cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7755505
title: A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.

description: A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques. Manuel Titos (1), Ligdamis Gutiérrez (2,3), Carmen Benítez (1), Pablo Rey Devesa (2,3), Ivan Koulakov (4) and Jesús. M. Ibáñez (2,3) <br> <strong>Institutions associated:</strong> (1) CITIC, Department of Signal Processing, Telematic and Communications, University of Granada, 18071. Granada. Spain.<br> (2) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.<br> (3) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.<br> (4) Laboratory for Seismic Forward and Inverse Problems, Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia <br> <strong>Acknowledgment:</strong> This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors. a) This work is part of the research by the Spanish FEMALE project (PID2019-106260GB-I00). <strong>FEMALE </strong>(<em>Forecasting Volcanic Eruptions Using Signal Processing and Machine Learning Techniques on Seismic Signals</em>) https://femalevolcanoes.es/ <br> b) JMI and LG were partially funded by the Spanish project PROOF-FOREVER (EUR2022.134044). <br> <strong>Keywords:</strong> Automatic volcanic monitoring, real-time monitoring, Artificial Intelligence, Transfer Learning, Recurrent Neural Networks, Temporal Convolutional Networks. <strong>Data availability statement:</strong> Seismic data from Bezymianny volcano (2017), Kamchatka, Russia. <strong>Contents:</strong> Seismic Data from Bezymianny volcano recorded at stations BZ01, BZ02, BZ06 and BZ10.<br> The data represent the vertical component of the seismic signal, associated to the period analyzed in the study:
DOI: 10.5281/zenodo.7761172
title: Application of two-step clustering algorithm to QuaLiKiz-v2.6.2 turbulent transport simulation data

description: QuaLiKiz simulation data in support of the two-step clustering algorithm, developed by Bart J. J. Kremers. The NETCDF file, generated via NETCDF4, contains the raw QuaLiKiz output for the 3-dimensional (2-input, 1-output) toy case used to develop the algorithm. Within the NETCDF file, the coordinates represent the code inputs and various vector indices and the data variables represent the code outputs. There are also 4 HDF5 files, containing the results from the two-step clustering reduction algorithm, where the data is saved under 2 keys: "/input" and "/flattened". The file names indicate the reduction algorithm settings used to produce the results within. The algorithm is available open-source at https://gitlab.com/BartKremers/two-step-clustering.
DOI: 10.5281/zenodo.7761171
title: Application of two-step clustering algorithm to QuaLiKiz-v2.6.2 turbulent transport simulation data

description: QuaLiKiz simulation data in support of the two-step clustering algorithm, developed by Bart J. J. Kremers. The NETCDF file, generated via NETCDF4, contains the raw QuaLiKiz output for the 3-dimensional (2-input, 1-output) toy case used to develop the algorithm. Within the NETCDF file, the coordinates represent the code inputs and various vector indices and the data variables represent the code outputs. There are also 4 HDF5 files, containing the results from the two-step clustering reduction algorithm, where the data is saved under 2 keys: "/input" and "/flattened". The file names indicate the reduction algorithm settings used to produce the results within. The algorithm is available open-source at https://gitlab.com/BartKremers/two-step-clustering.
DOI: 10.17909/rh19-k006
title: IUE spectra HD 133029
DOI: 10.5281/zenodo.6508639
title: DNS Threats Dataset

description: The dataset contains Normal, DGA and Tunneling domain names: i. the total number of normal domains are conformed by the Alexa top one million domains, 3,161 normal domains provided by the Bambenek Consulting feed, and another 177,017 normal domains; ii. the DGA domains were obtained from the repositories of DGA domains of Andrey Abakumov and John Bambenek, corresponding to 51 different malware families; iii. the DNS Tunneling consist of 8000 tunnel domains generated using a set of well known DNS tunneling tools under laboratory conditions: iodine, dnscat2 and dnsExfiltrator. The dataset is described in the paper:<br> Palau, F., Catania, C., Guerra, J., García, S. J., & Rigaki, M. (2019). Detecting DNS threats: A deep learning model to rule them all. In XX Simposio Argentino de Inteligencia Artificial (ASAI 2019)-JAIIO 48 (Salta).
description: The paper where the dataset is discussed can be found at http://sedici.unlp.edu.ar/handle/10915/87859
DOI: 10.5281/zenodo.6508640
title: DNS Threats Dataset

description: The dataset contains Normal, DGA and Tunneling domain names: i. the total number of normal domains are conformed by the Alexa top one million domains, 3,161 normal domains provided by the Bambenek Consulting feed, and another 177,017 normal domains; ii. the DGA domains were obtained from the repositories of DGA domains of Andrey Abakumov and John Bambenek, corresponding to 51 different malware families; iii. the DNS Tunneling consist of 8000 tunnel domains generated using a set of well known DNS tunneling tools under laboratory conditions: iodine, dnscat2 and dnsExfiltrator. The dataset is described in the paper:<br> Palau, F., Catania, C., Guerra, J., García, S. J., & Rigaki, M. (2019). Detecting DNS threats: A deep learning model to rule them all. In XX Simposio Argentino de Inteligencia Artificial (ASAI 2019)-JAIIO 48 (Salta).
description: The paper where the dataset is discussed can be found at http://sedici.unlp.edu.ar/handle/10915/87859
DOI: 10.15468/dl.44s8d4
title: Occurrence Download

description: A dataset containing 18686 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sequoia sempervirens (D.Don) Endl."
 ]
}

 The dataset includes 18686 records from 406 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086635-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.29sxqq
title: Occurrence Download

description: A dataset containing 50419 species occurrences available in GBIF matching the query:
{
 "PublishingOrg" : [
 "is ce3327e9-16df-4977-8024-114ec0a94377"
 ]
}

 The dataset includes 50419 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086634-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ap74p5
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Sitophilus granarius (C.Linnaeus, 1758)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 240 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086636-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g2xr9f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Fitchia cordata M.L.Grant & Carlquist)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086637-240626123714530/datasets/export for details.
DOI: 10.15468/dl.qb5aym
title: Occurrence Download

description: A dataset containing 6368 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Melitta haemorrhoidalis (Fabricius, 1775)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 6368 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086638-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nd96tn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Melicope fatuhivensis (F.Br.) T.G.Hartley & B.C.Stone)",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 100",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086643-240626123714530/datasets/export for details.
DOI: 10.15468/dl.22f4zu
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Sitophilus granarius (C.Linnaeus, 1758)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 240 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086642-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pxm5wz
title: Occurrence Download

description: A dataset containing 6530 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Database of Native Plants in Taiwan",
 "Geometry POLYGON((120.8297 24.78883,122.12378 24.65228,122.11436 25.36798,120.85889 25.38399,120.8297 24.78883))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6530 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086641-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t87b4k
title: Occurrence Download

description: A dataset containing 39112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Styphnolobium japonicum (L.) Schott"
 ]
}

 The dataset includes 39112 records from 374 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086640-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rrtqck
title: Occurrence Download

description: A dataset containing 4030 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Ceratina cyanea (Kirby, 1802)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 4030 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086644-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e8gse8
title: Occurrence Download

description: A dataset containing 315931 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lycopodiales)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Geometry POLYGON((-30 25,50 25,50 75,-30 75,-30 25))"
 ]
}

 The dataset includes 315931 records from 739 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086626-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh3283239.08fu
title: SH3283239.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283240.08fu
title: SH3283240.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283241.08fu
title: SH3283241.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283242.08fu
title: SH3283242.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283243.08fu
title: SH3283243.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283244.08fu
title: SH3283244.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283245.08fu
title: SH3283245.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283246.08fu
title: SH3283246.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh3283247.08fu
title: SH3283247.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.