cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1098539.10fu
title: SH1098539.10FU

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/sh1098543.10fu
title: SH1098543.10FU

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/sh1098544.10fu
title: SH1098544.10FU

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/sh1098545.10fu
title: SH1098545.10FU

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/sh1098546.10fu
title: SH1098546.10FU

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/sh1098547.10fu
title: SH1098547.10FU

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/sh1098548.10fu
title: SH1098548.10FU

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/sh1098550.10fu
title: SH1098550.10FU

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/sh1098552.10fu
title: SH1098552.10FU

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.8wy8sy
title: Occurrence Download

description: A dataset containing 180 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((119.77079 30.04498,120.43113 30.04535,120.42989 30.5084,119.77079 30.51195,119.77079 30.04498))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Callosciurus erythraeus",
 "Year 2020-2025"
 ]
}

 The dataset includes 180 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002361-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48544/0b74e7a0-6160-4791-bde8-bba6118d6e96
title: The Polder2C’s winter & summer schools ‘fieldwork for flood resilience'
DOI: 10.15468/dl.k68cq4
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caridina tonkinensis",
 "Country is Singapore",
 "OccurrenceStatus is Present",
 "EventDate is 1800,2026"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002363-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ryu5je
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.28_1",
 "TaxonKey is Salix matsudana"
 ]
}

 The dataset includes 26 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002364-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wefncf
title: Occurrence Download

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.28_1",
 "TaxonKey is Vicia cracca"
 ]
}

 The dataset includes 165 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002366-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ac8w6c
title: Occurrence Download

description: A dataset containing 13566 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((44.01147 39.41985,45.08058 36.19802,45.92745 35.66873,45.2923 33.65743,46.35088 32.84585,47.44475 32.06956,47.62118 31.32855,48.25632 30.51697,48.64447 29.81125,49.49479 29.65894,50.37348 29.63482,51.32621 27.34123,52.75952 26.17816,53.89978 25.79407,54.51192 25.56602,56.01225 26.50223,56.72497 27.12952,57.08049 26.02212,57.52513 25.47487,61.72417 25.26316,62.28874 26.14531,63.34589 26.8263,62.78275 28.05075,61.30074 29.03876,61.75945 30.73249,61.08902 31.64993,60.73616 33.97881,61.47717 36.23711,59.39529 37.57798,57.85331 37.88744,56.74884 38.38956,55.16097 37.93084,51.45594 36.80169,48.19115 38.77564,48.03319 39.65985,46.30872 38.92925,45.39973 39.10254,44.60846 39.78264,44.46808 39.70444,44.34915 39.388,44.1835 39.43047,44.01147 39.41985))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oenanthe"
 ]
}

 The dataset includes 13566 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002367-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tcw8st
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.28_1",
 "TaxonKey is Bassia hyssopifolia"
 ]
}

 The dataset includes 40 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002368-251025141854904/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.27016288.v2
title: <b>Water challenges in arid planted shrublands: precipitation non-responsiveness and high evapotranspiration potential</b>

description: Integrating a field precipitation manipulation experiment on <i>Tamarix spp.</i> in arid Northwest China with a global spatial data synthesis, this study investigated evapotranspiration (ET) patterns and hydrological responses in non-zonal large-statured planted shrublands. The field experiment revealed <i>Tamarix</i> transpiration was unresponsive to precipitation, with stand-scale ET primarily driven by bare soil evaporation. Spatial data showed severely water-stressed planted shrublands' ET approximated precipitation, while riparian ET decoupled and increased with potential evapotranspiration (PET). ET was broadly influenced by water availability and atmospheric evaporative demand. Crucially, water-limited planted shrublands exhibited weak responses to precipitation and PET, fundamentally reflecting water availability constraints. The strong influence of LAI on ET, coupled with high GEP-ET slopes, indicated a significant water cost for productivity. These findings challenge the scientific validity of pursuing functions beyond aeolian hazard control for planted shrublands in arid regions, underscoring the critical need for afforestation practices to align with regional water resource constraints.
DOI: 10.15468/dl.vjg7r4
title: Occurrence Download

description: A dataset containing 82709 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Africa)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lymantria dispar"
 ]
}

 The dataset includes 82709 records from 621 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002338-251025141854904/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.30385879
title: Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data for Therapy Response Prediction in Hormone Receptor-Positive Breast Cancer

