cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bk4egj
title: Occurrence Download

description: A dataset containing 709 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Ginkgo biloba L."
 ]
}

 The dataset includes 709 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001295-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.prupcx
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heloderma alvarezi Bogert & Martin Del Campo, 1956"
 ]
}

 The dataset includes 83 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001294-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15114512
title: Arctic Sea Ice Topography from a Triangulated Surface Model

description: Airborne Data

The "ATM_Elevations" and "ATM_TIN_model" datasets are derived from NASA Operation IceBridge Airborne Topographic Mapper (ATM) lidar data (Studinger, 2013) acquired on September 9, 2019. The ATM elevations and Triangulated Irregular Network (TIN) model are provided as ascii text (.txt) and .ply files, respectively.

The coordinates of the ATM elevations are provided in the NSIDC Sea Ice Polar Stereographic North projection (epsg: 3411) and the elevations are relative to the EGM2008 geoid model. The columns of the .txt files are as follows:



Projected Longitude (meters)

Projected Latitude (meters)

Elevation (meters, relative to the EGM2008 geoid)


ATM elevations and ATM TIN model data are provided in three parts/sections and in total cover ~72 km in length.

 

Spaceborne Data

The "UMDRDA_Elevation_&_Ridges" dataset is derived from the ICESat-2 Global Geolocated Photon Height Product (ATL03, Neumann et al., 2021), release 005, using the University of Maryland-Ridge Detection Algorithm (UMD-RDA, Duncan & Farrell, 2022). The UMD-RDA is applied to ATL03 granules on a per-shot basis, nominally resulting in elevation measurements at ICESat-2's along-track sampling of ~0.7 m.

The UMDRDA elevation and ridge locations are provided in the NSIDC Sea Ice Polar Stereographic North projection (epsg: 3411) and the elevations are relative to the EGM2008 geoid model. The columns of the .txt file are as follows:



Projected Longitude (meters)

Projected Latitude (meters)

Time (seconds since 2018-01-01)

Elevation (meters, relative to the EGM2008 geoid)

Ridge Flag (0 = not a ridge, 1 = ridge)


UMDRDA data cover approximately the same 72 km area as the ATM data.

 

For any questions regarding this dataset you can contact Kyle Duncan by email: kduncan at umd . edu
DOI: 10.5281/zenodo.15114513
title: Arctic Sea Ice Topography from a Triangulated Surface Model

description: Airborne Data

The "ATM_Elevations" and "ATM_TIN_model" datasets are derived from NASA Operation IceBridge Airborne Topographic Mapper (ATM) lidar data (Studinger, 2013) acquired on September 9, 2019. The ATM elevations and Triangulated Irregular Network (TIN) model are provided as ascii text (.txt) and .ply files, respectively.

The coordinates of the ATM elevations are provided in the NSIDC Sea Ice Polar Stereographic North projection (epsg: 3411) and the elevations are relative to the EGM2008 geoid model. The columns of the .txt files are as follows:



Projected Longitude (meters)

Projected Latitude (meters)

Elevation (meters, relative to the EGM2008 geoid)


ATM elevations and ATM TIN model data are provided in three parts/sections and in total cover ~72 km in length.

 

Spaceborne Data

The "UMDRDA_Elevation_&_Ridges" dataset is derived from the ICESat-2 Global Geolocated Photon Height Product (ATL03, Neumann et al., 2021), release 005, using the University of Maryland-Ridge Detection Algorithm (UMD-RDA, Duncan & Farrell, 2022). The UMD-RDA is applied to ATL03 granules on a per-shot basis, nominally resulting in elevation measurements at ICESat-2's along-track sampling of ~0.7 m.

The UMDRDA elevation and ridge locations are provided in the NSIDC Sea Ice Polar Stereographic North projection (epsg: 3411) and the elevations are relative to the EGM2008 geoid model. The columns of the .txt file are as follows:



Projected Longitude (meters)

Projected Latitude (meters)

Time (seconds since 2018-01-01)

Elevation (meters, relative to the EGM2008 geoid)

Ridge Flag (0 = not a ridge, 1 = ridge)


UMDRDA data cover approximately the same 72 km area as the ATM data.

 

For any questions regarding this dataset you can contact Kyle Duncan by email: kduncan at umd . edu
DOI: 10.15468/dl.ke6rbn
title: Occurrence Download

description: A dataset containing 6903 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Flueggea virosa (Roxb. ex Willd.) Royle",
 "Continent is Africa"
 ]
}

 The dataset includes 6903 records from 164 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001296-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ap3664
title: Occurrence Download

description: A dataset containing 129313 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-77.77626 4.4438,-73.50023 4.4438,-73.50023 8.8417,-77.77626 8.8417,-77.77626 4.4438))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dimerandra emarginata (G.Mey.) Hoehne"
 ]
}

 The dataset includes 129313 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001285-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8yfrpd
title: Occurrence Download

description: A dataset containing 270 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fuirena leptostachya Oliv.",
 "Continent is Africa"
 ]
}

