cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8307599
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2002)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8307604
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2004)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.15468/dl.6aqnk8
title: Occurrence Download

description: A dataset containing 239 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Macaca mulatta (Zimmermann, 1780)"
 ]
}

 The dataset includes 239 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027882-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ymztat
title: Occurrence Download

description: A dataset containing 219 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Enteroctopus dofleini (Wülker, 1910)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 219 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027883-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8281218
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2002)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.15468/dl.upfmkh
title: Occurrence Download

description: A dataset containing 1278015 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Botanical Database of Southern Africa (BODATSA): Botanical Collections"
 ]
}

 The dataset includes 1278015 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027873-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8281228
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2005)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8281227
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2005)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.15468/dl.mgzg8d
title: Occurrence Download

description: A dataset containing 170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balaenoptera acutorostrata Lacépède, 1804",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 170 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027885-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9zsz8y
title: Occurrence Download

description: A dataset containing 342 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eopsetta grigorjewi (Herzenstein, 1890)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 342 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027887-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bxpxu3
title: Occurrence Download

description: A dataset containing 31158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "GadmGid is FRA.1.7_1",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 31158 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027888-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8307605
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2004)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8287129
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2007)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8287128
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2007)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8288389
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2010)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8288388
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2010)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.15468/dl.z64zbv
title: Occurrence Download

description: A dataset containing 289 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Albatrossia pectoralis (Gilbert, 1892)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 289 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027889-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8289612
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2015)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8289613
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2015)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.34816/ifao.archives.141320
title: Ostracon hiératique Ifao - ODM 1138_nu_2012_17712

description: Référence : nu_2012_17712<br />Site : Deir el-Médina<br />Type : photographie<br />
DOI: 10.15468/dl.syvzdz
title: Occurrence Download

description: A dataset containing 1196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Colombia",
 "TaxonKey is Drosophilidae"
 ]
}

 The dataset includes 1196 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0153364-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34816/ifao.archives.141321
title: Ostracon hiératique Ifao - ODM 1138_nu_2012_17714

description: Référence : nu_2012_17714<br />Site : Deir el-Médina<br />Type : photographie<br />
DOI: 10.15468/dl.vezyaf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Icerya montserratensis Riley, 1890",
 {
 "not" : {
 "DatasetKey" : [
 "is Global database of alien macrofungi"
 ]
 }
 },
 {
 "or" : [
 "Country is United States of America",
 "Country is United States",
 "Country is United States of America",
 "Country is Canada",
 "Continent is North America"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0368643-210914110416597/datasets/export for details.
DOI: 10.15468/dl.x5t9un
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "TaxonKey is Tradescantia spathacea Sw."
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137480-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ebx9d
title: Occurrence Download

description: A dataset containing 53209 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is SysTax - Herbaria"
 ]
}

 The dataset includes 53209 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011841-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bzz5uy
title: Occurrence Download

description: A dataset containing 373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dryomys nitedula (Pallas, 1778))",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 373 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0239627-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.