cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28407831.v1
title: Penetrating Keratoplasty versus Deep Anterior Lamellar Keratoplasty for Keratoconus: A Systematic Review and Meta-Analysis of 27,018 Eyes

description: Keratoconus is a bilateral asymmetrical degeneration in the cornea. It is associated with local thinning in the cornea leading to visual problems such as myopia and astigmatism. Several treatments, such as lenses and keratoplasty surgeries, were utilized to treat this condition. In this study, we aim to compare deep anterior lamellar keratoplasty (DALK) to penetrating keratoplasty (PK). We registered our study protocol on PROSPERO (CRD42024559427). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched four electronic databases (PubMed, Scopus, Cochrane Library, and Web of Science) to identify eligible studies reported up to June 2024. Using STATA 17, we reported outcomes as risk ratios (RRs) or mean difference (MD) and confidence intervals (CIs). A P-value ≤.05 is considered as statistically significant. The meta-analysis included a total of 47 studies with a total number of 27,018 eyes. There was no significant difference between PK and DALK in terms of best-corrected visual acuity (BCVA) at 1 year (Hedge’s g: 0.03 with 95% CI [−0.13, 0.19], <i>p</i> = .71). PK was associated with a lower risk of graft failure than DALK (log RR: −0.41 with 95% CI [−0.74, −0.08], <i>p</i> = .01). PK was associated with a higher risk of graft rejection than DALK (log RR: 0.85 with 95% CI [0.35, 1.35], <i>p</i> &lt; .001). There was no significant difference between PK and DALK in terms of corneal astigmatism at 1 year (Hedge’s g: 0.03 with 95% CI [−0.24, 0.31], <i>p</i> = .82). The visual outcomes such as BCVA, astigmatism, and spherical equivalent refraction, of PK and DALK are comparable. However, PK was associated with a higher risk of complications and rejection episodes, but, a lower risk of graft failure.
DOI: 10.15468/dl.ncbe8h
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((77.72657 35.83875,74.77665 36.07172,73.12181 35.07689,73.12181 32.99023,73.5535 31.53007,74.99249 30.41969,76.79123 29.17097,78.44607 28.09745,79.74116 27.71595,81.5399 30.23338,81.18015 31.16138,80.10091 32.01939,80.17286 34.24839,80.17286 35.54658,77.72657 35.83875))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pinus roxburghii Sarg."
 ]
}

 The dataset includes 48 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056475-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.htarrm
title: Occurrence Download

description: A dataset containing 11002 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Dataset of Phenology of flora of mediterranean high-mountains meadows (Sierra Nevada)"
 ]
}

 The dataset includes 11002 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056476-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15696175
title: SpaceNet Comprehensive Astronomical Dataset

description: Description:















SpaceNet Comprehensive Astronomical Dataset is a hierarchically structured and high-quality astronomical image dataset, created using a novel double-stage augmentation process. This dataset, comprising approximately 12,900 images, is designed for both fine-grained and macro classification tasks. SpaceNet incorporates a range of resolutions from lower (LR) to higher resolution (HR) images, using standard augmentations and a diffusion approach for generating synthetic samples. This allows for superior generalization across various recognition tasks such as classification. The dataset also includes diverse celestial objects, making it a valuable resource for both academic research and practical applications in astronomy and astrophysics.

 











Download Dataset









Dataset Structure:



Fine-Grained Classes: The dataset includes 8 distinct classes: planets, galaxies, asteroids, nebulae, comets, black holes, stars, and constellations.


Dataset Composition:



Total Samples: Approximately 12,900 images

Fine-Grained Class Distribution:



Asteroid: 283 images

Black Hole: 656 images

Comet: 416 images

Constellation: 1,552 images

Galaxy: 3,984 images

Nebula: 1,192 images

Planet: 1,472 images

Star: 3,269 images




Usage: SpaceNet is ideal for:



Training and evaluating machine learning models on fine-grained and macro astronomical classification tasks.

Conducting research on hierarchical classification methods within the astronomy field.

