cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2epahv
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bursera palaciosii Rzed. & Calderón"
 ]
}

 The dataset includes 60 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032034-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a4rfmx
title: Occurrence Download

description: A dataset containing 2074 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-104.21015 30.22899,-103.70077 30.22899,-103.70077 30.82662,-104.21015 30.82662,-104.21015 30.22899))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2006-2016"
 ]
}

 The dataset includes 2074 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032035-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pnywg9
title: Occurrence Download

description: A dataset containing 949 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((71 33,98 33,98 51,71 51,71 33))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Scrophularia L."
 ]
}

 The dataset includes 949 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032036-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15420701
title: vSUB Fiber Photometry Recording Dataset
DOI: 10.5281/zenodo.15420702
title: vSUB Fiber Photometry Recording Dataset
DOI: 10.15468/dl.9ayp5y
title: Occurrence Download

description: A dataset containing 584 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bursera palmeri S.Watson"
 ]
}

 The dataset includes 584 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032038-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15400424
title: Data for the paper "Ab Initio Characterization of the CO2–Water Interface Using Unsupervised Machine Learning for Dimensionality Reduction"
DOI: 10.5281/zenodo.15400425
title: Data for the paper "Ab Initio Characterization of the CO2–Water Interface Using Unsupervised Machine Learning for Dimensionality Reduction"
DOI: 10.5281/zenodo.14619798
title: BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

description: Overview

BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 12 regions worldwide, with a particular focus on developing countries. About 4,500 paired optical and SAR images containing over 350,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings, making it ideal for precise damage assessment.

 

IEEE GRSS Data Fusion Contest 2025

BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II.

Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images. 

Benchmark code related to the DFC 2025 can be found at this Github repo. 

The official leaderboard is located on the Codalab-DFC2025-Track II page. 

 

Paper & Reference

Details of BRIGHT can be refer to our paper. 

If BRIGHT is useful to research, please kindly consider cite our paper 

@article{chen2025bright,
      title={BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response}, 
      author={Hongruixuan Chen and Jian Song and Olivier Dietrich and Clifford Broni-Bediako and Weihao Xuan and Junjue Wang and Xinlei Shao and Yimin Wei and Junshi Xia and Cuiling Lan and Konrad Schindler and Naoto Yokoya},
      journal={arXiv preprint arXiv:2501.06019},
      year={2025},
      url={https://arxiv.org/abs/2501.06019}, 
}

License

Label data of BRIGHT are provided under the same license as the optical images, which varies with different events.

With the exception of two events, Hawaii-wildfire-2023 and La Palma-volcano eruption-2021, all optical images are from Maxar Open Data Program, following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from High-Resolution Orthoimagery project of NOAA Office for Coastal Management. The optical images related to La Palma-volcano eruption-2021 are from IGN (Spain) following CC-BY 4.0 license. 

The SAR images of BRIGHT is provided by Capella Open Data Gallery and Umbra Space Open Data Program, following CC-BY-4.0 license.
DOI: 10.5281/zenodo.14950271
title: BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

description: Overview

BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 12 regions worldwide, with a particular focus on developing countries. About 4,500 paired optical and SAR images containing over 350,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings, making it ideal for precise damage assessment.

 

IEEE GRSS Data Fusion Contest 2025

BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II.

Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images. 

Please download dfc25_track2_test.zip and unzip it. It contains test images for the final test phase. 

Benchmark code related to the DFC 2025 can be found at this Github repo. 

The official leaderboard is located on the Codalab-DFC2025-Track II page. 

 

Paper & Reference

Details of BRIGHT can be refer to our paper. 

If BRIGHT is useful to research, please kindly consider cite our paper 

@article{chen2025bright,
      title={BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response}, 
      author={Hongruixuan Chen and Jian Song and Olivier Dietrich and Clifford Broni-Bediako and Weihao Xuan and Junjue Wang and Xinlei Shao and Yimin Wei and Junshi Xia and Cuiling Lan and Konrad Schindler and Naoto Yokoya},
      journal={arXiv preprint arXiv:2501.06019},
      year={2025},
      url={https://arxiv.org/abs/2501.06019}, 
}

License

Label data of BRIGHT are provided under the same license as the optical images, which varies with different events.

