cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29143817.v1
title: Deep learning computer vision system for estimating sheep age using teeth images

description: This study explores the use of deep learning neural networks and transfer learning to estimate the age of sheep from their dental images. This is an important aspect of agriculture for meat quality, animal welfare, breeding, and health management. Using cutting-edge techniques, MobileNet, ResNet50, and ResNet102, we compare two deep learning approaches: fine-tuning and feature extraction using the pre-trained version of these models as part of our investigation. We collected 540 images of sheep from nearby farms, concentrating on three age groups: young, middle-aged, and elderly, for the purpose of our study. With an interesting recognition accuracy of 96.9%, the experimental results demonstrate that ResNet102 is the best performer both when fine-tuned and when employing its deep features that are retrieved from its pre-trained version. These findings highlight how cutting-edge machine learning techniques have the potential to completely transform long-standing methods in the sheep sector and pave the way for developing a novel mobile application that improves economic outcomes and cultural conformity concerning sheep age recognition.
DOI: 10.6084/m9.figshare.30686537
title: MFITN-E2NetGA: a transformer-based multi-level fusion framework for multi-class respiratory disease classification from lung sounds

description: Respiratory diseases are among the most prevalent health challenges worldwide, and timely detection is critical for improving patient outcomes. Auscultation of lung sounds is often the first diagnostic step but heavily relies on the clinician’s expertise, which may lead to variability in assessments. Automating this process can enhance diagnostic efficiency and reliability. This study introduces an advanced Artificial Intelligence (AI) approach to improve lung sound classification by extracting relevant acoustic features and learning their relationships with various respiratory conditions. We propose a novel deep-learning pipeline using pulmonary sound data to classify multiple respiratory disorders. Three complementary audio representations—Mel-Frequency Cepstral Coefficients (MFCCs), Mel Spectrograms, and Cochleograms—are employed to capture time-frequency and perceptual characteristics of lung sounds. A Multi-level Feature Integration Transformer Network (MFITN) is developed to efficiently integrate these heterogeneous features through transformer-based attention mechanisms across abstraction layers. The fused representation is processed by our customized classifier, E2Net-GA—an enhanced EfficientNetV2 model augmented with a Global Attention Mechanism (GAM) and Lightweight Attention Network (LAN) modules. On benchmark datasets, the MFITN-E2Net-GA framework achieved superior performance: for the ICBHI−2017 dataset, accuracy of 98.75%, F1-score of 98.35%, precision of 98.10%, specificity of 97.45%, and recall of 98.75%; for another lung sound dataset, accuracy of 98.95%, F1-score of 98.48%, precision of 98.16%, specificity of 99.36%, and recall of 98.90%. By effectively capturing diverse acoustic features, the proposed multimodal approach enhances diagnostic accuracy, supporting early identification of lung diseases and contributing to improved clinical decision-making and patient care.
DOI: 10.6084/m9.figshare.30686537.v1
title: MFITN-E2NetGA: a transformer-based multi-level fusion framework for multi-class respiratory disease classification from lung sounds

description: Respiratory diseases are among the most prevalent health challenges worldwide, and timely detection is critical for improving patient outcomes. Auscultation of lung sounds is often the first diagnostic step but heavily relies on the clinician’s expertise, which may lead to variability in assessments. Automating this process can enhance diagnostic efficiency and reliability. This study introduces an advanced Artificial Intelligence (AI) approach to improve lung sound classification by extracting relevant acoustic features and learning their relationships with various respiratory conditions. We propose a novel deep-learning pipeline using pulmonary sound data to classify multiple respiratory disorders. Three complementary audio representations—Mel-Frequency Cepstral Coefficients (MFCCs), Mel Spectrograms, and Cochleograms—are employed to capture time-frequency and perceptual characteristics of lung sounds. A Multi-level Feature Integration Transformer Network (MFITN) is developed to efficiently integrate these heterogeneous features through transformer-based attention mechanisms across abstraction layers. The fused representation is processed by our customized classifier, E2Net-GA—an enhanced EfficientNetV2 model augmented with a Global Attention Mechanism (GAM) and Lightweight Attention Network (LAN) modules. On benchmark datasets, the MFITN-E2Net-GA framework achieved superior performance: for the ICBHI−2017 dataset, accuracy of 98.75%, F1-score of 98.35%, precision of 98.10%, specificity of 97.45%, and recall of 98.75%; for another lung sound dataset, accuracy of 98.95%, F1-score of 98.48%, precision of 98.16%, specificity of 99.36%, and recall of 98.90%. By effectively capturing diverse acoustic features, the proposed multimodal approach enhances diagnostic accuracy, supporting early identification of lung diseases and contributing to improved clinical decision-making and patient care.
DOI: 10.6084/m9.figshare.30158499
title: Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security

