cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0181031.10fu
title: SH0181031.10FU

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/sh0181032.10fu
title: SH0181032.10FU

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/sh0181033.10fu
title: SH0181033.10FU

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/sh0181034.10fu
title: SH0181034.10FU

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/sh0181035.10fu
title: SH0181035.10FU

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/sh0181036.10fu
title: SH0181036.10FU

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: 10.5281/zenodo.18200748
title: Pediatric choroid plexus tumors: dataset (English version)
DOI: 10.15156/bio/sh0181037.10fu
title: SH0181037.10FU

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/sh0181038.10fu
title: SH0181038.10FU

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: 10.5281/zenodo.18200747
title: Pediatric choroid plexus tumors: dataset (English version)
DOI: 10.17632/h7b9d97wv6.1
title: EB GAIT Deidentified Example Report

description: A deidentified report demonstrating Evidence Based Gait Analysis Interpretation Tools
DOI: 10.17632/v427v4bfkn.1
title: Suplementary materials of RCT study on waste segregation in campus setting

description: This study employed non-financial interventions to encourage waste segregation behavior among residents in a campus setting. The supplementary materials include: Pamphlet—Designed to provide information on the importance of waste segregation, proper waste-sorting guidelines, and the environmental and social benefits of responsible waste disposal. Scorecard—Developed to serve as a feedback mechanism, allowing residents to track their waste segregation performance over time. It aims to reinforce positive behavior by providing regular assessments and encouraging continuous improvement. These materials were utilized as intervention tools to assess their effectiveness in motivating households to improve their waste segregation practices.
DOI: 10.15468/dl.we3vva
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia mayae A.Vázquez & Pérez-Farr.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016154-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sdvs4nbgym.1
title: Jordan Unemployment Rates and Google Trends Data (2020-2024): Analyzing Economic Sentiment Through Search Behavior in Arabic and English

description: This dataset combines official unemployment rates in Jordan from 2020 to mid-2024 with Google Trends data capturing search behaviour in Arabic and English. The search phrases were curated by experts to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behavior and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.
DOI: 10.17632/sdvs4nbgym.2
title: Jordan Unemployment Rates and Google Trends Data (2020-2024): Analyzing Economic Sentiment Through Search Behavior in Arabic and English

description: This dataset combines official unemployment rates in Jordan from 2020 to mid-2024 with Google Trends data capturing search behaviour in Arabic and English. Experts curated the search phrases to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behaviour and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.
DOI: 10.17632/sdvs4nbgym.3
title: Jordan Unemployment Rates and Google Trends Data (2020-2024): Analyzing Economic Sentiment Through Search Behavior in Arabic and English

description: This dataset combines official unemployment rates in Jordan from 2020 to mid-2024 with Google Trends data capturing search behaviour in Arabic and English. Experts curated the search phrases to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behaviour and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.
DOI: 10.17632/sdvs4nbgym.4
title: Jordan Unemployment Rates and Google Trends Data (2010-2024): Analyzing Economic Sentiment Through Search Behavior in Arabic and English

description: This dataset combines official unemployment rates in Jordan from 2010 to mid-2024 with Google Trends data capturing search behaviour in Arabic and English. Experts curated the search phrases to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behaviour and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.
DOI: 10.17632/sdvs4nbgym.5
title: Jordan Unemployment Rates and Google Trends Data (2010-2024): Analyzing Economic Sentiment Through Search Behavior in Arabic and English

description: This dataset combines official unemployment rates in Jordan from 2010 to the end of September 2024 with Google Trends data capturing search behaviour in Arabic and English. Experts curated the search phrases to reflect terms associated with job searches, unemployment concerns, and expressions of economic frustration. This unique dataset offers a rich blend of traditional economic indicators and non-traditional data, such as search trends, providing a novel approach to understanding and predicting unemployment in an emergency context, particularly during and after the COVID-19 pandemic.

By leveraging search trends as proxies for real-time public sentiment and behaviours, this dataset opens the door to more immediate economic forecasts, bridging the gap between releasing official unemployment data and real-time indicators. Such insights are precious during economic uncertainty or crisis periods, where swift policy responses are critical. Researchers and policymakers can use this dataset to explore correlations between public search behaviour and unemployment rates, potentially allowing for earlier interventions based on predictive models incorporating traditional and non-traditional data.
DOI: 10.17632/srn5hc25bt.1
title: DataDeposit_Impact on efficacy of target reduction of two FDA-approved ASO drugs by cellular glucose levels in in vitro cell models

description: This dataset are raw and processed data for the investigation of the impact of glucose level on efficacy of two FDA approved ASO drugs. Data was classified into experiment designs.
DOI: 10.15468/dl.5tqjem
title: Occurrence Download

description: A dataset containing 270 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, Illinois Natural History Survey Insect Collection)",
 "GadmGid is one of (USA, CAN)",
 "OccurrenceStatus is Present",
 "TaxonKey is Antherophagus Dejean, 1821"
 ]
}

 The dataset includes 270 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016155-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x9tsye
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia mexicana DC.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 22 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016158-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fedkrq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia mixteca A.Vázquez & Domínguez-Yescas)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016159-250127130748423/datasets/export for details.
DOI: 10.15468/dl.vmxn4f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia montebelloensis A.Vázquez & Pérez-Farr.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016160-250127130748423/datasets/export for details.
DOI: 10.34863/bhnj-t333
title: Dataset: Ammonia Adsorption at High Temperature and Pressure Using Sodium Containing Aluminosilicates: Selectivity over Nitrogen and Multicycle Working Capacities
DOI: 10.15468/dl.44n26n
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia nuevoleonensis A.Vázquez & Domínguez-Yescas)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016161-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7r7ed7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Magnolia oaxacensis A.Vázquez)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016162-250127130748423/datasets/export for details.
DOI: 10.5281/zenodo.14812120
title: Pyrenophora teres f. teres 6A Annotation
DOI: 10.5281/zenodo.14812121
title: Pyrenophora teres f. teres 6A Annotation
DOI: 10.5281/zenodo.14811892
title: Pyrenophora teres f. maculata FGOB10Ptm-1 Annotation
DOI: 10.5281/zenodo.14811893
title: Pyrenophora teres f. maculata FGOB10Ptm-1 Annotation
DOI: 10.5281/zenodo.14727948
title: Pyrenophora teres f. maculata FGOB10Ptm-1 RNAseq
DOI: 10.5281/zenodo.14727947
title: Pyrenophora teres f. maculata FGOB10Ptm-1 RNAseq
DOI: 10.15468/dl.59fwde
title: Occurrence Download

description: A dataset containing 8920 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Pecari tajacu (Linnaeus, 1758)"
 ]
}

 The dataset includes 8920 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016163-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14812146
title: Pathogenicity of Paenibacillus spp. for honey bee brood

description: Raw data set of the article describing the pathogenicity of Paenibacillus spp. for honey bee brood.
DOI: 10.5281/zenodo.8114635
title: Pathogenicity of Paenibacillus spp. for honey bee brood

description: Raw data set of the article describing the pathogenicity of Paenibacillus spp. for honey bee brood.
DOI: 10.5281/zenodo.8114636
title: Pathogenicity of Paenibacillus spp. for honey bee brood

description: Relative raw data set of the article describing the pathogenicity of <em>Paenibacillus</em> spp. for honey bee brood.