cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0212243.10fu
title: SH0212243.10FU

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: 10.25318/1310090201-fra
title: Évolution des difficultés fonctionnelles chez les enfants et les jeunes entre 2019 et 2023, Enquête canadienne sur la santé des enfants et des jeunes, 2023 Échantillon longitudinal

description: Nombre et pourcentage d'enfants et de jeunes dont les difficultés fonctionnelles auront changé ou n'auront pas changé entre 2019 et 2023, selon le sexe et le groupe d'âge, Canada (sauf les territoires) et provinces.
DOI: 10.15468/dl.t3juyw
title: Occurrence Download

description: A dataset containing 36492 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Italy",
 "DatasetKey is one of (Wikiplantbase #Italia, Observation.org, Nature data from around the World, Segetal flora of Italy)",
 "Geometry POLYGON((7.05619 45.52327,7.05619 44.62527,8.31619 44.62527,8.31619 45.52327,7.05619 45.52327))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 36492 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000640-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.86f6cx
title: Occurrence Download

description: A dataset containing 5130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-82.82508 8.56411,-82.83794 8.4742,-82.81406 8.45785,-82.52388 8.39971,-82.34389 8.38336,-82.21532 8.33793,-82.17859 8.26705,-82.02799 8.22161,-81.78555 8.16889,-81.61842 8.12344,-81.34109 8.10526,-81.06376 8.0598,-80.94438 8.03252,-80.84796 8.05253,-80.71572 8.05253,-80.49716 8.08798,-80.48614 8.11435,-80.48706 8.17435,-80.46502 8.22979,-80.40258 8.3034,-80.33279 8.30249,-80.18218 8.3343,-80.10964 8.36791,-80.02423 8.4197,-79.97373 8.45603,-79.89383 8.52687,-79.88189 8.5414,-79.84608 8.61949,-79.83322 8.64764,-79.80843 8.65672,-79.77353 8.6776,-79.75241 8.70846,-79.75792 8.74658,-79.77262 8.772,-79.76986 8.79922,-79.75241 8.83189,-79.71476 8.86909,-79.68538 8.89359,-79.63487 8.88724,-79.566 8.90357,-79.56416 8.92172,-79.59079 8.97433,-79.57059 9.01968,-79.56324 9.03328,-79.58253 9.05052,-79.6165 9.05052,-79.6312 9.04507,-79.67344 9.01696,-79.71935 8.96526,-79.80935 8.90357,-79.86169 8.87998,-79.88832 8.83098,-79.9021 8.74114,-79.91404 8.68849,-79.9425 8.62948,-79.98291 8.56683,-80.03984 8.50871,-80.15004 8.45331,-80.23177 8.46602,-80.26667 8.49781,-80.29697 8.55684,-80.32819 8.56955,-80.3787 8.56138,-80.4641 8.49145,-80.5091 8.46784,-80.55685 8.41061,-80.57246 8.35792,-80.56236 8.2716,-80.64409 8.22252,-80.71939 8.19525,-80.76531 8.1498,-80.89663 8.16435,-80.9903 8.1798,-81.06927 8.22616,-81.13998 8.25796,-81.22263 8.25433,-81.2814 8.24979,-81.40813 8.25887,-81.45772 8.25342,-81.54312 8.24615,-81.60373 8.27523,-81.69464 8.28068,-81.75249 8.28432,-81.84984 8.31612,-81.92146 8.32975,-81.99768 8.32612,-82.06288 8.33884,-82.11523 8.39063,-82.24195 8.45421,-82.33654 8.47147,-82.4247 8.50599,-82.49265 8.51416,-82.57438 8.54322,-82.63407 8.56229,-82.72315 8.55139,-82.82508 8.56411))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (CR, EN, VU, NT)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 5130 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000641-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1610001101-eng
title: Manufacturing sales, by industry for 15 Selected Census Metropolitan Areas

description: Monthly Canadian manufacturer's sales for 15 census metropolitan areas (CMA) for durable and non-durable goods by North American Industry Classification System (NAICS). Data in thousands of dollars. Unadjusted and seasonally adjusted data available from January 2013 to the current reference month. Not all combinations are available.
DOI: 10.25318/1310090401-eng
title: Changes in the health characteristics of youth between 2019 and 2023, Canadian Health Survey of Children and youth, 2023 Longitudinal sample

