cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29118031.v1
title: Pathogenicity and transmissibility of bovine-derived HPAI H5N1 B3.13 virus in pigs

description: Since the first emergence of highly pathogenic avian influenza (HPAI) H5N1 viruses in dairy cattle, the virus has continued to spread, reaching at least 17 states and at least 950 dairy herds in the United States. Subsequently, spillovers of the virus from dairy cattle to humans have been reported. Pigs are an important reservoir in influenza ecology because they serve as a mixing vessel in which novel reassortant viruses with pandemic potential can be generated. Here, we show that oro-respiratory infection of pigs resulted in productive replication of a bovine-derived HPAI H5N1 B3.13 virus. Infectious virus was mainly identified in the lower respiratory tract of principal infected pigs, and sero-conversion was observed in most of the principal pigs at later time points, suggesting limited replication of the bovine-derived HPAI H5N1 B3.13 virus in pigs. In one animal, we detected the emergence of a mutation in hemagglutinin (HA) previously associated with increased affinity for “mammalian-type” α2,6-linked sialic acid receptors, but this mutation did not reach majority consensus levels. Sentinel contact pigs remained sero-negative throughout the study, indicating lack of transmission. These results support that pigs are susceptible to a bovine-derived HPAI H5N1 B3.13 virus, but this virus did not replicate as robustly in pigs as mink-derived HPAI H5N1 and swine-adapted influenza viruses.
DOI: 10.6084/m9.figshare.29118031.v2
title: Pathogenicity and transmissibility of bovine-derived HPAI H5N1 B3.13 virus in pigs

description: Since the first emergence of highly pathogenic avian influenza (HPAI) H5N1 viruses in dairy cattle, the virus has continued to spread, reaching 17 states and at least 1000 dairy herds in the United States. Subsequently, spillovers of the virus from dairy cattle to humans have been reported. Pigs are an important host in influenza ecology because they serve as a mixing vessel in which novel reassortant viruses with pandemic potential can be generated. Here, we show that oro-respiratory infection of pigs resulted in productive replication of a bovine-derived HPAI H5N1 B3.13 virus. Infectious virus and viral antigen were mainly identified in the lower respiratory tract of principal infected pigs, and sero-conversion was observed in most of the principal pigs at later time points, suggesting replication of the bovine-derived HPAI H5N1 B3.13 virus in pigs. In one animal, we detected the emergence of a mutation in the hemagglutinin (HA) previously associated with increased affinity for “mammalian-type” α2,6-linked sialic acid receptors, but this mutation did not reach majority consensus levels. Sentinel contact pigs remained sero-negative throughout the study, indicating lack of transmission. These results support that pigs are susceptible to a bovine-derived HPAI H5N1 B3.13 virus, but this virus did not replicate as robustly in pigs as swine-adapted influenza viruses.
DOI: 10.15468/dl.dv7uc8
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-1.03181 42.37353,0.76538 42.37353,0.76538 43.52769,-1.03181 43.52769,-1.03181 42.37353))",
 "OccurrenceStatus is Present",
 "TaxonKey is Lynx Kerr, 1792"
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080064-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7hfc4r
title: Occurrence Download

description: A dataset containing 13954 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solanum angustifolium Houst. ex Mill.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 13954 records from 285 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003357-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7797/26-200809-1-1-2
title: Gesundheit in Deutschland aktuell 2009 (GEDA 2009)
DOI: 10.15468/dl.u89m3w
title: Occurrence Download

description: A dataset listing the 4031954 species recorded in GBIF matching the query:
{ }

 The dataset's 4031954 records were derived from 71480 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003355-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bgfc8b
title: Occurrence Download

description: A dataset containing 4256 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solanum aviculare G.Forst.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 4256 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003311-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14942442
title: Metatranscriptomic Analyses Raw Sequence Data of Diclofenac Experiments from Enriched Nitrifying Sludge

description: Raw data for Metatranscriptome sequencing of Diclofenac 1 mg/L experiment.




