cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/y8g-fw2-f6t
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/75n-n8q-h37
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8mr-egs-3hb
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fcx-7jx-h80
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hbf-lkn-zp2
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/498-ymz-2zx
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j31-jhf-eaa
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.ejj962
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Salvia biserrata",
 "Year 1000-2025"
 ]
}

 The dataset includes 9 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023703-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/efp-z66-v5y
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/r0l-rv9-cwf
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.ckqk6j
title: Occurrence Download

description: A dataset containing 848 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cylindropuntia molesta"
 ]
}

 The dataset includes 848 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035601-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.abquas
title: Occurrence Download

description: A dataset containing 854404 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prunus spinosa"
 ]
}

 The dataset includes 854404 records from 3499 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035494-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cd4edu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Osmeriformes",
 "Geometry POLYGON ((26.9648 -16.1504, 17.9648 -16.1504, 17.9648 -25.1504, 26.9648 -25.1504, 26.9648 -16.1504))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008228-250525065834625/datasets/export for details.
DOI: 10.15468/dl.9tbay2
title: Occurrence Download

description: A dataset listing the 11 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies nordmanniana (Steven) Spach"
 ]
}

 The dataset's 11 records were derived from 298 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011013-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/sa3n-sdqw
title: Measurement of Ozone at Puy de Dôme

description: Measurement of Ozone at Puy de Dôme
DOI: 10.48597/wrz5-jjpz
title: Measurement of Ozone at Puy de Dôme

description: Measurement of Ozone at Puy de Dôme
DOI: 10.17632/jcpfjnk5c2.1
title: UrSEC: A Comprehensive Corpus for Emotion Recognition in Urdu Speech

description: Speech Emotion Recognition (SER) is a rapidly evolving field of research aimed at identifying and categorizing emotional states through the analysis of speech signals. As SER holds significant socio-cultural and commercial importance, researchers are increasingly leveraging machine learning and deep learning techniques to drive advancements in this domain. A high-quality dataset is an essential resource for SER studies in any language. Despite Urdu being the 10th most spoken language globally, there is a significant lack of robust SER datasets, creating a research gap. Existing Urdu SER datasets are often limited by their small size, narrow emotional range, and repetitive content, reducing their applicability in real-world scenarios. To address this gap, the Urdu Speech Emotion Corpus (UrSEC) was developed. This comprehensive dataset includes 3500 Urdu speech signals sourced from 10 professional actors, with an equal representation of male and female speakers from diverse age groups. The dataset encompasses seven emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad. The speech samples were curated from a wide collection of Pakistani Urdu drama serials and telefilms available on YouTube, ensuring diversity and natural delivery. Unlike conventional datasets, which rely on predefined dialogs recorded in controlled environments, UrSEC features unique and contextually varied utterances, making it more realistic and applicable for practical applications. To ensure balance and consistency, the dataset contains 500 samples per emotional class, with 50 samples contributed by each actor for each emotion. Additionally, an accompanying Excel file provides detailed metadata for each recording, including the file name, duration, format, sample rate, actor details, emotional state, and corresponding Urdu dialog. This metadata enables researchers to efficiently organize and utilize the dataset for their specific needs. The UrSEC dataset underwent rigorous validation, integrating expert evaluation and model-based validation to ensure its reliability, accuracy, and overall suitability for advancing research and development in Urdu Speech Emotion Recognition.
DOI: 10.17632/jcpfjnk5c2.2
title: UrduSER: A comprehensive dataset for speech emotion recognition in Urdu language

description: Speech Emotion Recognition (SER) is a rapidly evolving field of research aimed at identifying and categorizing emotional states through the analysis of speech signals. As SER holds significant socio-cultural and commercial importance, researchers are increasingly leveraging machine learning and deep learning techniques to drive advancements in this domain. A high-quality dataset is an essential resource for SER studies in any language. Despite Urdu being the 10th most spoken language globally, there is a significant lack of robust SER datasets, creating a research gap. Existing Urdu SER datasets are often limited by their small size, narrow emotional range, and repetitive content, reducing their applicability in real-world scenarios. To address this gap, the Urdu Speech Emotion Corpus (UrSEC) was developed. This comprehensive dataset includes 3500 Urdu speech signals sourced from 10 professional actors, with an equal representation of male and female speakers from diverse age groups. The dataset encompasses seven emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad. The speech samples were curated from a wide collection of Pakistani Urdu drama serials and telefilms available on YouTube, ensuring diversity and natural delivery. Unlike conventional datasets, which rely on predefined dialogs recorded in controlled environments, UrSEC features unique and contextually varied utterances, making it more realistic and applicable for practical applications. To ensure balance and consistency, the dataset contains 500 samples per emotional class, with 50 samples contributed by each actor for each emotion. Additionally, an accompanying Excel file provides detailed metadata for each recording, including the file name, duration, format, sample rate, actor details, emotional state, and corresponding Urdu dialog. This metadata enables researchers to efficiently organize and utilize the dataset for their specific needs. The UrSEC dataset underwent rigorous validation, integrating expert evaluation and model-based validation to ensure its reliability, accuracy, and overall suitability for advancing research and development in Urdu Speech Emotion Recognition.
DOI: 10.17632/jcpfjnk5c2.3
title: UrduSER: A Dataset for Urdu Speech Emotion Recognition

