cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3h2pg2
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Tentoriceps cristatus"
 ]
}

 The dataset includes 30 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001596-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18351254
title: AUW Dataset

description: General description of the dataset

The sample dataset, called the AUTH-Unreal-Wildfire (AUW) dataset, is a synthetic collection created to advance deep learning for wildfire segmentation. It addresses the critical challenge of obtaining accurately annotated training data in natural disaster management by using a novel, open-source pipeline built with the AirSim simulator. This pipeline uniquely integrates a custom particle segmentation camera and Procedural Content Generation (PCG) tools to produce photorealistic wildfire images paired with precise pixel-level segmentation masks—a feature previously difficult to achieve since fire assets are typically particle-based without a defined 3D mesh. The dataset consists of 1,500 training and 200 test images and was specifically designed to train and evaluate state-of-the-art segmentation models like PIDNet, both on its own and as a data augmentation resource to enhance performance on real-world wildfire imagery.

For a comprehensive explanation of the methodology and tools used to create this synthetic dataset, please refer to the full conference paper. This work is formally published and should be cited as follows: E. Spatharis, C. Papaioannidis, V. Mygdalis and I. Pitas, “UNREALFIRE: A synthetic dataset creation pipeline for annotated fire imagery in Unreal Engine”, IEEE International Conference on Image Processing (ICIP), Workshop on Bridging the Gap: Advanced Data Processing for Natural Disaster Management – Integrating Visual and Non-Visual Insights, Anchorage, Alaska, USA, 13-17 September, 2025. The paper is available at: https://aiia.csd.auth.gr/wp-content/uploads/2025/12/SPATHARIS_ICIP_2025.pdf and at https://zenodo.org/records/18198757 .

If one uses any part of these datasets in his/her work, he/she is kindly asked to cite the following paper:



E. Spatharis, C. Papaioannidis, V. Mygdalis and I.Pitas, "UNREALFIRE: A synthetic dataset creation pipeline for annotated fire imagery in Unreal Engine", IEEE International Conference on Image Processing (ICIP), Workshop on Bridging the Gap: Advanced Data Processing for Natural Disaster Management – Integrating Visual and Non-Visual Insights, Anchorage, Alaska, USA, 13-17 September, 2025


 

Dataset Structure

The dataset is organized into two primary directories representing the training and test sets, each of which contains the corresponding images and their annotation labels.

 

Details on acquiring the dataset can be found here
DOI: 10.5281/zenodo.18351255
title: AUW Dataset

description: General description of the dataset

The sample dataset, called the AUTH-Unreal-Wildfire (AUW) dataset, is a synthetic collection created to advance deep learning for wildfire segmentation. It addresses the critical challenge of obtaining accurately annotated training data in natural disaster management by using a novel, open-source pipeline built with the AirSim simulator. This pipeline uniquely integrates a custom particle segmentation camera and Procedural Content Generation (PCG) tools to produce photorealistic wildfire images paired with precise pixel-level segmentation masks—a feature previously difficult to achieve since fire assets are typically particle-based without a defined 3D mesh. The dataset consists of 1,500 training and 200 test images and was specifically designed to train and evaluate state-of-the-art segmentation models like PIDNet, both on its own and as a data augmentation resource to enhance performance on real-world wildfire imagery.

For a comprehensive explanation of the methodology and tools used to create this synthetic dataset, please refer to the full conference paper. This work is formally published and should be cited as follows: E. Spatharis, C. Papaioannidis, V. Mygdalis and I. Pitas, “UNREALFIRE: A synthetic dataset creation pipeline for annotated fire imagery in Unreal Engine”, IEEE International Conference on Image Processing (ICIP), Workshop on Bridging the Gap: Advanced Data Processing for Natural Disaster Management – Integrating Visual and Non-Visual Insights, Anchorage, Alaska, USA, 13-17 September, 2025. The paper is available at: https://aiia.csd.auth.gr/wp-content/uploads/2025/12/SPATHARIS_ICIP_2025.pdf and at https://zenodo.org/records/18198757 .

