cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.12562044
title: Dataset: Provident Bancorp, Inc. (PVBC) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562045
title: Dataset: PowerFleet, Inc. (PWFL) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562046
title: Dataset: PowerFleet, Inc. (PWFL) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562047
title: Dataset: Prestige Wealth Inc. (PWM) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562048
title: Dataset: Prestige Wealth Inc. (PWM) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.15468/dl.b2qqbs
title: Occurrence Download

description: A dataset containing 2765 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Monodelphis domestica (Wagner, 1842), Monodelphis dimidiata (Wagner, 1847), Monodelphis adusta (Thomas, 1897), Monodelphis americana (Müller, 1776), Monodelphis arlindor Pavan, Rossi & Schneider, 2012, Monodelphis brevicaudata (Erxleben, 1777), Monodelphis emiliae (Thomas, 1912), Monodelphis gardneri Solari, Pacheco, Vivar & Emmons, 2012, Monodelphis glirina (Wagner, 1842), Monodelphis iheringi (Thomas, 1888), Monodelphis kunsi Pine, 1975, Monodelphis osgoodi Doutt, 1938, Monodelphis palliolata (Osgood, 1914), Monodelphis peruviana (Osgood, 1913), Monodelphis reigi Lew & Pérez-Hernández, 2004, Monodelphis sanctaerosae Burnett, 1830, Monodelphis scalops (Thomas, 1888), Monodelphis touan (G.Shaw, 1800))"
 ]
}

 The dataset includes 2765 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001422-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9kbsas
title: Occurrence Download

description: A dataset containing 19067 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Material citation",
 "Country is Philippines",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 19067 records from 580 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001419-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2h9dzw
title: Occurrence Download

description: A dataset containing 287 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "TaxonKey is Lampyridae"
 ]
}

 The dataset includes 287 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001423-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pcw68d
title: Occurrence Download

description: A dataset containing 7958 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Berberis actinacantha Mart. ex Schult.fil., Berberis ×bidentata Lechl., Berberis buxifolia Lam., Berberis cabrerae Job, Berberis chilensis Gillies, Berberis comberi Sprague & Sandwith, Berberis congestiflora Gay, Berberis corymbosa Hook. & Arn., Berberis darwinii Hook., Berberis empetrifolia Lam., Berberis grevilleana Gillet, Berberis heterophylla Juss., Berberis hieronymi C.K.Schneid., Berberis horrida Gay, Berberis ilicifolia L.fil., Berberis jamesonii Lindl., Berberis linearifolia Phil., Berberis lutea Ruiz & Pav., Berberis microphylla G.Forst., Berberis montana Gay, Berberis negeriana Tischler, Berberis parodii Job, Berberis rotundifolia Poepp. & Endl., Berberis serrato-dentata Reiche, Berberis trigona Kunze, Berberis valdiviana Phil.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 7958 records from 187 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001424-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12562049
title: Dataset: Penns Woods Bancorp, Inc. (PWOD) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562050
title: Dataset: Penns Woods Bancorp, Inc. (PWOD) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562051
title: Dataset: Perella Weinberg Partners (PWP) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562052
title: Dataset: Perella Weinberg Partners (PWP) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562059
title: Dataset: PowerUp Acquisition Corp. (PWUPW) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562060
title: Dataset: PowerUp Acquisition Corp. (PWUPW) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562053
title: Dataset: PowerUp Acquisition Corp. (PWUP) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562054
title: Dataset: PowerUp Acquisition Corp. (PWUP) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562063
title: Dataset: Pixie Dust Technologies, Inc. (PXDT) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562064
title: Dataset: Pixie Dust Technologies, Inc. (PXDT) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562057
title: Dataset: PowerUp Acquisition Corp. (PWUPU) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562058
title: Dataset: PowerUp Acquisition Corp. (PWUPU) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12561795
title: Supplementary information: Motor point stimulation activates fewer Ia-sensory nerves than peripheral nerve stimulation in human soleus muscle
DOI: 10.5281/zenodo.12561796
title: Supplementary information: Motor point stimulation activates fewer Ia-sensory nerves than peripheral nerve stimulation in human soleus muscle
DOI: 10.15468/dl.778ayv
title: Occurrence Download

description: A dataset listing the 6373 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Northern Mariana Islands",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 6373 records were derived from 339 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001411-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12562067
title: Dataset: Invesco Dorsey Wright Energy Momentum ETF (PXI) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12562069
title: Dataset: Invesco Dorsey Wright Energy Momentum ETF (PXI) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.15468/dl.5nk3m6
title: Occurrence Download

description: A dataset listing the 111893 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Norway",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 111893 records were derived from 1909 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001412-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5dgeep
title: Occurrence Download

description: A dataset containing 283813 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tragopogon pratensis"
 ]
}

 The dataset includes 283813 records from 2269 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019288-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1383289.10fu
title: SH1383289.10FU

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/sh1383291.10fu
title: SH1383291.10FU

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/sh1383293.10fu
title: SH1383293.10FU

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/sh1383295.10fu
title: SH1383295.10FU

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/sh1383298.10fu
title: SH1383298.10FU

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/sh1383300.10fu
title: SH1383300.10FU

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: 10.5281/zenodo.17337772
title: Mixed Quantum States Datasets for Entanglement Classification

description: Dataset for Supervised Machine Learning Classification of Entanglement in Mixed Quantum States

This dataset is designed for the training and evaluation of supervised machine learning algorithms aimed at classifying quantum states. Specifically, the dataset contains labeled examples of 2-qubit and 3-qubit mixed quantum states, categorized as factorizable, separable, and states with negative partial transpose.

The data is provided in `.npy` format, compatible with common scientific computing libraries such as NumPy. Each sample represents a quantum state and is labeled according to its classification, enabling the development and benchmarking of models for quantum state discrimination.

Data Access and Structure

Upon loading, the dataset is structured as a dictionary with two main keys:

- 'matrices': contains the quantum state matrices, stored as NumPy arrays of dtype complex128;- 'labels': contains the corresponding classification labels.

Labeling Scheme





For general classification tasks, classes are ordered as: Fact (factorizable): 0 Sep (separable): 1 NPT (negative partial transpose for all possible bipartitions): 2




For fine-grained labeling of separable classes (e.g., ['NPT', 'Fact', 'Sep_1', 'Sep_2', 'Sep_3']), classes are ordered as: 'Fact': 0 'Sep_1': 1 'Sep_2': 2 'Sep_3': 3 'NPT': 4
DOI: 10.5281/zenodo.18229713
title: Mixed Quantum States Datasets for Entanglement Classification

description: Dataset for Supervised Machine Learning Classification of Entanglement in Mixed Quantum States

This dataset is designed for the training and evaluation of supervised machine learning algorithms aimed at classifying quantum states. Specifically, the dataset contains labeled examples of 2-qubit and 3-qubit mixed quantum states, categorized as factorizable, separable, and states with negative partial transpose.

The data is provided in `.npy` format, compatible with common scientific computing libraries such as NumPy. Each sample represents a quantum state and is labeled according to its classification, enabling the development and benchmarking of models for quantum state discrimination.

Data Access and Structure

Upon loading, the dataset is structured as a dictionary with two main keys:

- 'matrices': contains the quantum state matrices, stored as NumPy arrays of dtype complex128;- 'labels': contains the corresponding classification labels.

Labeling Scheme





For general classification tasks, classes are ordered as: Fact (factorizable): 0 Sep (separable): 1 NPT (negative partial transpose for all possible bipartitions): 2




For fine-grained labeling of separable classes (e.g., ['NPT', 'Fact', 'Sep_1', 'Sep_2', 'Sep_3']), classes are ordered as: 'Fact': 0 'Sep_1': 1 'Sep_2': 2 'Sep_3': 3 'NPT': 4