cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5h9xz2
title: Occurrence Download

description: A dataset containing 218583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Carcharhinus amblyrhynchos (Bleeker, 1856)"
 ]
}

 The dataset includes 218583 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012837-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.srtax7
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Rhododendron barbatum Wall. ex G.Don"
 ]
}

 The dataset includes 64 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012850-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.apj92x
title: Occurrence Download

description: A dataset containing 32298934 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Fabales"
 ]
}

 The dataset includes 32298934 records from 9281 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012830-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bmrfuw
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Celastrus orbiculatus",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 30 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049054-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qmqb7u
title: Occurrence Download

description: A dataset containing 211051 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuscuta L."
 ]
}

 The dataset includes 211051 records from 1838 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001012-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7k8pv
title: Occurrence Download

description: A dataset containing 358 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Platyrrhinus incarum (Thomas, 1912)"
 ]
}

 The dataset includes 358 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011823-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t5u528
title: Occurrence Download

description: A dataset containing 1379 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leopardus tigrinus"
 ]
}

 The dataset includes 1379 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024485-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w4gmu5
title: Occurrence Download

description: A dataset containing 23461467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pediobius multisetis Boucek, 1977, Emersonella palmae Boucek, 1977, Brachymeria mnestor (Walker, 1841), Trichospilus vorax Boucek, 1976, Brachymeria deesensis (Cameron, 1905), Pseudoligosita longiclavata (Viggiani, 1971), Erythmelus tingitiphagus (Soares, 1941), Brachymeria subconica Boucek, 1992, Horismenus carolinensis Burks, 1971, Pediobius vigintiquinque Kerrich, 1970, Pediobius setigerus Kerrich, 1970, Conura immaculata (Cresson, 1865), Pediobius anomalus (Gahan, 1920), Pleurotroppopsis podagrica (Waterston, 1925), Closterocerus africanus Waterston, 1925, Tineobius brachartonae (Gahan, 1927), Protoxerus stangeri eborivorus (Du Chaillu, 1860), Funisciurus lemniscatus lemniscatus, Heliosciurus rufobrachium aubryi (Milne-Edwards, 1867), Pteroteinon caenira (Hewitson, 1867), Elymnias bammakoo (Westwood), Lophoceros fasciatus (Shaw, 1812), Corythaeola cristata (Vieillot, 1816), Bycanistes albotibialis (Cabanis & Reichenow, 1877), Crinifer piscator (Boddaert, 1783), Ceratogymna atrata (Temminck, 1835), Tauraco persa (Linnaeus, 1758), Polyboroides typus A.Smith, 1829, Eulemur flavifrons (Gray, 1867), Gypohierax angolensis (Gmelin, 1788), Epixerus ebii wilsoni (Du Chaillu, 1860), Cathartes burrovianus Cassin, 1845, Bycanistes fistulator (Cassin, 1850), Colobus satanas Waterhouse, 1838, Poicephalus gulielmi (Jardine, 1849), Cercopithecus cephus (Linnaeus, 1758), Cercopithecus pogonias Bennett, 1833, Cercopithecus nictitans (Linnaeus, 1766), Gorilla gorilla (Savage, 1847), Strategus aloeus (Linnaeus, 1758), Coragyps atratus (Bechstein, 1793), Cercopithecus campbelli Waterhouse, 1838, Cathartes aura (Linnaeus, 1758), Psittacus erithacus Linnaeus, 1758, Mandrillus sphinx (Linnaeus, 1758), Milvus migrans (Boddaert, 1783), Grasidius hybridus O'Brien & Beserra, 2004, Piaractus brachypomus (Cuvier, 1818), Pseudotaenia ajax (Saunders, 1872), Bemisia gigantea Martin, 1999)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 23461467 records from 4722 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009390-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15139/s3/rfa3fp
title: Data Management and Sharing Plan for: Nitric oxide-releasing hyaluronic acid therapeutics for treating periodontal disease

description: The Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data.
description: Data Management and Sharing Plan
DOI: 10.5281/zenodo.14933635
title: Global Pasture Watch - Annual livestock headcount layers for cattle, goats, sheep, horses, and buffaloes at 1-km 2000–2022 (FAOSTAT-adjusted) (Part-1)

description: Global annual layers of livestock distribution of cattle, goats, sheep, horses and buffaloes at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).

Raster cell values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, ensuring that the adjusted pixel values accurately reflected the national totals. For applications relying on headcount data from FAOSTAT the provided layers serve as a fully compatible alternative.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.17491242
title: Global Pasture Watch - Annual livestock headcount layers for cattle, goats, sheep, horses, and buffaloes at 1-km 2000–2022 (FAOSTAT-adjusted) (Part-1)

description: Global annual layers of livestock distribution of cattle, goats, sheep, horses and buffaloes at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).

Raster cell values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, ensuring that the adjusted pixel values accurately reflected the national totals. For applications relying on headcount data from FAOSTAT the provided layers serve as a fully compatible alternative.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.17490692
title: Global Pasture Watch - Annual sheep density layers at 1-km for 2000–2022 (including 95% prediction interval)

description: Global annual layers of sheep density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 198,465 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). 

Raster cell values represent heads km2 including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.14933640
title: Global Pasture Watch - Annual sheep density layers at 1-km for 2000–2022 (including 95% prediction interval)

description: Global annual layers of sheep density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 198,465 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). 

Raster cell values represent heads km2 including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.34740/kaggle/dsv/13818919
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.17490457
title: Global Pasture Watch - Annual horse density layers at 1-km for 2000–2022 (including 95% prediction interval)

description: Global annual layers of horse density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 170,628 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). 

Raster cell values represent heads km2 including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.14933646
title: Global Pasture Watch - Annual horse density layers at 1-km for 2000–2022 (including 95% prediction interval)

description: Global annual layers of horse density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 170,628 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). 

Raster cell values represent heads km2 including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.17490112
title: Global Pasture Watch - Annual goat density layers at 1-km for 2000–2022 (including 95% prediction interval)

description: Global annual layers of goat density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 172,536 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). 

Raster cell values represent heads km2 including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.14933652
title: Global Pasture Watch - Annual goat density layers at 1-km for 2000–2022 (including 95% prediction interval)

description: Global annual layers of goat density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 172,536 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution). 

Raster cell values represent heads km2 including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle  Goats   Sheep  Horses Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine