cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.29617220
title: Time series of hourly resolution Solar-PV generation for European Scale Energy system studies

description: This dataset provides Pan-European simulated hourly time series of solar PV generation to be used with Balmorel energy system model which can be used to study future European scale energy system. In total, the dataset consists of three different technologies (rooftop PV, utility-scale PV with tracking, and utility-scale PV without tracking), each simulated across three resource grades (RGA, RGB, and RGC). Naming of the folders and their respective files are based on the technology type followed by resource grade. The resource grade RGA consists of best 10 % of locations (in terms of mean irradiance) within each region. Similarly, RGB consists of simulation of 10-50 % of the best location and RGC consists of the remaining 50 % of the locations. Each folder has one csv file which contains the hourly time-series and a map which shows the resulting capacity factors. The first column in csv file are the timestamps defined in GMT time and the rest of the columns represent Balmorel regions.For simulations a generic PV module and inverters are assumed. The meteorological data are from ERA5-Land and pvlib (version: 0.8.0) is used for the transformation to power generation (see references below). Three separate runs are performed:1. Utility scale without tracking: south-facing PVs with an optimal tilt and overplanting of 25%.2. Utility scale with 1-axis tracking: south-facing PVs with an optimal tilt and overplanting of 25%, and 1-axis tracking.3. Rooftop PV installations: The installation angles for each location are simulated from 2 Gaussian distributions, with the following parameters for orientation and tilt: μ = 180°, σ = 40 and μ = 25°, σ = 15° (tilt angles below 0 are forced to be 0), respectively. (link3)The optimal tilt was calculated based on the formula : β = 0.764 × Latitude + 2.14° (link4)This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.7964141
DOI: 10.11583/dtu.29617220.v1
title: Time series of hourly resolution Solar-PV generation for European Scale Energy system studies

description: This dataset provides Pan-European simulated hourly time series of solar PV generation to be used with Balmorel energy system model which can be used to study future European scale energy system. In total, the dataset consists of three different technologies (rooftop PV, utility-scale PV with tracking, and utility-scale PV without tracking), each simulated across three resource grades (RGA, RGB, and RGC). Naming of the folders and their respective files are based on the technology type followed by resource grade. The resource grade RGA consists of best 10 % of locations (in terms of mean irradiance) within each region. Similarly, RGB consists of simulation of 10-50 % of the best location and RGC consists of the remaining 50 % of the locations. Each folder has one csv file which contains the hourly time-series and a map which shows the resulting capacity factors. The first column in csv file are the timestamps defined in GMT time and the rest of the columns represent Balmorel regions.For simulations a generic PV module and inverters are assumed. The meteorological data are from ERA5-Land and pvlib (version: 0.8.0) is used for the transformation to power generation (see references below). Three separate runs are performed:1. Utility scale without tracking: south-facing PVs with an optimal tilt and overplanting of 25%.2. Utility scale with 1-axis tracking: south-facing PVs with an optimal tilt and overplanting of 25%, and 1-axis tracking.3. Rooftop PV installations: The installation angles for each location are simulated from 2 Gaussian distributions, with the following parameters for orientation and tilt: μ = 180°, σ = 40 and μ = 25°, σ = 15° (tilt angles below 0 are forced to be 0), respectively. (link3)The optimal tilt was calculated based on the formula : β = 0.764 × Latitude + 2.14° (link4)This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.7964141
DOI: 10.13140/rg.2.2.20111.30882
title: Mechanical error in Rit comparison R D G Rs Polyc BPReg RAC EAC cleaned (n = 22,824)
DOI: 10.13140/rg.2.2.27451.34082
title: Battle plans
DOI: 10.13140/rg.2.2.30282.49602
title: Data Utilisation des préservatifs chez les jeunes. Théorie du comportement planifié et identité prévention (n=400, année 2018)
DOI: 10.13140/rg.2.2.19587.02086
title: Gusano cortador (Agrotis ipsilon) que afecta los cultivos de hortalizas en Cerro Punta, Chiriquí, Panamá
DOI: 10.13140/rg.2.2.27398.91205
title: Data Satisfaction rythmes scolaires. Adaptation du modèle SERVQUAL (n=313, année 2015)
DOI: 10.15468/dl.n9had7
title: Occurrence Download

description: A dataset containing 1739 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is BatMapPH: Philippine Bat Species Occurrence Database"
 ]
}

