cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/sna-wbc-7gx
title: Rangifer tarandus (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ajk-b70-cd3
title: Bos taurus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wcj-w8j-w7l
title: Cythereis (Cythereis) Jones, 1849

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jcs-l75-1sc
title: Porifera Grant, 1836

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xwy-505-ef7
title: †Echinocorys Leske, 1778

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zah-81y-zw2
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4vg-l69-8j9
title: Heteroscleromorpha Cárdenas, Pérez & Boury-Esnault, 2012

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/m2m-240-3j5
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cly-nzv-tjz
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/thl-y4n-02d
title: Porifera Grant, 1836

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/7sp-0jf-qr9
title: Cellepora Linnaeus, 1767

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/svl-5xb-mgf
title: Scoliodon

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/y6q-3jf-wg1
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5kj-j8e-pmk
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/v4n-01q-q6w
title: Rangifer tarandus (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4ej-gvd-6y7
title: Porifera Grant, 1836

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d8r-1nw-96w
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/vbf-r1l-mme
title: Homo sapiens Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/azn-az1-89x
title: Ditrupa (Ditrupa) Berkeley, 1835

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/n4a-fh8-ga5
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cn4-c61-5mh
title: Cnidaria

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/d2z-sre-hql
title: Dipsastraea speciosa (Dana, 1846)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2ez-byt-52t
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5ns-bt5-53s
title: Equus (Equus) caballus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/g6x-5wl-d8r
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mq7-x8l-bmd
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qnm-tf8-9kl
title: Elephantidae Gray, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.ah39na
title: Occurrence Download

description: A dataset containing 2332 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Aporrectodea trapezoides",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 5000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 2332 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022473-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/eek-xl6-2wx
title: Neoaulaxinia Pisera & Lévi, 2002

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pjh-hap-h3a
title: Elephantidae Gray, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/2lx-cv6-ecn
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/mgn-7qb-nx5
title: Cervus elaphus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.15490793
title: Comprehensive High-Resolution Eggplant Leaf Image Dataset for Plant Disease Detection

description: This is a comprehensive version of the Eggplant Leaf Image Dataset, designed to support machine learning and deep learning research in agriculture, plant pathology, and computer vision. This dataset addresses class imbalance and model generalization challenges by including a significantly expanded collection of images through controlled data augmentation.

The dataset contains a total of 2,180 high-resolution images (4000×6000 pixels), evenly distributed across six categories of eggplant leaf conditions:

1. Healthy: 400 images (80 original + 320 augmented)2. Insect-Pest: 360 images (40 original + 320 augmented)3. Leaf-Spot: 350 images (50 original + 300 augmented)4. Mosaic-Virus: 360 images (15 original + 345 augmented)5. Small-Leaf: 360 images (20 original + 340 augmented)6. Wilt: 350 images (50 original + 300 augmented)

All original images were captured using a Canon EOS 1300D DSLR camera under consistent natural lighting conditions. Files are saved in JPG format, and image resolution is preserved within ±5% of the original dimensions to maintain visual fidelity.

Data augmentation was performed using the Albumentations library, applying transformations such as rotation, flipping, brightness and color shifts, and padding. The augmentation process was seeded to ensure reproducibility, and all configuration parameters are documented in the accompanying metadata.

The metadata.csv file provides a class-wise summary including original image count, augmented image count, augmentation ratios, and the exact augmentation pipeline used. The augmentation was seeded for reproducibility.

Note: Original and augmented images are stored together in each class folder. Augmented images are distinguishable by the inclusion of "_aug_" in their filenames. There is no separate folder for augmented images.

Files:EggplantLeaf-ImageDataset.zip — Contains all 2,180 images organized by class, along with metadata and readme.metadata.csv — Class-level summary and augmentation details.Readme.md — Technical documentation and usage notes.
DOI: 10.15468/dl.fzu7f6
title: Occurrence Download

description: A dataset containing 744 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterocarpus tinctorius Welw."
 ]
}

 The dataset includes 744 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005078-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13760109
title: Enhancing AI Research in Nanomaterials: FAIR Compliant Defected Graphene Dataset with STM Images and Transport Properties

description: This HDF5 dataset organizes data for graphene flakes. At the root level, global metadata and documentation are stored. Each flake is identified by an index and split into two groups: OptGeom (optimized geometry) and OptGeomElectrodes (geometry in an electrode setup).

