cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.k9t63e
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Micrurus steindachneri"
 ]
}

 The dataset includes 51 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021503-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jjb7xe
title: Occurrence Download

description: A dataset containing 386 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prociphilus fraxinifolii"
 ]
}

 The dataset includes 386 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021504-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c6m3vc
title: Occurrence Download

description: A dataset containing 27389 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Puma concolor"
 ]
}

 The dataset includes 27389 records from 452 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021505-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.24485.36329
title: Derived Parameter.pdf
DOI: 10.15468/dl.bq5gxa
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((95.90511 29.06205,123.09489 29.06205,123.09489 39.93795,95.90511 39.93795,95.90511 29.06205))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Longpotamon changanense"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021507-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mx7er
title: Occurrence Download

description: A dataset containing 13918 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((68 32,110 32,110 55,68 55,68 32))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Betula"
 ]
}

 The dataset includes 13918 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021506-251120083545085/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/13952438
title: ITB Official News Dataset (Berita Resmi ITB)
DOI: 10.5281/zenodo.17784537
title: DAO Analyzer dataset

description: DAO-Analyzer's dataset.


Explore data from Decentralized Autonomous Organizations deployed on the DAOstack, DAOhaus and Aragon platforms.
description: DAO-Analyzer is created under the umbrella of multiple research projects:



Chain Community, funded by the Spanish Ministry of Science and Innovation (RTI2018‐096820‐A‐I00) and led by Javier Arroyo and Samer Hassan

P2P Models, funded by the European Research Council (ERC-2017-STG 625 grant no.: 75920), led by Samer Hassan.

DAOapplications, funded by the Spanish Ministry of Science and Innovation (PID2021-127956OB-I00) and led by Javier Arroyo, Samer Hassan and maria Cruz Valiente
DOI: 10.5281/zenodo.17765250
title: Save the Bees: Long-Term Agricultural & Pollinator Dynamics Dataset

description: This dataset compiles multi-decadal information (1992–2020) on agricultural production, climate indicators, pesticide usage, and honey-bee colony dynamics across the United States.It was developed as part of an internal pilot study on long-term pollinator decline and its relationship with intensive agricultural practices.

The dataset integrates four major components:

1. Honey Bee Colony Statistics

Annual and quarterly estimates of colony counts, colony losses, renovation rates, and presence of stressors (varroa mites, diseases, parasites, pesticides exposure).Data were harmonized from multiple regional survey programs and digitized archival reports.

2. Agricultural Production

Crop yield data (hg/ha), fertilizer and pesticide usage metrics, cultivated area, and associated agronomic variables.These were aggregated from state-level agricultural monitoring programs and standardized to ensure comparability over time.

3. Climate and Environmental Indicators

Annual averages of temperature, precipitation, and extreme-weather incidence based on state-level agro-meteorological stations.

4. Derived Indicators

Several calculated fields are included to support modeling and forecasting tasks, such as:



colony loss ratios

pesticide-intensity indexes

yield-per-unit-input

multi-year moving averages

stressor-interaction indicators


Intended Use

This dataset is suitable for:



regression modeling

time-series forecasting

simulation studies

environmental impact assessment

teaching and academic exercises


The dataset does not contain sensitive information and was compiled solely for research and educational purposes.




Funding



Pollinator Resilience Seed Grant 2023 – North American Agroecology Foundation Environmental Data Innovation Grant #EDIG-2217
DOI: 10.5281/zenodo.17784533
title: Save the Bees: Long-Term Agricultural & Pollinator Dynamics Dataset

description: This dataset compiles multi-decadal information (1992–2020) on agricultural production, climate indicators, pesticide usage, and honey-bee colony dynamics across the United States.It was developed as part of an internal pilot study on long-term pollinator decline and its relationship with intensive agricultural practices.

The dataset integrates four major components:

1. Honey Bee Colony Statistics

Annual and quarterly estimates of colony counts, colony losses, renovation rates, and presence of stressors (varroa mites, diseases, parasites, pesticides exposure).Data were harmonized from multiple regional survey programs and digitized archival reports.

2. Agricultural Production

Crop yield data (hg/ha), fertilizer and pesticide usage metrics, cultivated area, and associated agronomic variables.These were aggregated from state-level agricultural monitoring programs and standardized to ensure comparability over time.