With the exception of two events, Hawaii-wildfire-2023 and La Palma-volcano eruption-2021, all optical images are from Maxar Open Data Program, following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from High-Resolution Orthoimagery project of NOAA Office for Coastal Management. The optical images related to La Palma-volcano eruption-2021 are from IGN (Spain) following CC-BY 4.0 license. 

The SAR images of BRIGHT is provided by Capella Open Data Gallery and Umbra Space Open Data Program, following CC-BY-4.0 license.
DOI: 10.5281/zenodo.15294434
title: BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

description: Overview

BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 14 regions worldwide, with a particular focus on developing countries. About 4,200 paired optical and SAR images containing over 380,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings, making it ideal for precise damage assessment.

 

IEEE GRSS Data Fusion Contest 2025

BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II.

Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images. 

Please download dfc25_track2_test.zip and unzip it. It contains test images for the final test phase. Now, DFC 2025 is over. Please download dfc25_track2_val_labels.zip for validation labels, redownload dfc25_track2_test_new.zip for test images with geo-coordinates and dfc25_track2_test_labels.zip for testing labels.

Benchmark code related to the DFC 2025 can be found at this Github repo. 

The official leaderboard is located on the Codalab-DFC2025-Track II page. 

 

Paper & Reference

Details of BRIGHT can be refer to our paper. 

If BRIGHT is useful to research, please kindly consider cite our paper 

@article{chen2025bright,
      title={BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response}, 
      author={Hongruixuan Chen and Jian Song and Olivier Dietrich and Clifford Broni-Bediako and Weihao Xuan and Junjue Wang and Xinlei Shao and Yimin Wei and Junshi Xia and Cuiling Lan and Konrad Schindler and Naoto Yokoya},
      journal={arXiv preprint arXiv:2501.06019},
      year={2025},
      url={https://arxiv.org/abs/2501.06019}, 
}

License

Label data of BRIGHT are provided under the same license as the optical images, which varies with different events.

With the exception of two events, Hawaii-wildfire-2023 and La Palma-volcano eruption-2021, all optical images are from Maxar Open Data Program, following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from High-Resolution Orthoimagery project of NOAA Office for Coastal Management. The optical images related to La Palma-volcano eruption-2021 are from IGN (Spain) following CC-BY 4.0 license. 

The SAR images of BRIGHT is provided by Capella Open Data Gallery and Umbra Space Open Data Program, following CC-BY-4.0 license.
DOI: 10.5281/zenodo.15322113
title: BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

description: Overview

BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 14 regions worldwide, with a particular focus on developing countries. About 4,200 paired optical and SAR images containing over 380,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings, making it ideal for precise damage assessment.

 

IEEE GRSS Data Fusion Contest 2025

BRIGHT also serves as the official dataset of IEEE GRSS DFC 2025 Track II.

Please download dfc25_track2_trainval.zip and unzip it. It contains training images & labels and validation images. 

Please download dfc25_track2_test.zip and unzip it. It contains test images for the final test phase. Now, DFC 2025 is over. Please download dfc25_track2_val_labels.zip for validation labels, redownload dfc25_track2_test_new.zip for test images with geo-coordinates and dfc25_track2_test_labels.zip for testing labels.

Benchmark code related to the DFC 2025 can be found at this Github repo. 

The official leaderboard is located on the Codalab-DFC2025-Track II page. 

 

Unsupervised multimodal image matching



BRIGHT supports the evaluation of Unsupervised Multimodal Image Matching (UMIM) algorithms for their performance in large-scale disaster scenarios. Please download data with the prefix "umim", such as umim_noto_earthquake.zip, and use our code to test the exsiting algorithms' performance. 


 

Paper & Reference

Details of BRIGHT can be refer to our paper. 

