cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0985118.10fu
title: SH0985118.10FU

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/sh0985119.10fu
title: SH0985119.10FU

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/sh0985120.10fu
title: SH0985120.10FU

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/sh0985121.10fu
title: SH0985121.10FU

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/sh0985122.10fu
title: SH0985122.10FU

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/sh0985123.10fu
title: SH0985123.10FU

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/sh0985124.10fu
title: SH0985124.10FU

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/sh0985125.10fu
title: SH0985125.10FU

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/sh0985126.10fu
title: SH0985126.10FU

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.dfyp4q
title: Occurrence Download

description: A dataset containing 1154670 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quiscalus major Vieillot, 1819",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 1154670 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097673-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.utmuwy
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation)",
 "Geometry POLYGON((-28.57506 38.34300,-28.00566 38.34300,-28.00566 38.60481,-28.57506 38.60481,-28.57506 38.34300))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chiroptera",
 "Year 2004-2024"
 ]
}

 The dataset's 1 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097671-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fw5rpm
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Roemeria procumbens Aarons. & Oppenh."
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097703-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.25358464
title: Supplementary Dataset - Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review

description: Extracted dataset for 'Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review.' Searches were performed in Web of Science and Scopus databases. The search was limited to papers published after 2014 and up to and including 25<sup>th</sup> July 2022. The terms “mass spec”, “metabo”, and volat” were required to appear in either the title, abstract or keywords. Papers were excluded if the terms “chromatograph”, “tandem mass spec”, “GC-MS”, “LC-MS”, or “review” appeared in the title, abstract or keywords. The citations identified were imported into Covidence. Duplicates were identified using the Covidence online software and removed. The abstracts were then screened by searching for the terms ‘GC’ and ‘LC’, and each appearance manually checked to ensure it referred to chromatography before exclusion.Articles were included in the study if they:Employed a direct mass spectrometry methodPerformed untargeted metabolomicsUsed a soft ionisation methodAnalysed gas-phase volatile organic compoundsArticles were excluded from the study if they:Did not present original mass spectrometry dataPerformed only tandem mass spectrometryOnly used mass spectrometry in combination with a separation method such as chromatographyWere not available in EnglishInformation regarding publication (author and publication year), sample type, data collection methods, data processing methods, statistical analysis, software and packages used to process the data, cited data processing workflows, and the data and code availability.
DOI: 10.5519/qd.cmnpg0m8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (10 records)
DOI: 10.17028/rd.lboro.25358464.v2
title: Supplementary Dataset - Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review

description: Extracted dataset for 'Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review.' Searches were performed in Web of Science and Scopus databases. The search was limited to papers published after 2014 and up to and including 25<sup>th</sup> July 2022. The terms “mass spec”, “metabo”, and volat” were required to appear in either the title, abstract or keywords. Papers were excluded if the terms “chromatograph”, “tandem mass spec”, “GC-MS”, “LC-MS”, or “review” appeared in the title, abstract or keywords. The citations identified were imported into Covidence. Duplicates were identified using the Covidence online software and removed. The abstracts were then screened by searching for the terms ‘GC’ and ‘LC’, and each appearance manually checked to ensure it referred to chromatography before exclusion.Articles were included in the study if they:Employed a direct mass spectrometry methodPerformed untargeted metabolomicsUsed a soft ionisation methodAnalysed gas-phase volatile organic compoundsArticles were excluded from the study if they:Did not present original mass spectrometry dataPerformed only tandem mass spectrometryOnly used mass spectrometry in combination with a separation method such as chromatographyWere not available in EnglishInformation regarding publication (author and publication year), sample type, data collection methods, data processing methods, statistical analysis, software and packages used to process the data, cited data processing workflows, and the data and code availability.
DOI: 10.17028/rd.lboro.25358464.v1
title: Supplementary Dataset - Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review.

description: Extracted dataset for 'Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review.' Searches were performed in Web of Science and Scopus databases. The search was limited to papers published after 2014 and up to and including 25<sup>th</sup> July 2022. The terms “mass spec”, “metabo”, and volat” were required to appear in either the title, abstract or keywords. Papers were excluded if the terms “chromatograph”, “tandem mass spec”, “GC-MS”, “LC-MS”, or “review” appeared in the title, abstract or keywords. The citations identified were imported into Covidence. Duplicates were identified using the Covidence online software and removed. The abstracts were then screened by searching for the terms ‘GC’ and ‘LC’, and each appearance manually checked to ensure it referred to chromatography before exclusion.Articles were included in the study if they:Employed a direct mass spectrometry methodPerformed untargeted metabolomicsUsed a soft ionisation methodAnalysed gas-phase volatile organic compoundsArticles were excluded from the study if they:Did not present original mass spectrometry dataPerformed only tandem mass spectrometryOnly used mass spectrometry in combination with a separation method such as chromatographyWere not available in EnglishInformation regarding publication (author and publication year), sample type, data collection methods, data processing methods, statistical analysis, software and packages used to process the data, cited data processing workflows, and the data and code availability.
DOI: 10.15468/dl.2hfj6c
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhododendron zhongdianense L.C.Hu"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097704-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yazcpa
title: Occurrence Download

description: A dataset containing 2932 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rottboellia rottboellioides (R.Br.) Druce"
 ]
}

 The dataset includes 2932 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097705-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10173293
title: BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020

description: Belgian National Burden of Disease Study


Estimates of the burden of disease


Causes of death


Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.


Years of Life Lost


In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.


Prevalence


Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.


Years Lived with Disability


In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.


Disability-Adjusted Life Years


Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.
description: For more information on the Parquet format, please see https://arrow.apache.org/docs/r/reference/read_parquet.html Explore the estimates via https://burden.sciensano.be/shiny/daly
DOI: 10.5281/zenodo.8263037
title: BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020

description: Belgian National Burden of Disease Study


Estimates of the burden of disease


Causes of death


Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.


Years of Life Lost


In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.


Prevalence


Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.


Years Lived with Disability


In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.


Disability-Adjusted Life Years


Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.
description: For more information on the Parquet format, please see https://arrow.apache.org/docs/r/reference/read_parquet.html Explore the estimates via https://burden.sciensano.be/shiny/daly
DOI: 10.15468/dl.yxv4ea
title: Occurrence Download

description: A dataset containing 588 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Roemeria refracta DC."
 ]
}

 The dataset includes 588 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097706-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.