description: Number and percentage of youth who report changes or no change in their health characteristics between 2019 and 2023 by gender and age group, Canada (excluding territories) and provinces.
DOI: 10.6084/m9.figshare.28409736
title: Novel human coronavirus in an infant patient with pneumonia, Republic of Korea

description: Coronaviruses (CoVs) pose a significant threat to public health, causing a wide spectrum of clinical manifestations and outcomes. Beyond precipitating global outbreaks, Human CoVs (HCoVs) are frequently found among patients with respiratory infections. To date, limited attention has been directed towards alphacoronaviruses due to their low prevalence and fatality rates. Nasal swab and serum samples were collected from a paediatric patient, and an epidemiological survey was conducted. Retrospective surveillance investigated the molecular prevalence of CoV in 880 rodents collected in the Republic of Korea (ROK) from 2018 to 2022. Next-generation sequencing (NGS) and phylogenetic analyses characterized the novel HCoV and closely related CoVs harboured by <i>Apodemus</i> spp. On 15 December 2022, a 103-day-old infant was admitted with fever, cough, sputum production, and rhinorrhea, diagnosed with human parainfluenza virus 1 (HPIV-1) and rhinovirus co-infection. Elevated AST/ALT levels indicated transient liver dysfunction on the fourth day of hospitalization. Metagenomic NGS (mNGS) identified a novel HCoV in nasal swab and serum samples. Retrospective rodent surveillance and phylogenetic analyses showed the novel HCoV was closely related to alphacoronaviruses carried by <i>Apodemus</i> spp. in the ROK and China. This case highlights the potential of mNGS to identify emerging pathogens and raises awareness of possible extra-respiratory manifestations, such as transient liver dysfunction, associated with novel HCoVs. While the liver injury in this case may be attributable to the novel HCoV, further research is necessary to elucidate its clinical significance, epidemiological prevalence, and zoonotic origins.
DOI: 10.6084/m9.figshare.28409736.v1
title: Novel human coronavirus in an infant patient with pneumonia, Republic of Korea

description: Coronaviruses (CoVs) pose a significant threat to public health, causing a wide spectrum of clinical manifestations and outcomes. Beyond precipitating global outbreaks, Human CoVs (HCoVs) are frequently found among patients with respiratory infections. To date, limited attention has been directed towards alphacoronaviruses due to their low prevalence and fatality rates. Nasal swab and serum samples were collected from a paediatric patient, and an epidemiological survey was conducted. Retrospective surveillance investigated the molecular prevalence of CoV in 880 rodents collected in the Republic of Korea (ROK) from 2018 to 2022. Next-generation sequencing (NGS) and phylogenetic analyses characterized the novel HCoV and closely related CoVs harboured by <i>Apodemus</i> spp. On 15 December 2022, a 103-day-old infant was admitted with fever, cough, sputum production, and rhinorrhea, diagnosed with human parainfluenza virus 1 (HPIV-1) and rhinovirus co-infection. Elevated AST/ALT levels indicated transient liver dysfunction on the fourth day of hospitalization. Metagenomic NGS (mNGS) identified a novel HCoV in nasal swab and serum samples. Retrospective rodent surveillance and phylogenetic analyses showed the novel HCoV was closely related to alphacoronaviruses carried by <i>Apodemus</i> spp. in the ROK and China. This case highlights the potential of mNGS to identify emerging pathogens and raises awareness of possible extra-respiratory manifestations, such as transient liver dysfunction, associated with novel HCoVs. While the liver injury in this case may be attributable to the novel HCoV, further research is necessary to elucidate its clinical significance, epidemiological prevalence, and zoonotic origins.
DOI: 10.6084/m9.figshare.28409797
title: Clinical manifestations and pathogenicity of Clade IIb monkeypox virus in rabbits

