cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.yscgnu
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Fossil, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salix suchowensis W.C.Cheng"
 ]
}

 The dataset includes 58 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056559-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jvumjy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.77088 -32.836359, 149.77088 -32.840851, 149.773126 -32.840851, 149.773126 -32.836359, 149.77088 -32.836359))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056560-240626123714530/datasets/export for details.
DOI: 10.15468/dl.cvjb45
title: Occurrence Download

description: A dataset containing 229 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Fossil, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Salix brachista C.K.Schneid., Salix pominica Z.Wang & P.Y.Fu)"
 ]
}

 The dataset includes 229 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056561-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25124210
title: <b>Nisin, a probiotic bacteriocin, modulates inflammatory and microbiome changes in female reproductive organs mediated by polymicrobial periodontal infection</b>

description: Periodontitis-related oral microbial dysbiosis is thought to contribute to adverse pregnancy outcomes (APO), infertility, and female reproductive inflammation. Since probiotics can modulate periodontitis and oral microbiome dysbiosis, this study examined the effects of a probiotic bacteriocin, nisin, in modulating the reproductive microbiome and inflammation triggered by periodontitis. A polymicrobial (Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Fusobacterium nucleatum) mouse model of periodontal disease was used to evaluate the effects of this disease on the female reproductive system with a focus on the microbiome, local inflammation, and nisin’s therapeutic potential in this context. 16s RNA sequencing was used to evaluate changes in the microbiome and RT-PCR was used to evaluate changes in inflammatory cytokines. Periodontal pathogen DNA was detected in the reproductive organs, and heart and aorta at the end of the experimental period, and DNA was especially elevated in the oral cavity in the infection group. Although only P. gingivalis was significantly elevated in the oral cavity and uterus, and T. forsythia and F. nucleatum were significantly elevated in the oral cavity. Nisin treatment significantly decreased the levels of P. gingivalis, T. forsythia, and F. nucleatum in the oral cavity. Since periodontal pathogen DNA was also detected in the heart and aorta, this suggests potential circulatory system transmission. The polymicrobial infection generally decreased microbiome diversity in the uterus, which was abrogated by nisin treatment. The polymicrobial infection compared to the control generally decreased the Firmicutes and increased the Bacteroidota in all reproductive organs with similar trends revealed in the heart. However, nisin treatment alone or in the presence of infection compared to the control or infection groups generally increased the Proteobacteria and reduced the Firmicutes and Bacteroidota in reproductive organs and the heart. Nisin treatment also altered the microbiome community structure in the reproductive tract to a new state that did not mirror the controls. Periodontal disease triggered an increase in inflammatory cytokines (IL-6, TNF-) in the uterus, which was abrogated by nisin treatment. Polymicrobial periodontal disease alters the reproductive tract microbial profile, microbiome, and inflammatory status. Nisin modulates the microbial profile and microbiome of the reproductive tract and mitigates the uterine elevated inflammatory cytokines triggered by the periodontal disease.
DOI: 10.6084/m9.figshare.25124210.v1
title: <b>Nisin, a probiotic bacteriocin, modulates inflammatory and microbiome changes in female reproductive organs mediated by polymicrobial periodontal infection</b>

