cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13544584.v1
title: Additional file 1 of Comparing the European Union System for the Evaluation of Substances (EUSES) environmental exposure calculations with monitoring data for alkyl sulphate surfactants

description: Additional file 1. ∑ C12, C14, C16 homologues (tonnages; Clocaleffluent concentrations).
DOI: 10.6084/m9.figshare.13544584
title: Additional file 1 of Comparing the European Union System for the Evaluation of Substances (EUSES) environmental exposure calculations with monitoring data for alkyl sulphate surfactants

description: Additional file 1. ∑ C12, C14, C16 homologues (tonnages; Clocaleffluent concentrations).
DOI: 10.6084/m9.figshare.13544771
title: Additional file 1 of Reliability and validity of the Persian version of 5-D itching scale among patients with chronic kidney disease

description: Additional file 1.
DOI: 10.6084/m9.figshare.13544771.v1
title: Additional file 1 of Reliability and validity of the Persian version of 5-D itching scale among patients with chronic kidney disease

description: Additional file 1.
DOI: 10.6084/m9.figshare.13544840.v1
title: Additional file 2 of The oncometabolite R-2-hydroxyglutarate dysregulates the differentiation of human mesenchymal stromal cells via inducing DNA hypermethylation

description: Additional file 2.
DOI: 10.6084/m9.figshare.13544840
title: Additional file 2 of The oncometabolite R-2-hydroxyglutarate dysregulates the differentiation of human mesenchymal stromal cells via inducing DNA hypermethylation

description: Additional file 2.
DOI: 10.25584/dh-172021/1756093
title: CESM1-MAM7 Simulations
DOI: 10.6084/m9.figshare.13545148
title: Additional file 5 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 5. Predicted mediators of JunD influence on CRC progression. Table 1. contains the model-predicted targets of JunD (top 500 RPOR scores in down-analysis) that were differentially expressed upon knock-down of JunD (p-value &lt; 0.05), and were differentially expressed between stages (p-value &lt; 0.05). Table 2. genes have the same characteristics as Table 1 genes except for being predicted as JunD targets in up-analysis.
DOI: 10.6084/m9.figshare.13545142.v1
title: Additional file 3 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 3. Annotated list of genes with SNVs that exhibited significant shifts in alternative allele frequency between M0 and M6.
DOI: 10.6084/m9.figshare.13545139.v1
title: Additional file 2 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 2. Gene set characterization. Gene set characterization of the DE genes between early and late stages. The downregulated genes are significantly enriched for cancer-related gene signatures from mSigDB.
DOI: 10.6084/m9.figshare.13545142
title: Additional file 3 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 3. Annotated list of genes with SNVs that exhibited significant shifts in alternative allele frequency between M0 and M6.
DOI: 10.6084/m9.figshare.13545145
title: Additional file 4 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 4: Table S1. List of HCT116 TFs and their corresponding files obtained from ENCODE portal ( https://www.encodeproject.org/ ). Table S2. Interpretations of the different combinations of signs of TF and histone mark change weights. Table S3. Expression changes in AP1-complex components upon JunD Knockdown. Table S4. Survival analysis on TCGA COAD cohort. Survival analysis of 374 patients was performed using different gene sets for clustering of omics profiles. Each row indicates the criterion used to pick the gene set (gene set type) and the setting used in the best clustering of patients in terms of survival analysis p-value (pval). The gene set was chosen to be of one of the following: 1. Using the data of all genes provided (Unfiltered). 2. Genes with the highest RPOR value in down-analysis (RPOR_down). 3. Genes with the highest RPOR value in up-analysis (RPOR_up). 4. Significantly differentially expressed genes (DE Genes). For all types of gene sets except “Unfiltered” sizes of 50, 70, and 100 were tested. To find the best clustering for the patients using each gene set, two types of networks, viz., protein-protein interaction network (PPI) and Enrichr pathway network were tested using the knowledge-guided clustering pipeline in KnowEnG; two different settings of smoothing (0.3 and 0.8), representing different levels of network-guidance, were explored. (Knowledge-guided clustering was applied only to the sparse somatic mutation data, while other types of omics profiles were clustered in the default mode.). Cluster sizes of 3 and 4 were tested. The total number of clusterings explored and followed by survival analysis, for RPOR-down and RPOR-up combined, DE Genes, and Unfiltered, are 48, 24, and 8 respectively, which were used to adjust the p-values by Bonferroni Correction (adj. p-val). Table S5. List of K562 TFs and their corresponding files obtained from ENCODE portal ( https://www.encodeproject.org/ ).
DOI: 10.6084/m9.figshare.13545145.v1
title: Additional file 4 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 4: Table S1. List of HCT116 TFs and their corresponding files obtained from ENCODE portal ( https://www.encodeproject.org/ ). Table S2. Interpretations of the different combinations of signs of TF and histone mark change weights. Table S3. Expression changes in AP1-complex components upon JunD Knockdown. Table S4. Survival analysis on TCGA COAD cohort. Survival analysis of 374 patients was performed using different gene sets for clustering of omics profiles. Each row indicates the criterion used to pick the gene set (gene set type) and the setting used in the best clustering of patients in terms of survival analysis p-value (pval). The gene set was chosen to be of one of the following: 1. Using the data of all genes provided (Unfiltered). 2. Genes with the highest RPOR value in down-analysis (RPOR_down). 3. Genes with the highest RPOR value in up-analysis (RPOR_up). 4. Significantly differentially expressed genes (DE Genes). For all types of gene sets except “Unfiltered” sizes of 50, 70, and 100 were tested. To find the best clustering for the patients using each gene set, two types of networks, viz., protein-protein interaction network (PPI) and Enrichr pathway network were tested using the knowledge-guided clustering pipeline in KnowEnG; two different settings of smoothing (0.3 and 0.8), representing different levels of network-guidance, were explored. (Knowledge-guided clustering was applied only to the sparse somatic mutation data, while other types of omics profiles were clustered in the default mode.). Cluster sizes of 3 and 4 were tested. The total number of clusterings explored and followed by survival analysis, for RPOR-down and RPOR-up combined, DE Genes, and Unfiltered, are 48, 24, and 8 respectively, which were used to adjust the p-values by Bonferroni Correction (adj. p-val). Table S5. List of K562 TFs and their corresponding files obtained from ENCODE portal ( https://www.encodeproject.org/ ).
DOI: 10.6084/m9.figshare.13545139
title: Additional file 2 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 2. Gene set characterization. Gene set characterization of the DE genes between early and late stages. The downregulated genes are significantly enriched for cancer-related gene signatures from mSigDB.
DOI: 10.6084/m9.figshare.13545148.v1
title: Additional file 5 of An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

