cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7483/opendata.cms.u6td.sm7y
title: /SUSY_WH_WToAll_HToAATo4B_M-30_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.4t90.9fnt
title: /SUSY_WH_WToAll_HToAATo4B_M-35_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.exv5.6ofh
title: /SUSY_WH_WToAll_HToAATo4B_M-35_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.6084/m9.figshare.28204739
title: Processed data for Figure 6

description: Measure of regulation of 10 TFs on a library of σ70 promoters.
DOI: 10.6084/m9.figshare.28204739.v1
title: Processed data for Figure 6

description: Measure of regulation of 10 TFs on a library of σ70 promoters.
DOI: 10.7483/opendata.cms.4hx1.oxer
title: /SUSY_WH_WToAll_HToAATo4B_M-40_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qxqd.l9rk
title: /SUSY_WH_WToAll_HToAATo4B_M-40_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5m9u.e1nq
title: /SUSY_WH_WToAll_HToAATo4B_M-45_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.8zln.9ds5
title: /SUSY_WH_WToAll_HToAATo4B_M-45_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.9cuz.f2c4
title: /SUSY_WH_WToAll_HToAATo4B_M-50_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.xceg.mq0t
title: /SUSY_WH_WToAll_HToAATo4B_M-50_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.11640826
title: FABLE Scenathon database 2023

description: This database contains key parameters and variables from the 2023 Scenathon which has been run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modelling approach with multiple models developed by different teams in the world at national and regional scales, and a methodology to link them ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. 

A description and analysis of the Scenathon 2023 pathways has been published in Sachs et al. (2024).

The Scenathon 2023 database includes results at the global, country and rest of the world regions levels, for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and input use in agriculture. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard.

Scope of the 2023 database:



Pathways: The Current Trends (CT) pathway, reflecting a low-ambition future shaped by existing policies; The National Commitments (NC) pathway, projecting how national strategies, pledges, and targets for climate, biodiversity, and food systems would shape future outcomes. The Global Sustainability (GS) pathway, identifying additional actions necessary to align national and regional pathways with global sustainability targets.

Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Denmark, Ethiopia, Finland, Germany, Greece, India, Indonesia, Mexico, Norway, Nepal, Russia, Rwanda, Sweden, Türkiye, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa.

Time: 2000-2050 with 2020 being the last calibration year. Results are provided for each 5 year-time step.

Trade adjustment: results are provided before total exports and total imports are balanced or after.


The readme worksheet provides all the relevant information on the indicators and acronyms definition used in the database.
description: Contact author: clara.douzal@unsdsn.or and info.fable@unsdsn.org

The FABLE Calculator has been used to compute all these results. You can review the FABLE Calculator 2020 documentation and get an Open FABLE Calculator. There is one FABLE Calculator per country or region.

The FABLE Scenathon 2023 has been supporte by: The Food Systems, Land Use and Restoration (FOLUR) Impact Program, Norway's International Climate and Forest Initiative (NICFI), and The International Climate Initiative (IKI).
DOI: 10.5281/zenodo.11640827
title: FABLE Scenathon database 2023

description: This database contains key parameters and variables from the 2023 Scenathon which has been run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modelling approach with multiple models developed by different teams in the world at national and regional scales, and a methodology to link them ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. 

A description and analysis of the Scenathon 2023 pathways has been published in Sachs et al. (2024).

The Scenathon 2023 database includes results at the global, country and rest of the world regions levels, for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and input use in agriculture. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard.

Scope of the 2023 database:



Pathways: The Current Trends (CT) pathway, reflecting a low-ambition future shaped by existing policies; The National Commitments (NC) pathway, projecting how national strategies, pledges, and targets for climate, biodiversity, and food systems would shape future outcomes. The Global Sustainability (GS) pathway, identifying additional actions necessary to align national and regional pathways with global sustainability targets.

Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Denmark, Ethiopia, Finland, Germany, Greece, India, Indonesia, Mexico, Norway, Nepal, Russia, Rwanda, Sweden, Türkiye, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa.

Time: 2000-2050 with 2020 being the last calibration year. Results are provided for each 5 year-time step.

Trade adjustment: results are provided before total exports and total imports are balanced or after.


The readme worksheet provides all the relevant information on the indicators and acronyms definition used in the database.
description: Contact author: clara.douzal@unsdsn.or and info.fable@unsdsn.org

The FABLE Calculator has been used to compute all these results. You can review the FABLE Calculator 2020 documentation and get an Open FABLE Calculator. There is one FABLE Calculator per country or region.

