cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1137034.10fu
title: SH1137034.10FU

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/sh1137037.10fu
title: SH1137037.10FU

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/sh1137041.10fu
title: SH1137041.10FU

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/sh1137044.10fu
title: SH1137044.10FU

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/sh1137045.10fu
title: SH1137045.10FU

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/sh1137047.10fu
title: SH1137047.10FU

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/sh1137050.10fu
title: SH1137050.10FU

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: 10.6084/m9.figshare.30466094.v1
title: R script and dataset for “Metabolic responses at sub-thermoneutral temperatures in captive common waxbills (<i>Estrilda astrild</i>)”

description: This repository contains the R script and dataset used in the study <i>“Metabolic responses at sub-thermoneutral temperatures in captive common waxbills (Estrilda astrild)”</i> by Pacioni et al. (2025).<br>The data and analyses quantify individual variation in metabolic rate and thermal reaction norms in common waxbills exposed to decreasing sub-thermoneutral temperatures.
DOI: 10.6084/m9.figshare.30466094
title: R script and dataset for “Metabolic responses at sub-thermoneutral temperatures in captive common waxbills (<i>Estrilda astrild</i>)”

description: This repository contains the R script and dataset used in the study <i>“Metabolic responses at sub-thermoneutral temperatures in captive common waxbills (Estrilda astrild)”</i> by Pacioni et al. (2025).<br>The data and analyses quantify individual variation in metabolic rate and thermal reaction norms in common waxbills exposed to decreasing sub-thermoneutral temperatures.
DOI: 10.15468/dl.kff6k8
title: Occurrence Download

description: A dataset containing 1771 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Radianthus magnifica (Quoy & Gaimard, 1833))"
 ]
}

 The dataset includes 1771 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021665-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sbadv4
title: Occurrence Download

description: A dataset containing 36147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thera juniperata (Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 36147 records from 171 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021664-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14294632
title: Supplementary data for: PseudoSorter: A Self-Supervised Spike Sorting Approach Applied to Reveal Tau-Induced Reductions in Neuronal Activity

description: Supplementary example data for the work presented in "PseudoSorter: A Self-Supervised Spike Sorting Approach Applied to Reveal Tau-Induced Reductions in Neuronal Activity". 

Abstract: 

Microelectrode arrays (MEAs) permit recordings with high electrode counts, thus generating complex datasets which would benefit from precise neuronal spike sorting for meaningful data extraction. Nevertheless, conventional spike sorting methods face limitations in recognising diverse spike shapes. Here, we introduce PseudoSorter, which employs self-supervised learning techniques, a density-based pseudo-labelling strategy, and an iterative fine-tuning process to enhance spike sorting accuracy. Through benchmarking, we demonstrate the superior performance of PseudoSorter compared to other spike sorting algorithms before applying PseudoSorter on MEA recordings from hippocampal neurons exposed to subneuronal concentrations of monomeric Tau as a model for Alzheimer's disease. Our results unveil that Tau diminishes the firing rate of a subset of neurons, which complement our findings observed using conventional electrophysiology analysis, and demonstrate that PseudoSorter’s high accuracy and throughput make it a valuable tool for studying neurodegenerative diseases, enhancing our understanding of their underlying mechanisms, as well as for therapeutic drug screening.
DOI: 10.5281/zenodo.15067557
title: Crowd Counting Dataset

description: Description:

The Crowd Counting Dataset is an extensive collection of high-resolution images capturing a wide variety of crowd scenes, with the number of individuals ranging from zero to 5,000 per image. The dataset is meticulously curated to include diverse scenarios such as public events, street gatherings, protests, festivals, and daily commutes, ensuring that it covers a broad spectrum of crowd densities and environmental conditions.

Download Dataset

Key Features:



Diverse Scenarios: The dataset features crowds in various settings, from dense urban environments to open public spaces, providing a comprehensive resource for developing and testing crowd counting algorithms across different contexts.

High-Quality Annotations: Each image in the dataset is paired with a JSON file that includes precise annotations for every individual in the crowd. The labeling details not only the count of people but also includes classification data such as gender, age group, and activity, enabling multi-dimensional analysis.

Versatility in Applications: This dataset is ideal for training and evaluating machine learning models for applications in public safety, event management, urban planning, and retail analysis, where accurate crowd estimation and behavior analysis are critical.

Scalable Data: With a wide range of crowd sizes, the dataset is suitable for both low-density and high-density crowd counting tasks, providing a robust foundation for developing scalable Al solutions.

Real-World Relevance: The images are sourced from real-world environments, ensuring that models trained on this dataset can generalize well to practical applications, enhancing their reliability in real-time deployments.


Dataset Structure:



Images: High-resolution images captured from various angles and lighting conditions.

Annotations: JSON files containing detailed labels for each individual, including:

Person count

Classification attributes (e.g., age group, gender, activity)

Positional information within the image for precise localization

Categories: The dataset is organized into different categories based on crowd density, scene type, and time of day, allowing users to focus on specific aspects of crowd analysis.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15067558
title: Crowd Counting Dataset

description: Description:

The Crowd Counting Dataset is an extensive collection of high-resolution images capturing a wide variety of crowd scenes, with the number of individuals ranging from zero to 5,000 per image. The dataset is meticulously curated to include diverse scenarios such as public events, street gatherings, protests, festivals, and daily commutes, ensuring that it covers a broad spectrum of crowd densities and environmental conditions.