 The dataset includes 1739 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019238-250920141307145/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.30218038
title: <b>Monitoring and Modeling Changes in Albedo and Drivers in Central Asia Over the Past 35 years</b>

description: Albedo is a crucial factor influencing regional radiative forcing and energy balance, exhibiting significant variability across ecological zones, from deserts to alpine areas. However, our understanding of surface albedo changes in Central Asia's arid regions is limited. This study analyzes the spatiotemporal variability of surface albedo from 1990 to 2025 using multi-sensor satellite datasets (MERRA-2, MODIS, VIIRS) and reanalysis products. It explores the relationships with climatic, environmental, anthropogenic, and biophysical drivers. Findings reveal a strong positive correlation between NDVI and surface albedo in vegetated areas, while pollution indices show a negative association. Arid lowlands display lower albedo (0.35-0.47), contrasting with higher values in humid highlands (0.6-0.73). Despite no significant long-term trends since 1990, a decline is anticipated post-2025 under the RCP 8.5 scenario. This study highlights albedo's sensitivity to both natural and human influences, providing valuable insights for climate resilience strategies.
DOI: 10.6084/m9.figshare.30218038.v1
title: <b>Monitoring and Modeling Changes in Albedo and Drivers in Central Asia Over the Past 35 years</b>

description: Albedo is a crucial factor influencing regional radiative forcing and energy balance, exhibiting significant variability across ecological zones, from deserts to alpine areas. However, our understanding of surface albedo changes in Central Asia's arid regions is limited. This study analyzes the spatiotemporal variability of surface albedo from 1990 to 2025 using multi-sensor satellite datasets (MERRA-2, MODIS, VIIRS) and reanalysis products. It explores the relationships with climatic, environmental, anthropogenic, and biophysical drivers. Findings reveal a strong positive correlation between NDVI and surface albedo in vegetated areas, while pollution indices show a negative association. Arid lowlands display lower albedo (0.35-0.47), contrasting with higher values in humid highlands (0.6-0.73). Despite no significant long-term trends since 1990, a decline is anticipated post-2025 under the RCP 8.5 scenario. This study highlights albedo's sensitivity to both natural and human influences, providing valuable insights for climate resilience strategies.
DOI: 10.15468/dl.rn9zsc
title: Occurrence Download

description: A dataset containing 1380 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((62.3761 -41.2386,171.1596 -41.2386,171.1596 39.2612,62.3761 39.2612,62.3761 -41.2386))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Arundinella setosa"
 ]
}

 The dataset includes 1380 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019239-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8frmw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Latvia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019240-250920141307145/datasets/export for details.
DOI: 10.15468/dl.ud4s7a
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Moldova, Republic of",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019241-250920141307145/datasets/export for details.
DOI: 10.15468/dl.74kv8h
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Nepal",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019242-250920141307145/datasets/export for details.
DOI: 10.13140/rg.2.2.21227.20000
title: Certificados HERGA ENERGY
DOI: 10.15468/dl.2ghb66
title: Occurrence Download

description: A dataset containing 414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudothemis zonata",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 414 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019243-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.enpzrc
title: Occurrence Download

description: A dataset containing 689 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Cimicifuga racemosa)"
 ]
}

 The dataset includes 689 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019244-250920141307145/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.30218182
title: Dataset for "Economic Burden of Work-related Musculoskeletal Disorders in Indonesia Before and During the COVID-19 Era: Analysis of Global Burden of Disease Estimates (2019–2021)"

description: The dataset contains province-level estimates of the health and economic burden of occupational ergonomics–related low back pain in Indonesia from 2019 to 2021. Variables include disability-adjusted life years (DALYs) and their annual shifts, provincial GDP and PPP conversion factors, life expectancy, and population counts. Economic valuation parameters such as the value of a statistical life (VSL), value of a statistical life year (VSLY), and value of lost welfare (VLW) were derived for each province and year. The dataset also includes VLW-to-GDP ratios and their year-to-year changes, allowing assessment of both absolute burden and relative economic impact across provinces and over time.
DOI: 10.6084/m9.figshare.30218182.v1
title: Dataset for "Economic Burden of Work-related Musculoskeletal Disorders in Indonesia Before and During the COVID-19 Era: Analysis of Global Burden of Disease Estimates (2019–2021)"

