cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.8mzbpf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Campylomormyrus cassaicus (Poll, 1967)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.2291 -34.019,27.3628 -33.9401,19.1382 -33.9401,19.1382 -35.0665,20.0413 -35.3323,20.7102 -34.9578,21.9201 -34.8682,22.6598 -34.5181,23.406 -34.611,23.7098 -34.5029,24.8326 -34.7174,25.2572 -34.5102,25.849 -34.5138,26.1077 -34.3123,27.2291 -34.019))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059323-250525065834625/datasets/export for details.
DOI: 10.15468/dl.csd8sm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gongrosira Kützing, 1843",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.2855 -34.7533,20.509 -34.5676,20.509 -32.9007,17.7723 -32.9007,17.8156 -33.0786,18.3471 -33.7176,18.3748 -33.8287,18.2181 -34.0492,18.3197 -34.3408,18.4452 -34.429,18.5435 -34.4213,18.5872 -34.3667,18.5363 -34.167,18.7345 -34.1508,18.7216 -34.3637,18.7808 -34.4521,19.0289 -34.4206,19.0831 -34.4835,19.2655 -34.5017,19.2032 -34.6157,19.2394 -34.6926,19.375 -34.6963,19.6091 -34.8524,19.8251 -34.8396,19.9798 -34.9064,20.1081 -34.8731,20.1517 -34.7847,20.2855 -34.7533))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059325-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15703109
title: Data Mentah Restaurant Jakarta
DOI: 10.5281/zenodo.15703382
title: Data Mentah Restaurant Jakarta
DOI: 10.15468/dl.hj63rb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gongrosira Kützing, 1843",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.628 -23.023,35.6007 -22.9702,35.6007 -23.165,35.628 -23.023))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059328-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15433569
title: Methodology for decentralized analysis: Detection, quantification and in situ monitoring of pharmaceutical formulations removal by electro-Fenton	J Electroanal Chem2022_909
DOI: 10.5281/zenodo.15433570
title: Methodology for decentralized analysis: Detection, quantification and in situ monitoring of pharmaceutical formulations removal by electro-Fenton	J Electroanal Chem2022_909
DOI: 10.15468/dl.gt6gc7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Campylomormyrus cassaicus (Poll, 1967)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.3628 -33.9401,28.1382 -33.4828,28.1382 -24.9401,19.1382 -24.9401,19.1382 -33.9401,27.3628 -33.9401))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059327-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15461843
title: Magnetic TiO2/Fe3O4-Chitosan Beads: A Highly Efficient and Reusable Catalyst for Photo-Electro-Fenton Process Catalyst2022_12
DOI: 10.5281/zenodo.15461844
title: Magnetic TiO2/Fe3O4-Chitosan Beads: A Highly Efficient and Reusable Catalyst for Photo-Electro-Fenton Process Catalyst2022_12
DOI: 10.5281/zenodo.15461896
title: Fluoride-Doped TiO2 Photocatalyst with Enhanced Activity for Stable Pollutant Degradation Catalysts 2022_12_1190
DOI: 10.5281/zenodo.15461897
title: Fluoride-Doped TiO2 Photocatalyst with Enhanced Activity for Stable Pollutant Degradation Catalysts 2022_12_1190
DOI: 10.15468/dl.xkqbje
title: Occurrence Download

description: A dataset containing 4197 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 acceptedtaxonkey,
 acceptedscientificname,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 7228684 OR occurrence.acceptedtaxonkey = 7228684 OR occurrence.kingdomkey = 7228684 OR occurrence.phylumkey = 7228684 OR occurrence.classkey = 7228684 OR occurrence.orderkey = 7228684 OR occurrence.familykey = 7228684 OR occurrence.genuskey = 7228684 OR occurrence.specieskey = 7228684)
 AND occurrence."year" = 2000
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.acceptedtaxonkey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence.acceptedtaxonkey,
 occurrence.acceptedscientificname,
 occurrence."year"

