cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25318/3310065601-fra
title: L’entreprise ou l’organisme prévoit déposer une demande de financement par emprunt, premier trimestre de 2023

description: L’entreprise ou l’organisme prévoit déposer une demande de financement par emprunt, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, premier trimestre de 2023.
DOI: 10.15468/dl.6yxv3b
title: Occurrence Download

description: A dataset containing 358 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Trichoderma reesei E.G.Simmons"
 ]
}

 The dataset includes 358 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000506-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13320304
title: Pettibone, 1982

description: Pettibone, M.H., 1982. Annelida. Synopsis and Classification of Living Organisms, Volume 2. https://repository.si.edu/bitstream/handle/10088/3418/Pettibone-1982-chapter.pdf
DOI: 10.5281/zenodo.13320305
title: Pettibone, 1982

description: Pettibone, M.H., 1982. Annelida. Synopsis and Classification of Living Organisms, Volume 2. https://repository.si.edu/bitstream/handle/10088/3418/Pettibone-1982-chapter.pdf
DOI: 10.25318/3310065701-eng
title: Ability for the business or organization to take on more debt, first quarter of 2023

description: Ability of the business or organization to take on more debt, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2023.
DOI: 10.15468/dl.6gtukj
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "OccurrenceStatus is Present",
 "TaxonKey is Bathynectes longispina Stimpson, 1871"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000508-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6k72pa
title: Occurrence Download

description: A dataset listing the 117 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is MarineGEO Global Seagrass Habitat Monitoring Data"
 ]
}

 The dataset's 117 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000503-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tx7yf7
title: Occurrence Download

description: A dataset containing 225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Rhodotorula toruloides (I.Banno) Q.M.Wang, F.Y.Bai, M.Groenew. & Boekhout"
 ]
}

 The dataset includes 225 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000509-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26655322
title: Additional file 14 of GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway

description: Additional file 14: Table S3. Primers of genes in this research for qRT‐PCR.
DOI: 10.6084/m9.figshare.26655322.v1
title: Additional file 14 of GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway

description: Additional file 14: Table S3. Primers of genes in this research for qRT‐PCR.
DOI: 10.6084/m9.figshare.26655325
title: Additional file 15 of GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway

description: Additional file 15: Table S4. details of primary antibodies applied in this study.
DOI: 10.6084/m9.figshare.26655325.v1
title: Additional file 15 of GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway

description: Additional file 15: Table S4. details of primary antibodies applied in this study.
DOI: 10.6084/m9.figshare.26655328
title: Additional file 16 of GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway

description: Additional file 16: Table S5. Sequence information used in this study.
DOI: 10.6084/m9.figshare.26655328.v1
title: Additional file 16 of GABA induced by sleep deprivation promotes the proliferation and migration of colon tumors through miR-223-3p endogenous pathway and exosome pathway

description: Additional file 16: Table S5. Sequence information used in this study.
DOI: 10.6084/m9.figshare.24864186
title: Additional file 1 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 1. Number of samples, predicted fusion transcripts and validated fusion transcripts.
DOI: 10.6084/m9.figshare.24864186.v1
title: Additional file 1 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 1. Number of samples, predicted fusion transcripts and validated fusion transcripts.
DOI: 10.6084/m9.figshare.24864275
title: Additional file 2 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 2. Validated fusion genes in TCGA samples.
DOI: 10.6084/m9.figshare.24864275.v1
title: Additional file 2 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 2. Validated fusion genes in TCGA samples.
DOI: 10.6084/m9.figshare.26655415
title: Additional file 3 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 3. Fusion transcript features used in machine learning for the FusionCatcher model.
DOI: 10.6084/m9.figshare.26655415.v1
title: Additional file 3 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 3. Fusion transcript features used in machine learning for the FusionCatcher model.
DOI: 10.6084/m9.figshare.26655418
title: Additional file 4 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 4. Performance metrics for machine learning-based filtering compared to classical filtering methods on fusions detected with FusionCatcher in an independent fusion dataset of 249 TNBC samples from the SCAN-B cohort.
DOI: 10.6084/m9.figshare.26655418.v1
title: Additional file 4 of Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification

description: Additional file 4. Performance metrics for machine learning-based filtering compared to classical filtering methods on fusions detected with FusionCatcher in an independent fusion dataset of 249 TNBC samples from the SCAN-B cohort.
DOI: 10.6084/m9.figshare.24864130
title: Additional file 1 of Can AMH levels predict the need to step up FSH dose for controlled ovarian stimulation following a long GnRH agonist protocol in PCOS women?