If one uses any part of these datasets in his/her work, he/she is kindly asked to cite the following paper:



E. Spatharis, C. Papaioannidis, V. Mygdalis and I.Pitas, "UNREALFIRE: A synthetic dataset creation pipeline for annotated fire imagery in Unreal Engine", IEEE International Conference on Image Processing (ICIP), Workshop on Bridging the Gap: Advanced Data Processing for Natural Disaster Management – Integrating Visual and Non-Visual Insights, Anchorage, Alaska, USA, 13-17 September, 2025


 

Dataset Structure

The dataset is organized into two primary directories representing the training and test sets, each of which contains the corresponding images and their annotation labels.

 

Details on acquiring the dataset can be found here
DOI: 10.15468/dl.a28x86
title: Occurrence Download

description: A dataset containing 668 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhododendron delavayi",
 "HasCoordinate is true"
 ]
}

 The dataset includes 668 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001597-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uuaka9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "DistanceFromCentroidInMeters is greater than or equal to 2000",
 "Geometry POLYGON ((105.610544 14.999999, 179.999961 14.999999, 179.999961 65.08290100000001, 105.610544 65.08290100000001, 105.610544 14.999999))",
 "TaxonKey is Porcostoma dentata"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001599-260226173443078/datasets/export for details.
DOI: 10.15468/dl.ntfh7t
title: Occurrence Download

description: A dataset containing 117114 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Galium aparine",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 117114 records from 471 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001574-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w6djze
title: Occurrence Download

description: A dataset containing 556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bivalvia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-80 -10,-70 -10,-70 0,-80 0,-80 -10))"
 ]
}

 The dataset includes 556 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019469-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udxkcq
title: Occurrence Download

description: A dataset containing 1841 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bivalvia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-90 -10,-80 -10,-80 0,-90 0,-90 -10))"
 ]
}

 The dataset includes 1841 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019470-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00006776.4
title: Available IPD datapackage for study 'A Multicentre, Randomized, Double-blind, Placebo-controlled, Parallel-group Study Of Oral Cp-690,550 As A Maintenance Therapy In Subjects With Ulcerative Colitis'

description: The study proposes to assess whether compared to placebo, CP-690,550 is effective, safe, and tolerable maintenance therapy in subjects with Ulcerative Colitis (UC). The study proposes to assess whether compared to placebo, CP-690,550 maintenance therapy more effectively achieves mucosal healing and improves quality of life in subjects with UC.The study proposes to assess CP-690,550 pharmacokinetic exposure during maintenance therapy in subjects over the age of 18 years with UC.
DOI: 10.15156/bio/sh1428892.10fu
title: SH1428892.10FU

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/sh1428893.10fu
title: SH1428893.10FU

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/sh1428894.10fu
title: SH1428894.10FU

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/sh1428895.10fu
title: SH1428895.10FU

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/sh1428896.10fu
title: SH1428896.10FU

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/sh1428897.10fu
title: SH1428897.10FU

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/sh1428898.10fu
title: SH1428898.10FU

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/sh1428899.10fu
title: SH1428899.10FU

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/sh1428900.10fu
title: SH1428900.10FU

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/sh1428901.10fu
title: SH1428901.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.7mdjwd
title: Occurrence Download

description: A dataset listing the 3444 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bignoniaceae"
 ]
}

 The dataset's 3444 records were derived from 2155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007721-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13929710
title: Online appendix C

description: Online appendix C for article in Capital Markets Law Journal entitled "Spoofing in U.S. Futures Markets: An Interdisciplinary Approach".
DOI: 10.15468/dl.xstayf
title: Occurrence Download

description: A dataset containing 16898 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus ponderosa Douglas ex C.Lawson"
 ]
}

 The dataset includes 16898 records from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0301138-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.iirxw6
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009972-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yen69s
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054029-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.