cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vfxh25
title: Occurrence Download

description: A dataset containing 39414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Continent derived from coordinates, Occurrence status inferred from individual count)",
 "TaxonKey is one of (Coyote Reeder & Roth, 1988, Canis latrans Say, 1823)",
 "Year 1000-2023"
 ]
}

 The dataset includes 39414 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029944-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ka8iat
title: Occurrence Download

description: A dataset containing 5338 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bellis sylvestris Cirillo"
 ]
}

 The dataset includes 5338 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003522-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mwkc2b
title: Occurrence Download

description: A dataset containing 5919 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Spizaetus tyrannus (Wied-Neuwied, 1820)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5919 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003522-170826194755519/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.65rhvs
title: Occurrence Download

description: A dataset containing 13642 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Monitoreo del Ictioplancton y potenciales Áreas de Desove en el marco del Proyecto Hidroeléctrico Ituango"
 ]
}

 The dataset includes 13642 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044502-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2jq069
title: Occurrence Download

description: A dataset containing 311 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "DecimalLatitude is greater than 30.894331514648",
 "DecimalLatitude is less than 31.4904677768502",
 "DecimalLongitude is greater than -81.7053689068108",
 "DecimalLongitude is less than -81.0766626842878"
 ]
}

 The dataset includes 311 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003522-171025204727650/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v5f66u
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Peces de la Ecoreserva San Antero (San Antero, Córdoba) - Proyecto Ecoreservas"
 ]
}

 The dataset includes 70 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044438-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7xt26a
title: Occurrence Download

description: A dataset containing 3065 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Parcela permanente de vegetación en bosque seco tropical en el cañón del río Cauca (Antioquia)"
 ]
}

 The dataset includes 3065 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044509-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wgre6o
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Croton patrum L.B.Sm. & Downs"
 ]
}

 The dataset includes 32 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003522-171113114016250/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t4rmc2
title: Occurrence Download

description: A dataset containing 2368 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Spain, Andorra, Portugal)",
 "TaxonKey is Coronella austriaca Laurenti, 1768"
 ]
}

 The dataset includes 2368 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012596-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eka545
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "TaxonKey is Geophis Wagler, 1830"
 ]
}

 The dataset includes 90 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008835-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/yftx-fp58
title: Data of Microlensing event MB10328
DOI: 10.5281/zenodo.15163596
title: Información básica de los centros de salud de Andalucía, Cataluña e Islas Baleares.
DOI: 10.5281/zenodo.15163597
title: Información básica de los centros de salud de Andalucía, Cataluña e Islas Baleares.
DOI: 10.15468/dl.xywbxh
title: Occurrence Download

description: A dataset containing 1754 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((62.01232 11.78680,167.98768 11.78680,167.98768 54.21320,62.01232 54.21320,62.01232 11.78680))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Castanopsis fargesii"
 ]
}

 The dataset includes 1754 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0065312-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7072195
title: InsectSet32: Dataset for automatic acoustic identification of insects (Orthoptera and Cicadidae)

description: This dataset contains recordings of 32 sound producing insect species with a total 335 files and a length of 57 minutes. The dataset was compiled for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was published in PLOS Computational Biology and this dataset can be used to replicate the results, as well as other uses. The scripts for audio processing and the machine learning implementations are published on Github.

The recordings are split into two datasets. Roughly half of the recordings (147) are of nine species belonging to the order Orthoptera. These recordings stem from a dataset that was originally compiled by Baudewijn Odé (unpublished). 

The remaining recordings (188) are of 23 species in the family Cicadidae. These recordings were selected from the Global Cicada Sound Collection hosted on Bioacoustica (doi.org/10.1093/database/bav054), including recordings published in doi.org/10.3897/BDJ.3.e5792 & doi.org/10.11646/zootaxa.4340.1. Many recordings from this collection included speech annotations in the beginning of the recordings, therefore the last ten seconds of audio were extracted and used in this dataset. 

All files were manually inspected and files with strong noise interference or with sounds of multiple species were removed. Between species, the number of files ranges from four to 22 files and the length from 40 seconds to almost nine minutes of audio material for a single species. The files range in length from less than one second to several minutes. All original files were available with sample rates of at least 44.1 kHz or higher but were resampled to 44.1 kHz mono WAV files for consistency. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).
DOI: 10.5281/zenodo.7072196
title: InsectSet32: Dataset for automatic acoustic identification of insects (Orthoptera and Cicadidae)

description: This dataset contains recordings of 32 sound producing insect species with a total 335 files and a length of 57 minutes. The dataset was compiled for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was published in PLOS Computational Biology and this dataset can be used to replicate the results, as well as other uses. The scripts for audio processing and the machine learning implementations are published on Github.