 The dataset includes 270 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001297-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zbab9s
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Homadaula albizziae Clarke, 1943",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001298-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7chs7j
title: Occurrence Download

description: A dataset containing 1902 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sitona lepidus Gyllenhal, 1834",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1902 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001300-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ex5hsf
title: Occurrence Download

description: A dataset containing 279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gymnetron pascuorum (Gyllenhal, 1813)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 279 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001299-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nh6nss
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heloderma alvarezi Bogert & Martin Del Campo, 1956"
 ]
}

 The dataset includes 83 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001301-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.erqpv8
title: Occurrence Download

description: A dataset containing 889 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fuirena stricta Steud.",
 "Continent is Africa"
 ]
}

 The dataset includes 889 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001302-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnhg58
title: Occurrence Download

description: A dataset containing 8239 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyclospermum leptophyllum (Pers.) Sprague ex Britton & P.Wilson"
 ]
}

 The dataset includes 8239 records from 252 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001303-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7m5gj5
title: Occurrence Download

description: A dataset containing 6036 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "Geometry POLYGON((42.74511 33.35268,65.64592 33.35268,65.64592 70.81126,42.74511 70.81126,42.74511 33.35268))",
 "OccurrenceStatus is Present",
 "TaxonKey is Parus major",
 "Year 1990-2020"
 ]
}

 The dataset includes 6036 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004573-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zdrtrc
title: Occurrence Download

description: A dataset containing 1828 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Australia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cladonia P.Browne, 1756"
 ]
}

 The dataset includes 1828 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018455-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yu52fx
title: Occurrence Download

description: A dataset containing 27110 species occurrences available in GBIF matching the query:
{
 "PublishingOrg" : [
 "is ef807e08-c954-487f-9e70-3cc75f173812"
 ]
}

 The dataset includes 27110 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018454-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15044574
title: Addendices for Korn & Poulsen "A Tree or Not: An East Iranian Experiment"

description: Appendices for the paper "A Tree or Not: At East Iranian Experiment" by Agnes Korn and Simon Poulsen, appearing in the cummulative PhD Theis of the latter.

List of appendices

Appendix 1: The data matrix1a: The data matrix proper (.csv)Appendix 2: The data matrix with outcomesAppendix 3: List of all statesAppendix 4: Condensed list of allowed directionalities and costs of transitionsAppendix 5: Costs of all allowed directionalities5a: phonological characters5b: morphological charactersAppendix 6: The 33 best trees of the final run (.trees)Appendix 7: A sample of the best trees of the final runAppendix 8: Inferred development of all characters on Tree 1 and Tree 2Appendix 9: A sample of the best trees of the alternative analysesAppendix 10: Data matrices and best trees of the alternative analyses (.zip)
DOI: 10.5281/zenodo.15044575
title: Addendices for Korn & Poulsen "A Tree or Not: An East Iranian Experiment"

description: Appendices for the paper "A Tree or Not: At East Iranian Experiment" by Agnes Korn and Simon Poulsen, appearing in the cummulative PhD Theis of the latter.

List of appendices

Appendix 1: The data matrix1a: The data matrix proper (.csv)Appendix 2: The data matrix with outcomesAppendix 3: List of all statesAppendix 4: Condensed list of allowed directionalities and costs of transitionsAppendix 5: Costs of all allowed directionalities5a: phonological characters5b: morphological charactersAppendix 6: The 33 best trees of the final run (.trees)Appendix 7: A sample of the best trees of the final runAppendix 8: Inferred development of all characters on Tree 1 and Tree 2Appendix 9: A sample of the best trees of the alternative analysesAppendix 10: Data matrices and best trees of the alternative analyses (.zip)
DOI: 10.15468/dl.rbases
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aeolothrips vittatus Haliday, 1836",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 67 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018456-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qf79eq
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cercopagis pengoi (Ostroumov, 1891))",
 "BasisOfRecord is one of (Human Observation)",
 "Year is greater than or equal to 1971",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 58 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018458-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15056804
title: Plant Disease Image Dataset

description: Description

This dataset offers an extensive collection of images and corresponding labels representing a wide array of plant diseases. Carefully curated from publicly available sources, it serves as a valuable resource for developing and evaluating machine learning models, particularly in the realms of image classification and Plant Disease Image Dataset.

Dataset Composition:

• Images: The dataset comprises high-quality images organized by plant species and disease type, providing a diverse range of visual data. It includes not only images of diseased plants but also healthy plant samples, ensuring a balanced dataset for training purposes.• Categories: The images are categorized into various classes based on the plant species and the specific disease affecting them. This categorization allows for more precise model training and testing.• Cleaned Data: All images have been meticulously cleaned and verified to remove any corrupt or unusable files, ensuring the dataset's reliability and usability.• Labeling: Each image is labeled with detailed information about the plant species and the type of disease, making it easier to use the dataset for supervised learning tasks.

Download Dataset

Applications:

This dataset is ideal for a variety of machine learning applications, including:• Disease Detection: Training models to identify and classify various plant diseases, which can be pivotal for early detection and prevention.• Image Classification: Developing and testing models to accurately classify images based on plant species and health status.• Agricultural Research: Supporting research in precision agriculture by providing data that can lead to better understanding and management of plant health.

