cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.acy4vs
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leporinus conirostris"
 ]
}

 The dataset includes 172 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018883-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6jcqy2
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Farsetia robecchiana"
 ]
}

 The dataset includes 41 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018881-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mr9mnq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ziziphus mauritiana",
 "Country is Saint Kitts and Nevis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018884-250711103210423/datasets/export for details.
DOI: 10.15468/dl.vv73ya
title: Occurrence Download

description: A dataset containing 16357 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tolpis staticifolia"
 ]
}

 The dataset includes 16357 records from 195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018885-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fa5gjj
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ziziphus mauritiana",
 "Country is Sudan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 18 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018887-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12479831
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.vsw7hv
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAX5394"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018888-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6druk7
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018889-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.amdyj2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ipomoea lacunosa",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018890-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr39284.v1
title: EDGE SENSE: Energy Decarbonization and GeoEngineering Survey Experiment for New Sampling and Mode Exploration, United States, 2023
title: Version 1

description: EDGE SENSE is a project to test innovations in measuring public opinion on energy decarbonization and geoengineering (also known as climate engineering). By running five surveys in the same timeframe, researchers compared how survey mode and sampling might affect the measurement of public sentiments. The five samples were selected via address-based sampling (ABS), random-digit-dial (RDD), an opt-in panel, Amazon Mechanical Turk (MTurk), and a college classroom. The RDD sample was surveyed over the phone; the rest were surveyed online.
With these five samples, researchers measured two key items. The first is the prioritization of energy decarbonization options. Respondents were asked to rank six options: 

Solar farms
Rooftop solar
On-land wind turbines
Offshore wind turbines
Nuclear
Fossil fuels with carbon capture and storage

The second phenomenon under study is how different online samples respond to an experiment providing information about climate engineering. (The RDD sample was not included in this.)
description: The purpose of this study was to test innovations in measuring public opinion on energy decarbonization and geoengineering (also known as climate engineering).
description: This study includes variables asking respondents about their belief in climate change, their thoughts on different decarbonization options, and demographic variables such as gender, age, race, political party affiliation, and education level.
description: The data contain the following weight variables:
<strong>BASE_WEIGHTS, REGION_WEIGHTS, INCOME_WEIGHTS, PARTY_WEIGHTS, INTER_WEIGHTS,</strong> and <strong>ALL_WEIGHTS</strong>.
description: Response Rates: RDD Sample: 4% 
ABS Sample:

Recruitment Rate - 20.6%
Retention Rate - 79.9%
Survey Completion Rate - 23.6%
Weighted Cumulative Response Rate - 3.9%

College Classroom: 90.7%
Opt-In and MTurk Samples: Not appropriate as there is no defined population.
description: Samplings included address-based sampling, random-digit-dial, opt-in survey panel, Amazon Mechanical Turk, and college classroom.
description: Datasets: 
DS0: Study-Level Files
DS1: ABS Data
DS2: RDD Data
DS3: Opt-In Data
DS4: MTurk Data
DS5: Classroom Data
description: ABS, RDD, Opt-in, and MTurk Samples: Adult noninstitutionalized population of the United States living in households.
College Sample: College students in the United States.. Smallest Geographic Unit: Region
description: computer-assisted telephone interview (CATI); web-based survey
DOI: 10.17632/89kfr84vm3
title: Dataset Automobile Brand Logo Description and Classification

description: This dataset provides a comprehensive overview of 50 top global automotive brands, offering insights into various facets of their identity and market presence, with a particular focus on their logo design elements.

Each entry in the dataset represents a unique automotive brand and includes the following attributes:

1. Auto Brand: The name of the automobile manufacturer (e.g., Audi, Ford, Toyota).
2. Origin: The primary country where the brand originated (e.g., Germany, USA, Japan).
3. Parent / Holding Group: The larger corporate entity that owns the brand, if applicable (e.g., Volkswagen Group, General Motors).
4. Headquarters: The main administrative location of the brand.
5. Establishment Year: The year the brand was established, providing historical context.
6. Units Sold (M): The approximate number of units sold globally in millions, offering an indication of market volume.
7. Logo Description: A textual summary detailing the symbolic meaning and key features of the brand's logo.
8. Logo Design Elements (Binary Flags): These columns indicate the presence (1) or absence (0) of specific design elements within the brand's primary logo:
 A. Abstract Mark: Presence of a non-representational graphic symbol.
 B. Pictorial Mark: Presence of a recognizable image (e.g., animal, object).
 C. Letter Mark: Presence of stylized initials or an acronym.
 D. Wordmark: Presence of the full brand name in a distinctive font.
 E. Combination: Indicates if the logo is a combination of multiple elements (e.g., abstract mark + wordmark).
9. Specific Pictorial Symbolism (Binary Flags): These columns denote the inclusion of particular types of pictorial marks, useful for categorizing symbolic themes:
 A. Birds & Wings: Indicates the presence of bird or wing imagery.
 B. Horses & Horns: Indicates the presence of horse or horn imagery.
 C. Lions & Jaguars: Indicates the presence of lion or jaguar imagery.
 D. Emblem & Shields: Indicates the presence of a crest, badge, or enclosed design.

This rich dataset enables detailed analysis of branding strategies, cultural influences on design, and correlations between visual identity and market performance within the global automotive industry.
DOI: 10.17632/89kfr84vm3.1
title: Dataset Automobile Brand Logo Description and Classification

description: This dataset provides a comprehensive overview of 50 top global automotive brands, offering insights into various facets of their identity and market presence, with a particular focus on their logo design elements.