The recordings are split into two datasets. Roughly half of the recordings (147) are of nine species belonging to the order Orthoptera. These recordings stem from a dataset that was originally compiled by Baudewijn Odé (unpublished). 

The remaining recordings (188) are of 23 species in the family Cicadidae. These recordings were selected from the Global Cicada Sound Collection hosted on Bioacoustica (doi.org/10.1093/database/bav054), including recordings published in doi.org/10.3897/BDJ.3.e5792 & doi.org/10.11646/zootaxa.4340.1. Many recordings from this collection included speech annotations in the beginning of the recordings, therefore the last ten seconds of audio were extracted and used in this dataset. 

All files were manually inspected and files with strong noise interference or with sounds of multiple species were removed. Between species, the number of files ranges from four to 22 files and the length from 40 seconds to almost nine minutes of audio material for a single species. The files range in length from less than one second to several minutes. All original files were available with sample rates of at least 44.1 kHz or higher but were resampled to 44.1 kHz mono WAV files for consistency. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).
DOI: 10.15468/dl.nwakbm
title: Occurrence Download

description: A dataset containing 4066 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trifolium montanum L., Trifolium repens L.)",
 "Country is Ukraine"
 ]
}

 The dataset includes 4066 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019615-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7828439
title: InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)

description: Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was published in PLOS Computational Biology and the machine learning implementations were published on Github.

These datasets expand on the previously published InsectSet32 by including recently published collections of insect recordings by citizen scientists from around the world. Recordings from BioAcoustica, xeno-canto and iNaturalist, as well as private collections by Baudewijn Odé were downloaded and manually inspected. Files with strong noise interference or intense filtering, as well as files containing sounds of multiple species were removed to compile these datasets. The files were standardised to 44.1 kHz mono WAV files ranging in length from less than one second to several minutes. Files containing long periods without insect sounds were edited into multiple smaller files with silent periods no longer than 5 seconds. These files are marked as edits in the annotation file and should be assigned together into train/validation/test sets to prevent data leakage. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).

InsectSet47 expands on InsectSet32 with recordings from xeno-canto and contains 1006 original recordings from 47 species, with at least ten files per species. The total length of InsectSet47 is 22 hours. InsectSet66 further expands on InsectSet47 by adding research-grade audio observations from iNaturalist, with a total of 1554 recordings from 66 species, a total length of over 24 hours and a minimum of ten files per species.

The datasets were split into the training, validation and test sets while ensuring a roughly equal distribution of audio files and audio material for every species in all three subsets. This resulted in a 60/20/20 split (train/validation/test) by file number and a 64/19.5/16.5 split by file length.

NOTE: Due to the inclusion of InsectSet47 and InsectSet66 in a coding challenge, the files included in the test datasets will be held back until the end of September 2023 as of now. The full datasets including the test sets will then be published as a new version of these datasets under the same identifier.
DOI: 10.5281/zenodo.7828438
title: InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)

description: Updated full version with training, validation and test sets.


Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was published in PLOS Computational Biology and the machine learning implementations were published on Github.


These datasets expand on the previously published InsectSet32 by including recently published collections of insect recordings by citizen scientists from around the world. Recordings from BioAcoustica, xeno-canto and iNaturalist, as well as private collections by Baudewijn Odé were downloaded and manually inspected. Files with strong noise interference or intense filtering, as well as files containing sounds of multiple species were removed to compile these datasets. The files were standardised to 44.1 kHz mono WAV files ranging in length from less than one second to several minutes. Files containing long periods without insect sounds were edited into multiple smaller files with silent periods no longer than 5 seconds. These files are marked as edits in the annotation file and should be assigned together into train/validation/test sets to prevent data leakage. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).


InsectSet47 expands on InsectSet32 with recordings from xeno-canto and contains 1006 original recordings from 47 species, with at least ten files per species. The total length of InsectSet47 is 22 hours. InsectSet66 further expands on InsectSet47 by adding research-grade audio observations from iNaturalist, with a total of 1554 recordings from 66 species, a total length of over 24 hours and a minimum of ten files per species.


