cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.rs2xmb
title: Occurrence Download

description: A dataset containing 937 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Saccostrea cuccullata"
 ]
}

 The dataset includes 937 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067690-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xzbse4
title: Occurrence Download

description: A dataset containing 1014 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhyacotriton cascadae"
 ]
}

 The dataset includes 1014 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067691-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18074722
title: GLOW26 - Datasets and Embeddings

description: Datasets (knowledge graphs) and their embeddings for reproducing experiments presented at GLOW26. Code is available at: https://github.com/SLIMlaboratory/glow26.
DOI: 10.5281/zenodo.18074721
title: GLOW26 - Datasets and Embeddings

description: Datasets (knowledge graphs) and their embeddings for reproducing experiments presented at GLOW26. Code is available at: https://github.com/SLIMlaboratory/glow26.
DOI: 10.15468/dl.sq97k7
title: Occurrence Download

description: A dataset containing 31216 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Spoladea recurvalis"
 ]
}

 The dataset includes 31216 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067692-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18075461
title: Astrophysical Reaction Rates for Charged-Particle Induced Reactions on Proton-Rich Nuclides Calculated with the Code SMARAGD

description: Astrophysical reaction reactivities (rates) N_A <sigma v> calculated with the statistical model (Hauser-Feshbach model) code SMARAGD, version v0.42.0s. Included are rates for captures and for reactions with positive reaction Q-value for elements Ne to Bi with isotopes from stability to the proton dripline. Projectiles are proton, alphas, and neutrons. 

See the file README_zenodo.txt for details on the file formats.

Details of the calculation are given in https://doi.org/10.48550/arXiv.2512.03206 .
description: Version 2: The header lines in the files fits_x.dat were corrected. The element symbol for the final nucleus was not shown correctly in the previous version (the numerical values for charge number and neutron number were correct, though). No other changes were made.
DOI: 10.5281/zenodo.17770706
title: Astrophysical Reaction Rates for Charged-Particle Induced Reactions on Proton-Rich Nuclides Calculated with the Code SMARAGD

description: Astrophysical reaction reactivities (rates) N_A <sigma v> calculated with the statistical model (Hauser-Feshbach model) code SMARAGD, version v0.42.0s. Included are rates for captures and for reactions with positive reaction Q-value for elements Ne to Bi with isotopes from stability to the proton dripline. Projectiles are proton, alphas, and neutrons. 

See the file README_zenodo.txt for details on the file formats.

Details of the calculation are given in https://doi.org/10.48550/arXiv.2512.03206 .
description: Version 2: The header lines in the files fits_x.dat were corrected. The element symbol for the final nucleus was not shown correctly in the previous version (the numerical values for charge number and neutron number were correct, though). No other changes were made.
DOI: 10.15468/dl.tphje4
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Hong Kong",
 "OccurrenceStatus is Present",
 "TaxonKey is Sousa chinensis"
 ]
}

 The dataset includes 88 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067694-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6k8ps
title: Occurrence Download

description: A dataset containing 25667 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((4.7024 47.19814,5.11807 47.19814,5.11807 47.44867,4.7024 47.44867,4.7024 47.19814))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepidoptera",
 "Year 2010-2025"
 ]
}

 The dataset includes 25667 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067696-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96kw27
title: Occurrence Download

description: A dataset containing 1201 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Saccostrea glomerata"
 ]
}

 The dataset includes 1201 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067695-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15699683
title: Sodium Chloride Electrolyte Equilibrium Molecular Dynamics Simulations

description: The repository contains inputs, outputs and workflow files of equilibrium molecular dynamics
simulations of sodium chloride electrolyte systems.
This repository is based on simulations discussed by
Gullbrekken et al. in
J. Phys. Chem. B 2023, 127, 12, 2729–2738,
of which the LAMMPS input files are provided in a related
Zenodo repository.
We have modified their inputs to create an example
of a conductivity calculation using STACIE.

See README.pdf for a full technical description.
DOI: 10.5281/zenodo.15699684
title: Sodium Chloride Electrolyte Equilibrium Molecular Dynamics Simulations

description: The repository contains inputs, outputs and workflow files of equilibrium molecular dynamics
simulations of sodium chloride electrolyte systems.
This repository is based on simulations discussed by
Gullbrekken et al. in
J. Phys. Chem. B 2023, 127, 12, 2729–2738,
of which the LAMMPS input files are provided in a related
Zenodo repository.
We have modified their inputs to create an example
of a conductivity calculation using STACIE.

See README.pdf for a full technical description.
DOI: 10.15468/dl.99fwve
title: Occurrence Download

description: A dataset containing 1077 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhyacotriton kezeri"
 ]
}

 The dataset includes 1077 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067697-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48623/aperta.286592
title: Performance Evaluation Of Photovoltaic Panels In Extreme Environments: A Machine Learning Approach On Horseshoe Island, Antarctica

description: Due to the supply problems of fossil-based energy sources, the tendency towards alternative energy sources is relatively high. For this reason, the use of solar energy systems is increasing today. This study combines experimental data and machine learning algorithms to evaluate the energy performance of four different photovoltaic (PV) panel designs (monocrystalline, polycrystalline, flexible, and transparent) under harsh environmental conditions on Horseshoe Island (Antarctica). In this research, the effects of environmental factors, such as solar radiation, temperature, humidity, and wind speed, on the panels were analyzed. Electrical power output of the PV panels are analyzed using six machine learning models. Random forest (RF) and CatBoost (CB) models showed the highest accuracy and reliability among these models. According to the experimental results, Monocrystalline PV provided the highest electrical power (20.5 Watts on average), and Flexible PV provided the highest energy efficiency (19.67%). However, Flexible PV was observed to have higher surface temperatures compared to the other panel types. Furthermore, using Monocrystalline PV resulted in an average reduction of 4.1 tons of CO<sub>2</sub> emissions per year, demonstrating the positive environmental impact of renewable energy systems. Thanks to this study, renewable energy research for temporary stations in Antarctica will focus on explainable and interpretable artificial intelligence models that will provide an understanding of the factors affecting the energy performance of PV panels. The research results will be an important guide for optimizing energy consumption, management, and demand forecasting in temporary research stations in Antarctica.
description: This dataset consists of experimental data obtained from the TAE-8 Antarctic expedition. This dataset is not a raw dataset organized into 30-minute intervals. The artificial intelligence modeling part of your work was performed using this dataset.
DOI: 10.15468/dl.8pw9pc
title: Occurrence Download

description: A dataset containing 2539 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhyacotriton olympicus"
 ]
}

 The dataset includes 2539 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067698-251120083545085/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/14320809
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18075441
title: chauna_chavaria_spades_01.fasta.gz
DOI: 10.5281/zenodo.18075442
title: chauna_chavaria_spades_01.fasta.gz
DOI: 10.15468/dl.fa86tn
title: Occurrence Download

description: A dataset containing 2762 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhyacotriton variegatus"
 ]
}

 The dataset includes 2762 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067700-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jrj7jm
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((13.19649 41.22494,13.72972 41.23206,13.72423 41.51365,13.1887 41.50646,13.19649 41.22494))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Upupa epops",
 "Year 1990-2025"
 ]
}

 The dataset includes 14 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067701-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbguvd
title: Occurrence Download

description: A dataset containing 951304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Country is Chinese Taipei",
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is TWN.6_1",
 "TaxonKey is Aves",
 "Year 2016-2025"
 ]
}

 The dataset includes 951304 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067693-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a6w2k7
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Atropoides"
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067702-251120083545085/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.30948506
title: Stack Overflow Recommendations Dataset (SORD) - Version 1.0 based on Stack Overflow Data Dump - October 2025

description: The dataset (including replication package) for Stack Overflow questions, answers and comments containing recommendation related keywords extracted from Stack Overflow data dump published in October 2025.
DOI: 10.6084/m9.figshare.30948506.v1
title: Stack Overflow Recommendations Dataset (SORD) - Version 1.0 based on Stack Overflow Data Dump - October 2025

description: The dataset (including replication package) for Stack Overflow questions, answers and comments containing recommendation related keywords extracted from Stack Overflow data dump published in October 2025.
DOI: 10.15468/dl.4n3ua6
title: Occurrence Download

description: A dataset containing 689 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Atheris"
 ]
}

 The dataset includes 689 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067705-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kw84n7
title: Occurrence Download

description: A dataset containing 226 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Salamandra algira"
 ]
}

 The dataset includes 226 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067706-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x99jks
title: Occurrence Download

description: A dataset containing 1016626 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is National insect monitoring in Norway"
 ]
}

 The dataset includes 1016626 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067704-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.csu262
title: Occurrence Download

description: A dataset containing 11618 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Crassostrea virginica"
 ]
}

 The dataset includes 11618 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067707-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.24jzae
title: Occurrence Download

description: A dataset containing 1526 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Salamandra corsica"
 ]
}

 The dataset includes 1526 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067708-251120083545085/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.30928907
title: <b>PEMDefect-1107</b>

description: <b>PEMDefect-1107</b> is a curated grayscale image dataset for <b>single-class defect localization</b> in proton-exchange membrane (PEM) fuel cell materials. The dataset is designed for <b>bounding-box–based defect detection</b> and supports research in materials-focused computer vision, data-centric machine learning, and defect localization under data-scarce conditions.The dataset contains <b>1,107 images in total</b> and is organized under a single root directory with <b>explicit train, validation, and test splits</b>. The splits consist of <b>1,045 training images</b>, <b>31 validation images</b>, and <b>31 test images</b>. Each split is stored in a dedicated subdirectory and follows a <b>split-aware directory structure</b> to prevent data leakage.All bounding-box annotations are provided in <b>YOLO format</b>, with one label file per image. This format ensures a strict one-to-one correspondence between images and annotations and avoids centralized annotation mismatch issues. A dataset configuration file (<code>dataset.yaml</code>) is included to define split paths and class metadata, enabling immediate use in standard object detection pipelines.All images are provided in <b>grayscale</b>, resized to a fixed resolution of <b>640 × 640 pixels</b>, and stored in <b>PNG format</b>. A consistent, physics-aware preprocessing pipeline was applied across the dataset, including conversion from microscopy acquisition formats, deterministic removal of scale bars via cropping, and global intensity normalization. Scale bars were removed to prevent shortcut learning from acquisition metadata and to ensure models learn from intrinsic material structure rather than imaging artifacts.The train/validation/test split was finalized <b>prior to any data augmentation</b>, ensuring strict separation between splits. Data augmentation was applied <b>exclusively to the training set</b>, while validation and test images remain unaugmented, enabling unbiased and reproducible evaluation of defect localization performance.The dataset underwent comprehensive integrity and validation checks, including verification of image–annotation correspondence, geometric consistency of bounding boxes, absence of truncation artifacts, detection of duplicate images, and confirmation of split integrity with no leakage across subsets. All hard validation criteria were satisfied in the final release.PEMDefect-1107 is suitable for training and benchmarking defect localization models such as Faster R-CNN and RetinaNet (via standard annotation conversion), as well as for studying data-centric effects, augmentation strategies, and robustness in materials-oriented computer vision tasks.
DOI: 10.6084/m9.figshare.30928907.v3
title: <b>PEMDefect-1107</b>

