cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34847/nkl.32dd53p0
title: Archéométallurgie des bronzes hallstattiens

description: Cette base de données est issue d'un post-doctorat réalisé d'octobre 2014 à septembre 2016, financé par la fondation F. Thyssen et hébergé par le RP Stuttgart, sur l'archéométallurgie des bronzes au premier âge du Fer dans la vallée du Rhin supérieur.
description: Cette base de données est issue d'un post-doctorat réalisé d'octobre 2014 à septembre 2016, financé par la fondation F. Thyssen et hébergé par le RP Stuttgart, sur l'archéométallurgie des bronzes au premier âge du Fer dans la vallée du Rhin supérieur. La composition élémentaire de 64 parures annulaires de types régionaux et 3 témoins d'activités métallurgiques a été analysée en collaboration avec B. Mille, au C2RMF, par la méthode PIXE. Les résultats de cette étude ont été publiés dans l'article suivant : L. Tremblay Cormier / B. Mille, Étude archéométallurgique de parures hallstattiennes en alliage cuivreux du Rhin supérieur. Bulletin de la Société Préhistorique Française 2016, 113/4, 785-808.
DOI: 10.15468/dl.76u84n
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Cyprus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074838-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.66edtz
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Egypt",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074839-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gwgf5b
title: Occurrence Download

description: A dataset containing 61562 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Megalops atlanticus Valenciennes, 1847"
 ]
}

 The dataset includes 61562 records from 164 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074828-250525065834625/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/12313448
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.wqm5fu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Ethiopia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074840-250525065834625/datasets/export for details.
DOI: 10.15468/dl.cj436e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Micronesia (Federated States of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074843-250525065834625/datasets/export for details.
DOI: 10.15468/dl.5jbgec
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Fiji",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074844-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ewj2cp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Ghana",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074846-250525065834625/datasets/export for details.
DOI: 10.15468/dl.x9dand
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Haiti",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074848-250525065834625/datasets/export for details.
DOI: 10.15468/dl.w2h4ar
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Hungary",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074849-250525065834625/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/12313477
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.15468/dl.ck9ptx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gambusia holbrooki Girard, 1859",
 "Country is Indonesia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0074852-250525065834625/datasets/export for details.
DOI: 10.15468/dl.xduvz3
title: Occurrence Download

description: A dataset containing 352 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magdalis barbicornis (P.A.Latreille, 1804)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 352 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001759-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15003721
title: EASE 2025 Dataset (BugRepo)

description: The following Datasets are for the submission of EASE 2025 dataset track

1) Bug report metadata dataset

2) Contributor Information Dataset

3) Bug reports and Comments Dataset
DOI: 10.5281/zenodo.15003722
title: EASE 2025 Dataset (BugRepo)

description: The following Datasets are for the submission of EASE 2025 dataset track

1) Bug report metadata dataset

2) Contributor Information Dataset

3) Bug reports and Comments Dataset
DOI: 10.15468/dl.6hum28
title: Occurrence Download

description: A dataset listing the 82 species recorded in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((136.21804 34.00437,136.24433 34.00437,136.24433 34.03076,136.23171 34.03043,136.21804 34.03076,136.21804 34.00437))"
 ]
}

 The dataset's 82 records were derived from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001758-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60656/0503d119021b4db9
title: Custom collection of synthetic earthcare radar data from Lampedusa between 2 Jan and 28 Feb 2025
DOI: 10.5281/zenodo.15004052
title: Damaged Traffic Signs Dataset

description: Description:







The Damaged Traffic Signs Dataset focuses on Portuguese traffic signs captured under a variety of conditions, providing a comprehensive collection of images showcasing signs in both their regular state and with various forms of damage or deterioration. The dataset is designed to support research and development efforts in the areas of traffic sign detection, classification, and damage recognition for AI and machine learning models.

Dataset Features:

This dataset includes traffic signs that have undergone several types of wear and tear, presenting realistic conditions encountered on the road. The goal is to offer a broad set of visual challenges for training models that can accurately identify and classify traffic signs, even in less-than-ideal circumstances.







Download Dataset









Conditions Captured:

The dataset encapsulates a range of deteriorations and flaws, including but not limited to:



Discoloration: Signs with faded colors due to prolonged exposure to sunlight or environmental factors.

Paint Peeling: Signs with paint coming off, making it harder to detect their design elements.

Scratches and Surface Damage: Signs damaged by vehicles or debris, which obscures the visibility of key information.

Bending or Warping: Traffic signs that are bent or physically distorted, altering their appearance from the standard design.

Rust: Older signs suffering from rust, particularly those made from metal.

Graffiti or Stickers: Signs vandalized with graffiti, stickers, or other forms of defacement.

Partial Occlusion: Signs partially blocked by objects such as tree branches, poles, or other obstructions.

Design and Manufacturing Flaws: Some images capture signs with design flaws or imperfections resulting from poor manufacturing.

Reflections and Glare: Signs affected by reflections from sunlight or vehicle headlights, which create visibility challenges.

Blurring: Signs captured under motion or with out-of-focus camera settings, simulating real-world challenges in detection.

Environmental Wear: The dataset also captures signs under various environmental factors such as rain, dirt, and dust.