The datasets were split into the training, validation and test sets while ensuring a roughly equal distribution of audio files and audio material for every species in all three subsets. This resulted in a 60/20/20 split (train/validation/test) by file number and a 64/19.5/16.5 split by file length.
DOI: 10.5281/zenodo.8252141
title: InsectSet47 & InsectSet66: Expanded datasets for automatic acoustic identification of insects (Orthoptera and Cicadidae)

description: <strong>Updated full version with training, validation and test sets.</strong> Two newly compiled datasets for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was published in PLOS Computational Biology and the machine learning implementations were published on Github. These datasets expand on the previously published InsectSet32 by including recently published collections of insect recordings by citizen scientists from around the world. Recordings from BioAcoustica, xeno-canto and iNaturalist, as well as private collections by Baudewijn Odé were downloaded and manually inspected. Files with strong noise interference or intense filtering, as well as files containing sounds of multiple species were removed to compile these datasets. The files were standardised to 44.1 kHz mono WAV files ranging in length from less than one second to several minutes. Files containing long periods without insect sounds were edited into multiple smaller files with silent periods no longer than 5 seconds. These files are marked as edits in the annotation file and should be assigned together into train/validation/test sets to prevent data leakage. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation). InsectSet47 expands on InsectSet32 with recordings from xeno-canto and contains 1006 original recordings from 47 species, with at least ten files per species. The total length of InsectSet47 is 22 hours. InsectSet66 further expands on InsectSet47 by adding research-grade audio observations from iNaturalist, with a total of 1554 recordings from 66 species, a total length of over 24 hours and a minimum of ten files per species. The datasets were split into the training, validation and test sets while ensuring a roughly equal distribution of audio files and audio material for every species in all three subsets. This resulted in a 60/20/20 split (train/validation/test) by file number and a 64/19.5/16.5 split by file length.
DOI: 10.34740/kaggle/dsv/9940172
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.15468/dl.mtgnr3
title: Occurrence Download

description: A dataset containing 2617 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Spiders (Aracnida:Araneae) of PSU’s Botanical Garden (Perm, Russia)"
 ]
}

 The dataset includes 2617 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019616-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n/chmo0000595.1
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1,8-dibromo-3,6-ditert-butyl-9H-carbazole)
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n/chmo0000596.2
title: distortionless enhancement with polarization transfer (DEPT) (1,8-dibromo-3,6-ditert-butyl-9H-carbazole)
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n/chmo0000596.3
title: distortionless enhancement with polarization transfer (DEPT) (1,8-dibromo-3,6-ditert-butyl-9H-carbazole)
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n/chmo0000599
title: correlation spectroscopy (COSY) (1,8-dibromo-3,6-ditert-butyl-9H-carbazole)
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: dataset for correlation spectroscopy (COSY)
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n/chmo0000601
title: heteronuclear multiple bond coherence (HMBC) (1,8-dibromo-3,6-ditert-butyl-9H-carbazole)
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: dataset for heteronuclear multiple bond coherence (HMBC)
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n/chmo0000604
title: heteronuclear single quantum coherence (HSQC) (1,8-dibromo-3,6-ditert-butyl-9H-carbazole)
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: dataset for heteronuclear single quantum coherence (HSQC)
DOI: 10.14272/ntucwbpmvkoids-uhfffaoysa-n.2
title: 1,8-dibromo-3,6-ditert-butyl-9H-carbazole
title: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C20H23Br2N/c1-19(2,3)11-7-13-14-8-12(20(4,5)6)10-16(22)18(14)23-17(13)15(21)9-11/h7-10,23H,1-6H3, and canonical SMILES descriptor[cheminf_000007]: Brc1cc(cc2c1[nH]c1c2cc(cc1Br)C(C)(C)C)C(C)(C)C, and by the IUPAC name[cheminf_000107]: 1,8-dibromo-3,6-ditert-butyl-9H-carbazole.
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-43870

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 225.0 (°C)
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0000596 | distortionless enhancement with polarization transfer (DEPT)
 
 CHMO:0000596 | distortionless enhancement with polarization transfer (DEPT)
 
 CHMO:0000599 | correlation spectroscopy (COSY)
 
 CHMO:0000604 | heteronuclear single quantum coherence (HSQC)
 
 CHMO:0000601 | heteronuclear multiple bond coherence (HMBC)
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations