cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14266395
title: Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

description: Glacial lake dataset for the paper "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset" (https://doi.org/10.1016/j.jhydrol.2025.133072)

The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.

GLID.rar contains the training dataset (16,000 samples),  and val.zip contains the validation dataset (2,367 samples).

GLID_annotation.zip contains the annotated shapefile of GLID with a CRS of WGS 84.

Optical_images_source.xlsx contains the optical images ID/names and acquisition time of each platform (e.g., WV2, LC08, S2B, and GF02) used in GLID.

Transferability validation.zip contains the images, labels, and predictions for transferability validation, which is independent of GLID (not used for model training or validation). The file structure is shown below:

Transferability validation.zip



images



AS.tif

GL.tif

NA.tif

SA.tif



labels



AS_gt.tif

GL_gt.tif

NA_gt.tif

SA_gt.tif



predictions



AS_pred.tif

GL_pred.tif

NA_pred.tif

SA_pred.tif




AS, GL, NA, and SA represent Asia, Greenland, North America, and South America, respectively. Four high-quality Landsat-8/9 images (each cloud cover less than 6%) were used for testing, and we manually annotated the glacial lakes in each image as labels. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.  Files in transferability validation.zip have a same CRS of WGS 84.

If you find this dataset is helpful in your research, please consider cite this paper:



Ma D, Li J, Jiang L. 2025. Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset. Journal of Hydrology 657: 133072.
DOI: 10.5281/zenodo.14266396
title: Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

description: Glacial lake dataset for paper "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.

GLID.rar contains the training dataset (16,000 samples),  and val.zip contains the validation dataset (2,367 samples).
DOI: 10.5281/zenodo.14607214
title: Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

description: Glacial lake dataset for paper "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.

GLID.rar contains the training dataset (16,000 samples),  and val.zip contains the validation dataset (2,367 samples).

GLID_annotation.zip contains the annotated shapefile of GLID with a CRS of WGS 84.

Optical_images_source.xlsx contains the optical images ID/names and acquisition time of each platform (e.g., WV2, LC08, S2B, and GF02) used in GLID.
DOI: 10.5281/zenodo.14838695
title: Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

description: Glacial lake dataset for the paper "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset" (https://doi.org/10.1016/j.jhydrol.2025.133072)

The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.

GLID.rar contains the training dataset (16,000 samples),  and val.zip contains the validation dataset (2,367 samples).

GLID_annotation.zip contains the annotated shapefile of GLID with a CRS of WGS 84.

Optical_images_source.xlsx contains the optical images ID/names and acquisition time of each platform (e.g., WV2, LC08, S2B, and GF02) used in GLID.

Transferability validation.zip contains the images, labels, and predictions for transferability validation, which is independent of GLID (not used for model training or validation). The file structure is shown below:

Transferability validation.zip



images



AS.tif

GL.tif

NA.tif

SA.tif



labels



AS_gt.tif

GL_gt.tif

NA_gt.tif

SA_gt.tif



predictions



AS_pred.tif

GL_pred.tif

NA_pred.tif

SA_pred.tif




AS, GL, NA, and SA represent Asia, Greenland, North America, and South America, respectively. Four high-quality Landsat-8/9 images (each cloud cover less than 6%) were used for testing, and we manually annotated the glacial lakes in each image as labels. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.  Files in transferability validation.zip have a same CRS of WGS 84.

If you find this dataset is helpful in your research, please consider cite this paper:



Ma D, Li J, Jiang L. 2025. Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset. Journal of Hydrology 657: 133072.
DOI: 10.5281/zenodo.15037204
title: AI-DRIVEN FORENSIC LOG AUTHENTICATION: ENHANCING INTEGRITY, ANOMALY DETECTION, AND COMPLIANCE IN DIGITAL EVIDENCE MANAGEMENT
DOI: 10.5281/zenodo.15037205
title: AI-DRIVEN FORENSIC LOG AUTHENTICATION: ENHANCING INTEGRITY, ANOMALY DETECTION, AND COMPLIANCE IN DIGITAL EVIDENCE MANAGEMENT
DOI: 10.15468/dl.q97dq9
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tribolium madens (Charpentier, 1825))",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 16 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011777-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cpwypd
title: Occurrence Download

description: A dataset containing 1166 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eucallipterus tiliae (Linnaeus, 1758))",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1166 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011780-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wtz27w
title: Occurrence Download

description: A dataset containing 4151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sceliphron curvatum (F.Smith, 1870))",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4151 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011778-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d8m3ve
title: Occurrence Download

description: A dataset containing 209 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Myotis bechsteinii (Kuhl, 1817)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2013",
 "Year is less than or equal to 2018",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 209 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011779-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z43q78
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Sinojackia rehderiana Hu"
 ]
}

 The dataset includes 22 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011781-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dkmrsj
title: Occurrence Download

description: A dataset containing 34818396 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 34818396 records from 3831 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011688-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-5034
title: Data repository for "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects"

description: &lt;p&gt;
This dataset provides reproduction code and experimental data for the publication "Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects". The repository contains an exact snapshot of the code version used to generate all results in the paper, ensuring full reproducibility.
&lt;/p&gt;

&lt;p&gt;
The repository is organized into three zip archives:&lt;br /&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;code&gt;code.zip&lt;/code&gt;: Contains the code used to generate and evaluate the data. This archive includes a README with instructions on how to use the code and integrate the two data archives.&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;data_quantum_devices.zip&lt;/code&gt;:Contains the raw and partially preprocessed experimental data obtained from quantum devices, as well as calibration data for the devices.&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;data_simulations.zip&lt;/code&gt;: Contains experimental data generated from simulations.&lt;/li&gt;
&lt;/ul&gt;
&lt;/p&gt;

&lt;p&gt;
For reference, this dataset includes a separate PDF file for each figure presented in the publication. These files were generated directly from the enclosed code and data, and they serve as benchmarks for visually verifying reproduced results.
&lt;/p&gt;