 The dataset's 4197 records were derived from 905 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059313-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15461942
title: Sustainable removal of antipyrine from wastewater via an Eco-Friendly heterogeneous Electro-Fenton-like process employing Zero-Valent iron nanoparticles loaded activated carbon Chem.Eng.J. 2024_493
DOI: 10.5281/zenodo.15461943
title: Sustainable removal of antipyrine from wastewater via an Eco-Friendly heterogeneous Electro-Fenton-like process employing Zero-Valent iron nanoparticles loaded activated carbon Chem.Eng.J. 2024_493
DOI: 10.5281/zenodo.15461985
title: Photoelectrocatalytic degradation of pharmaceuticals promoted by a metal-free g‑C3N4 catalyst Chem.Eng.J.2023_476
DOI: 10.5281/zenodo.15461986
title: Photoelectrocatalytic degradation of pharmaceuticals promoted by a metal-free g‑C3N4 catalyst Chem.Eng.J.2023_476
DOI: 10.5281/zenodo.15462011
title: Synthesis and characterization of eco-friendly cathodic electrodes incorporating nano Zero-Valent iron (NZVI) for the electro-fenton treatment of pharmaceutical wastewater Chem.Eng.J.2024_502
DOI: 10.5281/zenodo.15462012
title: Synthesis and characterization of eco-friendly cathodic electrodes incorporating nano Zero-Valent iron (NZVI) for the electro-fenton treatment of pharmaceutical wastewater Chem.Eng.J.2024_502
DOI: 10.5281/zenodo.15462047
title: Tuning graphitic carbon nitride (g-C3N4) electrocatalysts for efficient oxygen evolution reaction (OER) Fuel2024_360
DOI: 10.5281/zenodo.15462048
title: Tuning graphitic carbon nitride (g-C3N4) electrocatalysts for efficient oxygen evolution reaction (OER) Fuel2024_360
DOI: 10.15468/dl.czx3s7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gongrosira Kützing, 1843",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059329-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15462444
title: Synthesis and characterization of L-Arg-polypyrrole@g-C3N4 nanocomposite for highly efficient removal of orange G dye: Mechanistic insights and RSM@BBD optimization Inorganic Chem Communications 2025_113656
DOI: 10.5281/zenodo.15462445
title: Synthesis and characterization of L-Arg-polypyrrole@g-C3N4 nanocomposite for highly efficient removal of orange G dye: Mechanistic insights and RSM@BBD optimization Inorganic Chem Communications 2025_113656
DOI: 10.5281/zenodo.15462562
title: Structured organic frameworks as endocrine disruptor adsorbents suitable for Fenton regeneration and reuse J.Environ.Chem.Eng.2024_12_111820
DOI: 10.5281/zenodo.15462563
title: Structured organic frameworks as endocrine disruptor adsorbents suitable for Fenton regeneration and reuse J.Environ.Chem.Eng.2024_12_111820
DOI: 10.5281/zenodo.15462671
title: Screening of agroindustry residues for their usage as oxygen evolution reaction catalysts J.Environ.Chem.Eng.2024_12_114527
DOI: 10.5281/zenodo.15462670
title: Screening of agroindustry residues for their usage as oxygen evolution reaction catalysts J.Environ.Chem.Eng.2024_12_114527
DOI: 10.5281/zenodo.15463299
title: Disinfection through Advance Oxidation Processes: Optimization and Application on Real Wastewater Matrices Toxicts 2022_512
DOI: 10.5281/zenodo.15463300
title: Disinfection through Advance Oxidation Processes: Optimization and Application on Real Wastewater Matrices Toxicts 2022_512
DOI: 10.5281/zenodo.15480183
title: Efficient fluoxetine removal from water using Tunisian clay as adsorbent and its regeneration through NaOH-activated peroxymonosulfate Chem. Eng. Sci 2025_304
DOI: 10.5281/zenodo.15480184
title: Efficient fluoxetine removal from water using Tunisian clay as adsorbent and its regeneration through NaOH-activated peroxymonosulfate Chem. Eng. Sci 2025_304
DOI: 10.5281/zenodo.15482507
title: Efficiency of a novel cysteine-polypyrrole@silver phosphate nanocomposite for hazardous Cr(VI) removal: Experimental study and statistical optimization modeling J. Molecular Liquids 2024-126519
DOI: 10.5281/zenodo.15482508
title: Efficiency of a novel cysteine-polypyrrole@silver phosphate nanocomposite for hazardous Cr(VI) removal: Experimental study and statistical optimization modeling J. Molecular Liquids 2024-126519
DOI: 10.15468/dl.272aqu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Campylomormyrus cassaicus (Poll, 1967)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((29.1409 31.3397,28.1382 31.6271,28.1382 29.0599,35.2765 29.0599,35.4107 29.4968,34.5044 31.7516,34.206 31.8338,33.641 31.6189,33.1338 31.7313,32.728 31.6056,32.0818 31.9994,31.0088 32.1001,30.2163 31.954,29.1409 31.3397))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059330-250525065834625/datasets/export for details.
DOI: 10.34810/data454
title: OTMM Tonic Test Dataset v3.0.0

