cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0194302.10fu
title: SH0194302.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0194303.10fu
title: SH0194303.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0194304.10fu
title: SH0194304.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0194305.10fu
title: SH0194305.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0194306.10fu
title: SH0194306.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0194307.10fu
title: SH0194307.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0194308.10fu
title: SH0194308.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.ymfbc3
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chiropetalum ramboi (Allem & Irgang) Radcl.-Sm. & Govaerts"
 ]
}

 The dataset includes 11 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003515-171113114016250/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hhaqsz
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Observation)",
 "Continent is North America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Geophis semidoliatus (Duméril, Bibron & Duméril, 1854)",
 "Year 1998-2023"
 ]
}

 The dataset includes 18 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035103-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84ivmo
title: Occurrence Download

description: A dataset containing 643937 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-74.827880859375 4.390228926463396, -74.827880859375 7.27529233637217, -71.707763671875 7.27529233637217, -71.707763671875 4.390228926463396, -74.827880859375 4.390228926463396))"
 ]
}

 The dataset includes 643937 records from 455 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003515-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f38ygw
title: Occurrence Download

description: A dataset containing 2329 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((2.479608666666667 65.07073733333334, 2.479608666666667 82.15736799999999, -31.565747 82.15736799999999, -31.565747 65.07073733333334, 2.479608666666667 65.07073733333334))",
 "EventDate is 2020-09-01,2020-09-30"
 ]
}

 The dataset includes 2329 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030297-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zkow1i
title: Occurrence Download

description: A dataset containing 17272 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Netta rufina (Pallas, 1773)"
 ]
}

 The dataset includes 17272 records from 178 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003516-150316153013904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tw88ta
title: Occurrence Download

description: A dataset containing 263 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ehrharta ramosa Thunb."
 ]
}

 The dataset includes 263 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049316-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.86n5sr
title: Occurrence Download

description: A dataset containing 595866 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Betula pendula Roth)",
 "HasCoordinate is true",
 "Geometry POLYGON ((25.280527 74.385866, 34 74.516506, 34 35.773364, 32.861584 35.588421, 26.375071 34.609982, 22.816449 34.504241, 13.627633 36.690862, 10.001544 38.719154, -0.356862 36.404548, -9.323257 35.627079, -12.292937 57.408982, 25.280527 74.385866))"
 ]
}

 The dataset includes 595866 records from 1874 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049311-231002084531237/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.26626540.v1
title: Additional file 6 of Identify GADD45G as a potential target of 4-methoxydalbergione in treatment of liver cancer: bioinformatics analysis and in vivo experiment

description: Additional file 6: Table S5. List of KEGG category analysis of GADD45G co-expression genes.
DOI: 10.6084/m9.figshare.24309854
title: Additional file 1 of Therapeutic impact of epidermal growth factor receptor tyrosine kinase inhibitor with various treatment combinations for advanced lung adenocarcinoma

description: Additional file 1. Surgical patients with T and N stage.
DOI: 10.6084/m9.figshare.24309854.v1
title: Additional file 1 of Therapeutic impact of epidermal growth factor receptor tyrosine kinase inhibitor with various treatment combinations for advanced lung adenocarcinoma

description: Additional file 1. Surgical patients with T and N stage.
DOI: 10.6084/m9.figshare.26626546
title: Additional file 3 of Identifying quantitatively differential chromosomal compartmentalization changes and their biological significance from Hi-C data using DARIC

description: Additional file 3: Table 2. Analyzed Hi-C datasets from the 4DN consortium.
DOI: 10.6084/m9.figshare.26626546.v1
title: Additional file 3 of Identifying quantitatively differential chromosomal compartmentalization changes and their biological significance from Hi-C data using DARIC

description: Additional file 3: Table 2. Analyzed Hi-C datasets from the 4DN consortium.
DOI: 10.1594/pangaea.701615
title: Basic measurements of radiation at station Chesapeake Light (2007-08)
DOI: 10.6084/m9.figshare.24309905
title: Additional file 1 of MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