description: The 2022 monkeypox outbreak involved rapid global dissemination, prompting research into animal models for the monkeypox virus (MPXV), including non-human primates and mice. However, studies utilizing rabbits as models remain limited. In this study, we established three rabbit models using the current epidemic MPXV strain. Following intravenous MPXV injection, adult rabbits exhibited characteristic clinical manifestations, including widespread rash and fever, with viral replication in the skin, lungs, and testes, resulting in severe pathological damage by 6 days post-infection (dpi). Intradermal injection of MPXV into the dorsal skin of adult rabbits produced red lesions with central necrosis and hemorrhage accompanied by dense inflammatory infiltrates. Abundant viral particles were observed in epidermal cells at 6 dpi. Additionally, a fatal MPXV model was developed in 10-day-old rabbits using intranasal virus administration. These young rabbits exhibited lethargy and diarrhea beginning at 2 dpi, significant weight loss, and a 50% mortality rate by 15 dpi. Viral dissemination was detected in multiple organs, leading to extensive multi-organ damage. This study highlights the utility of rabbit models for MPXV, displaying typical clinical features and pathogenic mechanisms.
DOI: 10.25318/1310090401-fra
title: Évolution des caractéristiques de santé des jeunes entre 2019 et 2023, Enquête canadienne sur la santé des enfants et des jeunes, 2023 Échantillon longitudinal

description: Nombre et pourcentage de jeunes qui déclarent avoir changé ou n'avoir pas changé leurs caractéristiques de santé, par sexe et par groupe d'âge, Canada (sauf les territoires) et provinces.
DOI: 10.15468/dl.4rm64f
title: Occurrence Download

description: A dataset containing 273 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is one of (Digitalización y sistematización de las colecciones biológicas del INECOL (Herbario), Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (Plantas vasculares), Tropicos MO Specimen Data, Sistematización del Herbario Nacional Forestal Biól Luciano Vela Gálvez)",
 "Geometry POLYGON((-115.93674 11.72183,-77.06326 11.72183,-77.06326 27.27817,-115.93674 27.27817,-115.93674 11.72183))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vaccinium leucanthum Cham. & Schltdl."
 ]
}

 The dataset includes 273 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000644-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1510002801-fra
title: Fréquence d'utilisation du français au travail selon certaines professions de l'éducation, Canada hors Québec, 2011 et 2016

description: Présente de l’information sur l'utilisation totale, primaire, égale et secondaire du français au travail au Canada hors Québec pour les travailleurs employés dans certaines professions de l'éducation, pour obtenir plus de renseignements veuillez consulter les notes en bas de page. Ces données sont tirées de l’Enquête nationale auprès des ménages de 2011 et du Recensement de la population de 2016.
DOI: 10.5281/zenodo.14907472
title: PatFigVQA Dataset - Patent Figure Visual Question Answering Dataset

description: Dataset Summary

The PatFigVQA dataset is introduced in the paper Patent Figure Classification using Large Vision-language Models accepted at ECIR 2025.  The dataset is designed specifically for fine-tuning and evaluating Large Vision-language Models (LVLMs) in few-shot learning setting across multiple aspects, including type, projection, objects and USPC class.

The PatFigVQA dataset is used alongside another dataset called PatFigCLS, which is intended for patent figure classification and evaluation.

The dataset is sourced from two exisiting datasets:



Extended CLEF-IP 2011, and

DeepPatent2


Data Format

The dataset is stored in .tar files for fast and efficient read access.

Data Fields





__key__: unique sample id

image.png: patent figure file

task.txt: type of question (e.g. binary, multiple-choice or open-ended)

question.txt: natural language question asking about the concept depicted in figure

answer.txt: textual answer for the question

concept.txt: concept depicted in the patent figure for the given aspect



Data Splits



For each aspect, multiple data splits exist: train_9, train_18, train_27, train_54, train_81, train_150, val and test.


How to Use

The recommended approach is using the Python library `webdataset`. Below is an example code.



import io

from PIL import Image


from torchvision.transforms import Compose, ToTensor


import webdataset as wds

from braceexpand import braceexpand


def transform(image):

  return Compose([ToTensor()])(image)






dataset = (

    wds.WebDataset(

    braceexpand('PatFigVQA/object/train_150/shard-{000000..000042}.tar'),

    shardshuffle=1000,

   )

   .shuffle(1000)

   .to_tuple(

     '__key__',

     'image.png',

     'concept.txt',

     'task.txt',

     'question.txt',

     'answer.txt',

   )