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.24864130.v1
title: Additional file 1 of Can AMH levels predict the need to step up FSH dose for controlled ovarian stimulation following a long GnRH agonist protocol in PCOS women?

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.24864075
title: Additional file 1 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 1: Additional file 1. Table S1. Genotype data (numeric) of the 204 chile pepper genotypes for 14,922 single nucleotide polymorphism (SNP) markers used for genomic prediction. Table S2. Genotype data (hapmap) of the 204 chile pepper genotypes for 14,922 single nucleotide polymorphism (SNP) markers used for genomic prediction. Table S3. Chile pepper genotypes used to perform cross-validations and genomewide selection using ridge regression and deep learning prediction models.
DOI: 10.6084/m9.figshare.24864075.v1
title: Additional file 1 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 1: Additional file 1. Table S1. Genotype data (numeric) of the 204 chile pepper genotypes for 14,922 single nucleotide polymorphism (SNP) markers used for genomic prediction. Table S2. Genotype data (hapmap) of the 204 chile pepper genotypes for 14,922 single nucleotide polymorphism (SNP) markers used for genomic prediction. Table S3. Chile pepper genotypes used to perform cross-validations and genomewide selection using ridge regression and deep learning prediction models.
DOI: 10.6084/m9.figshare.24864286
title: Additional file 2 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 2: Additional file 2. Table S1. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for first pod date. Table S2. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for flowering time. Table S3. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for mature green yield. Table S4. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for plant height. Table S5. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for plant width. Table S6. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for mature red yield. Table S7. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for ten pod weight. Table S8. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for total yield per plant.
DOI: 10.6084/m9.figshare.24864286.v1
title: Additional file 2 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 2: Additional file 2. Table S1. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for first pod date. Table S2. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for flowering time. Table S3. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for mature green yield. Table S4. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for plant height. Table S5. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for plant width. Table S6. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for mature red yield. Table S7. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for ten pod weight. Table S8. Genomic prediction accuracy across different ridge regression and deep learning models for 10 iterations and 10-fold cross validations for subset of markers derived from linkage disequilibrium (LD) coefficients and 14,922 SNP loci for total yield per plant.
DOI: 10.6084/m9.figshare.26655493
title: Additional file 3 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 3: Additional file 3. Table S1. Genotype data (numeric) of the 204 chile pepper genotypes for 7,690 linkage disequilibrium (LD)-derived single nucleotide polymorphism (SNP) markers used for genomic prediction.
DOI: 10.6084/m9.figshare.26655493.v1
title: Additional file 3 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 3: Additional file 3. Table S1. Genotype data (numeric) of the 204 chile pepper genotypes for 7,690 linkage disequilibrium (LD)-derived single nucleotide polymorphism (SNP) markers used for genomic prediction.
DOI: 10.6084/m9.figshare.26655499
title: Additional file 5 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 5: Additional file 5. Figure S1. Heat map of the genomic relationship (kinship) matrix of the 204 chile pepper (Capsicum spp.) genotypes derived from 14,922 SNP markers.
DOI: 10.6084/m9.figshare.26655499.v1
title: Additional file 5 of Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

description: Supplementary Material 5: Additional file 5. Figure S1. Heat map of the genomic relationship (kinship) matrix of the 204 chile pepper (Capsicum spp.) genotypes derived from 14,922 SNP markers.
DOI: 10.6084/m9.figshare.24864293
title: Additional file 2 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 2: Includes 1 table for cell line data sets used in the study. The name of the table is: Table S1. Somatic SNVs detecting by RESA to full-length scRNA-seq data from 15 datasets.
DOI: 10.6084/m9.figshare.24864293.v1
title: Additional file 2 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 2: Includes 1 table for cell line data sets used in the study. The name of the table is: Table S1. Somatic SNVs detecting by RESA to full-length scRNA-seq data from 15 datasets.
DOI: 10.6084/m9.figshare.26655577
title: Additional file 3 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 3: Includes 1 table of the annotation for cell line data sets used in the study. The name of the table is: Table S1. Annotation of each mutation in 15 cell line datasets detected by RESA.
DOI: 10.6084/m9.figshare.26655577.v1
title: Additional file 3 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 3: Includes 1 table of the annotation for cell line data sets used in the study. The name of the table is: Table S1. Annotation of each mutation in 15 cell line datasets detected by RESA.
DOI: 10.6084/m9.figshare.26655580
title: Additional file 4 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 4: Includes 1 table of the statistical summary for cell line data sets. The name of the table is: Table S1. Summary statistic table of mutations detected by RESA.
DOI: 10.6084/m9.figshare.26655580.v1
title: Additional file 4 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 4: Includes 1 table of the statistical summary for cell line data sets. The name of the table is: Table S1. Summary statistic table of mutations detected by RESA.
DOI: 10.6084/m9.figshare.26655583
title: Additional file 5 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 5: Includes 1 table for melanoma data sets. The name of the table is: Table S1. 575 unique somatic SNVs of scRNA-seq of the melanoma detected by RESA, which mapped to 524 genes.
DOI: 10.6084/m9.figshare.26655583.v1
title: Additional file 5 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 5: Includes 1 table for melanoma data sets. The name of the table is: Table S1. 575 unique somatic SNVs of scRNA-seq of the melanoma detected by RESA, which mapped to 524 genes.
DOI: 10.6084/m9.figshare.26655586
title: Additional file 6 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 6: Includes 1 table of the stage enrichment for melanoma data sets. The name of the table is: Table S1. P values of the Hypergeometric test for stage-specific somatic SNVs enrichment in each stage.
DOI: 10.6084/m9.figshare.26655586.v1
title: Additional file 6 of De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