The OptGeom group contains the flake's atomic numbers, coordinates, number of atoms, and inertia tensor, with attributes such as formation energy. The OptGeomElectrodes group includes similar structural data but adds calculated properties like total energy, ionization potential (IP), electron affinity (EA), band gap, transport current, and STM images.
DOI: 10.5281/zenodo.15518927
title: Enhancing AI Research in Nanomaterials: FAIR Compliant Defected Graphene Dataset with STM Images and Transport Properties

description: This HDF5 dataset organizes data for graphene flakes. At the root level, global metadata and documentation are stored. Each flake is identified by an index and split into two groups: OptGeom (optimized geometry) and OptGeomElectrodes (geometry in an electrode setup).

The OptGeom group contains the flake's atomic numbers, coordinates, number of atoms, and inertia tensor, with attributes such as formation energy. The OptGeomElectrodes group includes similar structural data but adds calculated properties like total energy, ionization potential (IP), electron affinity (EA), band gap, transport current, and STM images.

 

HDF5 scientific file (application/x-hdf5)

FAIR metadata JSON-LD file (application/ld+json)
DOI: 10.5281/zenodo.15490792
title: Comprehensive High-Resolution Eggplant Leaf Image Dataset for Plant Disease Detection

description: This is a comprehensive version of the Eggplant Leaf Image Dataset, designed to support machine learning and deep learning research in agriculture, plant pathology, and computer vision. This dataset addresses class imbalance and model generalization challenges by including a significantly expanded collection of images through controlled data augmentation.

The dataset includes a total of 2,180 high-resolution images (6000×4000 pixels), categorized into six disease or health classes of Solanum melongena (eggplant) leaves:




Class
Original Images
Augmented Images
Total Images




Healthy
80
320
400


Insect-Pest
40
320
360


Leaf-Spot
50
300
350


Mosaic-Virus
15
345
360


Small-Leaf
20
340
360


Wilt
50
300
350




All original images were captured using a Canon EOS 1300D DSLR camera under consistent natural lighting conditions. Files are saved in JPG format, and image resolution is preserved within ±5% of the original dimensions to maintain visual fidelity.

To improve dataset usability for robust model training and generalization, controlled data augmentation was applied using the Albumentations library. The transformations include random rotation, horizontal flipping, brightness/contrast adjustments, slight color shifts, and padding to maintain aspect ratio. All augmentation procedures were consistently applied and seeded for reproducibility. Augmentation parameters are documented in detail in the metadata.

The metadata.csv file provides a class-wise summary including original image count, augmented image count, augmentation ratios, and the exact augmentation pipeline used. The augmentation was seeded for reproducibility.

Note: The original and augmented images are stored in separate folders under the "Original" and "Augmented" directories, respectively. Each directory is organized into six class-specific subfolders: Healthy, Insect-Pest, Leaf-Spot, Mosaic-Virus, Small-Leaf, and Wilt. Augmented images are clearly distinguishable by the inclusion of the substring "_aug_" in their filenames. This clear separation ensures reproducibility, transparency in data provenance, and ease of use for researchers who may wish to train models using only original, only augmented, or both types of data.

Files:



EggplantLeaf-ImageDataset.zip — Contains all files and folders, inclusind Original, Augmented, metadata and readme.

OriginalC — Contains only raw field-captured images grouped by class.

Augmented — Contains synthetically expanded datasets, also organized by class. Augmented filenames include the marker "aug" for easy identification.

metadata.csv — Class-level summary and augmentation details.

Readme.md — Technical documentation and usage notes.
DOI: 10.5281/zenodo.15527092
title: Comprehensive High-Resolution Eggplant Leaf Image Dataset for Plant Disease Detection

description: This is a comprehensive version of the Eggplant Leaf Image Dataset, designed to support machine learning and deep learning research in agriculture, plant pathology, and computer vision. This dataset addresses class imbalance and model generalization challenges by including a significantly expanded collection of images through controlled data augmentation.

