (DGIdb). Table S17. Three-level literature review of the genes associated with the non-T cell-inflamed phenotype (n = 29, from Fig. 3a) and genes associated with the T cell-inflamed phenotype (n = 3, from Fig. 3b). (XLSX 22 mb)
DOI: 10.6084/m9.figshare.13147219.v1
title: Additional file 1 of Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

description: Table S1. List of datasets used in this study (TCGA, external cancer genomic databases (ICGC, CPTAC, and MET500), and a cohort of patients treated with anti-PD1 from a published study). Table S2. List of cancer ID and description from TCGA included in this study. Table S3. List of the 160 genes from the T cell-inflamed expression signature. Table S4. List of mutated genes investigated in the individual cancer type analysis that are relatively enriched in non-T cell-inflamed relative to T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S5. List of mutated genes investigated in the individual cancer type analysis that are relatively enriched in T cell-inflamed relative to non-T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S6. List of mutated genes investigated in the pan-cancer analysis that are relatively enriched in non-T cell-inflamed relative to T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S7. List of mutated genes investigated in the pan-cancer analysis that are relatively enriched in T cell-inflamed relative to non-T cell-inflamed tumors. All genes are shown without filtering by p-values. Table S8. Annotation of mutation effects (LOF, GOF, etc.) for NSSMs from genes associated with non-T cell-inflamed phenotype (n = 29, from Fig. 3a) and genes associated with the T cell-inflamed phenotype (n = 3, from Fig. 3b). Table S9. Distribution of tumor samples carrying NSSMs in genes associated with non-T cell-inflamed phenotype (n = 29, from Fig. 3a) or genes associated with the T cell-inflamed phenotype (n = 3, from Fig. 3b) by mutation effect categories (LOF, GOF, etc.). Table S10. List of 266 pathways predicted to be activated in non-T cell-inflamed relative to T cell-inflamed tumors. 1 = activated; 0 = not-activated. Table S11. RPPA validation of activated transcriptional programs at protein level. Table S12. List of genes carrying NSSMs enriched in non-T cell-inflamed tumors (n = 29, from Fig. 3a) or transcriptional programs activated in non-T cell-inflamed tumors across at least 4 cancer types (n = 31, from Fig. 4a) at per patient level. Table S13. Summary of samples carrying NSSMs enriched in non-T cell-inflamed tumors (n = 29, from Fig. 3a) or transcriptional programs activated in non-T cell-inflamed tumors (n = 266). Table S14. Heterogeneity analysis of the NSSM and pathway score distribution across different tumor types. Table S15. Linear regression model of the T cell-inflamed gene expression and NSSM or pathway scores. Table S16. List of drugs targeting relevant molecular mechanisms identified in our study from The Drug Gene Interaction Database (DGIdb). Table S17. Three-level literature review of the genes associated with the non-T cell-inflamed phenotype (n = 29, from Fig. 3a) and genes associated with the T cell-inflamed phenotype (n = 3, from Fig. 3b). (XLSX 22 mb)
DOI: 10.6084/m9.figshare.13147240
title: Additional file 4 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 4: Table S1. Data output quality list.
DOI: 10.6084/m9.figshare.13147243
title: Additional file 5 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 5: Table S2. Reads and reference genome comparison list.
DOI: 10.6084/m9.figshare.13147240.v1
title: Additional file 4 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 4: Table S1. Data output quality list.
DOI: 10.6084/m9.figshare.13147246.v1
title: Additional file 6 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 6: Table S3. GO enrichment of DEGs between CK and GA-treated hairy roots. (XLS 3958 kb)
DOI: 10.6084/m9.figshare.13147249
title: Additional file 7 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 7: Table S4. KEGG Pathwayenrichment of DEGs between CK and GA-treated hairy roots. (XLS 246 kb)
DOI: 10.6084/m9.figshare.13147249.v1
title: Additional file 7 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 7: Table S4. KEGG Pathwayenrichment of DEGs between CK and GA-treated hairy roots. (XLS 246 kb)
DOI: 10.6084/m9.figshare.13147243.v1
title: Additional file 5 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 5: Table S2. Reads and reference genome comparison list.
DOI: 10.6084/m9.figshare.13147246
title: Additional file 6 of Transcriptomic analysis reveals the GRAS family genes respond to gibberellin in Salvia miltiorrhiza hairy roots