description: Cloud computing (CC) delivers processing power and data storage on demand. It is one of the most significant computer science technologies, contributing to healthcare, industry, and the Internet of Things. One of CC's biggest security concerns are intrusion detection and separating harmful from legitimate communication, similar to computer networks. Although a wide range of intrusion detection systems is available today, they often suffer from misclassification issues, where the system can fail to recognize an attack as a threat or to mark normal traffic as malicious. This research proposes classifying network traffic using a convolutional neural network and extreme gradient boosting model. Additionally, a modified sine cosine algorithm is used to tune model hyperparameters for optimal performance. The presented framework was tested on major real-world TON IoT intrusion detection datasets. The proposed optimizer is compared to many recent metaheuristics in a matched experimental setting. The simulation results show that the suggested technique is superior to other methods for both datasets, with the best-performing optimized models achieving an accuracy of 96.667 on Windows 10 and 98.6731 on Windows 7 simulation.
DOI: 10.6084/m9.figshare.30158499.v1
title: Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security

description: Cloud computing (CC) delivers processing power and data storage on demand. It is one of the most significant computer science technologies, contributing to healthcare, industry, and the Internet of Things. One of CC's biggest security concerns are intrusion detection and separating harmful from legitimate communication, similar to computer networks. Although a wide range of intrusion detection systems is available today, they often suffer from misclassification issues, where the system can fail to recognize an attack as a threat or to mark normal traffic as malicious. This research proposes classifying network traffic using a convolutional neural network and extreme gradient boosting model. Additionally, a modified sine cosine algorithm is used to tune model hyperparameters for optimal performance. The presented framework was tested on major real-world TON IoT intrusion detection datasets. The proposed optimizer is compared to many recent metaheuristics in a matched experimental setting. The simulation results show that the suggested technique is superior to other methods for both datasets, with the best-performing optimized models achieving an accuracy of 96.667 on Windows 10 and 98.6731 on Windows 7 simulation.
DOI: 10.6084/m9.figshare.28675370
title: Traits data of nestlings

description: Traits data of nestlings used in this study.
DOI: 10.6084/m9.figshare.28675370.v3
title: Traits data of nestlings

description: Traits data of nestlings used in this study.
DOI: 10.6084/m9.figshare.28675370.v4
title: Traits data of nestlings

description: Traits data of nestlings used in this study.
DOI: 10.15468/dl.hajxfj
title: Occurrence Download

description: A dataset containing 22987 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sulfitobacter"
 ]
}

 The dataset includes 22987 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059839-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rty35r
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON(((148.960209229542 -35.1737494980451,148.959885002657 -35.1746094181062,148.960916320091 -35.1747278326153,148.961125275452 -35.1747499813944,148.960633543388 -35.175121339286,148.960783353319 -35.17546324725,148.961403432124 -35.1759657976364,148.962282521808 -35.1772897516708,148.964630151001 -35.1760485557633,148.965727077691 -35.1747381778848,148.967097022312 -35.1726881476326,148.966312123451 -35.1728204624119,148.965560858682 -35.1729237744691,148.962442305162 -35.1733786828649,148.960209229542 -35.1737494980451)))"
 ]
}

 The dataset includes 23 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059842-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4zwk8t
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON(((149.191213642675 -35.2299471214001,149.191335482732 -35.2326043815181,149.193374982821 -35.2333262673634,149.194547695372 -35.2340065000697,149.195465470412 -35.2308551699448,149.19558444125 -35.2273705170318,149.195366718835 -35.227344968531,149.195375072122 -35.225099990632,149.19536271056 -35.2247652072027,149.19460658328 -35.2227701694333,149.193298329414 -35.2227876896477,149.192847946935 -35.2228928108543,149.191625480207 -35.2235585753342,149.190295779556 -35.2239615353923,149.189494871567 -35.2247963912335,149.18897581871 -35.2251143995111,149.188244426047 -35.2263671472674,149.188279816015 -35.2267526042245,149.187772559814 -35.2268007862154,149.186395383633 -35.2289488803198,149.185519286139 -35.2294412008459,149.185306089507 -35.2297997746859,149.184868152549 -35.2301076906746,149.18480163048 -35.2301484441738,149.186170876411 -35.2305740906083,149.189341761727 -35.2300986343387,149.18938772492 -35.2296998102687,149.191213642675 -35.2299471214001)))"
 ]
}

 The dataset includes 58 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059840-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zmvwb4
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON(((148.979688474996 -35.1626512081629,148.978053986032 -35.1620733228795,148.976274203209 -35.1612197919253,148.974173112878 -35.16246035412,148.969064399353 -35.1614077432729,148.968834202095 -35.1621563898689,148.969719507652 -35.1631721867885,148.970775661649 -35.1638451452612,148.970884383385 -35.1649244066574,148.970635876562 -35.1663464703929,148.973058818086 -35.1672352476001,148.973586405633 -35.1679334040723,148.979885162598 -35.1689476003032,148.987700640998 -35.1699317665089,148.987700640998 -35.1687336910434,148.984954710328 -35.1669709732106,148.982680473271 -35.1663237156655,148.980237774209 -35.1661309145499,148.979210155983 -35.1657039961672,148.97972591585 -35.1626621074087,148.979688474996 -35.1626512081629)))"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059841-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qgjr8r
title: Occurrence Download

description: A dataset containing 2883 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-6.5 34,12.5 34,12.5 44.8,-6.5 44.8,-6.5 34))",
 "OccurrenceStatus is Present",
 "TaxonKey is Caulerpa cylindracea",
 "Year 2010-2025"
 ]
}