The dataset includes a total of 2,180 high-resolution images (6000×4000 pixels), categorized into six disease or health classes of Solanum melongena (eggplant) leaves:




Class
Original Images
Augmented Images
Total Images




Healthy
80
320
400


Insect-Pest
40
320
360


Leaf-Spot
50
300
350


Mosaic-Virus
15
345
360


Small-Leaf
20
340
360


Wilt
50
300
350




All original images were captured using a Canon EOS 1300D DSLR camera under consistent natural lighting conditions. Files are saved in JPG format, and image resolution is preserved within ±5% of the original dimensions to maintain visual fidelity.

To improve dataset usability for robust model training and generalization, controlled data augmentation was applied using the Albumentations library. The transformations include random rotation, horizontal flipping, brightness/contrast adjustments, slight color shifts, and padding to maintain aspect ratio. All augmentation procedures were consistently applied and seeded for reproducibility. Augmentation parameters are documented in detail in the metadata.

The metadata.csv file provides a class-wise summary including original image count, augmented image count, augmentation ratios, and the exact augmentation pipeline used. The augmentation was seeded for reproducibility.

Note: The original and augmented images are stored in separate folders under the "Original" and "Augmented" directories, respectively. Each directory is organized into six class-specific subfolders: Healthy, Insect-Pest, Leaf-Spot, Mosaic-Virus, Small-Leaf, and Wilt. Augmented images are clearly distinguishable by the inclusion of the substring "_aug_" in their filenames. This clear separation ensures reproducibility, transparency in data provenance, and ease of use for researchers who may wish to train models using only original, only augmented, or both types of data.

Files:



EggplantLeaf-ImageDataset.zip — Contains all files and folders, inclusind Original, Augmented, metadata and readme.

OriginalC — Contains only raw field-captured images grouped by class.

Augmented — Contains synthetically expanded datasets, also organized by class. Augmented filenames include the marker "aug" for easy identification.

metadata.csv — Class-level summary and augmentation details.

Readme.md — Technical documentation and usage notes.
DOI: 10.15468/dl.xv58kj
title: Occurrence Download

description: A dataset containing 4802 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spheniscus magellanicus (J.R.Forster, 1781)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 10",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 4802 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010987-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y5vhy2
title: Occurrence Download

description: A dataset containing 36935 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Alchornea Sw."
 ]
}

 The dataset includes 36935 records from 725 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010986-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qjgfjd
title: Occurrence Download

description: A dataset containing 305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Asio abyssinicus)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 305 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0082838-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14974398
title: Dataset used for the needs of publication entitled "CHANGES IN THE MICROBIOME OF A HUMAN AND  IN THE SIMULATOR OF HUMAN INTESTINAL MICROBIAL  ECOSYSTEM (SHIME®) IN RESPONSE TO A DIET  AND PROBIOTIC SUPPLEMENTATION " DOI: 10.15193/zntj/2023/134/437

description: Possible applications of the Simulator of Human Microbial Ecosystem (SHIME) have been broadly discussed in the literature. However, so far, its reliability in providing data on the microbiota diversity mirroring humans has not been evaluated in detail. 

Our study aimed to provide sequencing data in order to allow such evaluation. A human (donor of a faecal microbiota sample for SHIME) was subjected to dietary (28 days) and probiotic (14 days) intervention in parallel with the system. Samples of microbiota from SHIME: both luminal and mucosal setup and faeces were collected and sequenced. 

We have found a rather low resemblance of the microbiota between SHIME (regardless of mucosal or luminal setups) and the donor.  This could have been caused by several factors of which, probably most significant were the use of a standard protocol for the initial stabilisation of microbiota in SHIME and discrepancies between food residues that could reach the colon of a human and SHIME. 

The relative abundance of Lactobacillus sp. was low and hence, probiotic Lactobacillus rhamnosus GG, contained in the intervention did not consistently appear in SHIME and human microbiota samples throughout 14 days of the supplementation.

Detailed data description was contained in the Readme File.txt. 

This research was funded by the National Science Centre, Poland 2021/05/X/NZ4/01613.

