cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.vzk45x
title: Occurrence Download

description: A dataset containing 11657 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ctenotus schomburgkii (Peters, 1863))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11657 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027304-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vepv3u
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Fossil",
 "OccurrenceStatus is Present",
 "TaxonKey is Keteleeria Carrière"
 ]
}

 The dataset includes 177 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002905-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjtfwk
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is New Zealand",
 "Geometry POLYGON((183.46752 -43.9753,183.55613 -43.9753,183.55613 -43.88492,183.46752 -43.88492,183.46752 -43.9753))",
 "HasCoordinate is true",
 "TaxonKey is Dromaius novaehollandiae (Latham, 1790)"
 ]
}

 The dataset includes 89 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002896-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/rq4cmv
title: Replication Data for Disaggregating Defection: Dissent Campaign Strategies and Security Force Disloyalty

description: This package contains the replication materials necessary to reproduce the findings presented in the article "Beyond Defection: Dissent Campaign Strategies and Security Force Disloyalty" by Marianne Dahl, Mauricio Rivera Celestino, and Scott Gates.
DOI: 10.15468/dl.zcqtha
title: Occurrence Download

description: A dataset containing 217 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Iran (Islamic Republic of)",
 "OccurrenceStatus is Present",
 "TaxonKey is Sus scrofa Linnaeus, 1758"
 ]
}

 The dataset includes 217 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002904-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nyamx7
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Fungarium, Oslo (O) UiO",
 "Country is Croatia"
 ]
}

 The dataset includes 73 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002907-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yu9fde
title: Occurrence Download

description: A dataset containing 434594 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is one of (Afghanistan, Bahrain, China, Egypt, Georgia, Greece, Iran (Islamic Republic of), Iraq, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Mongolia, Oman, Qatar, Russian Federation, Saudi Arabia, Chinese Taipei, Tajikistan, Turkmenistan, Turks and Caicos Islands, United Arab Emirates, Uzbekistan, Yemen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EN, VU, CR, EX, EW, NT, LC, DD, NE)",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 434594 records from 306 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002901-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ntmyxc
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Coleoptera Eastern Fennoscandia (Luomus) (FC)",
 "Country is Croatia"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002910-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15517621
title: Supporting data for "On the role of speech similarity in the detection of room acoustic differences"

description: Dataset supporting the study:McKenzie, T., Meyer-Kahlen, N., Schlecht S. J., "On the role of speech similarity in the detection of room acoustic differences", submitted to the Journal of the Acoustical Society of America, 2025

The dataset includes:



Binaural Room Impulse Responses (BRIRs) used in the experiment as sofa files. They can be loaded for example with sofar

Used diffuse field equalization filter (kemarDiffEq.mat)

Examples of listening test stimuli, created using source signals from https://doi.org/10.5281/zenodo.15034732.In the examples, a triplet "A," "B," and "C" would occur in one trial of the experiment. SS denotes "same speaker, same sentence," SD denotes "same speaker, different sentence," and DD stands for "different speaker, different sentence."
DOI: 10.5281/zenodo.15517620
title: Supporting data for "On the role of speech similarity in the detection of room acoustic differences"

description: Dataset supporting the study:McKenzie, T., Meyer-Kahlen, N., Schlecht S. J., "On the role of speech similarity in the detection of room acoustic differences", submitted to the Journal of the Acoustical Society of America, 2025

The dataset includes:



Binaural Room Impulse Responses (BRIRs) used in the experiment as sofa files. They can be loaded for example with sofar

Used diffuse field equalization filter (kemarDiffEq.mat)

Examples of listening test stimuli, created using source signals from https://doi.org/10.5281/zenodo.15034732.In the examples, a triplet "A," "B," and "C" would occur in one trial of the experiment. SS denotes "same speaker, same sentence," SD denotes "same speaker, different sentence," and DD stands for "different speaker, different sentence."
DOI: 10.15468/dl.h2y3c4
title: Occurrence Download

description: A dataset containing 141647 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Desulfobacterales"
 ]
}