3. Climate and Environmental Indicators

Annual averages of temperature, precipitation, and extreme-weather incidence based on state-level agro-meteorological stations.

4. Derived Indicators

Several calculated fields are included to support modeling and forecasting tasks, such as:



colony loss ratios

pesticide-intensity indexes

yield-per-unit-input

multi-year moving averages

stressor-interaction indicators


Intended Use

This dataset is suitable for:



regression modeling

time-series forecasting

simulation studies

environmental impact assessment

teaching and academic exercises


The dataset does not contain sensitive information and was compiled solely for research and educational purposes.




Funding



Pollinator Resilience Seed Grant 2023 – North American Agroecology Foundation Environmental Data Innovation Grant #EDIG-2217
DOI: 10.15468/dl.pvca97
title: Occurrence Download

description: A dataset containing 6889 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sorbus commixta"
 ]
}

 The dataset includes 6889 records from 207 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021508-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17747070
title: collaborative learning data
DOI: 10.15468/dl.cjf8rm
title: Occurrence Download

description: A dataset containing 197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "TaxonKey is Mystacidium venosum"
 ]
}

 The dataset includes 197 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021510-251120083545085/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.30758948
title: <b>Impact of Social Determinants of Health on Osteoporosis Risk: A Cross-Sectional Analysis of NHANES Data</b>

description: Background: Osteoporosis is a significant health issue, especially among older adults, leading to increased fracture risk and diminished quality of life. Social determinants of health (SDoH) have been shown to influence various health outcomes. However, the relationship between SDoH and osteoporosis remains underexplored.Methods: We conducted a cross-sectional analysis using National Health and Nutrition Examination Survey (NHANES) data from 2005-2010, 2013-2014, and 2017-2018, including 9,484 participants aged 50 and older. SDoH was assessed through components including employment status, poverty income ratio, food security, education level, access to healthcare, health insurance, home ownership, and marital status. SDoH scores ranged from 0 to 8, with higher scores indicating worse social conditions. Osteoporosis was defined based on bone mineral density (BMD) measurements of the femoral neck and total femur, with a T-score ≤ -2.5 indicating osteoporosis. Logistic and linear regression models were used to assess the association between SDoH scores and osteoporosis. Subgroup and sensitivity analyses were performed to test the robustness of the results. Receiver Operating Characteristic (ROC) analysis was used to assess the predictive value of SDoH for osteoporosis.Results: In the fully adjusted model, participants in the medium (3-5) and high (6-8) SDoH score groups had 1.76 (95% CI: 1.36–2.29; P &lt; 0.001) and 3.12 (95% CI: 1.88–5.18; P &lt; 0.001) times higher odds of osteoporosis, respectively, compared to the low (0-2) score group. All SDoH components, except access to healthcare, were significantly associated with osteoporosis. These findings remained consistent in subgroup and sensitivity analyses. ROC analysis showed that SDoH components, along with sex, age, race/ethnicity, and body mass index, effectively predicted osteoporosis (Sensitivity: 0.750, Specificity: 0.772, AUC: 0.839 [95% CI: 0.823–0.854], P &lt; 0.001).Conclusion: Higher SDoH scores are associated with an increased risk of osteoporosis. Addressing social disparities, such as improving food security and reducing poverty, could help mitigate osteoporosis risk. SDoH and easily accessible demographic data can effectively predict osteoporosis risk, offering potential for early identification and intervention.
DOI: 10.6084/m9.figshare.30758948.v1
title: <b>Impact of Social Determinants of Health on Osteoporosis Risk: A Cross-Sectional Analysis of NHANES Data</b>

