cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8308230
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2008)

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.8308231
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2008)

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.8310589
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2012)

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.8310590
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 grids (2012)

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.9ga3qu
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epinephelus septemfasciatus (Thunberg, 1793)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 63 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027869-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5fjfrd
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyporthodus octofasciatus (Griffin, 1926)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 19 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027870-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f7vszw
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Takifugu porphyreus (Temminck & Schlegel, 1850)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 89 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027871-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.czkgr4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Myrmecocystus testaceus Emery, 1893",
 "Country is Canada",
 {
 "not" : {
 "InstitutionCode" : [
 "is one of (iNaturalist)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027872-240626123714530/datasets/export for details.
DOI: 10.15468/dl.k9bg8c
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Macaca arctoides (I.Geoffroy Saint-Hilaire, 1831)"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027875-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4snj7t
title: Occurrence Download

description: A dataset containing 392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Decapterus maruadsi (Temminck & Schlegel, 1843)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 392 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027876-240626123714530/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.26317801
title: Supplementary Material for: Patient-centered care model’s effectiveness in reducing patient waiting time in the Emergency Department: A Systematic Literature Review

description: Introduction: The Emergency Department (ED) is an essential component of any hospital that offers round-the-clock urgent treatment to patients in critical condition. However, lengthy waiting times are expected, which may negatively affect the patient's health and overall experiences. This systematic review aims to assess the effectiveness of Patient-Centered Care (PCC) models in reducing the time patients spend waiting in the emergency room by synthesizing the available evidence. This was conducted by following the following three Research Objectives:
Objective 1: To identify patient-centered care models implementation in hospital settings.
Objective 2: To assess the effectiveness of patient-centered care models in improving patient outcomes in hospital settings.
Objective 3: To investigate the impact of prolonged waiting time on healthcare utilization in the emergency department.

Methods: In this systematic review design to be included, an article had to be: Full text, in English, peer-reviewed, address the evaluation of the effectiveness of the PCC model in reducing ED wait times, and the ED. Studies were excluded if they were abstracts, not fully available, published in any language other than English, were not peer-reviewed, did not meet the date of search, and were systematic reviews. The study was conducted by computerized databases which included PubMed, MEDLINE Embase, Scopus, Web of Science, and CINHAL were searched between April.01.2023 – April.17.2023. The study used the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement which has been updated to PRISMA 2020, a reporting guideline designed to address the issue of inadequate systematic review reporting. All the articles that satisfied the predefined inclusion criteria were independently assessed for risk of bias by two independent reviwer.


Results: A systematic review design searched the literature on ED context with PCC as the intervention to reduce the waiting time. Two independent reviewers screened the articles (3114) using the PRISMA and 27 articles were included. 

Conclusion: Long waiting times resulted in many adverse events for patients, such as delayed treatment and poorer clinical outcomes. PCC can exert a very impressive role contribution to emergency departments as patients should be integrated with health care providers in their health issues and treated from all perspectives
DOI: 10.15468/dl.ajux65
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Auxis rochei (Risso, 1810)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 128 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027877-240626123714530/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.24993984.v1
title: Dataset-code.zip

description: Data supporting "water additionbut not reduction alters plant biomass–diversity relationship".
DOI: 10.5281/zenodo.8288394
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2011)

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.8288395
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2011)

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.8307606
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 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.8307607
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 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.8310705
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 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.8310706
title: LGHAP v2:  Global daily 1-km gap-free PM2.5 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: 10.15468/dl.h2zsvx
title: Occurrence Download

description: A dataset containing 152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dexistes rikuzenius Jordan & Starks, 1904",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 152 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027879-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8288396
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2012)

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.8288397
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2012)

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.n9zm9w
title: Occurrence Download

description: A dataset listing the 1092917 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Plantae"
 ]
}

 The dataset's 1092917 records were derived from 16325 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027867-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3vh8hk
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cnidoscolus tridentifer Fern.Casas & J.M.Pizarro"
 ]
}

 The dataset includes 15 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0151114-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.