 The dataset includes 141647 records from 570 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002906-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3up5zw
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hamatocactus setispinus (Engelm.) Britton & Rose"
 ]
}

 The dataset includes 52 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002909-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10056453
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 100 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.10725850
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.10781120
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.10781134
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.10967867
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.15524472
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 154 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.8415542
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 154 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.5281/zenodo.8415543
title: RAS Dataset

description: The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 100 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods.
DOI: 10.15468/dl.rdwtww
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gazalina chrysolopha (Kollar, 1844)"
 ]
}

 The dataset includes 148 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002908-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/gtms-0d15
title: "Prueba de Validaci\u00f3n del Modelo"

description: "This dataset contains real and predicted 2D position coordinates (x, y) used for the validation of a deep learning-based indoor positioning model. The data were collected using Bluetooth Low Energy (BLE) signals emitted by four fixed ESP32 nodes and received by a mobile ESP32 device. The real positions were manually labeled, while the predicted positions were obtained from the trained model. It is particularly useful for researchers working on fingerprinting-based positioning systems, BLE signal processing, and deep learning applications in indoor localization."
DOI: 10.15468/dl.dsgkd2
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Codonopsis convolvulacea subsp. vinciflora (Kom.) D.Y.Hong"
 ]
}

 The dataset includes 37 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002912-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t9apyh
title: Occurrence Download

description: A dataset containing 449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Michigan Herbarium",
 "Country is Croatia"
 ]
}

 The dataset includes 449 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002911-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fmand4
title: Occurrence Download

description: A dataset containing 138 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Agave lophantha Schiede"
 ]
}

 The dataset includes 138 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002913-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12805810
title: CovDocker

description: The preprocessed CovDocker dataset for paper "CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions" with associated code at https://github.com/PoloWitty/CovDocker.

The dataset files are saved as lmdb file for the convenience of use.

File structure:



processed

    ├── bonded

    │   ├── 1A0L

    │   │     ├── 1A0L_10Apocket.pdb

    │   │     ├── 1A0L_5Apocket.pdb

    │   │     ├── 1A0L_8Apocket.pdb

    │   │     ├── 1A0L_chain_within_10A.pdb

    │   │     ├── 1A0L_ligand.pdb # ligand part from original complex pdb file

    │   │     ├── 1A0L_ligand.sdf # ligand pdb structure aligned with coresponding SMILES

    │   │     └── 1A0L_protein.pdb # protein part from original complex pdb file

    .....

    │   └── 9XIA

    ├── dataset # lmdb files used for deep learning model

    │   ├── docking

    │   ├── reaction

    │   └── reactive_site

    ├── dataset.csv # used for task2 and task3 (n=2754)

    ├── dataset.filtered.csv # used for task1 (n=2717)

    ├── dataset.filtered.unseen.csv # used for task1 unseen test set (67 unseen test samples)

    ├── dataset.unseen.csv # used for task2 and task3 unseen test set (68 unseen test samples)

    └── pdb2mechanism.csv # (n=2754)


Paper Abstract:



Molecular docking plays a crucial role in predicting the binding mode of ligands to target proteins, and covalent interactions, which involve the formation of a covalent bond between the ligand and the target, are particularly valuable due to their strong, enduring binding nature. However, most existing docking methods and deep learning approaches hardly account for the formation of covalent bonds and the associated structural changes. To address this gap, we introduce a comprehensive benchmark for covalent docking, CovDocker, which is designed to better capture the complexities of covalent binding. We decompose the covalent docking process into three main tasks: reactive location prediction, covalent reaction prediction, and covalent docking. By adapting state-of-the-art models, such as Uni-Mol and Chemformer, we establish baseline performances and demonstrate the effectiveness of the benchmark in accurately predicting interaction sites and modeling the molecular transformations involved in covalent binding. These results confirm the role of the benchmark as a rigorous framework for advancing research in covalent drug design. It underscores the potential of data-driven approaches to accelerate the discovery of selective covalent inhibitors and addresses critical challenges in therapeutic development. Our code is available at https://github.com/PoloWitty/CovDocker.