cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17183258
title: Global Hospital Capacity Management Solutions Market 2025 To 2034

description: Hospital Capacity Management Solutions Market Size, Trends and Insights By Solution Type (Capacity Management Software, Capacity Planning Software, Patient Flow Management, Others), By Hospital Type (Public Hospitals, Private Hospitals, Specialty Hospitals, Teaching Hospitals, Children’s Hospitals), By Workflow (Pre-Admission, During Admission, Post-Discharge, End-to-End), By Deployment Model (Cloud-Based, On-Premise), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

As per the Hospital Capacity Management Solutions Market analysis conducted by the CMI Team, the global Hospital capacity management solutions Market is expected to record a CAGR of 10.4% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 3.92 Billion. By 2034, the valuation is anticipated to reach USD 9.3 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=74249
DOI: 10.5281/zenodo.17183259
title: Global Hospital Capacity Management Solutions Market 2025 To 2034

description: Hospital Capacity Management Solutions Market Size, Trends and Insights By Solution Type (Capacity Management Software, Capacity Planning Software, Patient Flow Management, Others), By Hospital Type (Public Hospitals, Private Hospitals, Specialty Hospitals, Teaching Hospitals, Children’s Hospitals), By Workflow (Pre-Admission, During Admission, Post-Discharge, End-to-End), By Deployment Model (Cloud-Based, On-Premise), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

As per the Hospital Capacity Management Solutions Market analysis conducted by the CMI Team, the global Hospital capacity management solutions Market is expected to record a CAGR of 10.4% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 3.92 Billion. By 2034, the valuation is anticipated to reach USD 9.3 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=74249
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/lr0r-lr6/weighted/surface-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for LR0R-LR6 region and surface-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/lr0r-lr6/weighted/human-stool
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for LR0R-LR6 region and human-stool habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/lr0r-lr6/weighted/soil-non-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for LR0R-LR6 region and soil-non-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/lr0r-lr6/weighted/human-oral
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for LR0R-LR6 region and human-oral habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/average
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and average habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/animal-distal-gut
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and animal-distal-gut habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/animal-secretion
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and animal-secretion habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/animal-surface
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and animal-surface habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/water-non-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and water-non-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/animal-proximal-gut
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and animal-proximal-gut habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/animal-corpus
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and animal-corpus habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/plant-rhizosphere
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and plant-rhizosphere habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/water-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and water-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/sediment-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and sediment-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/plant-corpus
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and plant-corpus habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/sediment-non-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and sediment-non-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/plant-surface
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and plant-surface habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/surface-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and surface-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/soil-non-saline
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and soil-non-saline habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/human-oral
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and human-oral habitat (weighted)
DOI: 10.82364/138.2/lsu/ref-nr99/qiime2/2025.7/nl1-nl4/weighted/human-stool
title: SILVA Release 138.2 LSU Ref NR 99 QIIME2 classifier for NL1-NL4 region and human-stool habitat (weighted)
DOI: 10.15468/dl.ysckw3
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio squalidus",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 27 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008395-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jtm7hd
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senecio squalidus",
 "Country is Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 70 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008397-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kg9dnx
title: Occurrence Download

description: A dataset containing 168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India, Myanmar, Nepal, Bhutan, Lao People’s Democratic Republic, Viet Nam, Thailand, Cambodia, Philippines, Chinese Taipei, Malaysia, Indonesia, Bangladesh)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anthurium"
 ]
}

 The dataset includes 168 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008394-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xutjqm
title: Occurrence Download

description: A dataset containing 133648 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "HasCoordinate is true",
 "Year is 1963"
 ]
}

 The dataset includes 133648 records from 255 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008325-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.txkbvz
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anathallis corticicola (Schltr. ex Hoehne) Pridgeon & M.W.Chase"
 ]
}