description: <b>PEMDefect-1107</b> is a curated grayscale image dataset for single-class defect localization in proton-exchange membrane (PEM) fuel cell materials. The dataset is designed for bounding-box–based defect detection and supports research in materials AI, data-centric machine learning, and computer vision.The dataset contains <b>1,107 images in total</b> and is organized under a single root directory. Images are split into <b>1,045 training images</b>, <b>31 validation images</b>, and <b>31 test images</b>, with each split stored in a dedicated subdirectory under <code>images/</code>. All corresponding bounding-box annotations are provided in JSON format within the <code>all_annotations/</code> directory, with separate annotation files for the training, validation, and test splits.All images are provided in <b>grayscale</b>, resized to a fixed resolution of <b>640 × 640 pixels</b>, and stored in <b>PNG format</b>. A consistent, physics-aware preprocessing pipeline was applied across the dataset, including conversion from microscopy acquisition formats, deterministic removal of scale bars via cropping, and global intensity normalization. Scale bars were removed to prevent shortcut learning from acquisition metadata and to ensure models learn from physical structure rather than imaging artifacts.The train/validation/test split was finalized <b>prior to any data augmentation</b>, ensuring strict separation between splits. Data augmentation was applied exclusively to the training set, while validation and test images remain unaugmented, enabling unbiased and reproducible evaluation of defect localization performance.The dataset was subjected to comprehensive integrity and validation checks, including geometric consistency of bounding boxes, absence of truncation artifacts, statistical independence between original and augmented samples, and verification of dataset integrity (no exact duplicates or split leakage). All hard validation criteria were satisfied.<b>PEMDefect-1107</b> is suitable for training and benchmarking defect localization models on PEM fuel cell imagery, as well as for studying data-centric effects, augmentation strategies, and robustness in materials-focused computer vision.
DOI: 10.6084/m9.figshare.30928907.v4
title: <b>PEMDefect-1107</b>

description: <b>PEMDefect-1107</b> is a curated grayscale image dataset for <b>single-class defect localization</b> in proton-exchange membrane (PEM) fuel cell materials. The dataset is designed for <b>bounding-box–based defect detection</b> and supports research in materials-focused computer vision, data-centric machine learning, and defect localization under data-scarce conditions.The dataset contains <b>1,107 images in total</b> and is organized under a single root directory with <b>explicit train, validation, and test splits</b>. The splits consist of <b>1,045 training images</b>, <b>31 validation images</b>, and <b>31 test images</b>. Each split is stored in a dedicated subdirectory and follows a <b>split-aware directory structure</b> to prevent data leakage.All bounding-box annotations are provided in <b>YOLO format</b>, with one label file per image. This format ensures a strict one-to-one correspondence between images and annotations and avoids centralized annotation mismatch issues. A dataset configuration file (<code>dataset.yaml</code>) is included to define split paths and class metadata, enabling immediate use in standard object detection pipelines.All images are provided in <b>grayscale</b>, resized to a fixed resolution of <b>640 × 640 pixels</b>, and stored in <b>PNG format</b>. A consistent, physics-aware preprocessing pipeline was applied across the dataset, including conversion from microscopy acquisition formats, deterministic removal of scale bars via cropping, and global intensity normalization. Scale bars were removed to prevent shortcut learning from acquisition metadata and to ensure models learn from intrinsic material structure rather than imaging artifacts.The train/validation/test split was finalized <b>prior to any data augmentation</b>, ensuring strict separation between splits. Data augmentation was applied <b>exclusively to the training set</b>, while validation and test images remain unaugmented, enabling unbiased and reproducible evaluation of defect localization performance.The dataset underwent comprehensive integrity and validation checks, including verification of image–annotation correspondence, geometric consistency of bounding boxes, absence of truncation artifacts, detection of duplicate images, and confirmation of split integrity with no leakage across subsets. All hard validation criteria were satisfied in the final release.PEMDefect-1107 is suitable for training and benchmarking defect localization models such as Faster R-CNN and RetinaNet (via standard annotation conversion), as well as for studying data-centric effects, augmentation strategies, and robustness in materials-oriented computer vision tasks.
DOI: 10.15468/dl.h7e5jr
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Maxates thetydaria"
 ]
}

 The dataset includes 19 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067709-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.