Developing robust models that demonstrate excellent generalization across both in-domain and out-of-domain datasets.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15696176
title: SpaceNet Comprehensive Astronomical Dataset

description: Description:















SpaceNet Comprehensive Astronomical Dataset is a hierarchically structured and high-quality astronomical image dataset, created using a novel double-stage augmentation process. This dataset, comprising approximately 12,900 images, is designed for both fine-grained and macro classification tasks. SpaceNet incorporates a range of resolutions from lower (LR) to higher resolution (HR) images, using standard augmentations and a diffusion approach for generating synthetic samples. This allows for superior generalization across various recognition tasks such as classification. The dataset also includes diverse celestial objects, making it a valuable resource for both academic research and practical applications in astronomy and astrophysics.

 











Download Dataset









Dataset Structure:



Fine-Grained Classes: The dataset includes 8 distinct classes: planets, galaxies, asteroids, nebulae, comets, black holes, stars, and constellations.


Dataset Composition:



Total Samples: Approximately 12,900 images

Fine-Grained Class Distribution:



Asteroid: 283 images

Black Hole: 656 images

Comet: 416 images

Constellation: 1,552 images

Galaxy: 3,984 images

Nebula: 1,192 images

Planet: 1,472 images

Star: 3,269 images




Usage: SpaceNet is ideal for:



Training and evaluating machine learning models on fine-grained and macro astronomical classification tasks.

Conducting research on hierarchical classification methods within the astronomy field.

Developing robust models that demonstrate excellent generalization across both in-domain and out-of-domain datasets.


This dataset is sourced from Kaggle.
DOI: 10.15468/dl.m293jh
title: Occurrence Download

description: A dataset containing 75 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((77.72657 35.83875,74.77665 36.07172,73.12181 35.07689,73.12181 32.99023,73.5535 31.53007,74.99249 30.41969,76.79123 29.17097,78.44607 28.09745,79.74116 27.71595,81.5399 30.23338,81.18015 31.16138,80.10091 32.01939,80.17286 34.24839,80.17286 35.54658,77.72657 35.83875))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Pinus wallichiana A.B.Jacks., Pinus wallichiana var. wallichiana, Pinus wallichiana var. parva K.C.Sahni)"
 ]
}

 The dataset includes 75 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056477-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eukeu2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Synodontis zambezensis Peters, 1852",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((37.3005 22.3477,37.1382 22.4869,37.1382 20.0599,37.7663 20.0599,37.7668 21.2899,37.3972 21.7772,37.3005 22.3477))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056479-250525065834625/datasets/export for details.
DOI: 10.15468/dl.zyb7bn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caloneis bacillum (Grunow) Cleve, 1894",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1586 -33.5889,28.2048 -32.9007,25.5395 -32.9007,25.5395 -34.1098,25.6468 -34.1222,25.7787 -34.0734,25.7098 -33.9109,25.8639 -33.797,26.4806 -33.8473,27.1586 -33.5889))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056481-250525065834625/datasets/export for details.
DOI: 10.15468/dl.wuuhus
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((77.72657 35.83875,74.77665 36.07172,73.12181 35.07689,73.12181 32.99023,73.5535 31.53007,74.99249 30.41969,76.79123 29.17097,78.44607 28.09745,79.74116 27.71595,81.5399 30.23338,81.18015 31.16138,80.10091 32.01939,80.17286 34.24839,80.17286 35.54658,77.72657 35.83875))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus floribunda Lindl. ex A.Camus"
 ]
}

 The dataset includes 29 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056480-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x4c2p7
title: Occurrence Download

description: A dataset containing 3343 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phytophthora cinnamomi Rands"
 ]
}

 The dataset includes 3343 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056482-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p2ux85
title: Occurrence Download

description: A dataset containing 745 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "OccurrenceStatus is Present",
 "TaxonKey is Anogeissus latifolia (Roxb. ex DC.) Wall. ex Guill. & Perr."
 ]
}

 The dataset includes 745 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056485-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gfk8bt
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caloneis bacillum (Grunow) Cleve, 1894",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((18.2339 -32.5266,18.1411 -32.657,18.1007 -32.7021,18.0224 -32.6366,17.8923 -32.6419,17.754 -32.8255,17.7723 -32.9007,20.509 -32.9007,20.509 -28.5786,16.2782 -28.5786,16.4358 -28.7202,16.726 -29.1235,17.1957 -30.3752,17.6655 -31.1743,18.1322 -31.7679,18.2495 -32.2638,18.2339 -32.5266))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056484-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15294107
title: Forecasting Major Flares Using Magnetograms and Knowledge-Informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products

description: The repository includes the datasets and experimental code used in our research, along with descriptions of these files.