description: Background: Osteoporosis is a significant health issue, especially among older adults, leading to increased fracture risk and diminished quality of life. Social determinants of health (SDoH) have been shown to influence various health outcomes. However, the relationship between SDoH and osteoporosis remains underexplored.Methods: We conducted a cross-sectional analysis using National Health and Nutrition Examination Survey (NHANES) data from 2005-2010, 2013-2014, and 2017-2018, including 9,484 participants aged 50 and older. SDoH was assessed through components including employment status, poverty income ratio, food security, education level, access to healthcare, health insurance, home ownership, and marital status. SDoH scores ranged from 0 to 8, with higher scores indicating worse social conditions. Osteoporosis was defined based on bone mineral density (BMD) measurements of the femoral neck and total femur, with a T-score ≤ -2.5 indicating osteoporosis. Logistic and linear regression models were used to assess the association between SDoH scores and osteoporosis. Subgroup and sensitivity analyses were performed to test the robustness of the results. Receiver Operating Characteristic (ROC) analysis was used to assess the predictive value of SDoH for osteoporosis.Results: In the fully adjusted model, participants in the medium (3-5) and high (6-8) SDoH score groups had 1.76 (95% CI: 1.36–2.29; P &lt; 0.001) and 3.12 (95% CI: 1.88–5.18; P &lt; 0.001) times higher odds of osteoporosis, respectively, compared to the low (0-2) score group. All SDoH components, except access to healthcare, were significantly associated with osteoporosis. These findings remained consistent in subgroup and sensitivity analyses. ROC analysis showed that SDoH components, along with sex, age, race/ethnicity, and body mass index, effectively predicted osteoporosis (Sensitivity: 0.750, Specificity: 0.772, AUC: 0.839 [95% CI: 0.823–0.854], P &lt; 0.001).Conclusion: Higher SDoH scores are associated with an increased risk of osteoporosis. Addressing social disparities, such as improving food security and reducing poverty, could help mitigate osteoporosis risk. SDoH and easily accessible demographic data can effectively predict osteoporosis risk, offering potential for early identification and intervention.
DOI: 10.34740/kaggle/dsv/13952485
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.e4vgvy
title: Occurrence Download

description: A dataset containing 14271 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cephalotaxus"
 ]
}

 The dataset includes 14271 records from 454 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021511-251120083545085/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.30751034
title: Global Land-use Change Drives Nearly Half of Projected Diversity Shifts of Terrestrial Vertebrates

description: Climate change and land-use change are the two primary drivers of global biodiversity loss, while their relative contributions remain the subject of ongoing debate. Clarifying their respective roles is essential for designing effective conservation strategies aimed at protecting species from the most pressing threats in the near future. Our analysis of global biodiversity simulations indicates that nearly half of the world’s countries are projected to experience biodiversity declines by 2050 due to climate and land-use change. Global land-use change is projected to account for 48.5% of the variation in species richness, slightly less than the impact of climate change. Notably, tropical regions emerge as hotspots for future biodiversity loss. These findings highlight the urgent need to implement targeted conservation measures in priority areas to mitigate the substantial impacts of future climate and land-use changes, particularly in tropical regions.
DOI: 10.6084/m9.figshare.30751034.v1
title: Global Land-use Change Drives Nearly Half of Projected Diversity Shifts of Terrestrial Vertebrates

description: Climate change and land-use change are the two primary drivers of global biodiversity loss, while their relative contributions remain the subject of ongoing debate. Clarifying their respective roles is essential for designing effective conservation strategies aimed at protecting species from the most pressing threats in the near future. Our analysis of global biodiversity simulations indicates that nearly half of the world’s countries are projected to experience biodiversity declines by 2050 due to climate and land-use change. Global land-use change is projected to account for 48.5% of the variation in species richness, slightly less than the impact of climate change. Notably, tropical regions emerge as hotspots for future biodiversity loss. These findings highlight the urgent need to implement targeted conservation measures in priority areas to mitigate the substantial impacts of future climate and land-use changes, particularly in tropical regions.
DOI: 10.15468/dl.4jnn28
title: Occurrence Download

description: A dataset containing 4882 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (Thailand, India, Hong Kong, Nepal, China, Viet Nam, Bangladesh, Myanmar, Pakistan)",
 "TaxonKey is Macaca mulatta",
 "Year 2010-2025"
 ]
}

 The dataset includes 4882 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021512-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ujzwzf
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021515-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17596536
title: Data for "Variability of landslide susceptibility models under different ground motion scenarios in Rasuwa District, Nepal"

description: This dataset provides comprehensive geospatial information for analyzing landslide susceptibility variability under different peak ground acceleration (PGA) scenarios in the Rasuwa District, Nepal, following the 2015 Gorkha Earthquake. It is designed to support research on coseismic landslide distribution, seismic hazard assessment, and spatiotemporal modeling of landslide susceptibility.

Included Data

1. Coseismic Landslide Inventory (Shapefiles)





A collection of 890 mapped landslide polygons delineated after the 2015 Gorkha Earthquake.




