cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4z2p9h
title: Occurrence Download

description: A dataset containing 1233 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Species of Trichoptera in the collection of the Institute of Neotropical Biodiversity (IBN-CONICET-UNT), Tucumán, Argentina.",
 "Country is Argentina"
 ]
}

 The dataset includes 1233 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0158499-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wuxrmn
title: Occurrence Download

description: A dataset containing 1301532 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Madagascar"
 ]
}

 The dataset includes 1301532 records from 611 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005426-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.stbyft
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Alternaria metachromatica E.G.Simmons)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012544-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2422n2
title: Occurrence Download

description: A dataset containing 186 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterocarpus dalbergioides Roxb. ex DC."
 ]
}

 The dataset includes 186 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023586-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d52jkb
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Terminalia procera Roxb."
 ]
}

 The dataset includes 163 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023585-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mt3khq
title: Occurrence Download

description: A dataset containing 951 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pistacia weinmannifolia J.Poiss. ex Franch."
 ]
}

 The dataset includes 951 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038138-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kpj6e5
title: Occurrence Download

description: A dataset containing 461 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bulbophyllum odoratissimum (Sm.) Lindl. ex Wall."
 ]
}

 The dataset includes 461 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038136-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3wrjh8
title: Occurrence Download

description: A dataset containing 6601 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhizophora apiculata Blume"
 ]
}

 The dataset includes 6601 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023584-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yz3dzf
title: Occurrence Download

description: A dataset containing 376666 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Reynoutria japonica Houtt."
 ]
}

 The dataset includes 376666 records from 1342 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012540-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.avgwwb
title: Occurrence Download

description: A dataset containing 11571 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "GadmGid is CHE.26.13_1"
 ]
}

 The dataset includes 11571 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012547-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9896308
title: Impacts of Food Data
DOI: 10.57955/d9a53eab-aa03-4053-9142-90b9e751fbec
title: Digital Twin Templates for Intellectual Property Identification and Valuation in Manufacturing

description: Template files with proposed structure for representing intellectual property assets in digital twin format, and questionnaire for research study of IP management practices in manufacturing companies in UK and Brazil. 

Contains four files (+ a README file with further detail).

* IP_ASSET_MODEL_TEMPLATE.xlsx - Structure developed to represent intellectual property asset in a digital twin model; Excel format.

* IP_ASSET_MODEL_TEMPLATE.aasx - Structure developed to represent intellectual property asset in a digital twin model; Asset Administration Shell format.

* IP_Questionnaire.xlsx - Questions pre- and post- intervention in the intellectual property management study. In both English and Portuguese.

* IP_AASX_Structure.pdf - Screenshots from the Asset Administration Shell Explorer template file; shows the whole submodel structure. For illustration only (shows layout without needing to install AAS Package Explorer).
DOI: 10.15468/dl.ubybbn
title: Occurrence Download

description: A dataset containing 24242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-1019.0",
 "Geometry POLYGON((-126.44841 23.06021,-71.32437 23.06021,-71.32437 52.94969,-126.44841 52.94969,-126.44841 23.06021))",
 "TaxonKey is Lonicera japonica Thunb."
 ]
}

 The dataset includes 24242 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012545-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/kzr1-1w26
title: Low voltage DC series arc fault

description: This dataset is collected to investigate detection algorithms for DC series arc faults on resource constrained devices. The data is current signal collected from test-bench build to simulate arc fault under various conditions according to the standard UL1699B. Different condition includes the power mode regulated by the elctronic load in the system for the simulation of different system dynamics under topology series and parallel.The code attached is used in the comparative study for data evaluation with visulisation of the current signal and their representative FFT signal. Examples of detection algorithms, including a conventional method with threshold, SVM and CNN, are listed for cross-dataset validation and algorithm robustness evaluation.
DOI: 10.48328/tudatalib-1591.3
title: Dataset on Color Preference Rendition in White Lighting Applications

description: Collected database of psychophysical data on color preference evaluation of lighting scenarios. Including radiometric and photometric measures (SPD, light level...), experimental conditions (number of participants, scene...), z-scores based on average evaluations as well as calculated color metrics and measures.
DOI: 10.15468/dl.qejj4k
title: Occurrence Download