Download Dataset

Key Features:



Diverse Scenarios: The dataset features crowds in various settings, from dense urban environments to open public spaces, providing a comprehensive resource for developing and testing crowd counting algorithms across different contexts.

High-Quality Annotations: Each image in the dataset is paired with a JSON file that includes precise annotations for every individual in the crowd. The labeling details not only the count of people but also includes classification data such as gender, age group, and activity, enabling multi-dimensional analysis.

Versatility in Applications: This dataset is ideal for training and evaluating machine learning models for applications in public safety, event management, urban planning, and retail analysis, where accurate crowd estimation and behavior analysis are critical.

Scalable Data: With a wide range of crowd sizes, the dataset is suitable for both low-density and high-density crowd counting tasks, providing a robust foundation for developing scalable Al solutions.

Real-World Relevance: The images are sourced from real-world environments, ensuring that models trained on this dataset can generalize well to practical applications, enhancing their reliability in real-time deployments.


Dataset Structure:



Images: High-resolution images captured from various angles and lighting conditions.

Annotations: JSON files containing detailed labels for each individual, including:

Person count

Classification attributes (e.g., age group, gender, activity)

Positional information within the image for precise localization

Categories: The dataset is organized into different categories based on crowd density, scene type, and time of day, allowing users to focus on specific aspects of crowd analysis.


This dataset is sourced from Kaggle.
DOI: 10.15468/dl.cf56qq
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Astragalus membranaceus var. mongholicus (Bunge) P.K.Hsiao"
 ]
}

 The dataset includes 22 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021666-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6g8ef
title: Occurrence Download

description: A dataset containing 507 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Stichodactyla gigantea (Forsskål, 1775))"
 ]
}

 The dataset includes 507 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021667-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7r72t
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acanthospermum hispidum DC., Cardiospermum grandiflorum Sw., Chromolaena odorata (L.) R.M.King & H.Rob., Pontederia crassipes Mart., Imperata cylindrica (L.) Raeusch., Leucaena leucocephala (Lam.) de Wit, Murdannia nudiflora (L.) Brenan, Pistia stratiotes L., Salvinia ×molesta D.S.Mitch., Solanum sisymbriifolium Lam., Verbena brasiliensis Vell.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Country is one of (Congo)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 46 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021669-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hwy2ax
title: Occurrence Download

description: A dataset containing 9992 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((105.29 32.08,108.16 32.08,108.16 33.52,105.29 33.52,105.29 32.08))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 2000-2025"
 ]
}

 The dataset includes 9992 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021668-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nvuy5u
title: Occurrence Download

description: A dataset containing 316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Cryptodendrum adhaesivum (Klunzinger, 1877))"
 ]
}

 The dataset includes 316 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021670-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3e3hpp
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cales noacki Howard, 1907",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021671-250310093411724/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/11124104
title: Medical Imaging Bone Fracture Colorized Img Data
DOI: 10.5281/zenodo.15067571
title: Proof of Hodge's Conjecture

description: Finished
DOI: 10.5281/zenodo.15067572
title: Proof of Hodge's Conjecture

description: Finished
DOI: 10.15468/dl.233gw8
title: Occurrence Download

description: A dataset containing 399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Stichodactyla mertensii Brandt, 1835)"
 ]
}

 The dataset includes 399 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021673-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3cnqv
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)",
 "Country is China",
 "GadmGid is one of (CHN.16.5.11_1, CHN.16.5.10_1, CHN.16.5.8_1, CHN.16.5.7_1, CHN.16.6.6_1, CHN.16.6.1_1, CHN.16.10.9_1, CHN.16.10.4_1, CHN.16.10.2_1, CHN.16.10.3_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Accipiter virgatus (Temminck, 1822)",
 "Year 2004-2025"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021674-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4rw9kv
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)",
 "Country is China",
 "GadmGid is one of (CHN.16.5.11_1, CHN.16.5.10_1, CHN.16.5.8_1, CHN.16.5.7_1, CHN.16.6.6_1, CHN.16.6.1_1, CHN.16.10.9_1, CHN.16.10.4_1, CHN.16.10.2_1, CHN.16.10.3_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Accipiter nisus (Linnaeus, 1758)",
 "Year 2004-2025"
 ]
}

 The dataset includes 21 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021675-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.36cvq4
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)",
 "Country is China",
 "GadmGid is one of (CHN.16.5.11_1, CHN.16.5.10_1, CHN.16.5.8_1, CHN.16.5.7_1, CHN.16.6.6_1, CHN.16.6.1_1, CHN.16.10.9_1, CHN.16.10.4_1, CHN.16.10.2_1, CHN.16.10.3_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Accipiter gentilis (Linnaeus, 1758)",
 "Year 2004-2025"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021677-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qafe5j
title: Occurrence Download

description: A dataset containing 845 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Stichodactyla haddoni (Saville-Kent, 1893))"
 ]
}

 The dataset includes 845 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021678-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.