In this study, we construct two kinds of datasets from distinct time periods, both comprising line-of-sight magnetograms and knowledge-informed features. The first kind of dataset is based on SHARP LOS magnetograms and knowledge-informed features from May 1, 2010, to February 13, 2022. The zip files named “10_Fold_SHARP_CV_Image.zip” and “10_Fold_SHARP_CV_Knowledge_informed” provide the ten-fold cross-validation set, normalized and split using the AR-based cross-validation method, for SHARP LOS magnetograms and knowledge-informed features, respectively. The second kind of dataset consists of magnetograms and knowledge-informed features from SHARP, HMI, and FMG corresponding to the same ARs, spanning from April 1, 2023, to July 21, 2024. The zip files named “23_24_SHARP_HMI_FMG_Image.zip” and “23_24_SHARP_HMI_FMG_Knowledge_informed” provide magnetograms and knowledge-informed features from SHARP, HMI, and FMG, respectively.

The zip file named "CCMC_Compare.zip" provides the real-time solar flare forecast results from various models collected on the NASA/CCMC official website (https://ccmc.gsfc.nasa.gov/scoreboards/flare/) from April 1, 2023, to July 21, 2024, as well as the SHARP testing dataset we used, which is highly consistent with their time and AR numbers.

The file named “Correlation_Alignment.py” provides the cross-correlation method we used to align magnetograms and extract the same regions as the FMG from each complete HMI magnetogram.

The file named “Knowledge-informed_Features_Calculate.py” provides the method for calculating the 31 knowledge-informed features from the LOS magnetograms.

The file named “CCMC_Metric_Calculate.py” provides the code for calculating categorial and probabilistic forecast metrics based on the NASA/CCMC solar flare forecast results.

The zip file named "Visual.zip" provides visualizations for the CNN, CNN-BiLSTM, and ViT models, showing the regions of interest in the magnetograms that the models focus on.

In this paper, we design seven currently popular deep learning models, including four image-based models (e.g., CNN, CNN-BiLSTM, CNN-BiLSTM-Attention, and ViT) and three knowledge-informed models (e.g., BiLSTM, BiLSTM-Attention and iTransformer), for predicting ≥M-class solar flares within 24 hr. Additionally, we use a traditional NN model based on knowledge-informed features as the baseline model for comparison with other deep learning models. The zip file named "Model.zip" contains the eight solar flare prediction models used in this study, including the model architectures, saved model parameters, and the evaluation metric results from testing.
DOI: 10.5281/zenodo.15294108
title: Forecasting Major Flares Using Magnetograms and Knowledge-Informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products

description: The repository includes the datasets and experimental code used in our research, along with descriptions of these files.

In this study, we construct two kinds of datasets from distinct time periods, both comprising line-of-sight magnetograms and knowledge-informed features. The first kind of dataset is based on SHARP LOS magnetograms and knowledge-informed features from May 1, 2010, to February 13, 2022. The zip files named “10_Fold_SHARP_CV_Image.zip” and “10_Fold_SHARP_CV_Knowledge_informed” provide the ten-fold cross-validation set, normalized and split using the AR-based cross-validation method, for SHARP LOS magnetograms and knowledge-informed features, respectively. The second kind of dataset consists of magnetograms and knowledge-informed features from SHARP, HMI, and FMG corresponding to the same ARs, spanning from April 1, 2023, to July 21, 2024. The zip files named “23_24_SHARP_HMI_FMG_Image.zip” and “23_24_SHARP_HMI_FMG_Knowledge_informed” provide magnetograms and knowledge-informed features from SHARP, HMI, and FMG, respectively.

The zip file named "CCMC_Compare.zip" provides the real-time solar flare forecast results from various models collected on the NASA/CCMC official website (https://ccmc.gsfc.nasa.gov/scoreboards/flare/) from April 1, 2023, to July 21, 2024, as well as the SHARP testing dataset we used, which is highly consistent with their time and AR numbers.

The file named “Correlation_Alignment.py” provides the cross-correlation method we used to align magnetograms and extract the same regions as the FMG from each complete HMI magnetogram.

