cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14197456
title: Example Dataset

description: This dataset contains multiple files uploaded programmatically.
DOI: 10.5281/zenodo.14196729
title: Example Dataset with Multiple Files

description: This is an example dataset containing multiple files.
DOI: 10.5281/zenodo.14197097
title: Example Dataset with Multiple Files

description: This is an example dataset containing multiple files.
DOI: 10.23637/ri7j79zo
title: Baseline and business-as-usual (BAU) agricultural footprints for major farm types across England
DOI: 10.5281/zenodo.14216789
title: Training images for physical stress image face recognition

description: A proof-of-concept dataset for the initial training for physical stress recognition based on patient's faces. The dataset consists on multiple frames of a training session containing 11 different individuals.
DOI: 10.5281/zenodo.14216790
title: Training images for physical stress image face recognition

description: A proof-of-concept dataset for the initial training for physical stress recognition based on patient's faces. The dataset consists on multiple frames of a training session containing 11 different individuals.
DOI: 10.6084/m9.figshare.27901167.v1
title: The genome and annotation of <i>Pristella maxillaris</i>

description: The genome and annotation of<i> </i><i>Pristella maxillaris</i>
DOI: 10.6084/m9.figshare.27901296
title: Medical student data set from somewhere university in Indonesia.xlsx

description: Data consist anonym ID student, gender, study year, GPA, SWLS score for each item and total score of medical student from an university in Indonesia.
DOI: 10.6084/m9.figshare.27901296.v2
title: Medical student data set from somewhere university in Indonesia.xlsx

description: Data consist anonym ID student, gender, study year, GPA, SWLS score for each item and total score of medical student from an university in Indonesia.
DOI: 10.6084/m9.figshare.27901296.v1
title: Medical student data set from somewhere university in samarinda.xlsx

description: Data consist anonym ID student, gender, study year, GPA, SWLS score for each item and total score of medical student at somewhere Samarinda University, Kalimantan Timur, Indonesia.
DOI: 10.5281/zenodo.14216822
title: Long-term monitoring reveals sex- and age-related survival patterns in griffon vultures

description: Excel dataset
DOI: 10.5281/zenodo.14216823
title: Long-term monitoring reveals sex- and age-related survival patterns in griffon vultures

description: Excel dataset
DOI: 10.57745/tcxgqn
title: 20- FHNW - AgriLoop RP1_M18-Results and Impact.xlsx

description: datatable D2.1
DOI: 10.15468/dl.d3qq33
title: Occurrence Download

description: A dataset containing 412522 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Living Specimen",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 412522 records from 167 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032980-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/3629
title: DPO-En-Zh-20k
DOI: 10.15468/dl.axa56z
title: Occurrence Download

description: A dataset containing 233 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Prieskum chránených druhov rastlín, Chopok juh, lyžiarske stredisko"
 ]
}

 The dataset includes 233 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033034-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epf6gg
title: Occurrence Download

description: A dataset containing 1695488 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "Year is greater than or equal to 2013",
 {
 "or" : [
 "TaxonKey is Bombus Latreille, 1802",
 "TaxonKey is Melipona Illiger, 1806"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 "Country is one of (Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Switzerland, Cyprus, Czechia, Germany, Denmark, Estonia, Spain, Finland, France, Georgia, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Kazakhstan, Liechtenstein, Lithuania, Luxembourg, Latvia, Monaco, Moldova, Republic of, Montenegro, North Macedonia, Malta, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Russian Federation, Sweden, Slovenia, Slovakia, San Marino, Türkiye, Ukraine, United Kingdom of Great Britain and Northern Ireland, Holy See)"
 ]
}

 The dataset includes 1695488 records from 1132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033024-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14197475
title: Example Dataset

description: This dataset contains multiple files uploaded programmatically.
DOI: 10.5281/zenodo.14197460
title: Example Dataset

description: This dataset contains multiple files uploaded programmatically.
DOI: 10.25403/upresearchdata.27867849
title: Investigation of dietary practices of mother-child pairs in rural and urban settings in South Africa during the COVID-19 pandemic

description: This study undertaken is a descriptive, quantitative, cross-sectional study. In line with the study's Aims, the research collected data on dietary practices and nutritional statuses of mothers and children aged 6-23 months. Dietary practices were determined using the 24-hour recall, which was used to determine the nutrient intakes of mothers and children and food group intakes to determine the minimum dietary diversity. Additionally, breastfeeding practices were determined using a structured questionnaire. The nutritional status was determined using anthropometric measurements including; weight, height or length for children and mid-upper arm circumference (MUAC). Weight and height are used to determine the body mass index (BMI) for mothers and to compute Z-scores in line with the World Health Organization's growth charts for children. The data derived from these investigations provided information on the proportions of stunted, wasted and underweight children as well as underweight and overweight/obese mothers. Nutritional data sheds light on the average (mean) intakes and standard deviations of the essential nutrients necessary to sustain health during the COVID-19 pandemic
DOI: 10.25403/upresearchdata.27867849.v1
title: Investigation of dietary practices of mother-child pairs in rural and urban settings in South Africa during the COVID-19 pandemic

description: This study undertaken is a descriptive, quantitative, cross-sectional study. In line with the study's Aims, the research collected data on dietary practices and nutritional statuses of mothers and children aged 6-23 months. Dietary practices were determined using the 24-hour recall, which was used to determine the nutrient intakes of mothers and children and food group intakes to determine the minimum dietary diversity. Additionally, breastfeeding practices were determined using a structured questionnaire. The nutritional status was determined using anthropometric measurements including; weight, height or length for children and mid-upper arm circumference (MUAC). Weight and height are used to determine the body mass index (BMI) for mothers and to compute Z-scores in line with the World Health Organization's growth charts for children. The data derived from these investigations provided information on the proportions of stunted, wasted and underweight children as well as underweight and overweight/obese mothers. Nutritional data sheds light on the average (mean) intakes and standard deviations of the essential nutrients necessary to sustain health during the COVID-19 pandemic
DOI: 10.5281/zenodo.14197487
title: Example Dataset

description: This dataset contains multiple files uploaded programmatically.
DOI: 10.5281/zenodo.14196672
title: Example Dataset

description: This is an example dataset created via the Zenodo API.
DOI: 10.5281/zenodo.14196627
title: Ejemplo de Dataset

description: Este es un ejemplo de dataset con metadatos.
DOI: 10.15468/dl.8xj3xn
title: Occurrence Download

description: A dataset containing 132991 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "StateProvince is JiangSu",
 "Year 2019-2024"
 ]
}