Sample Name
DCF_1


Protocol REF
Nucleic acid extraction


Extract Name
DCF_1


Protocol REF
RNA library construction


Parameter Value[library layout]
Pair-end


Protocol REF
Nucleic acid sequencing


Parameter Value[sequencing instrument]
Illumina NovaSeq X Plus Sequencing System


Assay Name
DCF _1_Metatranscriptome


Raw Data File
DCF_1_1.fg.gz, DCF_1_2.fg.gz


Protocol REF
Sequence analysis data transformation




 

 

Raw data for Metatranscriptome sequencing of Diclofenac 10 mg/L experiment.




Sample Name
DCF_10


Protocol REF
Nucleic acid extraction


Extract Name
DCF_10


Protocol REF
RNA library construction


Parameter Value[library layout]
Pair-end


Protocol REF
Nucleic acid sequencing


Parameter Value[sequencing instrument]
Illumina NovaSeq X Plus Sequencing System


Assay Name
DCF _10_Metatranscriptome


Raw Data File
DCF_10_1.fg.gz, DCF_10_2.fg.gz


Protocol REF
Sequence analysis data transformation




 

Raw data for Metatranscriptome sequencing of Diclofenac 15 mg/L experiment.




Sample Name
DCF_15


Protocol REF
Nucleic acid extraction


Extract Name
DCF_15


Protocol REF
RNA library construction


Parameter Value[library layout]
Pair-end


Protocol REF
Nucleic acid sequencing


Parameter Value[sequencing instrument]
Illumina NovaSeq X Plus Sequencing System


Assay Name
DCF _15_Metatranscriptome


Raw Data File
DCF_15_1.fg.gz, DCF_15_2.fg.gz


Protocol REF
Sequence analysis data transformation
DOI: 10.5281/zenodo.14942443
title: Metatranscriptomic Analyses Raw Sequence Data of Diclofenac Experiments from Enriched Nitrifying Sludge

description: Raw data for Metatranscriptome sequencing of Diclofenac 1 mg/L experiment.




Sample Name
DCF_1


Protocol REF
Nucleic acid extraction


Extract Name
DCF_1


Protocol REF
RNA library construction


Parameter Value[library layout]
Pair-end


Protocol REF
Nucleic acid sequencing


Parameter Value[sequencing instrument]
Illumina NovaSeq X Plus Sequencing System


Assay Name
DCF _1_Metatranscriptome


Raw Data File
DCF_1_1.fg.gz, DCF_1_2.fg.gz


Protocol REF
Sequence analysis data transformation




 

 

Raw data for Metatranscriptome sequencing of Diclofenac 10 mg/L experiment.




Sample Name
DCF_10


Protocol REF
Nucleic acid extraction


Extract Name
DCF_10


Protocol REF
RNA library construction


Parameter Value[library layout]
Pair-end


Protocol REF
Nucleic acid sequencing


Parameter Value[sequencing instrument]
Illumina NovaSeq X Plus Sequencing System


Assay Name
DCF _10_Metatranscriptome


Raw Data File
DCF_10_1.fg.gz, DCF_10_2.fg.gz


Protocol REF
Sequence analysis data transformation




 

Raw data for Metatranscriptome sequencing of Diclofenac 15 mg/L experiment.




Sample Name
DCF_15


Protocol REF
Nucleic acid extraction


Extract Name
DCF_15


Protocol REF
RNA library construction


Parameter Value[library layout]
Pair-end


Protocol REF
Nucleic acid sequencing


Parameter Value[sequencing instrument]
Illumina NovaSeq X Plus Sequencing System


Assay Name
DCF _15_Metatranscriptome


Raw Data File
DCF_15_1.fg.gz, DCF_15_2.fg.gz


Protocol REF
Sequence analysis data transformation
DOI: 10.5281/zenodo.14048341
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023. 