description: Additional file 6: Includes 1 table of the stage enrichment for melanoma data sets. The name of the table is: Table S1. P values of the Hypergeometric test for stage-specific somatic SNVs enrichment in each stage.
DOI: 10.6084/m9.figshare.24864106
title: Additional file 1 of Quantitative thresholds for variant enrichment in 13,845 cases: improving pathogenicity classification in genetic hearing loss

description: Additional file 1: Table S1. List of 115 SNVs and three CNVs in SNPscan assay. Table S2. 157 hearing loss related genes included in the CDGC-HL panel. Table S3. 9,050 variants included in this study. Table S4. Allele frequency thresholds for variants could reach p-value&lt;0.05 with different case sample size. Table S5. Variants in truth subset 1. Table S6. Variants in truth subset 2. Table S7. Variants in truth subset 3. Table S8. Evaluation metrics and positive likelihood ratio with different cutoffs in Truth Subset 1 and Truth Subset 3. Table S9. Summary of variants meeting the proposed PS4 thresholds. Table S10. The clinical and genetic findings of the 8 patients with upgraded P/LP variants. Table S11. CNVplex results of F3:II-1. Table S12. Correlation between PS4 and PM3 in the truth subset 3.
DOI: 10.6084/m9.figshare.24864106.v1
title: Additional file 1 of Quantitative thresholds for variant enrichment in 13,845 cases: improving pathogenicity classification in genetic hearing loss

description: Additional file 1: Table S1. List of 115 SNVs and three CNVs in SNPscan assay. Table S2. 157 hearing loss related genes included in the CDGC-HL panel. Table S3. 9,050 variants included in this study. Table S4. Allele frequency thresholds for variants could reach p-value&lt;0.05 with different case sample size. Table S5. Variants in truth subset 1. Table S6. Variants in truth subset 2. Table S7. Variants in truth subset 3. Table S8. Evaluation metrics and positive likelihood ratio with different cutoffs in Truth Subset 1 and Truth Subset 3. Table S9. Summary of variants meeting the proposed PS4 thresholds. Table S10. The clinical and genetic findings of the 8 patients with upgraded P/LP variants. Table S11. CNVplex results of F3:II-1. Table S12. Correlation between PS4 and PM3 in the truth subset 3.
DOI: 10.6084/m9.figshare.24864296
title: Additional file 2 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

description: Additional file 2: Table S3. Differentially expressed genes identified by tradeSeq in MS dataset.
DOI: 10.6084/m9.figshare.24864296.v1
title: Additional file 2 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

description: Additional file 2: Table S3. Differentially expressed genes identified by tradeSeq in MS dataset.
DOI: 10.6084/m9.figshare.26655604
title: Additional file 3 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

description: Additional file 3: Table S4. Differentially expressed genes in transitioning cells in MS dataset.
DOI: 10.6084/m9.figshare.26655604.v1
title: Additional file 3 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

description: Additional file 3: Table S4. Differentially expressed genes in transitioning cells in MS dataset.
DOI: 10.6084/m9.figshare.26655607
title: Additional file 4 of scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles

description: Additional file 4: Table S5. Gene Ontology canonical pathways in transitioning cells in MS dataset.
DOI: 10.15468/dl.4s6yyp
title: Occurrence Download

description: A dataset containing 13908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juniperus thurifera L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 13908 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004442-240130105604617/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rqhwfv
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chuquiraga erinacea subsp. hystrix (Don) C.Ezcurra"
 ]
}

 The dataset includes 135 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107297-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.