Additional Information:



Resolution: Images are captured at various resolutions, ensuring diversity in image quality.

Weather Conditions: The dataset includes images taken in different weather conditions, such as sunny, rainy, and foggy days, which add further complexity.

Lighting Conditions: Photographs were taken under a mix of lighting conditions, including bright daylight, evening, and low-light situations.


Applications:

This dataset is an excellent resource for developing and training machine learning models for:



Traffic sign recognition and detection systems.

Damage classification for traffic sign maintenance and road safety assessment.

Autonomous vehicle navigation and obstacle detection.

Government or municipal agencies for traffic infrastructure analysis.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15004051
title: Damaged Traffic Signs Dataset

description: Description:







The Damaged Traffic Signs Dataset focuses on Portuguese traffic signs captured under a variety of conditions, providing a comprehensive collection of images showcasing signs in both their regular state and with various forms of damage or deterioration. The dataset is designed to support research and development efforts in the areas of traffic sign detection, classification, and damage recognition for AI and machine learning models.

Dataset Features:

This dataset includes traffic signs that have undergone several types of wear and tear, presenting realistic conditions encountered on the road. The goal is to offer a broad set of visual challenges for training models that can accurately identify and classify traffic signs, even in less-than-ideal circumstances.







Download Dataset









Conditions Captured:

The dataset encapsulates a range of deteriorations and flaws, including but not limited to:



Discoloration: Signs with faded colors due to prolonged exposure to sunlight or environmental factors.

Paint Peeling: Signs with paint coming off, making it harder to detect their design elements.

Scratches and Surface Damage: Signs damaged by vehicles or debris, which obscures the visibility of key information.

Bending or Warping: Traffic signs that are bent or physically distorted, altering their appearance from the standard design.

Rust: Older signs suffering from rust, particularly those made from metal.

Graffiti or Stickers: Signs vandalized with graffiti, stickers, or other forms of defacement.

Partial Occlusion: Signs partially blocked by objects such as tree branches, poles, or other obstructions.

Design and Manufacturing Flaws: Some images capture signs with design flaws or imperfections resulting from poor manufacturing.

Reflections and Glare: Signs affected by reflections from sunlight or vehicle headlights, which create visibility challenges.

Blurring: Signs captured under motion or with out-of-focus camera settings, simulating real-world challenges in detection.

Environmental Wear: The dataset also captures signs under various environmental factors such as rain, dirt, and dust.


Additional Information:



Resolution: Images are captured at various resolutions, ensuring diversity in image quality.

Weather Conditions: The dataset includes images taken in different weather conditions, such as sunny, rainy, and foggy days, which add further complexity.

Lighting Conditions: Photographs were taken under a mix of lighting conditions, including bright daylight, evening, and low-light situations.


Applications:

This dataset is an excellent resource for developing and training machine learning models for:



Traffic sign recognition and detection systems.

Damage classification for traffic sign maintenance and road safety assessment.

Autonomous vehicle navigation and obstacle detection.

Government or municipal agencies for traffic infrastructure analysis.


This dataset is sourced from Kaggle.
DOI: 10.15468/dl.72z9uw
title: Occurrence Download

description: A dataset containing 2020 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nanocnide japonica Blume"
 ]
}

 The dataset includes 2020 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001760-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ad3vn
title: Occurrence Download

description: A dataset containing 110521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Carduus nutans L."
 ]
}

 The dataset includes 110521 records from 1339 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001754-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.acx3vt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mercetaspis halli (Green, 1923)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001761-250310093411724/datasets/export for details.
DOI: 10.21227/bgkd-ke34
title: PRIVACY-TEXT—IMAGE(section)

description: Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
DOI: 10.18150/3qwigv
title: Has bitcoin been dethroned too quickly?

description: Zbiór zawiera dane wykorzystane w artykule Będowska-Sójka B., Wójcik P., Giordano S., Has bitcoin been dethroned too quickly? The cryptocurrency return networks, Network Science, 2024, 1-20. DOI:10.1017/nws.2024.17
DOI: 10.15468/dl.7fyzwm
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Fossil",
 "Geometry POLYGON((17.571760585619078 59.78985113773633,17.698097528216895 59.78985113773633,17.698097528216895 59.916188080334145,17.571760585619078 59.916188080334145,17.571760585619078 59.78985113773633))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ephedra distachya L."
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001763-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4wrpnp
title: Occurrence Download

description: A dataset containing 107852990 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 107852990 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001744-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j4jgen
title: Occurrence Download

description: A dataset containing 88326 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is KUO Lepidoptera collections (NW-Europe)"
 ]
}

 The dataset includes 88326 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001762-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/w5dj-yw42
title: PRIVACY-TEXT—IMAGE(section)

description: Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
DOI: 10.21227/02fa-nv65
title: PRIVACY-TEXT—IMAGE(section)

description: Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
DOI: 10.15468/dl.f2qwf4
title: Occurrence Download

description: A dataset containing 32353 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Carduus pycnocephalus L."
 ]
}

 The dataset includes 32353 records from 671 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001764-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1074067.10fu
title: SH1074067.10FU

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/sh1074071.10fu
title: SH1074071.10FU

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/sh1074073.10fu
title: SH1074073.10FU

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/sh1074077.10fu
title: SH1074077.10FU

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.