description: A dataset containing 370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Abies cephalonica Loudon, Abies cephalonica Link, 1842)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 370 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012548-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48328/tudatalib-1591
title: Dataset on Color Preference Rendition in White Lighting Applications

description: Collected database of psychophysical data on color preference evaluation of lighting scenarios. Including radiometric and photometric measures (SPD, light level...), experimental conditions (number of participants, scene...), z-scores based on average evaluations as well as calculated color metrics and measures.
DOI: 10.6084/m9.figshare.27695190
title: 2.Eggnog6_orthology

description: These datasets are the result of the identification of orthologous genes between <i>Tribolium</i> and <i>Drosophila</i>.
DOI: 10.5281/zenodo.12698581
title: Data for "Machine learning of reduced quantum channels on NISQ devices"

description: This dataset contains the data, figures and code of the publication "Machine learning of reduced quantum channels on NISQ devices". I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.
DOI: 10.5281/zenodo.11091626
title: Data for "Machine learning of reduced quantum channels on NISQ devices"

description: This dataset contains the data, figures and code of the publication "Machine learning of reduced quantum channels on NISQ devices". I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.
DOI: 10.5281/zenodo.11091627
title: Data for "Machine learning of quantum channels on NISQ devices"

description: This dataset contains the data, figures and code of the publication "Machine learning of quantum channels on NISQ devices". I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.
DOI: 10.5281/zenodo.11351085
title: Data for "Machine learning of quantum channels on NISQ devices"

description: This dataset contains the data, figures and code of the publication "Machine learning of quantum channels on NISQ devices". I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.
DOI: 10.5281/zenodo.14045894
title: Data for "Machine learning of reduced quantum channels on NISQ devices"

description: This dataset contains the data, figures and code of the publication "Machine learning of reduced quantum channels on NISQ devices". I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.
DOI: 10.15468/dl.ynjkme
title: Occurrence Download

description: A dataset containing 41156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acacia dealbata Link, Acacia dealbata A.Cunn.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 41156 records from 281 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012549-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0184822.10fu
title: SH0184822.10FU

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/sh0184823.10fu
title: SH0184823.10FU

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/sh0184824.10fu
title: SH0184824.10FU

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: 10.60594/w4ww2j
title: Aerial Orthoimagery from the 2022 Department of Defense Wildland Fire Science Initiative Fort Stewart Campaign

description: This dataset contains 15-cm 4-band (red, green, blue, near-infrared) color aerial orthoimagery across burn units E16.2, E16.3, F6.3 and F6.4 in Fort Stewart, GA. Kucera International acquired digital orthoimagery on November 23, 2021 (before prescribed fire), and on March 13, 2022 (after prescribed fire); prescribed fires occurred from March 2 to 5, 2022. Orthoimagery was acquired to characterize pre- and post-fire vegetation and estimate fire-caused biomass consumption.This dataset is part of the Wildland Fire Science Initiative (WFSI) research burn campaign held at Fort Stewart from March 2, 2022 to March 5, 2022. In addition, pre- and post-fire data collection occurred before and after these dates. See the most recent version of the contextual dataset "Fort Stewart 2022: Contextual data for Wildland Fire Science Initiative (WFSI) Research Burn Campaign" for more information about the campaign (see Annotations section for hasMetadata link).
DOI: 10.15156/bio/sh0184825.10fu
title: SH0184825.10FU

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/sh0184826.10fu
title: SH0184826.10FU

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: 10.15468/dl.ukhys5
title: Occurrence Download

description: A dataset containing 496 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Naturalis Biodiversity Center (NL) - Botany",
 "TaxonKey is Meteorium"
 ]
}

 The dataset includes 496 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001815-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0184827.10fu
title: SH0184827.10FU

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/sh0184828.10fu
title: SH0184828.10FU

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/sh0184829.10fu
title: SH0184829.10FU

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/sh0184830.10fu
title: SH0184830.10FU

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.