description: Additional file 5. Predicted mediators of JunD influence on CRC progression. Table 1. contains the model-predicted targets of JunD (top 500 RPOR scores in down-analysis) that were differentially expressed upon knock-down of JunD (p-value &lt; 0.05), and were differentially expressed between stages (p-value &lt; 0.05). Table 2. genes have the same characteristics as Table 1 genes except for being predicted as JunD targets in up-analysis.
DOI: 10.6084/m9.figshare.13545211
title: Additional file 2 of Genomic Regions Involved in Differences in Eating and Cooking Quality Other than Wx and Alk Genes between indica and japonica Rice Cultivars

description: Additional file 2: Table S1. Graphical genotypes of eight CSSLs in the ‘Koshihikari’ genetic background. Table S2. Eating quality traits and agronomic traits of 41 CSSLs in the ‘Koshihikari’ genetic background in 2016. Table S3. Eating quality traits and agronomic traits of 41 CSSLs in the ‘Koshihikari’ genetic background in 2017. Table S4. Eating quality traits and agronomic traits of 39 CSSLs in the ‘Takanari’ genetic background in 2018. Table S5. Eating quality traits and agronomic traits of eight CSSLs carrying chromosome segments from five indica rice cultivars in the ‘Koshihikari’ genetic background in 2018.
DOI: 10.6084/m9.figshare.13545211.v1
title: Additional file 2 of Genomic Regions Involved in Differences in Eating and Cooking Quality Other than Wx and Alk Genes between indica and japonica Rice Cultivars

description: Additional file 2: Table S1. Graphical genotypes of eight CSSLs in the ‘Koshihikari’ genetic background. Table S2. Eating quality traits and agronomic traits of 41 CSSLs in the ‘Koshihikari’ genetic background in 2016. Table S3. Eating quality traits and agronomic traits of 41 CSSLs in the ‘Koshihikari’ genetic background in 2017. Table S4. Eating quality traits and agronomic traits of 39 CSSLs in the ‘Takanari’ genetic background in 2018. Table S5. Eating quality traits and agronomic traits of eight CSSLs carrying chromosome segments from five indica rice cultivars in the ‘Koshihikari’ genetic background in 2018.
DOI: 10.6084/m9.figshare.13545368
title: Additional file 6 of Factors influencing the implementation of screening and brief interventions for alcohol use in primary care practices: a systematic review using the COM-B system and Theoretical Domains Framework

description: Additional file 6. Facilitators linked to the identified themes of barriers. This file details the facilitators extracted after full-text analysis.
DOI: 10.6084/m9.figshare.13545368.v1
title: Additional file 6 of Factors influencing the implementation of screening and brief interventions for alcohol use in primary care practices: a systematic review using the COM-B system and Theoretical Domains Framework

description: Additional file 6. Facilitators linked to the identified themes of barriers. This file details the facilitators extracted after full-text analysis.
DOI: 10.6084/m9.figshare.13545365.v1
title: Additional file 5 of Factors influencing the implementation of screening and brief interventions for alcohol use in primary care practices: a systematic review using the COM-B system and Theoretical Domains Framework

description: Additional file 5. Title of data: Themes of barriers within each of the components of the COM-B system and domains of the Theoretical Domains Framework. This file details the barriers extracted after full-text analysis.
DOI: 10.6084/m9.figshare.13545365
title: Additional file 5 of Factors influencing the implementation of screening and brief interventions for alcohol use in primary care practices: a systematic review using the COM-B system and Theoretical Domains Framework

description: Additional file 5. Title of data: Themes of barriers within each of the components of the COM-B system and domains of the Theoretical Domains Framework. This file details the barriers extracted after full-text analysis.
DOI: 10.6084/m9.figshare.13545376
title: Additional file 2 of Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon

description: Additional file 2: Table S1. Contaminating features removed from the ASV table.
DOI: 10.6084/m9.figshare.13545376.v1
title: Additional file 2 of Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon

description: Additional file 2: Table S1. Contaminating features removed from the ASV table.
DOI: 10.6084/m9.figshare.13545379
title: Additional file 3 of Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon

description: Additional file 3: Table S2. The prevalence of core ASVs in different sample types.
DOI: 10.6084/m9.figshare.13545379.v1
title: Additional file 3 of Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon

description: Additional file 3: Table S2. The prevalence of core ASVs in different sample types.
DOI: 10.6084/m9.figshare.13545535
title: Additional file 2 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 2: Table S2. Spearman’s rank correlation coefficients and adjusted P values.
DOI: 10.6084/m9.figshare.13545532.v1
title: Additional file 1 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 1: Table S1. Counts per million reads of 15 small RNA libraries.
DOI: 10.6084/m9.figshare.13545532
title: Additional file 1 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 1: Table S1. Counts per million reads of 15 small RNA libraries.
DOI: 10.6084/m9.figshare.13545538.v1
title: Additional file 3 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 3: Table S3. Coordinates (i.e. correlation) of miRNA expression on the first five principal components. Table S4. Quality of representation of each miRNA (expressed as a squared cosine) for each of the first five principal components. Table S5. Contribution of 85 miRNAs to the first five principal components. Table S6. The amount of variation explained by each of the 14 principal components expressed as eigenvalue, percentage of total variance and cumulative percentage. Table S7. MiRNAs whose expression levels significantly correlated with at least one of the first principal components.
DOI: 10.6084/m9.figshare.13545541
title: Additional file 4 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 4: Table S8. Potential target genes of differentially expressed miRNAs. Table S9. GO terms for the target genes of differentially expressed miRNAs. Table S10. Potential target genes of regression predictor miRNAs. Table S11. GO terms for the potential target genes of the regression predictor miRNAs.
DOI: 10.6084/m9.figshare.13545538
title: Additional file 3 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 3: Table S3. Coordinates (i.e. correlation) of miRNA expression on the first five principal components. Table S4. Quality of representation of each miRNA (expressed as a squared cosine) for each of the first five principal components. Table S5. Contribution of 85 miRNAs to the first five principal components. Table S6. The amount of variation explained by each of the 14 principal components expressed as eigenvalue, percentage of total variance and cumulative percentage. Table S7. MiRNAs whose expression levels significantly correlated with at least one of the first principal components.
DOI: 10.6084/m9.figshare.13545541.v1
title: Additional file 4 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 4: Table S8. Potential target genes of differentially expressed miRNAs. Table S9. GO terms for the target genes of differentially expressed miRNAs. Table S10. Potential target genes of regression predictor miRNAs. Table S11. GO terms for the potential target genes of the regression predictor miRNAs.
DOI: 10.6084/m9.figshare.13545535.v1
title: Additional file 2 of The micro-RNA content of unsorted cryopreserved bovine sperm and its relation to the fertility of sperm after sex-sorting

description: Additional file 2: Table S2. Spearman’s rank correlation coefficients and adjusted P values.
DOI: 10.6084/m9.figshare.13535207
title: Additional file 1 of Improved selection of participants in genetic longevity studies: family scores revisited

description: Additional file 1.
DOI: 10.6084/m9.figshare.13535207.v1
title: Additional file 1 of Improved selection of participants in genetic longevity studies: family scores revisited

description: Additional file 1.
DOI: 10.6084/m9.figshare.13545649
title: Additional file 1 of Evaluation of the potential of Pap test fluid and cervical swabs to serve as clinical diagnostic biospecimens for the detection of ovarian cancer by mass spectrometry-based proteomics

description: Additional file 1: Supplemental Excel file. Lists of the proteins identified in each subset of the Venn diagram (Fig 2). Each tab shows the Protein accession numbers, Entry, Entry name, Protein names, and Gene names for proteins that were identified in each sample set: (i) 158 unique to the Pap and Swab samples, (ii) 186 unique to the Pap and Tumor samples, (iii) 1423 unique to the Swab and Tumor samples, (iv) 2293 present in the Pap, Swab and Tumor samples, (v) 64 unique to the Pap test sample, (vi) 320 unique to the Swab sample, and (vii) 490 unique to the Tumor sample.
DOI: 10.6084/m9.figshare.13545649.v1
title: Additional file 1 of Evaluation of the potential of Pap test fluid and cervical swabs to serve as clinical diagnostic biospecimens for the detection of ovarian cancer by mass spectrometry-based proteomics

description: Additional file 1: Supplemental Excel file. Lists of the proteins identified in each subset of the Venn diagram (Fig 2). Each tab shows the Protein accession numbers, Entry, Entry name, Protein names, and Gene names for proteins that were identified in each sample set: (i) 158 unique to the Pap and Swab samples, (ii) 186 unique to the Pap and Tumor samples, (iii) 1423 unique to the Swab and Tumor samples, (iv) 2293 present in the Pap, Swab and Tumor samples, (v) 64 unique to the Pap test sample, (vi) 320 unique to the Swab sample, and (vii) 490 unique to the Tumor sample.
DOI: 10.6084/m9.figshare.13545646
title: Additional file 1 of Whole genomic comparative analysis of Streptococcus pneumoniae serotype 1 isolates causing invasive and non-invasive infections among children under 5 years in Casablanca, Morocco

description: Additional file 1.
DOI: 10.6084/m9.figshare.13545646.v1
title: Additional file 1 of Whole genomic comparative analysis of Streptococcus pneumoniae serotype 1 isolates causing invasive and non-invasive infections among children under 5 years in Casablanca, Morocco

description: Additional file 1.
DOI: 10.6084/m9.figshare.13545676
title: Additional file 1 of Does burnout affect clinical reasoning? An observational study among residents in general practice

description: Additional file 1. All data of participants including MBI-HSS responses, SCT scores, and descriptive statistical analysis.
DOI: 10.6084/m9.figshare.13545755
title: Additional file 1 of Trimester effects of source-specific PM10 on birth weight outcomes in the Avon Longitudinal Study of Parents and Children (ALSPAC)

description: Additional file 1.
DOI: 10.6084/m9.figshare.13545755.v1
title: Additional file 1 of Trimester effects of source-specific PM10 on birth weight outcomes in the Avon Longitudinal Study of Parents and Children (ALSPAC)

description: Additional file 1.
DOI: 10.6084/m9.figshare.13545836.v1
title: Additional file 6 of Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

description: Additional file 6.
DOI: 10.6084/m9.figshare.13545833
title: Additional file 5 of Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

description: Additional file 5.
DOI: 10.6084/m9.figshare.13545836
title: Additional file 6 of Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

description: Additional file 6.
DOI: 10.6084/m9.figshare.13545833.v1
title: Additional file 5 of Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

description: Additional file 5.
DOI: 10.6084/m9.figshare.13545839
title: Additional file 7 of Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

description: Additional file 7.
DOI: 10.6084/m9.figshare.13545839.v1
title: Additional file 7 of Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

description: Additional file 7.
DOI: 10.6084/m9.figshare.13545848
title: Dataset - All are equal before the law: comparison between students who were admitted through regular overall competition or social quota systems in a public Medical School in Brazil

description: The files represent the raw data collected in this research.
DOI: 10.6084/m9.figshare.13545848.v1
title: Dataset - All are equal before the law: comparison between students who were admitted through regular overall competition or social quota systems in a public Medical School in Brazil

description: The files represent the raw data collected in this research.
DOI: 10.25912/rdf_1610082905190
title: Survey data from online experiment testing correctness of bird sound descriptions against bird calls
DOI: 10.21227/b0rm-dq07
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.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 294,491 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.5281/zenodo.4426825
title: Machine learning reactive mixing dataset-12

description: Reactive-mixing dataset-3.3 for machine learning analyses
description: For DIB article