 The dataset includes 71 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006165-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dvzqxb
title: Occurrence Download

description: A dataset containing 15639 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cotoneaster franchetii Boiss., Cotoneaster mairei H.Lév., Pyrus klotzii M.F.Fay & Christenh., Cotoneaster franchetii var. sternianus Turrill, Cotoneaster wardii Anon., Cotoneaster tengyuehensis J.Fryer & B.Hylmö, Cotoneaster vilmorinianus G.Klotz, Cotoneaster franchetii var. cinerascens Rehder, Cotoneaster insculptus Diels, Cotoneaster dielsianus f. major Rehder & E.H.Wilson, Pyrus turdus M.F.Fay & Christenh., Cotoneaster mairei var. albiflorus H.Lév., Pyrus variabilis M.F.Fay & Christenh., Cotoneaster wardii W.W.Sm., Cotoneaster induratus J.Fryer & B.Hylmö, Pyrus sterniana (Turrill) M.F.Fay & Christenh., Cotoneaster sternianus (Turrill) Boom, Pyrus cinerascens (Rehder) M.F.Fay & Christenh., Cotoneaster cinerascens (Rehder) Flinck & B.Hylmö, Pyrus insculpta (Diels) M.F.Fay & Christenh., Pyrus franchetii (Bois) M.F.Fay & Christenh., Pyrus wardii (W.W.Sm.) M.F.Fay & Christenh., Pyrus indurata (J.Fryer & B.Hylmö) M.F.Fay & Christenh.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 15639 records from 225 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006167-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2bcrjv
title: Occurrence Download

description: A dataset containing 576 species occurrences available in GBIF matching the query:
{
 "and" : [
 "InstitutionKey is e71f36f1-e271-4720-a604-3c5a1418e7aa",
 {
 "and" : [
 "Geometry POLYGON((22.1459592783514 6.686713967612164,22.1459592783514 -5.150996227601254,45.17059937015631 -5.150996227601254,45.17059937015631 6.686713967612164,22.1459592783514 6.686713967612164))",
 "TaxonKey is one of (Artiodactyla, Perissodactyla)"
 ]
 }
 ]
}

 The dataset includes 576 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006168-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hy37qz
title: Occurrence Download

description: A dataset containing 93490 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Parnassius apollo (Linnaeus, 1758)",
 "Year 1000-2025"
 ]
}

 The dataset includes 93490 records from 448 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006164-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12759247
title: Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"

description: Abstract

Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.

Data Sources

- Solar and Wind Time Series (1950-2020): Bloomfield and Brayshaw (2021)- Techno-Economic Assumptions: technology-data (v0.8.1), Danish Energy Agency- Demand Elasticity Assumptions: Hirth et al. (2024), Arnold (2023)

Installation

Use conda environment manager:

conda update condaconda env create -f workflow/envs/environment.fixed.yamlconda activate price-formation

Main Dependencies:



pypsa (v0.27.1)

linopy (v0.3.8)

snakemake (v8.5)

gurobi (v11.0.2)


Run

From the root of the repository:

snakemake -call --use-conda --conda-frontend conda

Or with a specific scenario configuration file:

snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml

Cluster

On an HPC cluster, run:

snakemake -call --profile slurm --use-conda --conda-frontend conda

Compress Results

Use tar to compress results (excluding the report directory):

tar -cJf price-formation-results.tar.xz \    config data figures results resources workflow \    .gitignore .pre-commit-config.yaml .syncignore-receive \    .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md

Licenses

The code in this repository is MIT licensed.

The data in this repository is CC-BY-4.0 licensed.

Amendments

In v0.2.0, we added the file revision-1-amendments.tar.xz, which includes additional results for sensitivity runs with cross-elastic terms.
DOI: 10.5281/zenodo.12759248
title: Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"

description: Abstract

Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.

Data Sources

- Solar and Wind Time Series (1950-2020): Bloomfield and Brayshaw (2021)- Techno-Economic Assumptions: technology-data (v0.8.1), Danish Energy Agency- Demand Elasticity Assumptions: Hirth et al. (2024), Arnold (2023)

Installation

Use conda environment manager:

conda update condaconda env create -f workflow/envs/environment.fixed.yamlconda activate price-formation

Main Dependencies:



pypsa (v0.27.1)

linopy (v0.3.8)

snakemake (v8.5)

gurobi (v11.0.2)


Run

From the root of the repository:

snakemake -call --use-conda --conda-frontend conda

Or with a specific scenario configuration file:

snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml

Cluster

On an HPC cluster, run:

snakemake -call --profile slurm --use-conda --conda-frontend conda

Compress Results

Use tar to compress results (excluding the report directory):

tar -cJf price-formation-results.tar.xz \    config data figures results resources workflow \    .gitignore .pre-commit-config.yaml .syncignore-receive \    .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md

Licenses

The code in this repository is MIT licensed.

The data in this repository is CC-BY-4.0 licensed.
DOI: 10.5281/zenodo.14778947
title: Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"

description: Abstract

Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.

Data Sources

- Solar and Wind Time Series (1950-2020): Bloomfield and Brayshaw (2021)- Techno-Economic Assumptions: technology-data (v0.8.1), Danish Energy Agency- Demand Elasticity Assumptions: Hirth et al. (2024), Arnold (2023)

Installation

Use conda environment manager:

conda update condaconda env create -f workflow/envs/environment.fixed.yamlconda activate price-formation

Main Dependencies:



pypsa (v0.27.1)

linopy (v0.3.8)

snakemake (v8.5)

gurobi (v11.0.2)


Run

From the root of the repository:

snakemake -call --use-conda --conda-frontend conda

Or with a specific scenario configuration file:

snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml

Cluster

On an HPC cluster, run:

snakemake -call --profile slurm --use-conda --conda-frontend conda

Compress Results

Use tar to compress results (excluding the report directory):

tar -cJf price-formation-results.tar.xz \    config data figures results resources workflow \    .gitignore .pre-commit-config.yaml .syncignore-receive \    .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md

Licenses

The code in this repository is MIT licensed.

The data in this repository is CC-BY-4.0 licensed.

Amendments

In v0.2.0, we added the file revision-1-amendments.tar.xz, which includes additional results for sensitivity runs with cross-elastic terms.
DOI: 10.15468/dl.yq5rn8
title: Occurrence Download

description: A dataset containing 17085 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Russula delica Fr."
 ]
}

 The dataset includes 17085 records from 295 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006166-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14778732
title: Inventory of Open Access Databases for Conservation Planning

description: Database compilation of global and regional data sources used in the three case studies. Some of the sources included in this database were identified in collaboration with the regional stakeholders. This inventory of Open Access databases is relevant to multi-objective, multi-realm and multi-time planning in Europe.
DOI: 10.5281/zenodo.14778733
title: Inventory of Open Access Databases for Conservation Planning

description: Database compilation of global and regional data sources used in the three case studies. Some of the sources included in this database were identified in collaboration with the regional stakeholders. This inventory of Open Access databases is relevant to multi-objective, multi-realm and multi-time planning in Europe.
DOI: 10.5281/zenodo.14779351
title: gplasCC Benchmark dataset
DOI: 10.5281/zenodo.14779352
title: gplasCC Benchmark dataset
DOI: 10.15468/dl.es22j7
title: Occurrence Download

description: A dataset containing 11312 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 Astacus astacus (Linnaeus, 1758)"
 ]
}

 The dataset includes 11312 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006169-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5523/bris.p75j112xrzs31zlmj3aa3ca59
title: An Innovative Approach to Characterizing the Refractive Indices and Effective Densities of Internally Mixed Light-Absorbing Aerosol Particles

description: Data associated with publication with title, "An Innovative Approach to Characterizing the Refractive Indices and Effective Densities of Internally Mixed Light-Absorbing Aerosol Particles"
DOI: 10.15468/dl.p7754h
title: Occurrence Download

description: A dataset containing 849 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Machilus japonica Siebold & Zucc.",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 849 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006170-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jcp8pm
title: Occurrence Download

description: A dataset containing 5301508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bucephala clangula (Linnaeus, 1758)"
 ]
}

 The dataset includes 5301508 records from 918 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006160-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.