The FABLE Scenathon 2023 has been supporte by: The Food Systems, Land Use and Restoration (FOLUR) Impact Program, Norway's International Climate and Forest Initiative (NICFI), and The International Climate Initiative (IKI).
DOI: 10.7483/opendata.cms.0d28.n41x
title: /SUSY_WH_WToAll_HToAATo4B_M-55_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.wn47hm
title: Occurrence Download

description: A dataset containing 555 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anomius castaneus (Illiger, 1803)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 555 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070513-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.ib9v.70cb
title: /SUSY_WH_WToAll_HToAATo4B_M-55_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.jm7ruc
title: Occurrence Download

description: A dataset containing 70044 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-5.56332 35.99762,-4.59241 36.55181,-6.01688 37.4123,-6.43501 36.68264,-6.1161 36.21519,-5.56332 35.99762))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 70044 records from 374 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070511-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.npxput
title: Occurrence Download

description: A dataset containing 67113 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Trichaptum brastagii (Corner) T.Hatt., Hirschioporus fuscoviolaceus (Ehrenb.) Donk, Trichaptum fuscoviolaceum (Ehrenb.) Ryvarden, Hirschioporus abietinus (Pers. ex J.F.Gmel.) Donk, Trichaptum abietinum (Pers. ex J.F.Gmel.) Ryvarden)"
 ]
}

 The dataset includes 67113 records from 391 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070509-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.5ztp.8td4
title: /SUSY_WH_WToAll_HToAATo4B_M-60_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4far.pzbm
title: /SUSY_WH_WToAll_HToAATo4B_M-60_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.u36u.kh0y
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-12_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.73jw.gbgx
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-12_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.guqeqd
title: Occurrence Download

description: A dataset containing 97095 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Inventaire des Mammifères de France - Atlas des ongulés et Lagomorphes de France (Métropole et outre-mer). 2000-2020.",
 "TaxonKey is Leporidae"
 ]
}

 The dataset includes 97095 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070510-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.s34g.cp6g
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-15_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.15468/dl.jyf3re
title: Occurrence Download

description: A dataset containing 1392 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterosiphonia complanata (Clemente) Falkenberg"
 ]
}

 The dataset includes 1392 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070516-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.hano.7stv
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-15_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tpol.q9ad
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-20_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.mr4i.rhs9
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-20_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ohnf.v7bv
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-25_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.fpcy.p2f4
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-25_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.klrc.py0s
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-30_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.5281/zenodo.13732411
title: VoiceWukong: Benchmarking Deepfake Voice Detection (part_ab)

description: VoiceWukong

VoiceWukong is a comprehensive benchmark for deepfake voice detection, designed to evaluate the performance of various detectors in real-world application scenarios.

Dataset Features



Large Scale: Contains 265,200 English and 148,200 Chinese deepfake voice samples

Diverse Sources: Covers voice samples generated by 19 commercial tools and 15 open-source tools

Real-world Scenarios: Constructed 38 data variants covering 6 types of audio manipulations common in practical applications

Bilingual Support: Supports evaluation in both Chinese and English languages


Evaluation Results



Conducted comprehensive evaluations on 12 state-of-the-art deepfake voice detectors

AASIST2 achieved the best performance with an Equal Error Rate (EER) of 13.50%

Other detectors showed EERs exceeding 20%

Results indicate significant challenges for current detectors in practical applications


Human-Machine Comparison Study



Conducted user studies with over 300 participants

Comparative analysis of detection capabilities among humans, detectors, and multimodal large language models (Qwen2-Audio)

Different detectors and humans showed varying identification capabilities for deepfake voices at different deception levels

Multimodal large language models demonstrated no effective detection ability


Dataset

This is the second part of the dataset, and it requires the complete download of both part_aa and part_ab for proper extraction and use. Please ensure that both files are in the same folder. For a detailed introduction to the data, please refer to our paper (to be made available).

The first part (part_aa) is at part_aaextract command : cat VoiceWukong.part_* | tar -xz

Leaderboard

Our leaderboard presents comprehensive evaluation results in three main sections:



Overall Performance - General evaluation metrics for each detector across the entire dataset, providing a broad view of detection capabilities.

Manipulation-specific Performance - Detailed results showing how each detector performs under different types of audio manipulations, offering insights into specific strengths and weaknesses.

User Study-based Evaluation - Performance analysis of detectors on deepfake voices categorized by difficulty levels based on our user study results, demonstrating detector effectiveness across varying deception capabilities.