Due to data upload size limitation, we provide DOIs for different model data of NHMSD in (NHMSD 1980-2100.txt), which can be downloaded by users as needed.
DOI: 10.5281/zenodo.14048342
title: Northern Hemisphere Monthly Downscaled Snow Depth (NHMSD) Datasets at 0.05° Resolution from 21 CMIP6 Models for Ski Seasons

description: Accurate snow depth datasets are crucial for advancing the winter tourism industry, especially for ski industry planning, amidst rapid climate change. This study combines the delta and snow cover probability-based downscaling methods to generate monthly downscaled 0.05° ski season snow depth datasets for the northern hemisphere (NHMSD), using 21 major general circulation models and four shared socioeconomic pathways from the Sixth Coupled Model Intercomparison Project, covering 1980–2014 (historical) and 2015–2100 (projected). Validation with 1965 ground snow depth observations demonstrated that NHMSD outperformed reanalysis datasets, including the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land and the Global Land Data Assimilation System, in terms of root mean square error, bias, and mean absolute error for 1980–2014 and 2015–2023. Moreover, spatiotemporal analysis using NHMSD projects ski season snow depth to increase during the ski season in Eastern Eurasia and decrease in North America. These findings provide valuable insights for Northern Hemisphere snow depth changes, which are beneficial for the planning of snow-related industries, such as ski resort site selection.

Due to data upload size limitation, we provide DOIs for different modal data of NHMSD in (NHMSD 1980-2100.txt), which can be downloaded by users as needed.
DOI: 10.5281/zenodo.14575858
title: Northern Hemisphere Monthly Downscaled Snow Depth (NHMSD) Datasets at 0.05° Resolution from 21 CMIP6 Models for Ski Seasons

description: Accurate snow depth datasets are crucial for advancing the winter tourism industry, especially for ski industry planning, amidst rapid climate change. This study combines the delta and snow cover probability-based downscaling methods to generate monthly downscaled 0.05° ski season snow depth datasets for the northern hemisphere (NHMSD), using 21 major general circulation models and four shared socioeconomic pathways from the Sixth Coupled Model Intercomparison Project, covering 1980–2014 (historical) and 2015–2100 (projected). Validation with 1965 ground snow depth observations demonstrated that NHMSD outperformed reanalysis datasets, including the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land and the Global Land Data Assimilation System, in terms of root mean square error, bias, and mean absolute error for 1980–2014 and 2015–2023. Moreover, spatiotemporal analysis using NHMSD projects ski season snow depth to increase during the ski season in Eastern Eurasia and decrease in North America. These findings provide valuable insights for Northern Hemisphere snow depth changes, which are beneficial for the planning of snow-related industries, such as ski resort site selection.

Due to data upload size limitation, we provide DOIs for different modal data of NHMSD in (NHMSD 1980-2100.txt), which can be downloaded by users as needed.
DOI: 10.7797/29-201213-1-1-1
title: Gesundheit in Deutschland aktuell 2012 (GEDA 2012)
DOI: 10.5281/zenodo.14958730
title: Replication data for: Rural Pensions, Labor Reallocation, and Aggregate Income: An Empirical and Quantitative Analysis of China

description: This is the replication package for the paper titled "Rural Pensions, Labor Reallocation, and Aggregate Income: An Empirical and Quantitative Analysis of China."
DOI: 10.5281/zenodo.14958731
title: Replication data for: Rural Pensions, Labor Reallocation, and Aggregate Income: An Empirical and Quantitative Analysis of China

description: This is the replication package for the paper titled "Rural Pensions, Labor Reallocation, and Aggregate Income: An Empirical and Quantitative Analysis of China."
DOI: 10.5281/zenodo.13932377
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from NorESM2-LM model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13932462
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from NorESM2-MM model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13929458
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from TaiESM1 model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13929459
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from TaiESM1 model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13932463
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from NorESM2-MM model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13927357
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from MIROC-ES2L model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13927491
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from MRI-ESM2-0 model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.
DOI: 10.5281/zenodo.13927492
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from MRI-ESM2-0 model

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.