cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1141126.10fu
title: SH1141126.10FU

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/sh1141127.10fu
title: SH1141127.10FU

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/sh1141130.10fu
title: SH1141130.10FU

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/sh1141132.10fu
title: SH1141132.10FU

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/sh1141134.10fu
title: SH1141134.10FU

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/sh1141136.10fu
title: SH1141136.10FU

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/sh1141137.10fu
title: SH1141137.10FU

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/sh1141138.10fu
title: SH1141138.10FU

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/sh1141142.10fu
title: SH1141142.10FU

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.epf53d
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ameroglossum asperifolium"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004765-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.avbpyy
title: Occurrence Download

description: A dataset containing 466 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is PSU-Seagrass Collection"
 ]
}

 The dataset includes 466 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004766-251025141854904/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.26180461
title: Code and data used in the study - "<b>Canopy height and biomass distribution across the forests of Iberian Peninsula</b> "

description: This is the initial version of the data and code used in the paper, "<b>Canopy height and biomass distribution across the forests of Iberian Peninsula</b>".<b>Authors</b>Yang Su <sup>a, b, c</sup>, Martin Schwartz <sup>b</sup>, Ibrahim Fayad <sup>b</sup>, García Alonso Mariano <sup>d</sup>, Miguel A. Zavala <sup>e</sup>, Julián Tijerín-Triviño <sup>e</sup>, Julen Astigarraga <sup>e</sup>, Verónica Cruz <sup>f</sup>, Siyu Liu <sup>g</sup>, Xianglin Zhang <sup>c, h</sup>, Songchao Chen <sup>h,i</sup>, François Ritter <sup>b</sup>, Nikola Besic <sup>j</sup>, Alexandre d'Aspremont <sup>a</sup>, Philippe Ciais <sup>b</sup><br><b>Affiliations</b><sup>a</sup> Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France<sup>b</sup> Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France<sup>c </sup>UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France<sup>d</sup> University of Alcalá, Department of Geology, Geography and the Environment, Enviromental Remote Sensing Research Group, 28801 Alcalá de Henares, Spain<sup>e</sup> University of Alcalá, Department of Life Sciences, 28801 Alcalá de Henares, Spain<sup>f</sup> Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain<sup>g</sup> Department of Geosciences and Natural Resource Management, Copenhagen University, 1958 Frederiksberg, Denmark<sup>h</sup> College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China<sup>i </sup>ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China<sup>j </sup>IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France<br><b>Corresponding Author</b>Yang Suyang.su@ens.fr+33 1 89 10 07 67École normale supérieure - PSL<br>To use the data and code, please cite this study, and in case of difficulties when using the model and data, please contact the corresponding author for further details and possible assistance.<br>The maps of canopy height and above-ground biomass provided by this study can be found on GEE, detailed information about how to access those datasets can be found in Table 1 and Table 2 in this study. A preview of those maps can be found here: https://ens-yangsu-forest-spain-als.projects.earthengine.app/view/ai4forest-iberian-peninsula
DOI: 10.6084/m9.figshare.26180461.v2
title: Code used in the study - "<b>Canopy height and biomass distribution across the forests of Iberian Peninsula</b> "

description: This is the initial version of the data and code used in the paper, "<b>Canopy height and biomass distribution across the forests of Iberian Peninsula</b>".<b>Authors</b>Yang Su <sup>a, b, c</sup>, Martin Schwartz <sup>b</sup>, Ibrahim Fayad <sup>b</sup>, García Alonso Mariano <sup>d</sup>, Miguel A. Zavala <sup>e</sup>, Julián Tijerín-Triviño <sup>e</sup>, Julen Astigarraga <sup>e</sup>, Verónica Cruz <sup>f</sup>, Siyu Liu <sup>g</sup>, Xianglin Zhang <sup>c, h</sup>, Songchao Chen <sup>h,i</sup>, François Ritter <sup>b</sup>, Nikola Besic <sup>j</sup>, Alexandre d'Aspremont <sup>a</sup>, Philippe Ciais <sup>b</sup><br><b>Affiliations</b><sup>a</sup> Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France<sup>b</sup> Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France<sup>c </sup>UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France<sup>d</sup> University of Alcalá, Department of Geology, Geography and the Environment, Enviromental Remote Sensing Research Group, 28801 Alcalá de Henares, Spain<sup>e</sup> University of Alcalá, Department of Life Sciences, 28801 Alcalá de Henares, Spain<sup>f</sup> Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain<sup>g</sup> Department of Geosciences and Natural Resource Management, Copenhagen University, 1958 Frederiksberg, Denmark<sup>h</sup> College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China<sup>i </sup>ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China<sup>j </sup>IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France<br><b>Corresponding Author</b>Yang Suyang.su@ens.fr+33 1 89 10 07 67École normale supérieure - PSL<br>To use the model and data, please contact the correspondin author for more details.<br>The maps of canopy height and above-ground biomass provided by this study can be found on GEE, detailed information about how to access those datasets can be found in Table 2 in this study. A preview of those maps can be found here: https://ens-yangsu-forest-spain-als.projects.earthengine.app/view/ai4forest-iberian-peninsula
DOI: 10.15468/dl.mtzwjj
title: Occurrence Download

