cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3sdcvd
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aristolochia haitiensis Ekman & O.C.Schmidt"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050276-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dybnfy
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((93.65076 -9.41122,123.34924 -9.41122,123.34924 11.41122,93.65076 11.41122,93.65076 -9.41122))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Adina eurhyncha (Miq.) Å.Krüger & Löfstrand"
 ]
}

 The dataset includes 53 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068279-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5xe673
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gonocephalus sophiae (Gray, 1845))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 154 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052498-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n96wtc
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lupinus muelleri Standl."
 ]
}

 The dataset includes 51 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014726-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v5c4hp
title: Occurrence Download

description: A dataset containing 26942 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Terathopius ecaudatus",
 "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 26942 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0087019-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u2aqev
title: Occurrence Download

description: A dataset containing 19379798 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.64347 35.95021,19.1482 29.94512,34.20685 30.57958,36.77784 35.8014,29.43216 41.39381,20.25006 42.07898,12.90438 45.90828,3.35499 43.69351,-5.64347 35.95021))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2011-2023"
 ]
}

 The dataset includes 19379798 records from 2473 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060814-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25935/7t8g-d241
title: ORN NDA NewRoutine Jupiter EDR FITS data collection

description: This data collection is composed of daily decametric observations of the Sun acquired by the Nançay Decameter Array (NDA) with its NewRoutine (NR) receiver at the Observatoire Radioastronomique de Nançay (ORN) in Nançay, France. On top of scientific measurements, the dataset also includes pointing and instrumental setup parameters.
DOI: 10.15468/dl.re6jk8
title: Occurrence Download

description: A dataset containing 22939 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Isolepis fluitans, Baeothryon fluitans, Clavula fluitans, Cyperus fluitans, Dichostylis fluitans, Eleocharis fluitans, Eleogiton fluitans, Heleogiton fluitans, Heleogiton fluitans, Schoenoplectus fluitans)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 22939 records from 384 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000290-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbpgve
title: Occurrence Download

description: A dataset containing 324 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidium chichicara"
 ]
}

 The dataset includes 324 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000291-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6q6vnp
title: Occurrence Download

description: A dataset containing 328 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Colletotrichum siamense"
 ]
}

 The dataset includes 328 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000292-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqq9hk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brachiaria dictyoneura",
 "Country is Fiji",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000294-250717081556266/datasets/export for details.
DOI: 10.15468/dl.usf7ey
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brachiaria dictyoneura",
 "Country is Brazil",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000293-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jfg2ps
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brachiaria dictyoneura",
 "Country is Niue",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000295-250717081556266/datasets/export for details.
DOI: 10.6084/m9.figshare.29589686
title: Jurnal_Kepuasan_Sobat_IndiHome_Kuantitatif.pdf

description: Sobat IndiHome adalah aplikasi layanan pelanggan digital milik PT Telkom Indonesia yang digunakan untuk membantu pelanggan dalam memantau, melaporkan, dan mengelola layanan internet rumah. Namun, kualitas jaringan serta respon aplikasi masih menjadi keluhan utama pengguna. Penelitian ini bertujuan untuk menganalisis tingkat kepuasan pengguna terhadap jaringan aplikasi Sobat IndiHome melalui pendekatan kuantitatif. Data dikumpulkan menggunakan kuesioner yang disebarkan kepada 100 responden pengguna aktif aplikasi Sobat IndiHome. Analisis data dilakukan dengan metode statistik deskriptif dan regresi linier sederhana. Hasil menunjukkan bahwa kualitas jaringan (X) berpengaruh signifikan terhadap tingkat kepuasan pengguna (Y) dengan nilai R² sebesar 0,62. Temuan ini menunjukkan bahwa semakin baik kualitas jaringan dan respons sistem aplikasi, semakin tinggi kepuasan pengguna.<br>
DOI: 10.6084/m9.figshare.29589686.v1
title: Jurnal_Kepuasan_Sobat_IndiHome_Kuantitatif.pdf

description: Sobat IndiHome adalah aplikasi layanan pelanggan digital milik PT Telkom Indonesia yang digunakan untuk membantu pelanggan dalam memantau, melaporkan, dan mengelola layanan internet rumah. Namun, kualitas jaringan serta respon aplikasi masih menjadi keluhan utama pengguna. Penelitian ini bertujuan untuk menganalisis tingkat kepuasan pengguna terhadap jaringan aplikasi Sobat IndiHome melalui pendekatan kuantitatif. Data dikumpulkan menggunakan kuesioner yang disebarkan kepada 100 responden pengguna aktif aplikasi Sobat IndiHome. Analisis data dilakukan dengan metode statistik deskriptif dan regresi linier sederhana. Hasil menunjukkan bahwa kualitas jaringan (X) berpengaruh signifikan terhadap tingkat kepuasan pengguna (Y) dengan nilai R² sebesar 0,62. Temuan ini menunjukkan bahwa semakin baik kualitas jaringan dan respons sistem aplikasi, semakin tinggi kepuasan pengguna.<br>
DOI: 10.5281/zenodo.16027045
title: North America Oral Care Market 2025 To 2034

description: North America Oral Care Market Size, Trends and Insights By Product Type (Toothbrush, Manual, Electric (Rechargeable), Battery-powered (Non-rechargeable), Others, Toothpaste, Gel, Polish, Paste, Powder, Mouthwash/Rinse, Medicated, Non-medicated, Denture Products, Cleaners, Fixatives, Floss, Others, Dental Accessories, Cosmetic Whitening Products, Fresh Breath Dental Chewing Gum, Tongue Scrapers, Fresh Breath Strips, Others, Oral Irrigators, Mouth Freshener Sprays), By Age Group (Kids, Adults, Geriatric), By Type (Countertop, Cordless), By Application (Home, Dentistry), By Sales Channel (Hypermarkets/Supermarkets, Specialty Stores, Drug Stores &Pharmacies, Convenience Stores, Online Sales Channel), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