Dataset Structure:

• Organized Folders: The images are structured into folders corresponding to each plant species and disease type, facilitating easy access and manipulation of data.• Healthy Samples Included: To ensure balanced datasets, healthy plant samples are included alongside diseased ones, enabling models to learn to differentiate between healthy and diseased plants.• Versatile Use: The dataset's structure and labeling make it suitable for a wide range of research and commercial applications in agriculture and plant biology.

Conclusion:

The Plant Disease Dataset is a comprehensive and well-organized collection, ideal for anyone working on machine learning models in the field of agriculture. Whether you're developing new algorithms for disease detection or enhancing existing models, this dataset provides the rich and diverse data necessary to achieve accurate and reliable results.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15056805
title: Plant Disease Image Dataset

description: Description

This dataset offers an extensive collection of images and corresponding labels representing a wide array of plant diseases. Carefully curated from publicly available sources, it serves as a valuable resource for developing and evaluating machine learning models, particularly in the realms of image classification and Plant Disease Image Dataset.

Dataset Composition:

• Images: The dataset comprises high-quality images organized by plant species and disease type, providing a diverse range of visual data. It includes not only images of diseased plants but also healthy plant samples, ensuring a balanced dataset for training purposes.• Categories: The images are categorized into various classes based on the plant species and the specific disease affecting them. This categorization allows for more precise model training and testing.• Cleaned Data: All images have been meticulously cleaned and verified to remove any corrupt or unusable files, ensuring the dataset's reliability and usability.• Labeling: Each image is labeled with detailed information about the plant species and the type of disease, making it easier to use the dataset for supervised learning tasks.

Download Dataset

Applications:

This dataset is ideal for a variety of machine learning applications, including:• Disease Detection: Training models to identify and classify various plant diseases, which can be pivotal for early detection and prevention.• Image Classification: Developing and testing models to accurately classify images based on plant species and health status.• Agricultural Research: Supporting research in precision agriculture by providing data that can lead to better understanding and management of plant health.

Dataset Structure:

• Organized Folders: The images are structured into folders corresponding to each plant species and disease type, facilitating easy access and manipulation of data.• Healthy Samples Included: To ensure balanced datasets, healthy plant samples are included alongside diseased ones, enabling models to learn to differentiate between healthy and diseased plants.• Versatile Use: The dataset's structure and labeling make it suitable for a wide range of research and commercial applications in agriculture and plant biology.

Conclusion:

The Plant Disease Dataset is a comprehensive and well-organized collection, ideal for anyone working on machine learning models in the field of agriculture. Whether you're developing new algorithms for disease detection or enhancing existing models, this dataset provides the rich and diverse data necessary to achieve accurate and reliable results.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14963136
title: ChemTastesDB: A Curated Database of Molecular Tastants

description: ChemTastesDB is a database that includes curated information of 4075 molecular tastants. ChemTastesDB is distributed to the scientific community to expand the information of molecular tastants, which could assist the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR/QSPR) studies for taste prediction. Examples of QSPR approaches for the prediction of molecular taste are given in the following publication: Rojas, C., Abril-González, M., Ballabio, D. & García, F. (2025). ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants (Under review).

The 4075 molecular tastants are categorized into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, non-bitter, multitaste and miscellaneous). The molecules are categorized into following ten classes: sweet (1313), bitter (1615), umami (220), sour (49), salty (16), multitaste (179), tasteless (232), non-sweet (304), non-bitter (28), and miscellaneous (119).

ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. In addition, the molecular structure in the HyperChem (.hin) format of each compound is provided.

This is version 2.0 of the ChemTastesDB. In this new version, 1131 newly curated compounds were added. These new molecules were retrieved from 52 new bibliographic references.
DOI: 10.5281/zenodo.15051366
title: ChemTastesDB: A Curated Database of Molecular Tastants

description: ChemTastesDB is a database that includes curated information of 4075 molecular tastants. ChemTastesDB is distributed to the scientific community to expand the information of molecular tastants, which could assist the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR/QSPR) studies for taste prediction. Examples of QSPR approaches for the prediction of molecular taste are given in the following publication: Rojas, C., Abril-González, M., Ballabio, D. & García, F. (2025). ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants. Chemometrics and Intelligent Laboratory Systems. 261, 105380. https://doi.org/10.1016/j.chemolab.2025.105380.

The 4075 molecular tastants are categorized into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, non-bitter, multitaste and miscellaneous). The molecules are categorized into following ten classes: sweet (1313), bitter (1615), umami (220), sour (49), salty (16), multitaste (179), tasteless (232), non-sweet (304), non-bitter (28), and miscellaneous (119).

ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. In addition, the molecular structure in the HyperChem (.hin) format of each compound is provided.

This is version 2.1 of the ChemTastesDB. In this new version, 1131 newly curated compounds were added. These new molecules were retrieved from 52 new bibliographic references.