description: This repository contains datasets of annotated tonic frequencies of the audio recordings of Ottoman-Turkish makam music. The annotations are compiled from several research papers published under the CompMusic project. For more information about the original datasets, please refer to the relevant paper.
description: Each annotated recording is uniquely identified with a MusicBrainz MBID. The tonic symbol is also for each recording given in the format [letter][octave][accidental][comma], e.g. B4b1 (according to AEU theory)./n/n Each recording is annotated by at least expert musician or musicologists. The annotations are stored as a list with each annotation including the annotated frequency, source dataset, relevant publication, additional observations written by the annotator and whether the octave of the annotated value is considered (for example, the octave is ambiguous in orchestral instrumental recordings)./n The data is stored as JSON file and organized as a dictionary of recordings. An example recording is displayed below:/n/n```json/n{/n "mbid": "http://musicbrainz.org/recording/e3a22684-d237-48b5-ac27-e9b77ddd3c18", /n "verified": true, /n "annotations": [/n {/n "source": "https://github.com/MTG/otmm_tonic_dataset/blob/7f28c1a3261b9146042155ee5e0f9e644d9ebcfa/senturk2013karar_ismir/tonic_annotations.csv", /n "citation": "?entürk, S., Gulati, S., and Serra, X. (2013). Score Informed Tonic Identification for Makam Music of Turkey. In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR 2013), pages 175–180, Curitiba, Brazil.", /n "octave_wrapped": true, /n "observations": "", /n "value": 296.9597/n }, /n {/n "source": "https://github.com/MTG/otmm_tonic_dataset/blob/7f28c1a3261b9146042155ee5e0f9e644d9ebcfa/atli2015tonic_fma/TD2.csv", /n "citation": "Atl?, H. S., Bozkurt, B., ?entürk, S. (2015). A Method for Tonic Frequency Identification of Turkish Makam Music Recordings. In Proceedings of 5th International Workshop on Folk Music Analysis (FMA 2015), pages 119–122, Paris, France.", /n "octave_wrapped": true, /n "observations": "", /n "value": 296/n }/n ], /n "tonic_symbol": "D4"/n}/n```/n/n/nMost of the recordings in this dataset cannot be shared due to copyright. However relevant features are already computed and they can be downloaded from the [Dunya-makam](dunya.compmusic.upf.edu/makam) after registration. Please refer to the API documentation (http://dunya.compmusic.upf.edu/docs/makam.html) to how to access the data.
DOI: 10.5281/zenodo.14221137
title: SenseCobotFusion
title: SenseCobotFusion Dataset: Unlocking New Avenues for Stress Detection in Industry 5.0

description: SenseCobotFusion dataset has been created as a natural evolution of SenseCobot dataset.

SenseCobotFusion dataset collects metrics extracted from ElectroCardioGram (ECG), Galvanic Skin Response (GSR), ElectroEncephaloGram (EEG), and emotion signals obtained with professional biosensors, according to modern state-of-the-art signal processing methods, labeled with a subjective evaluation obtained from widely used NASA-TLX questionnaire.

The signals used for this processing have been obtained from 21 participants engaged in collaborative robotics programming tasks, organized in three phases:  an introduction to learning materials, a baseline measurement task to establish reference conditions, and hands-on practice, organized in tasks of increasing complexity: Task 1, Task 2, Task 3, Task 4 and Task 5. 

SenseCobotFusion is organized to facilitate statistical investigations, data mining, and machine learning applications as much as possible, and divided into participants and tasks performed: a practical Readme.txt file contains details of the metrics extracted, the nature of the signals of origin, and information on the use of the dataset itself.

A Python code present in this repository has been implemented and optimized with modern state-of-the-art libraries and algorithms to support the researcher in analyzing SenseCobot data (https://zenodo.org/records/10124005), similar datasets, or new related biological signals.