description: The dataset contains province-level estimates of the health and economic burden of occupational ergonomics–related low back pain in Indonesia from 2019 to 2021. Variables include disability-adjusted life years (DALYs) and their annual shifts, provincial GDP and PPP conversion factors, life expectancy, and population counts. Economic valuation parameters such as the value of a statistical life (VSL), value of a statistical life year (VSLY), and value of lost welfare (VLW) were derived for each province and year. The dataset also includes VLW-to-GDP ratios and their year-to-year changes, allowing assessment of both absolute burden and relative economic impact across provinces and over time.
DOI: 10.15468/dl.rannsp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Romania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019245-250920141307145/datasets/export for details.
DOI: 10.15468/dl.gxj2zn
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019246-250920141307145/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.30218152
title: Seasonal Fire Weather Index Analysis – Italy (2007–2024)

description: 📌 OverviewThis repository contains all the necessary scripts and input data to reproduce the analysis of the <b>seasonal Fire Weather Index (FWI)</b> indicators for Italy between <b>2007–2024 (18 years)</b>.The workflow consists of <b>three R scripts</b> that calculate mean/median FWI values, burned area statistics, and relationships between fire danger indicators and observed burned areas.⚠️ Important Notes🔹 Dataset provenanceThe FWI dataset used here originates from the Copernicus Climate Data Store.This dataset is <b>no longer supported by the data providers</b>. Data and documentation are provided <i>as is</i>. Users are encouraged to consult the CDS Forum for any discussions or clarifications.The original <b>NetCDF (nc) files</b> contained:<b>Seasonal data</b>: June–September (fire season)<b>Indicator</b>: Fire Weather Index (FWI)<b>Scenario</b>: RCP2.6<b>Aggregation</b>: Multi-model mean caseThese NetCDF files were <b>reprojected using Climate Data Operators (CDO)</b> to a regular lon/lat grid before being included here.Reprojection grid specification:gridtype = lonlatxsize = 1000ysize = 460xfirst = -45xinc = 0.11yfirst = 22yinc = 0.11📂 Folder StructureAfter downloading, place the folder in the following path:C:/NERO2025/The folder should contain:C:/NERO2025/│── Script 01 C3S Analysis.R # Step 1 – FWI annual mean & median│── Script 02 Burned Areas Per Region.R # Step 2 – Burned areas per ecoregion│── Script 03 Scatter Plots FWI vs Burned Area.R # Step 3 – FWI–burned area link│── IB_ITA_2007_2024_v_1.shp # Shapefile of burned areas│── subsez_ecoregioni_IT_proj.shp # Shapefile of Italian ecoregions│── other shapefile files (.dbf, .shx, etc.)▶️ Workflow<b>Step 1: Annual FWI statistics</b>Run <code><strong>Script 01 C3S Analysis.R</strong></code>Calculates <b>mean and median seasonal FWI values</b> per year for Italy and per ecoregion.Outputs:<code>FWI_Mean_Table.csv</code><code>FWI_Median_Table.csv</code><b>Step 2: Burned areas per ecoregion</b>Run <code><strong>Script 02 Burned Areas Per Region.R</strong></code>Intersects burned areas with Italian ecoregions.Calculates:Absolute burned area per year & ecoregionRelative burned area (% of ecoregion burned)Performs <b>trend analysis</b> using linear regression & Mann-Kendall.Outputs:<code>BurnedArea_Ecoregions.csv</code><code>RelativeBurnedArea_Ecoregions.csv</code><code>BurnedArea_Ecoregions_Trends.csv</code>Plots saved in <code>BurnedArea_Trends_Plots/</code><b>Step 3: Linking FWI and burned areas</b>Run <code><strong>Script 03 Scatter Plots FWI vs Burned Area.R</strong></code>Merges FWI tables (Step 1) with burned area statistics (Step 2).Generates <b>scatterplots with regression fits</b>, showing the relationship between FWI and burned areas.Outputs:Plots saved in <code>FWI_Burned_ScatterPlots/</code>CSVs of data used for each plot⚠️ <b>Dependencies</b>:<br><b>Script 03 </b>requires the results of <b>Scripts 01 & 02</b>. Therefore, always run them in sequence.
DOI: 10.6084/m9.figshare.30218152.v1
title: Seasonal Fire Weather Index Analysis – Italy (2007–2024)