description: Speech Emotion Recognition (SER) is a rapidly evolving field of research aimed at identifying and categorizing emotional states through the analysis of speech signals. As SER holds significant socio-cultural and commercial importance, researchers are increasingly leveraging machine learning and deep learning techniques to drive advancements in this domain. A high-quality dataset is an essential resource for SER studies in any language. Despite Urdu being the 10th most spoken language globally, there is a significant lack of robust SER datasets, creating a research gap. Existing Urdu SER datasets are often limited by their small size, narrow emotional range, and repetitive content, reducing their applicability in real-world scenarios. To address this gap, the Urdu Speech Emotion Corpus (UrSEC) was developed. This comprehensive dataset includes 3500 Urdu speech signals sourced from 10 professional actors, with an equal representation of male and female speakers from diverse age groups. The dataset encompasses seven emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad. The speech samples were curated from a wide collection of Pakistani Urdu drama serials and telefilms available on YouTube, ensuring diversity and natural delivery. Unlike conventional datasets, which rely on predefined dialogs recorded in controlled environments, UrSEC features unique and contextually varied utterances, making it more realistic and applicable for practical applications. To ensure balance and consistency, the dataset contains 500 samples per emotional class, with 50 samples contributed by each actor for each emotion. Additionally, an accompanying Excel file provides detailed metadata for each recording, including the file name, duration, format, sample rate, actor details, emotional state, and corresponding Urdu dialog. This metadata enables researchers to efficiently organize and utilize the dataset for their specific needs. The UrSEC dataset underwent rigorous validation, integrating expert evaluation and model-based validation to ensure its reliability, accuracy, and overall suitability for advancing research and development in Urdu Speech Emotion Recognition.
DOI: 10.17632/jcpfjnk5c2.4
title: UrduSER: A Dataset for Urdu Speech Emotion Recognition

description: Speech Emotion Recognition (SER) is a rapidly evolving field of research aimed at identifying and categorizing emotional states through the analysis of speech signals. As SER holds significant socio-cultural and commercial importance, researchers are increasingly leveraging machine learning and deep learning techniques to drive advancements in this domain. A high-quality dataset is an essential resource for SER studies in any language. Despite Urdu being the 10th most spoken language globally, there is a significant lack of robust SER datasets, creating a research gap. Existing Urdu SER datasets are often limited by their small size, narrow emotional range, and repetitive content, reducing their applicability in real-world scenarios. To address this gap, the Urdu Speech Emotion Recognition (UrduSER) was developed. This comprehensive dataset includes 3500 Urdu speech signals sourced from 10 professional actors, with an equal representation of male and female speakers from diverse age groups. The dataset encompasses seven emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad. The speech samples were curated from a wide collection of Pakistani Urdu drama serials and telefilms available on YouTube, ensuring diversity and natural delivery. Unlike conventional datasets, which rely on predefined dialogs recorded in controlled environments, UrduSER features unique and contextually varied utterances, making it more realistic and applicable for practical applications. To ensure balance and consistency, the dataset contains 500 samples per emotional class, with 50 samples contributed by each actor for each emotion. Additionally, an accompanying Excel file provides detailed metadata for each recording, including the file name, duration, format, sample rate, actor details, emotional state, and corresponding Urdu dialog. This metadata enables researchers to efficiently organize and utilize the dataset for their specific needs. The UrduSER dataset underwent rigorous validation, integrating expert evaluation and model-based validation to ensure its reliability, accuracy, and overall suitability for advancing research and development in Urdu Speech Emotion Recognition.
DOI: 10.5285/5934d2a5706c4a3c9caa15188d9ed24b
title: Met Office Cardington: vertical profile measurements from Vaisala radiosonde ascents, 1996-2024

description: This repository provides data from all radiosondes launched at Cardington between 1996-2024.
The sonde unit was operated by the Met Office Observation-based research Boundary Layer Facility, at the semi-rural field site (18 Ha) of Cardington (52° 06′ N, 00° 25′ W, 29 m ± 1 m amsl) in central-southern England.

Sonde launches were performed with a mean ascent rate of 2.5 m s-1. The slower ascent rate compared to an operational sonde ascent rate enables improved vertical sampling resolution in the atmospheric boundary layer whilst maintaining a sufficient ventilation rate over the sensors. From 1996-2002, a RS80 device was used with ThermoCap, HumiCap, BaroCap sensors, and between 2006-2014 a RS92-SGPB device was used with ThermoCap, HumiCap, capacitive pressure sensors. Sonde launches from 2014-2024 used an RS41-SG(P) device with PRT, silicon capacitive pressure (SGP), and HumiCap sensors.

The launches of radiosondes were performed on a project related basis only and all available ascent data has been provided from the Cardington facility. Please note that there were no ascents in 2022.