description: A dataset containing 74791 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Procambarus clarkii (Girard, 1852)"
 ]
}

 The dataset includes 74791 records from 542 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022691-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11132786
title: Egypt Gold Prices | Daily Updated
DOI: 10.15468/dl.gy6vah
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Grus monacha Temminck, 1835",
 "Year 2010-2010"
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022694-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15011941
title: Data for research

description: Raw data for research on:

Diagnostic Accuracy of STOP-BANG Score in Detecting Obstructive Sleep Apnea Among Patients at the Rizal Medical Center
DOI: 10.5281/zenodo.15011940
title: Data for research

description: Raw data for research on:

Diagnostic Accuracy of STOP-BANG Score in Detecting Obstructive Sleep Apnea Among Patients at the Rizal Medical Center
DOI: 10.5281/zenodo.15069709
title: Data for research

description: Raw data for research on:

Diagnostic Accuracy of STOP-BANG Score in Detecting Obstructive Sleep Apnea Among Patients at the Rizal Medical Center
DOI: 10.15468/dl.nfk52h
title: Occurrence Download

description: A dataset containing 459 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Order Carnivora"
 ]
}

 The dataset includes 459 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022695-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ksmwgt
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (January, February)",
 "OccurrenceStatus is Present",
 "TaxonKey is Grus monacha Temminck, 1835",
 "Year 2011-2011"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022696-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.64f9rd
title: Occurrence Download

description: A dataset containing 152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Anser canagicus (Sevastianov, 1802)",
 "Year 2010-2010"
 ]
}

 The dataset includes 152 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022697-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rdkstv
title: Occurrence Download

description: A dataset containing 74791 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Procambarus clarkii (Girard, 1852)"
 ]
}

 The dataset includes 74791 records from 542 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022693-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/z7s9-t949
title: Ecological carrying capacity assessment

description: Ecological carrying capacity (ECC) is central to assessing the sustainability of ecosystems, aiming to quantify the limits of natural systems to support human activities while maintaining biodiversity and resource regeneration. To assess ECC, earlier studies typically used the analytic hierarchy process (AHP) method for modeling. In this study, we developed an AHP-EW method based on a combination of AHP and entropy weight method, which considered important indicators including land use, vegetation, soil, location, topography, climate, and socio-economics, and constructed an ECC evaluation system. The new AHP-EW method was applied to analyze the spatiotemporal ECC patterns in Urumqi from 2000 to 2020. The results showed a general decreasing trend in ECC during the period 2000-2020. Among them, the ECC decreased significantly by 19.05% from 2000 to 2010, while after 2010, the decrease slowed down to 14.12%, owing to the implementation of ecological conservation policies. In addition, Midong District, Dabancheng District, and Urumqi County had worse ECC. Still, in general, the distribution of ECC in each district and county showed a trend of decreasing in areas with low ECC and increasing in areas with high ECC. Cluster analysis showed that ECC improved in ecological reserve areas, while some built-up areas showed a decrease in ECC due to economic development and human activities. Driving factors analysis shows that NDVI, climate change, and land use conversion are the key factors influencing the change of ECC in Urumqi. This study provides new ideas and technical support for ECC assessment in the global arid areas, which can help formulate more effective ecological protection strategies and promote the healthy and stable development of regional ecosystems.
DOI: 10.15468/dl.gds9kt
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)",
 "Country is China",
 "GadmGid is one of (CHN.16.5.11_1, CHN.16.5.10_1, CHN.16.5.8_1, CHN.16.5.7_1, CHN.16.6.6_1, CHN.16.6.1_1, CHN.16.10.9_1, CHN.16.10.4_1, CHN.16.10.2_1, CHN.16.10.3_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Pucrasia macrolopha (R.Lesson, 1829)",
 "Year 2004-2025"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022699-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12559239
title: PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset

description: PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at 1 km x 1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas defined as 'peat-dominated' by the Global Peat Map v2.0 obtained from the Global Peat Database.