description: 📌 OverviewThis repository contains all the necessary scripts and input data to reproduce the analysis of the <b>seasonal Fire Weather Index (FWI)</b> indicators for Italy between <b>2007–2024 (18 years)</b>.The workflow consists of <b>three R scripts</b> that calculate mean/median FWI values, burned area statistics, and relationships between fire danger indicators and observed burned areas.⚠️ Important Notes🔹 Dataset provenanceThe FWI dataset used here originates from the Copernicus Climate Data Store.This dataset is <b>no longer supported by the data providers</b>. Data and documentation are provided <i>as is</i>. Users are encouraged to consult the CDS Forum for any discussions or clarifications.The original <b>NetCDF (nc) files</b> contained:<b>Seasonal data</b>: June–September (fire season)<b>Indicator</b>: Fire Weather Index (FWI)<b>Scenario</b>: RCP2.6<b>Aggregation</b>: Multi-model mean caseThese NetCDF files were <b>reprojected using Climate Data Operators (CDO)</b> to a regular lon/lat grid before being included here.Reprojection grid specification:gridtype = lonlatxsize = 1000ysize = 460xfirst = -45xinc = 0.11yfirst = 22yinc = 0.11📂 Folder StructureAfter downloading, place the folder in the following path:C:/NERO2025/The folder should contain:C:/NERO2025/│── Script 01 C3S Analysis.R # Step 1 – FWI annual mean & median│── Script 02 Burned Areas Per Region.R # Step 2 – Burned areas per ecoregion│── Script 03 Scatter Plots FWI vs Burned Area.R # Step 3 – FWI–burned area link│── IB_ITA_2007_2024_v_1.shp # Shapefile of burned areas│── subsez_ecoregioni_IT_proj.shp # Shapefile of Italian ecoregions│── other shapefile files (.dbf, .shx, etc.)▶️ Workflow<b>Step 1: Annual FWI statistics</b>Run <code><strong>Script 01 C3S Analysis.R</strong></code>Calculates <b>mean and median seasonal FWI values</b> per year for Italy and per ecoregion.Outputs:<code>FWI_Mean_Table.csv</code><code>FWI_Median_Table.csv</code><b>Step 2: Burned areas per ecoregion</b>Run <code><strong>Script 02 Burned Areas Per Region.R</strong></code>Intersects burned areas with Italian ecoregions.Calculates:Absolute burned area per year & ecoregionRelative burned area (% of ecoregion burned)Performs <b>trend analysis</b> using linear regression & Mann-Kendall.Outputs:<code>BurnedArea_Ecoregions.csv</code><code>RelativeBurnedArea_Ecoregions.csv</code><code>BurnedArea_Ecoregions_Trends.csv</code>Plots saved in <code>BurnedArea_Trends_Plots/</code><b>Step 3: Linking FWI and burned areas</b>Run <code><strong>Script 03 Scatter Plots FWI vs Burned Area.R</strong></code>Merges FWI tables (Step 1) with burned area statistics (Step 2).Generates <b>scatterplots with regression fits</b>, showing the relationship between FWI and burned areas.Outputs:Plots saved in <code>FWI_Burned_ScatterPlots/</code>CSVs of data used for each plot⚠️ <b>Dependencies</b>:<br><b>Script 03 </b>requires the results of <b>Scripts 01 & 02</b>. Therefore, always run them in sequence.
DOI: 10.15468/dl.dc8bja
title: Occurrence Download

description: A dataset containing 126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhyothemis regia",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 126 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019248-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13178694
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.18112/openneuro.ds006720.v1.0.0
title: Alpha power indexes working memory load for durations
DOI: 10.15468/dl.qcuvte
title: Occurrence Download

description: A dataset containing 14520 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Cistus creticus)"
 ]
}

 The dataset includes 14520 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019247-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hqfrjf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cryphonectria parasitica",
 "Country is Bosnia and Herzegovina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019249-250920141307145/datasets/export for details.
DOI: 10.15468/dl.77rd9u
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cryphonectria parasitica",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 11 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019250-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96ntkq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cryphonectria parasitica",
 "Country is Georgia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019251-250920141307145/datasets/export for details.
DOI: 10.6084/m9.figshare.30218194
title: Database PAPER

description: Database Paper Grit
DOI: 10.6084/m9.figshare.30218194.v1
title: Database PAPER

description: Database Paper Grit
DOI: 10.15468/dl.brpsf4
title: Occurrence Download

description: A dataset containing 641 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhyothemis variegata",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 641 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019252-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.