 The dataset includes 2883 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059844-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17977493
title: Global Distribution Characteristics Dataset of Soil Microplastics (2005–2025)

description: The dataset comprises global microplastic distribution characteristics, with 2,611 data entries extracted from 189 published papers between 2005 and 2025. It includes literature titles on microplastic research, countries, sampling sites and geographic locations, microplastic abundance and units, sampling depth, pretreatment temperatures in experiments, dissolution methods, density separation techniques, filter pore sizes, and additionally extracts methods for microplastic identification and observation.
DOI: 10.5281/zenodo.17977494
title: Global Distribution Characteristics Dataset of Soil Microplastics (2005–2025)

description: The dataset comprises global microplastic distribution characteristics, with 2,611 data entries extracted from 189 published papers between 2005 and 2025. It includes literature titles on microplastic research, countries, sampling sites and geographic locations, microplastic abundance and units, sampling depth, pretreatment temperatures in experiments, dissolution methods, density separation techniques, filter pore sizes, and additionally extracts methods for microplastic identification and observation.
DOI: 10.7910/dvn/apyus3
title: FISH FOR LIVELIHOODS (F4L) MIDLINE STUDY DATASET IN MYANMAR

description: A comprehensive dataset was collected in 2024 from 800 households across 6 diverse regions of Myanmar to discover the impact of adopting Better Management Practices (BMPs) on local livelihoods. Data collection focused on assessing socio-economic characteristics, production characteristics, household-level knowledge, attitudes, and practices concerning improved aquaculture techniques.
description: Type: Dataset; Sub-type(s): Survey Data
DOI: 10.5281/zenodo.17983059
title: 血清代谢组的遗传调控及其与2型糖尿病等复杂表型的多组学关联研究
DOI: 10.5281/zenodo.17978045
title: 血清代谢组的遗传调控及其与2型糖尿病等复杂表型的多组学关联研究
DOI: 10.15468/dl.xkda29
title: Occurrence Download

description: A dataset containing 426 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-6.5 34,12.5 34,12.5 44.8,-6.5 44.8,-6.5 34))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Womersleyella setacea",
 "Year 2010-2025"
 ]
}

 The dataset includes 426 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059845-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17982994
title: Fraud, Older Adults, and the Digital Age: A Systematic Literature Review

description: This data contains the protocol, data extraction, quality analysis, and other details of the review.
DOI: 10.15468/dl.rckm7b
title: Occurrence Download

description: A dataset containing 105230 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nerium oleander",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 105230 records from 531 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059830-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c6bnys
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sicyopus multisquamatus de Beaufort, 1912"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0340603-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qggf81
title: Occurrence Download

description: A dataset containing 342 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Salix myricoides Muhl.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 342 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009612-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f472hz
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (BRA.1_1, BRA.22_1)",
 "TaxonKey is Bertholletia Bonpl."
 ]
}

 The dataset includes 101 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0204607-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q8hhw3
title: Occurrence Download

description: A dataset containing 5948 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)",
 "Year 1990-2022"
 ]
}

 The dataset includes 5948 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0172714-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u5gd48
title: Occurrence Download

description: A dataset containing 182154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tragopogon pratensis L., Trametes gibbosa (Pers.) Fr., Trametes hirsute (Fr.) Pilát., 1939, Trametes ochracea (Pers.) Gilb. & Ryvarden)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 182154 records from 685 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0329609-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7gzup
title: Occurrence Download

description: A dataset containing 186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ascodichaena rugosa Butin",
 {
 "or" : [
 "Country is United States of America",
 "Country is Canada"
 ]
 },
 {
 "not" : {
 "DatasetKey" : [
 "is Global database of alien macrofungi"
 ]
 }
 }
 ]
}

 The dataset includes 186 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0102528-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7casvy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lobocheilos tenura Kottelat & Tan, 2008"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0329679-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4scesp
title: Occurrence Download

description: A dataset containing 1513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Saguinus oedipus (Linnaeus, 1758)"
 ]
}

 The dataset includes 1513 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0172701-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jwgqh3
title: Occurrence Download

description: A dataset containing 602 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Spilornis rufipectus",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 602 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084984-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ty37sc
title: Occurrence Download

description: A dataset containing 140 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xenophallus umbratilis (Meek, 1912)"
 ]
}

 The dataset includes 140 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0329723-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d62wck
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sciurus oculatus tolucae Nelson, 1898"
 ]
}

 The dataset includes 11 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027435-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.