If BRIGHT is useful to research, please kindly consider cite our paper 

@article{chen2025bright,
      title={BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response}, 
      author={Hongruixuan Chen and Jian Song and Olivier Dietrich and Clifford Broni-Bediako and Weihao Xuan and Junjue Wang and Xinlei Shao and Yimin Wei and Junshi Xia and Cuiling Lan and Konrad Schindler and Naoto Yokoya},
      journal={arXiv preprint arXiv:2501.06019},
      year={2025},
      url={https://arxiv.org/abs/2501.06019}, 
}

License

Label data of BRIGHT are provided under the same license as the optical images, which varies with different events.

With the exception of two events, Hawaii-wildfire-2023 and La Palma-volcano eruption-2021, all optical images are from Maxar Open Data Program, following CC-BY-NC-4.0 license. The optical images related to Hawaii-wildifire-2023 are from High-Resolution Orthoimagery project of NOAA Office for Coastal Management. The optical images related to La Palma-volcano eruption-2021 are from IGN (Spain) following CC-BY 4.0 license. 

The SAR images of BRIGHT is provided by Capella Open Data Gallery and Umbra Space Open Data Program, following CC-BY-4.0 license.
DOI: 10.15468/dl.ykezyh
title: Occurrence Download

description: A dataset containing 1063 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-104.21015 30.22899,-103.70077 30.22899,-103.70077 30.82662,-104.21015 30.82662,-104.21015 30.22899))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Squamata",
 "Year 2006-2016"
 ]
}

 The dataset includes 1063 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032040-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kzq4xb
title: Occurrence Download

description: A dataset containing 240267 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Clemensia albata Packard, 1864, Caripeta angustiorata Walker, Eupithecia mutata Pearsall, 1908, Hydriomena divisaria (Walker, 1860), Hypagyrtis piniata (Packard, 1870), Macaria fissinotata Walker, Tolype laricis (Fitch, 1856), Acronicta innotata Guenée, 1852, Aplectoides condita Guenée, 1852, Eueretagrotis perattentus Grote, 1876, Xestia badicollis Grote, 1873, Zanclognatha protumnusalis Walker, 1859, Archips dissitana (Grote, 1879), Archips strianus Fernald, 1905, Sparganothis tristriata Kearfott, 1907, Pero morrisonaria (H.Edwards, 1881), Eucopina tocullionana (Heinrich, 1920), Archips pinus Freeman, Idia aemula Hübner, 1814, Probole amicaria (Herrich-Schäffer, 1855), Macaria oweni (Swett, 1907), Manulea bicolor (Grote, 1864), Eupithecia palpata Packard, 1873, Hypoprepia fucosa Hübner, 1831, Iridopsis ephyraria (Walker, 1860), Nepytia canosaria (Walker), Phlogophora periculosa Guenée, 1852, Zanclognatha cruralis Guenée, 1854, Macaria bisignata Walker, 1866, Spiramater lutra Guenée, 1852, Panthea acronyctoides Walker, 1861, Eulithis explanata (Walker, 1862), Syngrapha rectangula W.Kirby, 1837, Nematocampa resistaria (Herrich-Schäffer), Macaria signaria (Hübner), Speranza pustularia (Guenée, 1858), Archips fumiferana Clemens, Nemoria mimosaria (Guenée, 1857), Eutrapela clemataria (Smith, 1797), Hyalophora columbia Smith, 1865, Elaphria versicolor Grote, 1875, Palthis angulalis Hübner, 1796, Feralia comstocki Grote, 1874, Protoboarmia porcelaria (Guenée, 1857), Hydriomena renunciata Walker, 1862, Tetracis cachexiata Guenée, Orgyia leucostigma J.E.Smith, 1797, Aphrophora cribrata Lethierry, 1890, Castor canadensis Kuhl, 1820, Alces americanus (Clinton, 1822))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 240267 records from 283 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032015-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4fqm6a
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crataegus songarica K.Koch",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 22 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032043-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aceqe9
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorbus sibirica Hedl.",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032042-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.czhueb
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus domestica L.",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 108 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032044-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tmvb9q
title: Occurrence Download

description: A dataset containing 171 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chiasmocleis panamensis Dunn, Trapido & Evans, 1948"
 ]
}