description: Additional file 1: Fig. S1. Resolving shortest paths with loops in GFA. Green segment is the start and end of the loop. 1. Loop that begins and ends on the different sides of the start-segment. Resolved by generating two paths (A,B,C,D) and (A,D,C,B). Note that the sequence direction of A differs in two paths. 2. Loop that begins and ends on the same end of the start-segment. Resolved similar to Loop 1, but the direction of A is identical in both paths. 3. Hairpin loop with repeated segments A, B and C. Resolved by creating two paths (A,B,C,D,E,F) and (A,B,C,F,E,D). 4. Hairpin loop with different start- (A) and end- (H) segments. Resolved by removing all path data (G and H) after the repeated segment (C), reducing the problem to the hairpin loop in example 3 with the same solutions: (A,B,C,D,E,F) and (A,B,C,F,E,D). Fig. S2. Example of the evaluation of homology matches. The seed segments of ARG1 and ARG2 both match a reference genome at the same position, indicating they refer to the same ARG. The position of segment 4 in the reference genome does not align with the expected distance from the ARG as represented in the GFA of ARG 1 suggesting it likely represents a false positive match, and therefore will be excluded from further analysis. Fig. S3. Bacteria associated with the 6 bacteria used in validation. This heatmap shows which bacterial sequences (both genome or plasmid) also tend to score high when the known presence is one of the 6 used in validation. It reflects the uncertainty that comes with bacterial calling in metagenomics. Fig. S4. MGS2AMR run time and memory usage for 5 benchmarking samples. All tools were allowed to use up to 8 CPUs. The numbers 1 through 5 refer to the file ID in Table S3. The four main pipeline steps are denoted as follows: A. MetaCherchant (existing tool). B. The MetaCherchant output pre-processing for BLAST (novel R scripts). C. BLAST+ (existing tool) D. ARG annotation (novel R scripts). Note that the large leap in memory for BLASTn is nearly entirely explained by having to load the nucleotide database into memory (~150 GB).
DOI: 10.6084/m9.figshare.24309905.v1
title: Additional file 1 of MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

description: Additional file 1: Fig. S1. Resolving shortest paths with loops in GFA. Green segment is the start and end of the loop. 1. Loop that begins and ends on the different sides of the start-segment. Resolved by generating two paths (A,B,C,D) and (A,D,C,B). Note that the sequence direction of A differs in two paths. 2. Loop that begins and ends on the same end of the start-segment. Resolved similar to Loop 1, but the direction of A is identical in both paths. 3. Hairpin loop with repeated segments A, B and C. Resolved by creating two paths (A,B,C,D,E,F) and (A,B,C,F,E,D). 4. Hairpin loop with different start- (A) and end- (H) segments. Resolved by removing all path data (G and H) after the repeated segment (C), reducing the problem to the hairpin loop in example 3 with the same solutions: (A,B,C,D,E,F) and (A,B,C,F,E,D). Fig. S2. Example of the evaluation of homology matches. The seed segments of ARG1 and ARG2 both match a reference genome at the same position, indicating they refer to the same ARG. The position of segment 4 in the reference genome does not align with the expected distance from the ARG as represented in the GFA of ARG 1 suggesting it likely represents a false positive match, and therefore will be excluded from further analysis. Fig. S3. Bacteria associated with the 6 bacteria used in validation. This heatmap shows which bacterial sequences (both genome or plasmid) also tend to score high when the known presence is one of the 6 used in validation. It reflects the uncertainty that comes with bacterial calling in metagenomics. Fig. S4. MGS2AMR run time and memory usage for 5 benchmarking samples. All tools were allowed to use up to 8 CPUs. The numbers 1 through 5 refer to the file ID in Table S3. The four main pipeline steps are denoted as follows: A. MetaCherchant (existing tool). B. The MetaCherchant output pre-processing for BLAST (novel R scripts). C. BLAST+ (existing tool) D. ARG annotation (novel R scripts). Note that the large leap in memory for BLASTn is nearly entirely explained by having to load the nucleotide database into memory (~150 GB).
DOI: 10.15468/dl.uns7qa
title: Occurrence Download

description: A dataset containing 1406 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((108.061 17.6189,121.42568 21.13712,123.42064 31.70196,123.88547 33.50661,124.43233 44.30712,110.55117 40.83003,108.061 17.6189))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Calidris pygmaea (Linnaeus, 1758)",
 "Year 2014-2024"
 ]
}