description: Periodontitis-related oral microbial dysbiosis is thought to contribute to adverse pregnancy outcomes (APO), infertility, and female reproductive inflammation. Since probiotics can modulate periodontitis and oral microbiome dysbiosis, this study examined the effects of a probiotic bacteriocin, nisin, in modulating the reproductive microbiome and inflammation triggered by periodontitis. A polymicrobial (Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Fusobacterium nucleatum) mouse model of periodontal disease was used to evaluate the effects of this disease on the female reproductive system with a focus on the microbiome, local inflammation, and nisin’s therapeutic potential in this context. 16s RNA sequencing was used to evaluate changes in the microbiome and RT-PCR was used to evaluate changes in inflammatory cytokines. Periodontal pathogen DNA was detected in the reproductive organs, and heart and aorta at the end of the experimental period, and DNA was especially elevated in the oral cavity in the infection group. Although only P. gingivalis was significantly elevated in the oral cavity and uterus, and T. forsythia and F. nucleatum were significantly elevated in the oral cavity. Nisin treatment significantly decreased the levels of P. gingivalis, T. forsythia, and F. nucleatum in the oral cavity. Since periodontal pathogen DNA was also detected in the heart and aorta, this suggests potential circulatory system transmission. The polymicrobial infection generally decreased microbiome diversity in the uterus, which was abrogated by nisin treatment. The polymicrobial infection compared to the control generally decreased the Firmicutes and increased the Bacteroidota in all reproductive organs with similar trends revealed in the heart. However, nisin treatment alone or in the presence of infection compared to the control or infection groups generally increased the Proteobacteria and reduced the Firmicutes and Bacteroidota in reproductive organs and the heart. Nisin treatment also altered the microbiome community structure in the reproductive tract to a new state that did not mirror the controls. Periodontal disease triggered an increase in inflammatory cytokines (IL-6, TNF-) in the uterus, which was abrogated by nisin treatment. Polymicrobial periodontal disease alters the reproductive tract microbial profile, microbiome, and inflammatory status. Nisin modulates the microbial profile and microbiome of the reproductive tract and mitigates the uterine elevated inflammatory cytokines triggered by the periodontal disease.
DOI: 10.15468/dl.7chkz6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.831517 -32.834113, 149.831517 -32.836359, 149.82478 -32.836359, 149.82478 -32.840851, 149.822534 -32.840851, 149.822534 -32.843097, 149.831517 -32.843097, 149.831517 -32.840851, 149.833763 -32.840851, 149.833763 -32.834113, 149.831517 -32.834113))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056562-240626123714530/datasets/export for details.
DOI: 10.15468/dl.ftrv7a
title: Occurrence Download

description: A dataset containing 23152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Felis silvestris Schreber, 1777",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 23152 records from 290 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056563-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s6w8bx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.299882 -32.840851, 148.299882 -32.843097, 148.306619 -32.843097, 148.306619 -32.840851, 148.299882 -32.840851))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056564-240626123714530/datasets/export for details.
DOI: 10.15468/dl.5akhwk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.746796 -32.840851, 148.746796 -32.845342, 148.749042 -32.845342, 148.749042 -32.840851, 148.746796 -32.840851))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056566-240626123714530/datasets/export for details.
DOI: 10.15468/dl.5hrtjb
title: Occurrence Download

description: A dataset containing 249 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Fossil, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Salix dunnii C.K.Schneid., Salix changchowensis F.P.Metcalf, Pleiarina dunnii (C.K.Schneid.) N.Chao & G.T.Gong)"
 ]
}

 The dataset includes 249 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056567-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xncc4n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.780483 -32.840851, 148.780483 -32.843097, 148.782729 -32.843097, 148.782729 -32.840851, 148.780483 -32.840851))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056568-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.10483643
title: Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 1

description: This resource contains data corresponding to the study titled "Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.10483642
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 1

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.13244810
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 1

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.15468/dl.5u6sga
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.88379 -32.840851, 148.88379 -32.843097, 148.888281 -32.843097, 148.888281 -32.845342, 148.890527 -32.845342, 148.890527 -32.843097, 148.895019 -32.843097, 148.895019 -32.840851, 148.88379 -32.840851))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056570-240626123714530/datasets/export for details.
DOI: 10.15468/dl.2a3f7k
title: Occurrence Download

description: A dataset containing 7414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Fossil, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Salix schwerinii E.L.Wolf, Salix viminalis L., Salix gmelinii Pall., Salix viminalis var. gmelinii Turcz., Salix dailingensis Y.L.Chou & C.Y.King)"
 ]
}

 The dataset includes 7414 records from 214 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056571-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10483701
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 2

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.10483702
title: Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 2

description: This resource contains data corresponding to the study titled "Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.13254908
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 2

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.10483938
title: Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 3

description: This resource contains data corresponding to the study titled "Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.10483937
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 3

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.13256125
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 3

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.15468/dl.hujea5
title: Occurrence Download

description: A dataset containing 2749 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen, Fossil, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salix udensis (Wimm.) Trautv. & C.A.Mey."
 ]
}

 The dataset includes 2749 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056572-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13272729
title: Vegetation Cover Pattern Detecting
DOI: 10.5281/zenodo.13272730
title: Vegetation Cover Pattern Detecting
DOI: 10.5281/zenodo.10483963
title: Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 4

description: This resource contains data corresponding to the study titled "Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

For further information on this resource, please contact abhigupta.1611@gmail.com
DOI: 10.5281/zenodo.10483964
title: Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 4

description: This resource contains data corresponding to the study titled "Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed.

For further information on this resource, please contact abhigupta.1611@gmail.com