description: The data folder contains the data files that were used in the tutorial "Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data for Therapy Response Prediction in Hormone Receptor-Positive Breast Cancer". Raw data can be downloaded at: <b>GEO Accession:</b> GSE300475Folder 1- H5AD Files: The folder contains the <b>AnnData objects </b>of raw and preprocessed transcriptomic datagene_expression_annotated.h5ad: This file contains the combined raw gene expression data of all samples, with cells annotated by Patient ID and Treatment Response Labels.gene_expression_processed1.h5ad: This file contains the preprocessed data after performing quality control and removing doublets.Folder 2- CSV Files: The folder contains All <code>.csv</code> files used for the tutorial, including:hvg_genes.csv: List of highly variable genes used for feature selectiontranscriptomic_features.csv: Transcriptomic feature matrix (samples × genes)fluxomic_features.csv: Flux-based feature matrix (samples × reactions)metadata.csv: Sample-level metadata with response labels (Responder / Non-responder)The tutorial code is available at: Occhipinti-Lab/HRplus-BC-Multimodal: Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data
DOI: 10.6084/m9.figshare.30385879.v3
title: Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data for Therapy Response Prediction in Hormone Receptor-Positive Breast Cancer

description: The data folder contains the data files that were used in the tutorial "Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data for Therapy Response Prediction in Hormone Receptor-Positive Breast Cancer". Raw data can be downloaded at: <b>GEO Accession:</b> GSE300475Folder 1- H5AD Files: The folder contains the <b>AnnData objects </b>of raw and preprocessed transcriptomic datagene_expression_annotated.h5ad: This file contains the combined raw gene expression data of all samples, with cells annotated by Patient ID and Treatment Response Labels.gene_expression_processed1.h5ad: This file contains the preprocessed data after performing quality control and removing doublets.Folder 2- CSV Files: The folder contains All <code>.csv</code> files used for the tutorial, including:hvg_genes.csv: List of highly variable genes used for feature selectiontranscriptomic_features.csv: Transcriptomic feature matrix (samples × genes)fluxomic_features.csv: Flux-based feature matrix (samples × reactions)metadata.csv: Sample-level metadata with response labels (Responder / Non-responder)The tutorial code is available at: Occhipinti-Lab/HRplus-BC-Multimodal: Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data
DOI: 10.48544/a9183bb6-bcca-44fe-b254-d13faba67c60
title: Animals on the levee
DOI: 10.6084/m9.figshare.30385879.v2
title: Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data for Therapy Response Prediction in Hormone Receptor-Positive Breast Cancer

description: The data folder contains the data files that were used in the tutorial "Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data for Therapy Response Prediction in Hormone Receptor-Positive Breast Cancer". Raw data can be downloaded at: <b>GEO Accession:</b> GSE300475Folder 1- H5AD Files: The folder contains the <b>AnnData objects </b>of raw and preprocessed transcriptomic datagene_expression_annotated.h5ad: This file contains the combined raw gene expression data of all samples, with cells annotated by Patient ID and Treatment Response Labels.gene_expression_processed1.h5ad: This file contains the preprocessed data after performing quality control and removing doublets.Folder 2- CSV Files: The folder contains All <code>.csv</code> files used for the tutorial, including:hvg_genes.csv: List of highly variable genes used for feature selectiontranscriptomic_features.csv: Transcriptomic feature matrix (samples × genes)fluxomic_features.csv: Flux-based feature matrix (samples × reactions)metadata.csv: Sample-level metadata with response labels (Responder / Non-responder)The tutorial code is available at: Occhipinti-Lab/HRplus-BC-Multimodal: Multiscale Flux Balance Analysis: Bridging Multi-Omics and Single-Cell Data
DOI: 10.25375/uct.30449312
title: Order-flow and long-memory in a simulated financial market

description: This is the dataset used in our Honours Project on "Order-flow and long-memory in a simulated financial market". It can be used hand-in-hand with our code to replicate our findings. The data consists of intraday quote and trade tick (trade-by-trade) data for the top 42 stocks listed on the JSE, from January to December 2015. This data does not contain Trader IDs (a way of knowing which trade is associated with which trader) and thus one needs to apply a method for generating synthetic trader IDs for these trades. We used the Maitrier-Loeper-Bouchaud (MLB) algorithm once we had processed this raw data. Trader IDs are required in order to calculate metaorder lengths, which is a key component needed when estimating the microscopic exponent 'alpha'.
DOI: 10.25375/uct.30449312.v1
title: Order-flow and long-memory in a simulated financial market

description: This is the dataset used in our Honours Project on "Order-flow and long-memory in a simulated financial market". It can be used hand-in-hand with our code to replicate our findings. The data consists of intraday quote and trade tick (trade-by-trade) data for the top 42 stocks listed on the JSE, from January to December 2015. This data does not contain Trader IDs (a way of knowing which trade is associated with which trader) and thus one needs to apply a method for generating synthetic trader IDs for these trades. We used the Maitrier-Loeper-Bouchaud (MLB) algorithm once we had processed this raw data. Trader IDs are required in order to calculate metaorder lengths, which is a key component needed when estimating the microscopic exponent 'alpha'.
DOI: 10.15468/dl.4t8xpw
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.28_1",
 "TaxonKey is Cystopteris montana"
 ]
}

 The dataset includes 32 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002371-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vvqk5j
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.28_1",
 "TaxonKey is Smelowskia bifurcata"
 ]
}

 The dataset includes 22 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002370-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.