Landslides were mapped manually by visually interpreting cloud-free, high-resolution satellite imagery (WorldView series, GeoEye-1, and RapidEye) acquired by Maxar Technologies between:





April 14, 2015 (pre-earthquake), and




June 10, 2015 (post-earthquake).






Imagery was accessed and analyzed using Google Earth Pro.




The shapefile dataset provides accurate spatial extents of coseismic landslides for hazard evaluation and model validation.



2. Seismic Hazard Maps and Landslide Susceptibility Maps (.tif)

This dataset includes a series of raster layers representing both seismic hazard and modeled landslide susceptibility:





Four probabilistic seismic hazard maps (PSHA-based) corresponding to 50-year probability of exceedance levels:





50%, 10%, 5%, and 2% probability.






Each seismic hazard map is paired with a landslide susceptibility map generated using logistic regression models.




To isolate the spatiotemporal effects of varying PGA, all non-PGA covariates (e.g., topographic, geological, hydrological factors) were held static during susceptibility modeling.




The resulting raster outputs allow temporal comparison of susceptibility patterns under different seismic loading conditions.
DOI: 10.5281/zenodo.17596537
title: Data for "Variability of landslide susceptibility models under different ground motion scenarios in Rasuwa District, Nepal"

description: This dataset provides comprehensive geospatial information for analyzing landslide susceptibility variability under different peak ground acceleration (PGA) scenarios in the Rasuwa District, Nepal, following the 2015 Gorkha Earthquake. It is designed to support research on coseismic landslide distribution, seismic hazard assessment, and spatiotemporal modeling of landslide susceptibility.

Included Data

1. Coseismic Landslide Inventory (Shapefiles)





A collection of 890 mapped landslide polygons delineated after the 2015 Gorkha Earthquake.




Landslides were mapped manually by visually interpreting cloud-free, high-resolution satellite imagery (WorldView series, GeoEye-1, and RapidEye) acquired by Maxar Technologies between:





April 14, 2015 (pre-earthquake), and




June 10, 2015 (post-earthquake).






Imagery was accessed and analyzed using Google Earth Pro.




The shapefile dataset provides accurate spatial extents of coseismic landslides for hazard evaluation and model validation.



2. Seismic Hazard Maps and Landslide Susceptibility Maps (.tif)

This dataset includes a series of raster layers representing both seismic hazard and modeled landslide susceptibility:





Four probabilistic seismic hazard maps (PSHA-based) corresponding to 50-year probability of exceedance levels:





50%, 10%, 5%, and 2% probability.






Each seismic hazard map is paired with a landslide susceptibility map generated using logistic regression models.




To isolate the spatiotemporal effects of varying PGA, all non-PGA covariates (e.g., topographic, geological, hydrological factors) were held static during susceptibility modeling.




The resulting raster outputs allow temporal comparison of susceptibility patterns under different seismic loading conditions.
DOI: 10.15468/dl.jpgv7p
title: Occurrence Download

description: A dataset containing 20666 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-44.52365 -2.35737,-52.37642 4.93581,-55.38593 5.2707,-59.71213 7.36178,-63.44153 7.09371,-67.36963 7.57721,-68.24354 4.67271,-71.50401 2.48239,-73.63686 0.01065,-74.51764 -4.01866,-74.37944 -8.1371,-72.33391 -12.30368,-68.2621 -16.20595,-62.36204 -19.78556,-60.00504 -17.0637,-56.70469 -11.11607,-48.58858 -7.00297,-44.52365 -2.35737))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bromeliaceae"
 ]
}

 The dataset includes 20666 records from 176 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021516-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3z8anw
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudopyricularia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021517-251120083545085/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.30758888
title: RF regression model data

description: Measured soil and mapped environmental variables to be used in a Random Forest model. d13C and d15N were also measured, and will be published seperately. Until then, d13C and d15N data are available upon request.Random Forest regression model used to understand relationship between subsoil OC and soil and environmental covariates to elucidate the controls on subsoil OC formation under perennial pasture grazing in Northern NSW, Australia.
DOI: 10.6084/m9.figshare.30758888.v1
title: RF regression model data

description: Measured soil and mapped environmental variables to be used in a Random Forest model. d13C and d15N were also measured, and will be published seperately. Until then, d13C and d15N data are available upon request.Random Forest regression model used to understand relationship between subsoil OC and soil and environmental covariates to elucidate the controls on subsoil OC formation under perennial pasture grazing in Northern NSW, Australia.