As per the North America Oral Care Market analysis conducted by the CMI Team, the North America Oral Care Market is expected to record a CAGR of 5.67% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 13.89 Billion. By 2034, the valuation is anticipated to reach USD 22.97 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=71178
DOI: 10.5281/zenodo.16027046
title: North America Oral Care Market 2025 To 2034

description: North America Oral Care Market Size, Trends and Insights By Product Type (Toothbrush, Manual, Electric (Rechargeable), Battery-powered (Non-rechargeable), Others, Toothpaste, Gel, Polish, Paste, Powder, Mouthwash/Rinse, Medicated, Non-medicated, Denture Products, Cleaners, Fixatives, Floss, Others, Dental Accessories, Cosmetic Whitening Products, Fresh Breath Dental Chewing Gum, Tongue Scrapers, Fresh Breath Strips, Others, Oral Irrigators, Mouth Freshener Sprays), By Age Group (Kids, Adults, Geriatric), By Type (Countertop, Cordless), By Application (Home, Dentistry), By Sales Channel (Hypermarkets/Supermarkets, Specialty Stores, Drug Stores &Pharmacies, Convenience Stores, Online Sales Channel), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034.

Reports Description

As per the North America Oral Care Market analysis conducted by the CMI Team, the North America Oral Care Market is expected to record a CAGR of 5.67% from 2025 to 2034. In 2025, the market size is projected to reach a valuation of USD 13.89 Billion. By 2034, the valuation is anticipated to reach USD 22.97 Billion.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=71178
DOI: 10.6084/m9.figshare.29588849
title: A sample set of original and generated images using the 14 cluster model.

description: Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diﬀusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diﬀusion model in the healthcare domain. Specifically, we trained a latent diﬀusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diﬀusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.
DOI: 10.6084/m9.figshare.29588849.v1
title: A sample set of original and generated images using the 14 cluster model.

description: Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diﬀusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diﬀusion model in the healthcare domain. Specifically, we trained a latent diﬀusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diﬀusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.
DOI: 10.15468/dl.6zt8m9
title: Occurrence Download

description: A dataset containing 5941 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lathyrus linifolius linifolius, Lathyrus linifolius montanus, Lathyrus linifolius variegatus, Lathyrus linifolius montanus, Lathyrus linifolius tenuifolius, Lathyrus linifolius varifolius, Lathyrus linnaei, Lathyrus linnaei orientalis, Lathyrus luteus, Lathyrus macrorrhizus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 5941 records from 208 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000296-250717081556266/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.29588807
title: Trained model parameters.

description: Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diﬀusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diﬀusion model in the healthcare domain. Specifically, we trained a latent diﬀusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diﬀusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.
DOI: 10.6084/m9.figshare.29588807.v1
title: Trained model parameters.

description: Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diﬀusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diﬀusion model in the healthcare domain. Specifically, we trained a latent diﬀusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diﬀusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.
DOI: 10.15468/dl.496b8c
title: Occurrence Download

description: A dataset containing 187 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidium flexicaule"
 ]
}

 The dataset includes 187 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000299-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3pamnf
title: Occurrence Download

description: A dataset containing 757 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Gentiana straminea"
 ]
}

 The dataset includes 757 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000300-250717081556266/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.29588651
title: Training dataset. Randomly cropped patches from histopathological images of KPC mice.

description: Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diﬀusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diﬀusion model in the healthcare domain. Specifically, we trained a latent diﬀusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diﬀusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.
DOI: 10.6084/m9.figshare.29588651.v1
title: Training dataset. Randomly cropped patches from histopathological images of KPC mice.

description: Generative image models have revolutionized artificial intelligence by enabling the synthesis of high-quality, realistic images. These models utilize deep learning techniques to learn complex data distributions and generate novel images that closely resemble the training dataset. Recent advancements, particularly in diﬀusion models, have led to remarkable improvements in image fidelity, diversity, and controllability. In this work, we investigate the application of a conditional latent diﬀusion model in the healthcare domain. Specifically, we trained a latent diﬀusion model using unlabeled histopathology images. Initially, these images were embedded into a lower-dimensional latent space using a Vector Quantized Generative Adversarial Network (VQ-GAN). Subsequently, a diffusion process was applied within this latent space, and clustering was performed on the resulting latent features. The clustering results were then used as a conditioning mechanism for the diﬀusion model, enabling conditional image generation. Finally, we determined the optimal number of clusters using cluster validation metrics and assessed the quality of the synthetic images through quantitative methods. To enhance the interpretability of the synthetic image generation process, expert input was incorporated into the cluster assignments.
DOI: 10.15468/dl.wax6t9
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidium santacruzensis"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000297-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bfv58t
title: Occurrence Download

description: A dataset containing 248 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalaris canariensis",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 248 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000302-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.