cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10798151
title: ToneTwist AFx Dataset - External: Universal Audio 6176 Vintage Channel Strip - 1176LN Limiter

description: Dry



Dry inputs are from:







IDMT-SMT-Audio-Effects





Settings












Attack






Release










Input












Output












Ratio






10
10
4
7
All





 

From source publication:





1176LN Limiter: Similarly, the wildly used field-effect transistor limiter 1176LN is sampled from the same 6176 Vintage Channel Strip unit. The limiter samples are recorded with the following settings: attack 800us, release 1100 ms, input level 4, output level 7 and ratio ALL. We use the slowest attack and release settings in order to further test the long-term memory of the models. The compression ratio value of ALL corresponds to all the ratio buttons of an original 1176 being pushed simultaneously. Thus, this setting also introduces distortion due to the variation of attack and release times.


Github

https://github.com/mcomunita/tonetwist-afx-dataset



Source



@article{martinez2020deep,
  title={Deep learning for black-box modeling of audio effects},
  author={Mart{\'\i}nez Ram{\'\i}rez, Marco A and Benetos, Emmanouil and Reiss, Joshua D},
  journal={Applied Sciences},
  volume={10},
  number={2},
  pages={638},
  year={2020},
  publisher={MDPI}}


Web



https://mchijmma.github.io/DL-AFx


Code

N.A.

Original Data





https://zenodo.org/records/3562442
DOI: 10.5281/zenodo.10901327
title: ToneTwist AFx Dataset: Amplitube DComp

description: Settings




Output
Sensitivity


-14dB
10




Dry with markers


Dry inputs are a selection of clean guitar and bass recordings from different sources:



IDMT-SMT-GUITAR - dataset 2 (7:23 min)

IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min)

IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min)

IDMT-SMT-Bass-Single-Track - (5:58 min)

NAM: Neural Amp Modeler - (3:11 min)

Private Guitar Data - (5:19 min)

YouTube Bass Recordings - (10:09 min)


Pre-processing:



All:



synchronization markers (2 impulses) added at start and end of every file



IDMT-SMT-GUITAR - dataset 2:



peak normalized to -6dBFS



NAM:



no pre-processing



Others:



peak normalized to -0.1dBFS

signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])






Authors

Marco Comunità - Centre for Digital Music, Queen Mary University of London

Github

https://github.com/mcomunita/tonetwist-afx-dataset

Reference

If you make use of AUDIO-EFFECTS-DATASET, please cite the following publication:

@misc{comunità2025nablafxframeworkdifferentiableblackbox,
      title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, 
      author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss},
      year={2025},
      eprint={2502.11668},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.11668}, 
}
DOI: 10.5281/zenodo.10901328
title: ToneTwist AFx Dataset: Amplitube DComp

description: Settings




Output
Sensitivity


-14dB
10




Dry with markers


Dry inputs are a selection of clean guitar and bass recordings from different sources:



IDMT-SMT-GUITAR - dataset 2 (7:23 min)

IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min)

IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min)

IDMT-SMT-Bass-Single-Track - (5:58 min)

NAM: Neural Amp Modeler - (3:11 min)

Private Guitar Data - (5:19 min)

YouTube Bass Recordings - (10:09 min)


Pre-processing:



All:



synchronization markers (2 impulses) added at start and end of every file



IDMT-SMT-GUITAR - dataset 2:



peak normalized to -6dBFS



NAM:



no pre-processing



Others:



peak normalized to -0.1dBFS

signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])






Authors

Marco Comunità - Centre for Digital Music, Queen Mary University of London

Github

https://github.com/mcomunita/tonetwist-afx-dataset

Reference

If you make use of AUDIO-EFFECTS-DATASET, please cite the following publication:

@misc{comunità2025nablafxframeworkdifferentiableblackbox,
      title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, 
      author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss},
      year={2025},
      eprint={2502.11668},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.11668}, 
}
DOI: 10.5281/zenodo.10901425
title: ToneTwist AFx Dataset: Dry

description: Dry inputs are a selection of clean guitar and bass recordings from different sources:



IDMT-SMT-GUITAR - dataset 2 (7:23 min)

IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min)

IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min)

IDMT-SMT-Bass-Single-Track - (5:58 min)

NAM: Neural Amp Modeler - (3:11 min)

Private Guitar Data - (5:19 min)

YouTube Bass Recordings - (10:09 min)


Pre-processing:



All:



synchronization markers (2 impulses) added at start and end of every file



IDMT-SMT-GUITAR - dataset 2:



peak normalized to -6dBFS



NAM:



no pre-processing



Others:



peak normalized to -0.1dBFS

signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])






Authors

Marco Comunità - Centre for Digital Music, Queen Mary University of London

Github

https://github.com/mcomunita/tonetwist-afx-dataset

Reference

If you make use of this dataset, please cite the following publications:

@misc{comunità2025nablafxframeworkdifferentiableblackbox,
      title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, 
      author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss},
      year={2025},
      eprint={2502.11668},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.11668}, 
}
DOI: 10.5281/zenodo.10901426
title: ToneTwist AFx Dataset: Dry

description: Dry inputs are a selection of clean guitar and bass recordings from different sources:



IDMT-SMT-GUITAR - dataset 2 (7:23 min)

IDMT-SMT-GUITAR - dataset 4 - Career SG (6:08 min)

IDMT-SMT-GUITAR - dataset 4 - Ibanez 2820 (5:14 min)

IDMT-SMT-Bass-Single-Track - (5:58 min)

NAM: Neural Amp Modeler - (3:11 min)

Private Guitar Data - (5:19 min)

YouTube Bass Recordings - (10:09 min)


Pre-processing:



All:



synchronization markers (2 impulses) added at start and end of every file



IDMT-SMT-GUITAR - dataset 2:



peak normalized to -6dBFS



NAM:



no pre-processing



Others:



peak normalized to -0.1dBFS

signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])






Authors

Marco Comunità - Centre for Digital Music, Queen Mary University of London

Github

https://github.com/mcomunita/tonetwist-afx-dataset

Reference

If you make use of this dataset, please cite the following publications:

@misc{comunità2025nablafxframeworkdifferentiableblackbox,
      title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, 
      author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss},
      year={2025},
      eprint={2502.11668},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.11668}, 
}
DOI: 10.15468/dl.5crdge
title: Occurrence Download

description: A dataset containing 146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Human Observation)",
 "Continent is Asia",
 "Country is one of (China, India, Bangladesh, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pterospermum acerifolium (L.) Willd."
 ]
}

 The dataset includes 146 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004401-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10798110
title: ToneTwist AFx Dataset - External: Universal Audio 6176 Vintage Channel Strip - 610B Mic Preamp

description: Dry



Dry inputs are from:







IDMT-SMT-Audio-Effects





Settings








Gain
Level
Input
High
Low


10dB
6
Line
0dB
0dB





 

From source publication:





610B Mic Preamp: This microphone tube preamplifier (preamp) is sampled from a 6176 Vintage Channel Strip unit. In order to obtain an output signal with high harmonic distortion, the preamp is overdriven with the following settings: gain +10 dB, level 6, line impedance and high and low boost/cut 0 dB.




Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source



@article{martinez2020deep,
  title={Deep learning for black-box modeling of audio effects},
  author={Mart{\'\i}nez Ram{\'\i}rez, Marco A and Benetos, Emmanouil and Reiss, Joshua D},
  journal={Applied Sciences},
  volume={10},
  number={2},
  pages={638},
  year={2020},
  publisher={MDPI}}


Web



https://mchijmma.github.io/DL-AFx


Code

N.A.

Original Data





https://zenodo.org/records/3562442
DOI: 10.5281/zenodo.10798111
title: ToneTwist AFx Dataset - External: Universal Audio 6176 Vintage Channel Strip - 610B Mic Preamp

description: Dry



Dry inputs are from:







IDMT-SMT-Audio-Effects





Settings








Gain
Level
Input
High
Low


10dB
6
Line
0dB
0dB





 

From source publication:





610B Mic Preamp: This microphone tube preamplifier (preamp) is sampled from a 6176 Vintage Channel Strip unit. In order to obtain an output signal with high harmonic distortion, the preamp is overdriven with the following settings: gain +10 dB, level 6, line impedance and high and low boost/cut 0 dB.




Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source



@article{martinez2020deep,
  title={Deep learning for black-box modeling of audio effects},
  author={Mart{\'\i}nez Ram{\'\i}rez, Marco A and Benetos, Emmanouil and Reiss, Joshua D},
  journal={Applied Sciences},
  volume={10},
  number={2},
  pages={638},
  year={2020},
  publisher={MDPI}}


Web



https://mchijmma.github.io/DL-AFx


Code

N.A.

Original Data





https://zenodo.org/records/3562442
DOI: 10.5281/zenodo.10794425
title: ToneTwist AFx Dataset - External: Blackstar HT1 - Channel: Overdrive

description: Settings






Gain
Volume


5
10





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{wright2019real,
  title={Real-time black-box modelling with recurrent neural networks},
  author={Wright, Alec and Damsk{\"a}gg, Eero-Pekka and V{\"a}lim{\"a}ki, Vesa and others},
  booktitle={22nd international conference on digital audio effects (DAFx-19)},
  pages={1--8},
  year={2019}}

Web

http://research.spa.aalto.fi/publications/papers/dafx19-rnn

Code

https://github.com/Alec-Wright/Automated-GuitarAmpModelling

Original Data

https://github.com/Alec-Wright/Automated-GuitarAmpModelling/tree/main/Data
DOI: 10.5281/zenodo.10794424
title: ToneTwist AFx Dataset - External: Blackstar HT1 - Channel: Overdrive

description: Settings






Gain
Volume


5
10





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{wright2019real,
  title={Real-time black-box modelling with recurrent neural networks},
  author={Wright, Alec and Damsk{\"a}gg, Eero-Pekka and V{\"a}lim{\"a}ki, Vesa and others},
  booktitle={22nd international conference on digital audio effects (DAFx-19)},
  pages={1--8},
  year={2019}}