The file named “Knowledge-informed_Features_Calculate.py” provides the method for calculating the 31 knowledge-informed features from the LOS magnetograms.

The file named “CCMC_Metric_Calculate.py” provides the code for calculating categorial and probabilistic forecast metrics based on the NASA/CCMC solar flare forecast results.

The zip file named "Visual.zip" provides visualizations for the CNN, CNN-BiLSTM, and ViT models, showing the regions of interest in the magnetograms that the models focus on.

In this paper, we design seven currently popular deep learning models, including four image-based models (e.g., CNN, CNN-BiLSTM, CNN-BiLSTM-Attention, and ViT) and three knowledge-informed models (e.g., BiLSTM, BiLSTM-Attention and iTransformer), for predicting ≥M-class solar flares within 24 hr. Additionally, we use a traditional NN model based on knowledge-informed features as the baseline model for comparison with other deep learning models. The zip file named "Model.zip" contains the eight solar flare prediction models used in this study, including the model architectures, saved model parameters, and the evaluation metric results from testing.
DOI: 10.15468/dl.98j688
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Synodontis zambezensis Peters, 1852",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.9094 1.1982,46.1382 1.9309,46.1382 2.0599,37.1382 2.0599,37.1382 -6.9401,40.3593 -6.9401,40.412 -6.7736,40.0778 -6.426,39.9615 -5.9146,40.3471 -5.2827,40.3421 -4.7259,40.0631 -4.4307,40.3789 -3.7221,40.7318 -3.0429,41.8321 -2.1586,43.8394 0.2906,44.9094 1.1982))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056483-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15679246
title: VeriExploit

description: Updated README, replaced the virtual machine with a lighter version to ease downloads.
DOI: 10.15468/dl.5npndf
title: Occurrence Download

description: A dataset containing 22081 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Salix lanata subsp. richardsonii (Hook.) A.K.Skvortsov, Salix pyrifolia Andersson, Salix planifolia Pursh, Salix reticulata L., Salix vestita Pursh)",
 "Geometry POLYGON ((-180 45.53015, 180 45.53015, 180 83.62313, -180 83.62313, -180 45.53015))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than or equal to 50000"
 ]
}

 The dataset includes 22081 records from 164 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056488-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nkbkhu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Synodontis zambezensis Peters, 1852",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.5146 10.8961,44.3186 11.0599,37.1382 11.0599,37.1382 2.0599,46.1382 2.0599,46.1382 11.0599,45.0861 11.0599,44.7982 10.9157,44.5146 10.8961))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056486-250525065834625/datasets/export for details.
DOI: 10.15468/dl.u9un5n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caloneis bacillum (Grunow) Cleve, 1894",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056487-250525065834625/datasets/export for details.
DOI: 10.15468/dl.dubzv2
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((77.72657 35.83875,74.77665 36.07172,73.12181 35.07689,73.12181 32.99023,73.5535 31.53007,74.99249 30.41969,76.79123 29.17097,78.44607 28.09745,79.74116 27.71595,81.5399 30.23338,81.18015 31.16138,80.10091 32.01939,80.17286 34.24839,80.17286 35.54658,77.72657 35.83875))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus leucotrichophora A.Camus"
 ]
}

 The dataset includes 33 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056491-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f29qne
title: Occurrence Download

description: A dataset containing 525 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aspidelaps Fitzinger, 1843"
 ]
}

 The dataset includes 525 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056490-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nmtmxa
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Culicoides paraensis (Goeldi, 1905)"
 ]
}

 The dataset includes 128 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056492-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15675937
title: Supported data for manuscript "Electrospun Mo-SiO₂ Nanofibers as Heterogeneous Catalysts for Propylene Metathesis"

description: "Dataset for archived manuscript DOI 10.26434/chemrxiv-2025-kdxwh. Synthesis of Mo-SiO2 nanofibers via electrospinning, structural characterization of prepared nanofibres (EM, N2 and Ar adsorption, XPS, insitu DRUV, XRD, Raman spectroscopy), and study of propylene metathesis (heterogeneous catalysis, fixed bed reactor) over electrospun Mo-SiO₂ nanofibers at 200 °C. Detailed descriptions are in the README text documents in subdirectories.

Keywords: Mo-SiO₂; hetogeneous catalysis; propylene metathesis; electrospinnig."