cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13922388
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from CanESM5 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.13923222
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from CanESM5-1 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: 10.7797/31-201920-1-1-1
title: Gesundheit in Deutschland aktuell 2019/2020-EHIS (GEDA 2019/2020-EHIS)
DOI: 10.5281/zenodo.13922357
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from BCC-CSM2-MR 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.13922387
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from CanESM5 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.13923221
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from CanESM5-1 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.13922358
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from BCC-CSM2-MR 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.13920166
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from MIROC6 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.13911683
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from CMCC-CM2-SR5 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.13911684
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from CMCC-CM2-SR5 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.13911590
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from FGOALS-g3 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.13911591
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from FGOALS-g3 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.13920165
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from MIROC6 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: 10.7797/27-200910-1-1-2
title: Gesundheit in Deutschland aktuell 2010 (GEDA 2010)
DOI: 10.7797/27-200910-1-1-1
title: Gesundheit in Deutschland aktuell 2010 (GEDA 2010)
DOI: 10.7797/26-200809-1-1-1
title: Gesundheit in Deutschland aktuell 2009 (GEDA 2009)
DOI: 10.7797/19-201415-1-1-1
title: Gesundheit in Deutschland aktuell 2014/2015-EHIS (GEDA 2014/2015-EHIS)
DOI: 10.7797/9-200306-1-1-5
title: KiGGS-Basiserhebung - „Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland“
DOI: 10.7797/9-200306-1-1-3
title: KiGGS-Basiserhebung - „Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland“
DOI: 10.7797/9-200306-1-1-4
title: KiGGS-Basiserhebung - „Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland“
DOI: 10.5281/zenodo.14361229
title: [Dataset & Models] Online material to publication "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints"

description: Supporting material for publication "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints" (Munch, Kemper, Khan, Helffrich, Giardini).

This repository contains:



SCEM_nmodes_catalog.xls: Compilation of normal mode centre frequencies and quality (attenuation) factors used on the study. 

SCEM_bestfit.bm: Best-fitting self-consistent Earth model (SCEM).

P-PEM_bestfit.bm: Best-fitting physically-parameterised Earth model (P-PEM).

forward_calculator.zip: Python script to compute normal mode centre frequencies and quality factors for a given 1D model (in .bm format) and compare predictions with observations (in .csv format). Running this script locally requires installation of package specnm (https://johkem1.gitlab.io/specnm/). Alternatively, the script can also be run online on the following link: https://colab.research.google.com/drive/1jaFiMHTlUW_kTOdtLCyOpN5StGxWtqk7?usp=sharing

Kernel_Gallery.zip: Gallery of normal modes sensitivity kernels (pdf format).
DOI: 10.7797/17-201417-1-1-1
title: KiGGS Welle 2 - „Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland“
DOI: 10.7797/15-200912-1-1-1
title: KiGGS Welle 1 - „Studie zur Gesundheit von Kindern und Jugendlichen in Deutschland“
DOI: 10.15468/dl.965mpa
title: Occurrence Download

description: A dataset containing 3031425 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3031425 records from 1169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003310-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.krggf6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cossyphus reticulatus Valenciennes, 1839",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019054-241126133413365/datasets/export for details.
DOI: 10.15468/dl.t3wfhm
title: Occurrence Download

description: A dataset containing 376851 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Year 2001-2024"
 ]
}

 The dataset includes 376851 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019021-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4fukkv
title: Occurrence Download

description: A dataset containing 17225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.30_1",
 "Geometry POLYGON((99 26,100 26,100 27,99 27,99 26))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 17225 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019053-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5aqdz
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleginus gracilis (Tilesius, 1810)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 37 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019057-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qakquu
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ficus insipida Willd.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019056-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e85bpv
title: Occurrence Download

description: A dataset containing 38379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Espeletia Mutis ex Humb. & Bonpl.",
 "HasCoordinate is true",
 "Country is Colombia"
 ]
}

 The dataset includes 38379 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019055-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6yt3nn
title: Occurrence Download

description: A dataset containing 110144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-106.74313 25.44749,-103.23011 25.44749,-103.23011 26.85374,-106.74313 26.85374,-106.74313 25.44749))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 110144 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019024-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pjyymy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gadus gracilis Tilesius, 1810",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019058-241126133413365/datasets/export for details.
DOI: 10.15468/dl.64wwbw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ficus lacor Buch.-Ham.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019059-241126133413365/datasets/export for details.
DOI: 10.15468/dl.asznkh
title: Occurrence Download

description: A dataset containing 252 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scomber australasicus Cuvier, 1832",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 252 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019060-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9p8kb4
title: Occurrence Download

description: A dataset containing 1849 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycodes diapterus Gilbert, 1892",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1849 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019062-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4wg4kr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ficus lutea Vahl",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019063-241126133413365/datasets/export for details.
DOI: 10.15468/dl.3uydwh
title: Occurrence Download

description: A dataset containing 38379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Espeletia Mutis ex Humb. & Bonpl.",
 "HasCoordinate is true",
 "Country is Colombia"
 ]
}

 The dataset includes 38379 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019061-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.