A full list of NetCDF variables can be found in “A continuous hydrometeorological record (2004–2024) at the Met Office surface site of Cardington, UK.” Osborne et al. (2025). This paper should be referenced in any research/publications pertaining to this dataset.

To ensure optimal traceability and transparency of data, comprehensive metadata is included.
DOI: 10.5281/zenodo.17591320
title: Dataset to "Mesoporous Carbon Thin Films with Large Mesopores as Model Material for Electrochemical Applications"

description: This collection contains the raw data to the Advanced-Functional-Materials article "Mesoporous Carbon Thin Films with Large Mesopores as Model Material for Electrochemical Applications" (DOI: 10.1002/adfm.202521031).
DOI: 10.5281/zenodo.17591319
title: Dataset to "Mesoporous Carbon Thin Films with Large Mesopores as Model Material for Electrochemical Applications"

description: This collection contains the raw data to the Advanced-Functional-Materials article "Mesoporous Carbon Thin Films with Large Mesopores as Model Material for Electrochemical Applications" (DOI: 10.1002/adfm.202521031).
DOI: 10.5281/zenodo.14205511
title: Unveiling Paris's Future Climate: Urban Heat Island and Temperature Extremes with Deep Learning Downscaling

description: Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available.
DOI: 10.5281/zenodo.17522415
title: Unveiling Paris's Future Climate: Urban Heat Island and Temperature Extremes with Deep Learning Downscaling

description: Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available.
DOI: 10.5281/zenodo.14706526
title: Data and code for Chakraborty T., et al. (2025), American Journal of Biological Anthropology

description: Data and R code used for the study Exploring the relationship between mandibular morphology, dental eruption and chronological age in modern human juveniles through geometric morphometrics, by Chakraborty, T., Arnaud, J., and Buzi, C.
DOI: 10.5281/zenodo.15268324
title: Table 1 in A new Late Viséan ammonoid assemblage from the Anti-Atlas (Early Carboniferous, Morocco)

description: Table 1. Conch dimensions and ratios of selected specimens of Xenoglyphioceras eidos gen. et sp. nov. from the Gara el Itima.
dmwwwhuwahww/dmww/whuw/dmWERIZRMB.C.32198.161.023.325.011.910.80.380.930.201.480.5751.020.820.08.6–0.411.040.17––MB.C.32198.237.718.517.54.96.30.491.060.131.440.6432.017.014.83.3–0.531.150.10––
description: Published as part of Korn, Dieter, 2025, A new Late Viséan ammonoid assemblage from the Anti-Atlas (Early Carboniferous, Morocco), pp. 257 in European Journal of Taxonomy 987 on page 257, DOI: 10.5852/ejt.2025.987.2873, http://zenodo.org/record/15260621
DOI: 10.15468/dl.tmnz2v
title: Occurrence Download

description: A dataset containing 13584 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lanius minor Gmelin, 1788",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 13584 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004488-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pvctdu
title: Occurrence Download

description: A dataset containing 460924 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((6.51932 42.66987,20.92713 42.66987,20.92713 49.08524,6.51932 49.08524,6.51932 42.66987))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (CR, EN, VU, NT, NE)"
 ]
}

 The dataset includes 460924 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004470-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rwzt87
title: Occurrence Download

description: A dataset containing 4946 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lanius nubicus Lichtenstein, 1823",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 4946 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004490-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d9wzsp
title: Occurrence Download

description: A dataset containing 1029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lanius phoenicuroides (Schalow, 1875)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 1029 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004491-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gegf2w
title: Occurrence Download

description: A dataset containing 460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Myrmica fracticornis Forel, 1901",
 "Country is Canada",
 {
 "not" : {
 "InstitutionCode" : [
 "is one of (iNaturalist)"
 ]
 }
 }
 ]
}

 The dataset includes 460 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004492-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9yqjk8
title: Occurrence Download

description: A dataset containing 3208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Myanmar, Viet Nam, Cambodia, Indonesia, Nepal, Philippines, Malaysia, Chinese Taipei, Bangladesh, Lao People’s Democratic Republic, Thailand, Bhutan)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Leymus Hochst."
 ]
}

 The dataset includes 3208 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004494-250515123054153/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.27857970
title: Spatiotemporal variation in the dissolved black carbon concentrations in three estuaries

description: This dataset contains the following data: sampling date, sampling locations, sample depths, salinity, total suspended matter (TSM) concentration; dissolved black carbon; and the deviation of dissolved black carbon to the conservative mixing between fresh and saline end-members.
DOI: 10.15468/dl.2qpbm4
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Oncorhynchus chrysogaster (Needham & Gard, 1964)"
 ]
}

 The dataset includes 151 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004496-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kfp32e
title: Occurrence Download

description: A dataset containing 14350 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lanius schach Linnaeus, 1758",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 14350 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004497-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nke6pd
title: Occurrence Download

description: A dataset containing 27663 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Lepista nuda (Bull.) Cooke",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 27663 records from 383 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004498-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.