   .map_tuple(

     lambda key: key,

     lambda image: transform(Image.open(io.BytesIO(image))),

     lambda concept: concept.decode('utf-8'),

     lambda task: task.decode('utf-8'),

     lambda question: question.decode('utf-8'),

     lambda answer: answer.decode('utf-8'),

   )

)




dataloder = wds.WebLoader(dataset)


Source Code

The source code used to produce this dataset can be found at https://github.com/TIBHannover/patent-figure-classification

Licensing Information

PatFigCLS dataset is released under GNU General Public License v3.0.
DOI: 10.5281/zenodo.14905550
title: PatFigCLS Dataset - Patent Figure Classification Dataset

description: Dataset Summary

The PatFigCLS dataset is introduced in the paper Patent Figure Classification using Large Vision-language Models accepted at ECIR 2025. The dataset is designed specifically for patent figure classification and evaluation across multiple aspects, including type, projection, objects and USPC class.

The PatFigCLS dataset is used alongside another dataset called PatFigVQA, which is intended for fine-tuning and evaluating Large Vision-language Models (LVLMs) in few-shot learning setting for patent figure visual question answering.

The dataset is sourced from two exisiting datasets:



Extended CLEF-IP 2011, and

DeepPatent2


Data Format

The dataset is stored in .tar files for fast and efficient read access.

Data Fields



 __key__: unique sample id

image.png: patent figure file

label.txt:  classification label


Data Splits

For each classification aspect, three data splits exist: `train_150`, `val` and `test`.

How to Use

The recommended approach is using the Python library `webdataset`. Below is an example code.



import io

from PIL import Image


from torchvision.transforms import Compose, ToTensor


import webdataset as wds

from braceexpand import braceexpand


def transform(image):

  return Compose([ToTensor()])(image)


dataset = (

  wds.WebDataset(

    braceexpand('PatFigCLS/object/train_150/shard-{000000..000042}.tar'),

    shardshuffle=1000

  )

  .shuffle(1000)

  .to_tuple('__key__', 'image.png', 'label.txt')

  .map_tuple(

    lambda key: key,

    lambda image: transform(Image.open(io.BytesIO(image))),

    lambda label: label.decode('utf-8'),

  )

)


dataloder = wds.WebLoader(dataset)


Source Code

The source code used to produce this dataset can be found at https://github.com/TIBHannover/patent-figure-classification

Licensing Information

PatFigCLS dataset is released under GNU General Public License v3.0.
DOI: 10.5281/zenodo.14905551
title: PatFigCLS Dataset - Patent Figure Classification Dataset

description: Dataset Summary

The PatFigCLS dataset is introduced in the paper Patent Figure Classification using Large Vision-language Models accepted at ECIR 2025. The dataset is designed specifically for patent figure classification and evaluation across multiple aspects, including type, projection, objects and USPC class.

The PatFigCLS dataset is used alongside another dataset called PatFigVQA, which is intended for fine-tuning and evaluating Large Vision-language Models (LVLMs) in few-shot learning setting for patent figure visual question answering.

The dataset is sourced from two exisiting datasets:



Extended CLEF-IP 2011, and

DeepPatent2


Data Format

The dataset is stored in .tar files for fast and efficient read access.

Data Fields



 __key__: unique sample id

image.png: patent figure file

label.txt:  classification label


Data Splits

For each classification aspect, three data splits exist: `train_150`, `val` and `test`.

How to Use

The recommended approach is using the Python library `webdataset`. Below is an example code.



import io

from PIL import Image


from torchvision.transforms import Compose, ToTensor


import webdataset as wds

from braceexpand import braceexpand


def transform(image):

  return Compose([ToTensor()])(image)


dataset = (

  wds.WebDataset(

    braceexpand('PatFigCLS/object/train_150/shard-{000000..000042}.tar'),

    shardshuffle=1000

  )

  .shuffle(1000)

  .to_tuple('__key__', 'image.png', 'label.txt')

  .map_tuple(

    lambda key: key,

    lambda image: transform(Image.open(io.BytesIO(image))),

    lambda label: label.decode('utf-8'),

  )

)


dataloder = wds.WebLoader(dataset)


Source Code

The source code used to produce this dataset can be found at https://github.com/TIBHannover/patent-figure-classification

Licensing Information

PatFigCLS dataset is released under GNU General Public License v3.0.
DOI: 10.5281/zenodo.14907473
title: PatFigVQA Dataset - Patent Figure Visual Question Answering Dataset

description: Dataset Summary

The PatFigVQA dataset is introduced in the paper Patent Figure Classification using Large Vision-language Models accepted at ECIR 2025.  The dataset is designed specifically for fine-tuning and evaluating Large Vision-language Models (LVLMs) in few-shot learning setting across multiple aspects, including type, projection, objects and USPC class.