 The dataset includes 171 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032045-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28905209.v2
title: <b>Appendix 1. Statistical Descriptive</b>: Table 1.8 Sampling Data between Districts and Gender

description: <b>Disclaimer:</b>The raw data of this crosstabulation originates from the 2023 Medan City MSME survey undertaken by the Medan City administration. All MSMEs in this study possess a business registration or operating permit in Medan City. The researcher has received authorization from the Medan City Government to analyze and publish this data by permit number 000.9/1826 in 2023.<b>Interpretation:</b>This table presents a demographic breakdown of sampled entrepreneurial individuals across 21 districts in Medan City. The table categorises participants or business owners by gender (men and women) and provides a <b>total count</b> for each district.<b>Key Insights:</b><br><b>Overall Gender Distribution</b>:<b>Women (962 individuals) make up 67.8% of the sample.</b><b>Men (458 individuals) account for 32.2%.</b>The total sample size is <b>1,420 individuals</b>.<b>Districts with the Highest Women's Representation</b>:<b>Medan Helvetia</b>: <b>143 women (77.3%)</b>.<b>Medan Sunggal</b>: <b>92 women (63.0%)</b>.<b>Medan Barat</b>: <b>56 women (73.7%)</b>.<b>Districts with the Highest Men's Representation</b>:<b>Medan Sunggal</b>: <b>54 men (37.0%)</b>.<b>Medan Denai</b>: <b>42 men (40.4%)</b>.<b>Medan Helvetia</b>: <b>42 men (22.7%)</b>.<b>District with Lowest Sample Size</b>:<b>Medan Maimun</b>: <b>Only 20 individuals sampled</b> (7 men and 13 women).<br><b>Potential Research Implications</b>:<br><b>Gender-based entrepreneurial activity</b>: Certain districts may have higher concentrations of women entrepreneurs, which can influence policy or support programs.<b>Regional demographic disparities</b>: The variation in sample sizes across districts may reflect differences in population density or business registration patterns.<b>Further statistical modelling</b>: Logistic regression or clustering analysis could reveal more profound insights into gender distribution patterns.<br>
DOI: 10.6084/m9.figshare.28905209
title: <b>Appendix 1. Statistical Descriptive</b>: Table 1.8 Sampling Data between Districts and Gender

description: <b>Disclaimer:</b>The raw data of this crosstabulation originates from the 2023 Medan City MSME survey undertaken by the Medan City administration. All MSMEs in this study possess a business registration or operating permit in Medan City. The researcher has received authorization from the Medan City Government to analyze and publish this data by permit number 000.9/1826 in 2023.<b>Interpretation:</b>This table presents a demographic breakdown of sampled entrepreneurial individuals across 21 districts in Medan City. The table categorises participants or business owners by gender (men and women) and provides a <b>total count</b> for each district.<b>Key Insights:</b><br><b>Overall Gender Distribution</b>:<b>Women (962 individuals) make up 67.8% of the sample.</b><b>Men (458 individuals) account for 32.2%.</b>The total sample size is <b>1,420 individuals</b>.<b>Districts with the Highest Women's Representation</b>:<b>Medan Helvetia</b>: <b>143 women (77.3%)</b>.<b>Medan Sunggal</b>: <b>92 women (63.0%)</b>.<b>Medan Barat</b>: <b>56 women (73.7%)</b>.<b>Districts with the Highest Men's Representation</b>:<b>Medan Sunggal</b>: <b>54 men (37.0%)</b>.<b>Medan Denai</b>: <b>42 men (40.4%)</b>.<b>Medan Helvetia</b>: <b>42 men (22.7%)</b>.<b>District with Lowest Sample Size</b>:<b>Medan Maimun</b>: <b>Only 20 individuals sampled</b> (7 men and 13 women).<br><b>Potential Research Implications</b>:<br><b>Gender-based entrepreneurial activity</b>: Certain districts may have higher concentrations of women entrepreneurs, which can influence policy or support programs.<b>Regional demographic disparities</b>: The variation in sample sizes across districts may reflect differences in population density or business registration patterns.<b>Further statistical modelling</b>: Logistic regression or clustering analysis could reveal more profound insights into gender distribution patterns.<br>