 The dataset includes 132991 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033027-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ke3dsk
title: Occurrence Download

description: A dataset containing 678 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Metlapilcoatlus borealis Tepos-Ramírez, Flores-Villela, Velasco, Lara, García-Rubio & Jadin, 2021"
 ]
}

 The dataset includes 678 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033035-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y4vc3t
title: Occurrence Download

description: A dataset containing 353 records in GBIF matching the query:
 SELECT "year", GBIF_EEARGCode( 1000, decimalLatitude, decimalLongitude, COALESCE(coordinateUncertaintyInMeters, 1000) ) AS eeaCellCode, classKey, class, speciesKey, species, COUNT(*) AS occurrences, MIN(COALESCE(coordinateUncertaintyInMeters, 1000)) AS minCoordinateUncertaintyInMeters, MIN(GBIF_TemporalUncertainty(eventDate)) AS minTemporalUncertainty, IF(ISNULL(classKey), NULL, SUM(COUNT(*)) OVER (PARTITION BY classKey)) AS classCount FROM occurrence WHERE occurrenceStatus = 'PRESENT' AND speciesKey IN ( 1314773 ) AND continent = 'EUROPE' AND "year" &gt;= 1900 AND hasCoordinate = TRUE AND speciesKey IS NOT NULL AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') GROUP BY "year", eeaCellCode, classKey, class, speciesKey, species ORDER BY "year" DESC, eeaCellCode ASC, speciesKey ASC; 

 The dataset's 353 records were derived from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033020-241107131044228/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.24088407
title: Data accompanying the manuscript The silence of the LLMs: Cross-lingual analysis of guardrail-related political bias and false information prevalence in ChatGPT, Google Bard (Gemini), and Bing Chat

description: The dataset of the queries and chatbot responses for the paper The silence of the LLMs: Cross-lingual analysis of guardrail-related political bias and false information prevalence in ChatGPT, Google Bard (Gemini), and Bing Chat
DOI: 10.15468/dl.nwx69j
title: Occurrence Download

description: A dataset containing 81400 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Castor canadensis Kuhl, 1820"
 ]
}

 The dataset includes 81400 records from 189 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033033-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17617/3.ninn3q
title: Data for Combined X-ray Emission Spectroscopy at Phosphorus and Nickel: Detecting Subtle Changes in Catalyst Electronic Structure at High Resolution

description: Data supporting the publication "Combined X-ray Emission Spectroscopy at Phosphorus and Nickel: Detecting Subtle Changes in Catalyst Electronic Structure at High Resolution", including experimental data, calculated data, and ORCA input files.
description: ORCA, 6.0
DOI: 10.17632/jjhykb7v9w.1
title: Comprehensive Mango Leaf Images Dataset for Multi-Class Disease Classification and Automated Plant Disease Detection

description: This dataset contains high-resolution images of mango (Mangifera indica) leaves collected from Rajbari and Pabna districts in Bangladesh. The purpose of this dataset is to support machine learning and deep learning-based classification and detection of various mango leaf diseases. The dataset is organized into six distinct classes based on leaf conditions and visual symptoms.All images were manually collected using a smartphone camera and later resized for efficient processing and analysis. The dataset includes images of healthy leaves as well as five common disease categories: Anthracnose, Dried Leaves, Gall Midge, Powdery Mildew, and Senescent Leaves.
This dataset can be used for training, testing, and validating image classification models aimed at automated plant disease detection to assist farmers and researchers in crop management.

Dataset Classes and Image Counts:
Anthracnose: 500 images
Dried: 500 images
Gall Midge: 500 images
Healthy: 1000 images
Powdery Mildew: 500 images
Senescent Leaves: 1000 images

Image Details:
Original image resolution: 4096 x 3072 pixels
Resized image resolution: 480 x 560 pixels
Image format: JPG
Color mode: RGB
Collection device: Smartphone camera
Location: Rajbari and Pabna districts, Bangladesh
DOI: 10.17632/jnrdksfsyp
title: PITA Data

description: Datasets for LJ-13, Alanine Dipeptide and Alanine Tripeptide experiments for Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities (https://www.arxiv.org/abs/2506.16471)
DOI: 10.17632/jnrdksfsyp.1
title: PITA Data

description: Datasets for LJ-13, Alanine Dipeptide and Alanine Tripeptide experiments for Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities (https://www.arxiv.org/abs/2506.16471)
DOI: 10.15468/dl.w6tj7e
title: Occurrence Download

description: A dataset containing 7013 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is one of (Oligoryzomys andinus (Osgood, 1914), Merganetta armata Gould, 1842)"
 ]
}

 The dataset includes 7013 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069865-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0209532.10fu
title: SH0209532.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209533.10fu
title: SH0209533.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209534.10fu
title: SH0209534.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209535.10fu
title: SH0209535.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209536.10fu
title: SH0209536.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209537.10fu
title: SH0209537.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209538.10fu
title: SH0209538.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0209539.10fu
title: SH0209539.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.