description: Additional file 6: Table S3. GO enrichment of DEGs between CK and GA-treated hairy roots. (XLS 3958 kb)
DOI: 10.25910/q139-4d51
title: Claims Submitted to the Multilateral Development Bank Accountability Mechanisms – 1994-2020

description: The dataset represents a summary depiction of grievance cases brought to the Accountability Mechanisms (AMs) of the six Multilateral Development Banks (MDBs) from 1994 to 2018: The Asian Development Bank, the African Development Bank, the Inter-American Development Bank, the European Bank for Reconstruction and Development, the World Bank (IBRD/IDA) and the World Bank Group (IFC and MIGA). These Accountability Mechanisms were created to enable people adversely or potentially adversely affected by a project or program financed by the MDBs to take their concerns to the Banks for recourse. The Accountability Mechanisms generally do not stop the project or provide material reparations. They seek to stop or prevent harm and mitigate the negative aspects of a development project. The objective of the database is to be able to garner a quick qualitative snapshot of any given case, as well as to be able to aggregate quantitative data for each AM as a whole and to identify trends over time. Each of the grievance mechanisms has a specific name, which may have changed over time. It may also be comprised of more than one office with separate functions (i.e. consulting with affected people versus a compliance investigation). Most of the Accountability Mechanisms now have separate functions, with the consultation process also called problem solving. The compliance phase is to investigate whether the MDBs have complied with their environmental and social policies and whether this has led to harm. The Accountability Mechanisms are detailed below. The dataset includes cases received by the AMs from the beginning of each mechanism until 22 August 2020. The last update for cases is August 22 2020. Access note: Please notify Susan Park for any changes made to the data set. Contact email: susan.park@sydney.edu.au
DOI: 10.21227/dtxh-cc48
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 249,133 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.
DOI: 10.15472/gl8uox
title: Especies florísticas presentes en el humedal Costero Obregón, Guapi, Cauca - 2013

description: Los humedales costeros de agua dulce, albergan una vegetación especializada con adaptaciones morfológicas o fisiológicas, que les permiten crecer y sobrevivir exitosamente en este tipo de ambientes y además, estrategias reproductivas únicas para poder desarrollarse. La vegetación presente en estos favorecen el desarrollo de una rica biodiversidad local, constituida por organismos que trabajan de manera interrelacionada, en donde cada integrante es beneficiado por la simbiosis generada dando como resultado un ecosistema altamente productivo, el cual provee de alimento y protección a los organismos que lo integran, y gracias al ambiente que otorga, facilita la reproducción y multiplicación de los mismos. A pesar de la importancia que representa la flora en este tipo de ecosistemas existen muchos vacíos de información, los estudios encaminados al conocimiento de los humedales se centran específicamente en los interiores y en las presiones que han sufrido los manglares cercanos a estos ecosistemas y al reconocimiento que se le ha dado a los mismos en años recientes, dejando de lado la vegetación que se establece en los humedales costeros de agua dulce que reviste de gran importancia para el establecimiento de la biota que allí reside. Por lo anterior en este capítulo se presentan los resultados de la composición, estructura y análisis de la vegetación presente en el humedal Obregón, además del estado de conservación de la flora aquí establecida. Esperamos que con la información obtenida y puesta a disposición, se contribuya al conocimiento y a la divulgación de este particular ecosistema y de igual manera sirva de base a las autoridades ambientales y a la comunidad de esta importante región para tomar decisiones que contribuyan al manejo y conservación del humedal.
DOI: 10.5281/zenodo.4134099
title: Data for: Machine learning identifies robust matrisome markers and regulatory mechanisms in cancer

description: The expression and regulation of matrisome genes - the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors - is of paramount importance for the many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.
description: This study was supported by the Academy of Finland grant 329742
DOI: 10.5281/zenodo.4134098
title: Data for: Machine learning identifies robust matrisome markers and regulatory mechanisms in cancer

description: The expression and regulation of matrisome genes - the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors - is of paramount importance for the many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.
description: This study was supported by the Academy of Finland grant 329742
DOI: 10.6084/m9.figshare.13147138
title: Additional file 2 of How important are parents in the development of child anxiety and depression? A genomic analysis of parent-offspring trios in the Norwegian Mother Father and Child Cohort Study (MoBa)

description: Additional file 2. Code for running the Relatedness Disequilibrium Regression model.