Visit our leaderboard(github.io) for detailed performance metrics and rankings. Additionally, we provide a copy of the leaderboard code here for permanent storage.



Evaluated Detectors' Weighted Models



All evaluated detectors’ weighted models can be obtained from huggingface.co. Additionally, we provide a copy of the weights files here for premanent storage.


User Study Results & Original Outputs



This code repository(github) stores our user study results and the original outputs of the evaluation detectors. Additionally, we provide a copy of the code repository here for permanent storage.



Note: VoiceWukong prohibits use for commercial purposes.
DOI: 10.5281/zenodo.13732412
title: VoiceWukong: Benchmarking Deepfake Voice Detection (part_ab)

description: VoiceWukong

VoiceWukong is a comprehensive benchmark for deepfake voice detection, designed to evaluate the performance of various detectors in real-world application scenarios.

Dataset Features



Large Scale: Contains 265,200 English and 148,200 Chinese deepfake voice samples

Diverse Sources: Covers voice samples generated by 19 commercial tools and 15 open-source tools

Real-world Scenarios: Constructed 38 data variants covering 6 types of audio manipulations common in practical applications

Bilingual Support: Supports evaluation in both Chinese and English languages


Evaluation Results



Conducted comprehensive evaluations on 12 state-of-the-art deepfake voice detectors

AASIST2 achieved the best performance with an Equal Error Rate (EER) of 13.50%

Other detectors showed EERs exceeding 20%

Results indicate significant challenges for current detectors in practical applications


Human-Machine Comparison Study



Conducted user studies with over 300 participants

Comparative analysis of detection capabilities among humans, detectors, and multimodal large language models (Qwen2-Audio)

Different detectors and humans showed varying identification capabilities for deepfake voices at different deception levels

Multimodal large language models demonstrated no effective detection ability


Dataset

This is the second part of the dataset, and it requires the complete download of both part_aa and part_ab for proper extraction and use. Please ensure that both files are in the same folder. For a detailed introduction to the data, please refer to our paper (to be made available).

The first part (part_aa) is at part_aaextract command : cat VoiceWukong.part_* | tar -xz

Leaderboard

Our leaderboard presents comprehensive evaluation results in three main sections:



Overall Performance - General evaluation metrics for each detector across the entire dataset, providing a broad view of detection capabilities.

Manipulation-specific Performance - Detailed results showing how each detector performs under different types of audio manipulations, offering insights into specific strengths and weaknesses.

User Study-based Evaluation - Performance analysis of detectors on deepfake voices categorized by difficulty levels based on our user study results, demonstrating detector effectiveness across varying deception capabilities.


Visit our leaderboard(github.io) for detailed performance metrics and rankings. Additionally, we provide a copy of the leaderboard code here for permanent storage.



Evaluated Detectors' Weighted Models



All evaluated detectors’ weighted models can be obtained from huggingface.co. Additionally, we provide a copy of the weights files here for premanent storage.


User Study Results & Original Outputs



This code repository(github) stores our user study results and the original outputs of the evaluation detectors. Additionally, we provide a copy of the code repository here for permanent storage.



Note: VoiceWukong prohibits use for commercial purposes.
DOI: 10.7483/opendata.cms.et4k.8kll
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-30_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.7qgc.xzfy
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-35_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.may5.4qpl
title: /SUSY_WH_WToAll_HToAATo4B_Pt150_M-35_TuneCP5_13TeV_madgraph_pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.u9huz3
title: Occurrence Download

description: A dataset containing 6648 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aristolochia baetica L."
 ]
}

 The dataset includes 6648 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070517-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8kuesb
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Auriglobus remotus (Roberts, 1982)"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0329586-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84pkeu
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Plectranthus elegans Britten"
 ]
}

 The dataset includes 56 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084931-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f62r2g
title: Occurrence Download

description: A dataset containing 897 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Raja undulata Lacepède, 1802"
 ]
}

 The dataset includes 897 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0205731-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c9mrmc
title: Occurrence Download

description: A dataset containing 2195 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Occurrence evidence"
 ]
 },
 "GadmGid is ESP.6_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Genetta genetta (Linnaeus, 1758)"
 ]
}

 The dataset includes 2195 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0381916-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uqtuga
title: Occurrence Download

description: A dataset containing 346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Sylvilagus cunicularius (Waterhouse, 1848)"
 ]
}

 The dataset includes 346 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0202296-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xyndwc
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhipidoglossum tanneri (P.J.Cribb) Senghas"
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102491-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ccbu2
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Nano and microzooplankton abundance and biomass at station NBP96-04A-11-5"
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0180552-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.