The PatFigVQA dataset is used alongside another dataset called PatFigCLS, which is intended for patent figure classification and evaluation.

The dataset is sourced from two exisiting datasets:



Extended CLEF-IP 2011, and

DeepPatent2


Data Format

The dataset is stored in .tar files for fast and efficient read access.

Data Fields





__key__: unique sample id

image.png: patent figure file

task.txt: type of question (e.g. binary, multiple-choice or open-ended)

question.txt: natural language question asking about the concept depicted in figure

answer.txt: textual answer for the question

concept.txt: concept depicted in the patent figure for the given aspect



Data Splits



For each aspect, multiple data splits exist: train_9, train_18, train_27, train_54, train_81, train_150, val and test.


How to Use

The recommended approach is using the Python library `webdataset`. Below is an example code.



import io

from PIL import Image


from torchvision.transforms import Compose, ToTensor


import webdataset as wds

from braceexpand import braceexpand


def transform(image):

  return Compose([ToTensor()])(image)






dataset = (

    wds.WebDataset(

    braceexpand('PatFigVQA/object/train_150/shard-{000000..000042}.tar'),

    shardshuffle=1000,

   )

   .shuffle(1000)

   .to_tuple(

     '__key__',

     'image.png',

     'concept.txt',

     'task.txt',

     'question.txt',

     'answer.txt',

   )

   .map_tuple(

     lambda key: key,

     lambda image: transform(Image.open(io.BytesIO(image))),

     lambda concept: concept.decode('utf-8'),

     lambda task: task.decode('utf-8'),

     lambda question: question.decode('utf-8'),

     lambda answer: answer.decode('utf-8'),

   )

)




dataloder = wds.WebLoader(dataset)


Source Code

The source code used to produce this dataset can be found at https://github.com/TIBHannover/patent-figure-classification

Licensing Information

PatFigCLS dataset is released under GNU General Public License v3.0.
DOI: 10.25318/1610001101-fra
title: Ventes des industries manufacturières, pour 15 régions métropolitaines de recensement sélectionnées

description: Valeur mensuelle des ventes, pour 15 régions métropolitaines de recensement (RMR) sélectionnées, pour les biens durables et non durables du secteur de la fabrication au Canada, selon le Système de classification des industries de l'Amérique du Nord (SCIAN). Données exprimées en milliers de dollars. Les données non désaisonnnalisées et désaisonnalisées sont disponibles pour les périodes de référence allant de janvier 2013 au mois de référence en cours. Les combinaisons ne sont pas toutes disponibles.
DOI: 10.15468/dl.mvz8kf
title: Occurrence Download

description: A dataset containing 2930 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Polylepis Ruiz & Pav."
 ]
}

 The dataset includes 2930 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000649-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.36zz9v
title: Occurrence Download

description: A dataset containing 623 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hodomys alleni (Merriam, 1892)"
 ]
}

 The dataset includes 623 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000650-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1510003001-eng
title: Frequency of French use at work by selected education occupations, Quebec, 2011 and 2016

description: Provides information on the total, primary, equal and secondary use of French at work in Quebec for workers employed in selected education occupations, please see footnotes for more details. These data are based on the 2016 Census of Population as well as the 2011 National Household Survey.
DOI: 10.5066/f7cz3626
title: Influenza A Viruses and Antibody Response in High-Latitude Urban Wintering Mallards (Anas platyrhynchos), Alaska, 2012-2015

description: This data set contains information regarding the sampling of avian influenza viruses from mallard ducks at locations in Anchorage and Fairbanks, Alaska 2012-2015. Data pertaining to wild birds (mallards) sampled includes band numbers, age and sex, location and timing of sampling. Laboratory specific data is also included and used to identify presence and absence of avian influenza viruses either during active infection or previous exposure (serostatus).