 The dataset includes 1406 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069895-240626123714530/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.26626555
title: Additional file 3 of MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

description: Additional file 3: Metadata utilized for the validation of the MGS2AMR pipeline. ARG: The list of all antimicrobial resistance genes (ARG) evaluated by the pipeline. Backgrounds: Information on metagenomic sequencing data from healthy individuals, sourced from previously published studies (control samples). These data serve as the background for the 1200 samples generated for validation. GeneLinks: The linkage between genotypic information and the ARG tables. Genotypes: Details on the antimicrobial resistance genotypes of all bacteria used in the generation of the 1200 validation samples. Phenotypes: Information on the antimicrobial resistance phenotypes exhibited by all bacteria used in the generation of the 1200 validation samples. SampleInfo: Details on the sequencing data used in the generation of the 1200 validation samples. SEQ2MGS_1200: Input parameters for SEQ2MGS to generate the 1200 validation samples.
DOI: 10.6084/m9.figshare.26626555.v1
title: Additional file 3 of MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

description: Additional file 3: Metadata utilized for the validation of the MGS2AMR pipeline. ARG: The list of all antimicrobial resistance genes (ARG) evaluated by the pipeline. Backgrounds: Information on metagenomic sequencing data from healthy individuals, sourced from previously published studies (control samples). These data serve as the background for the 1200 samples generated for validation. GeneLinks: The linkage between genotypic information and the ARG tables. Genotypes: Details on the antimicrobial resistance genotypes of all bacteria used in the generation of the 1200 validation samples. Phenotypes: Information on the antimicrobial resistance phenotypes exhibited by all bacteria used in the generation of the 1200 validation samples. SampleInfo: Details on the sequencing data used in the generation of the 1200 validation samples. SEQ2MGS_1200: Input parameters for SEQ2MGS to generate the 1200 validation samples.
DOI: 10.1594/pangaea.423019
title: Hourly fast delivery sea level from coastal tide gauge station Dakar-E in 1996
DOI: 10.6084/m9.figshare.24309857
title: Additional file 1 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 1
DOI: 10.6084/m9.figshare.24309857.v1
title: Additional file 1 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 1
DOI: 10.6084/m9.figshare.24310009
title: Additional file 2 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 2
DOI: 10.6084/m9.figshare.24310009.v1
title: Additional file 2 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 2
DOI: 10.6084/m9.figshare.26626666
title: Additional file 3 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 3
DOI: 10.6084/m9.figshare.26626666.v1
title: Additional file 3 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 3
DOI: 10.6084/m9.figshare.26626669
title: Additional file 4 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 4
DOI: 10.6084/m9.figshare.26626669.v1
title: Additional file 4 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 4
DOI: 10.6084/m9.figshare.26626672
title: Additional file 5 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 5
DOI: 10.6084/m9.figshare.26626672.v1
title: Additional file 5 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 5
DOI: 10.6084/m9.figshare.26626675
title: Additional file 6 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 6
DOI: 10.6084/m9.figshare.26626675.v1
title: Additional file 6 of Association of smoking with cartilage loss of knee osteoarthritis: data from two longitudinal cohorts

description: Additional file 6
DOI: 10.1594/pangaea.229781
title: Sound velocity of Hole 70-508
DOI: 10.6084/m9.figshare.24355117.v1
title: Additional file 2 of Integrative proteomic and phosphoproteomic analysis in the female goat hypothalamus to study the onset of puberty

description: Supplementary Material 2
DOI: 10.1594/pangaea.871473
title: Table S47 - Astronomical tuning age tie points