Classic machine learning models such as Decision Tree, Random Forest, SVM, and XGBoost have been trained on SenseCobotFusion to showcase the dataset potential and uploaded to this repository in pickle file format.

Integration with its predecessor SenseCobot dataset, allows the user to implement various types of analysis, such as time series and deep learning approaches.

The SenseCobotFusion dataset, building on the SenseCobot dataset, supports HRC research by providing high-quality, multimodal metrics on mental effort and stress during cobot programming, offering valuable insights for developing intuitive programming interfaces, predictive machine-learning models for real-time stress monitoring, and enhancing human-robot collaboration, while also enabling integration with other datasets, statistical investigation of physical and mental states in Industry 5.0, user-specific machine learning model customization, and the creation of adaptive platforms or technologies aligned with the SenseCobotFusion protocol.If the SenseCobotFusion_Code code or the SenseCobotFusion dataset is used in whole or in part please credit the authors and this repository.
description: See "SenseCobotFusion Dataset: Unlocking New Avenues for Stress Detection in Industry 5.0" at link https://ieeexplore.ieee.org/document/11038711 for more details.
DOI: 10.5281/zenodo.14753191
title: SenseCobotFusion
title: SenseCobotFusion Dataset: Unlocking New Avenues for Stress Detection in Industry 5.0

description: SenseCobotFusion dataset has been created as a natural evolution of SenseCobot dataset.

SenseCobotFusion dataset collects metrics extracted from ElectroCardioGram (ECG), Galvanic Skin Response (GSR), ElectroEncephaloGram (EEG), and emotion signals obtained with professional biosensors, according to modern state-of-the-art signal processing methods, labeled with a subjective evaluation obtained from widely used NASA-TLX questionnaire.

The signals used for this processing have been obtained from 21 participants engaged in collaborative robotics programming tasks, organized in three phases:  an introduction to learning materials, a baseline measurement task to establish reference conditions, and hands-on practice, organized in tasks of increasing complexity: Task 1, Task 2, Task 3, Task 4 and Task 5. 

SenseCobotFusion is organized to facilitate statistical investigations, data mining, and machine learning applications as much as possible, and divided into participants and tasks performed: a practical Readme.txt file contains details of the metrics extracted, the nature of the signals of origin, and information on the use of the dataset itself.

A Python code present in this repository has been implemented and optimized with modern state-of-the-art libraries and algorithms to support the researcher in analyzing SenseCobot data (https://zenodo.org/records/10124005), similar datasets, or new related biological signals.

Classic machine learning models such as Decision Tree, Random Forest, SVM, and XGBoost have been trained on SenseCobotFusion to showcase the dataset potential and uploaded to this repository in pickle file format.

Integration with its predecessor SenseCobot dataset, allows the user to implement various types of analysis, such as time series and deep learning approaches.

The SenseCobotFusion dataset, building on the SenseCobot dataset, supports HRC research by providing high-quality, multimodal metrics on mental effort and stress during cobot programming, offering valuable insights for developing intuitive programming interfaces, predictive machine-learning models for real-time stress monitoring, and enhancing human-robot collaboration, while also enabling integration with other datasets, statistical investigation of physical and mental states in Industry 5.0, user-specific machine learning model customization, and the creation of adaptive platforms or technologies aligned with the SenseCobotFusion protocol.If the SenseCobotFusion_Code code or the SenseCobotFusion dataset is used in whole or in part please credit the authors and this repository.
description: See "SenseCobotFusion Dataset: Unlocking New Avenues for Stress Detection in Industry 5.0" at link https://ieeexplore.ieee.org/document/11038711 for more details.
DOI: 10.15468/dl.cp748s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gymnodinium F.Stein, 1878",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059331-250525065834625/datasets/export for details.
DOI: 10.15468/dl.jzky3b
title: Occurrence Download

description: A dataset containing 3408 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 acceptedtaxonkey,
 acceptedscientificname,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 13 OR occurrence.acceptedtaxonkey = 13 OR occurrence.kingdomkey = 13 OR occurrence.phylumkey = 13 OR occurrence.classkey = 13 OR occurrence.orderkey = 13 OR occurrence.familykey = 13 OR occurrence.genuskey = 13 OR occurrence.specieskey = 13)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.acceptedtaxonkey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence.acceptedtaxonkey,
 occurrence.acceptedscientificname,
 occurrence."year"

 The dataset's 3408 records were derived from 422 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059322-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.