Web

http://research.spa.aalto.fi/publications/papers/dafx19-rnn

Code

https://github.com/Alec-Wright/Automated-GuitarAmpModelling

Original Data

https://github.com/Alec-Wright/Automated-GuitarAmpModelling/tree/main/Data
DOI: 10.5281/zenodo.14886956
title: cml
DOI: 10.5281/zenodo.14886957
title: cml
DOI: 10.15468/dl.q3qjgc
title: Occurrence Download

description: A dataset listing the 200 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "DatasetKey is Observation.org, Nature data from around the World",
 "OccurrenceStatus is Present",
 "TaxonKey is Squamata",
 "Year 2018-2020"
 ]
}

 The dataset's 200 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004400-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10796840
title: ToneTwist AFx Dataset - External: MesaBoogie Mark V - Channel: Crunch

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796841
title: ToneTwist AFx Dataset - External: MesaBoogie Mark V - Channel: Crunch

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.15468/dl.4ygz3c
title: Occurrence Download

description: A dataset containing 822 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Gabon",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ardisia Sw."
 ]
}

 The dataset includes 822 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004402-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y59rg8
title: Occurrence Download

description: A dataset containing 300116 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ictaluridae"
 ]
}

 The dataset includes 300116 records from 504 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004395-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10796536
title: ToneTwist AFx Dataset - External: Ibanez TSA15 - Channel: Crunch

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796535
title: ToneTwist AFx Dataset - External: Ibanez TSA15 - Channel: Crunch

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796524
title: ToneTwist AFx Dataset - External: Engl Retro Tube 50 - Channel: Drive

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796525
title: ToneTwist AFx Dataset - External: Engl Retro Tube 50 - Channel: Drive

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796557
title: ToneTwist AFx Dataset - External: MesaBoogie 550 - Channel: Clean

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796556
title: ToneTwist AFx Dataset - External: MesaBoogie 550 - Channel: Clean

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10891515
title: ToneTwist AFx Dataset - External: Electro Harmonix Big Muff

description: Settings






Sustain
Volume


5
10





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{wright2019real,
  title={Real-time black-box modelling with recurrent neural networks},
  author={Wright, Alec and Damsk{\"a}gg, Eero-Pekka and V{\"a}lim{\"a}ki, Vesa and others},
  booktitle={22nd international conference on digital audio effects (DAFx-19)},
  pages={1--8},
  year={2019}}

Web

http://research.spa.aalto.fi/publications/papers/dafx19-rnn

Code

https://github.com/Alec-Wright/Automated-GuitarAmpModelling

Original Data

https://github.com/Alec-Wright/Automated-GuitarAmpModelling/tree/main/Data
DOI: 10.5281/zenodo.10891514
title: ToneTwist AFx Dataset - External: Electro Harmonix Big Muff

description: Settings






Sustain
Volume


5
10





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{wright2019real,
  title={Real-time black-box modelling with recurrent neural networks},
  author={Wright, Alec and Damsk{\"a}gg, Eero-Pekka and V{\"a}lim{\"a}ki, Vesa and others},
  booktitle={22nd international conference on digital audio effects (DAFx-19)},
  pages={1--8},
  year={2019}}

Web

http://research.spa.aalto.fi/publications/papers/dafx19-rnn

Code

https://github.com/Alec-Wright/Automated-GuitarAmpModelling

Original Data

https://github.com/Alec-Wright/Automated-GuitarAmpModelling/tree/main/Data
DOI: 10.5281/zenodo.10796863
title: ToneTwist AFx Dataset - External: MesaBoogie Mark V - Channel: Extreme

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.5281/zenodo.10796864
title: ToneTwist AFx Dataset - External: MesaBoogie Mark V - Channel: Extreme

description: Settings






Gain


5





Github

https://github.com/mcomunita/tonetwist-afx-dataset

Source

Data used in the following publication:

@inproceedings{schmitz2019objective,  title={Objective and subjective comparison of several machine learning techniques          applied for the real-time emulation of the guitar amplifier nonlinear behavior},  author={Schmitz, Thomas and Embrechts, Jean-Jacques},  booktitle={Audio Engineering Society Convention 146},  year={2019},  organization={Audio Engineering Society}}

Web

N.A.

Code

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master

Original Data

https://github.com/TSchmitzULG/NeuralNetwork_Comparison/tree/master/Datasets
DOI: 10.15468/dl.5vf3xm
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Human Observation)",
 "Continent is Asia",
 "Country is one of (China, India, Bangladesh, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Eurya acuminata DC."
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004405-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.