This dataset was used for the needs of publication entitled "CHANGES IN THE MICROBIOME OF A HUMAN AND  IN THE SIMULATOR OF HUMAN INTESTINAL MICROBIAL  ECOSYSTEM (SHIME®) IN RESPONSE TO A DIET  AND PROBIOTIC SUPPLEMENTATION " DOI: 10.15193/zntj/2023/134/437
DOI: 10.5281/zenodo.14974399
title: Dataset used for the needs of publication entitled "CHANGES IN THE MICROBIOME OF A HUMAN AND  IN THE SIMULATOR OF HUMAN INTESTINAL MICROBIAL  ECOSYSTEM (SHIME®) IN RESPONSE TO A DIET  AND PROBIOTIC SUPPLEMENTATION " DOI: 10.15193/zntj/2023/134/437

description: Possible applications of the Simulator of Human Microbial Ecosystem (SHIME) have been broadly discussed in the literature. However, so far, its reliability in providing data on the microbiota diversity mirroring humans has not been evaluated in detail. 

Our study aimed to provide sequencing data in order to allow such evaluation. A human (donor of a faecal microbiota sample for SHIME) was subjected to dietary (28 days) and probiotic (14 days) intervention in parallel with the system. Samples of microbiota from SHIME: both luminal and mucosal setup and faeces were collected and sequenced. 

We have found a rather low resemblance of the microbiota between SHIME (regardless of mucosal or luminal setups) and the donor.  This could have been caused by several factors of which, probably most significant were the use of a standard protocol for the initial stabilisation of microbiota in SHIME and discrepancies between food residues that could reach the colon of a human and SHIME. 

The relative abundance of Lactobacillus sp. was low and hence, probiotic Lactobacillus rhamnosus GG, contained in the intervention did not consistently appear in SHIME and human microbiota samples throughout 14 days of the supplementation.

Detailed data description was contained in the Readme File.txt. 

This research was funded by the National Science Centre, Poland 2021/05/X/NZ4/01613.

This dataset was used for the needs of publication entitled "CHANGES IN THE MICROBIOME OF A HUMAN AND  IN THE SIMULATOR OF HUMAN INTESTINAL MICROBIAL  ECOSYSTEM (SHIME®) IN RESPONSE TO A DIET  AND PROBIOTIC SUPPLEMENTATION " DOI: 10.15193/zntj/2023/134/437
DOI: 10.5281/zenodo.14978150
title: Dataset used for the needs of publication entitled "CHANGES IN THE MICROBIOME OF A HUMAN AND  IN THE SIMULATOR OF HUMAN INTESTINAL MICROBIAL  ECOSYSTEM (SHIME®) IN RESPONSE TO A DIET  AND PROBIOTIC SUPPLEMENTATION " DOI: 10.15193/zntj/2023/134/437

description: Possible applications of the Simulator of Human Microbial Ecosystem (SHIME) have been broadly discussed in the literature. However, so far, its reliability in providing data on the microbiota diversity mirroring humans has not been evaluated in detail. 

Our study aimed to provide sequencing data in order to allow such evaluation. A human (donor of a faecal microbiota sample for SHIME) was subjected to dietary (28 days) and probiotic (14 days) intervention in parallel with the system. Samples of microbiota from SHIME: both luminal and mucosal setup and faeces were collected and sequenced. 

We have found a rather low resemblance of the microbiota between SHIME (regardless of mucosal or luminal setups) and the donor.  This could have been caused by several factors of which, probably most significant were the use of a standard protocol for the initial stabilisation of microbiota in SHIME and discrepancies between food residues that could reach the colon of a human and SHIME. 

The relative abundance of Lactobacillus sp. was low and hence, probiotic Lactobacillus rhamnosus GG, contained in the intervention did not consistently appear in SHIME and human microbiota samples throughout 14 days of the supplementation.

Detailed data description was contained in the Readme File.txt. 

This research was funded by the National Science Centre, Poland 2021/05/X/NZ4/01613.

This dataset was used for the needs of publication entitled "CHANGES IN THE MICROBIOME OF A HUMAN AND  IN THE SIMULATOR OF HUMAN INTESTINAL MICROBIAL  ECOSYSTEM (SHIME®) IN RESPONSE TO A DIET  AND PROBIOTIC SUPPLEMENTATION " DOI: 10.15193/zntj/2023/134/437