Each entry in the dataset represents a unique automotive brand and includes the following attributes:

1. Auto Brand: The name of the automobile manufacturer (e.g., Audi, Ford, Toyota).
2. Origin: The primary country where the brand originated (e.g., Germany, USA, Japan).
3. Parent / Holding Group: The larger corporate entity that owns the brand, if applicable (e.g., Volkswagen Group, General Motors).
4. Headquarters: The main administrative location of the brand.
5. Establishment Year: The year the brand was established, providing historical context.
6. Units Sold (M): The approximate number of units sold globally in millions, offering an indication of market volume.
7. Logo Description: A textual summary detailing the symbolic meaning and key features of the brand's logo.
8. Logo Design Elements (Binary Flags): These columns indicate the presence (1) or absence (0) of specific design elements within the brand's primary logo:
 A. Abstract Mark: Presence of a non-representational graphic symbol.
 B. Pictorial Mark: Presence of a recognizable image (e.g., animal, object).
 C. Letter Mark: Presence of stylized initials or an acronym.
 D. Wordmark: Presence of the full brand name in a distinctive font.
 E. Combination: Indicates if the logo is a combination of multiple elements (e.g., abstract mark + wordmark).
9. Specific Pictorial Symbolism (Binary Flags): These columns denote the inclusion of particular types of pictorial marks, useful for categorizing symbolic themes:
 A. Birds & Wings: Indicates the presence of bird or wing imagery.
 B. Horses & Horns: Indicates the presence of horse or horn imagery.
 C. Lions & Jaguars: Indicates the presence of lion or jaguar imagery.
 D. Emblem & Shields: Indicates the presence of a crest, badge, or enclosed design.

This rich dataset enables detailed analysis of branding strategies, cultural influences on design, and correlations between visual identity and market performance within the global automotive industry.
DOI: 10.15468/dl.6k28z4
title: Occurrence Download

description: A dataset containing 10121 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tordylium apulum"
 ]
}

 The dataset includes 10121 records from 199 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018891-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.73q939
title: Occurrence Download

description: A dataset containing 1346 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heliophila linearis"
 ]
}

 The dataset includes 1346 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018892-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nu6r96
title: Occurrence Download

description: A dataset containing 131 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Isatis multicaulis"
 ]
}

 The dataset includes 131 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018893-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rets4a
title: Occurrence Download

description: A dataset containing 128419 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 128419 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018868-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bmhj94
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Solanum carolinense",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018894-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kwg7nz
title: Occurrence Download

description: A dataset containing 1632 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Torenia asiatica"
 ]
}

 The dataset includes 1632 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018895-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8nhhg8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ziziphus mauritiana",
 "Country is Syrian Arab Republic",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018896-250711103210423/datasets/export for details.
DOI: 10.15468/dl.9zm7ns
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidium botschantsevianum"
 ]
}

 The dataset includes 12 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018898-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vy4r76
title: Occurrence Download

description: A dataset containing 7005 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "Geometry POLYGON((-72.5401 -43.93258,-67.94332 -43.93258,-67.94332 -40.03522,-72.5401 -40.03522,-72.5401 -43.93258))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Larus dominicanus"
 ]
}

 The dataset includes 7005 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018897-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g652ye
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018900-250711103210423/datasets/export for details.
DOI: 10.15468/dl.pba9w4
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018901-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kccr44
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oryctolagus cuniculus",
 "Country is Cuba",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018899-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2skewc
title: Occurrence Download

description: A dataset containing 962 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Torenia violacea"
 ]
}

 The dataset includes 962 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018902-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bncfhxbzrv
title: LungCancer4Types-ImageDataset

description: Lung Cancer Image Dataset: A Comprehensive Collection 2024
Explore the intricacies of lung cancer with our curated dataset, consisting of high-resolution CT scan images. This dataset is designed to aid researchers, clinicians, and machine learning/ Deep learning enthusiasts in studying the diverse manifestations of lung cancer.

Key Features:
CT Scan Images:
Our dataset comprises CT scan images, providing detailed insights into lung cancer morphology. Each image is a visual representation of the complex nature of lung tumors.

Split for Comprehensive Analysis:

Training Set (613 Images): A robust training set containing 613 images meticulously labeled into four distinct classes, allowing for in-depth model training and understanding.
Testing Set (315 Images): Evaluate the model's performance on a diverse range of 315 images, each belonging to one of the four well-defined lung cancer classes.
Validation Set (72 Images): A curated validation set of 72 images, essential for fine-tuning models and ensuring generalizability.
Classes:
Class 1: adenocarcinoma
Class 2: large.cell.carcinoma
Class 3: normal
DOI: 10.17632/bncfhxbzrv.1
title: LungCancer4Types-ImageDataset

description: Lung Cancer Image Dataset: A Comprehensive Collection 2024
Explore the intricacies of lung cancer with our curated dataset, consisting of high-resolution CT scan images. This dataset is designed to aid researchers, clinicians, and machine learning/ Deep learning enthusiasts in studying the diverse manifestations of lung cancer.

Key Features:
CT Scan Images:
Our dataset comprises CT scan images, providing detailed insights into lung cancer morphology. Each image is a visual representation of the complex nature of lung tumors.

Split for Comprehensive Analysis:

Training Set (613 Images): A robust training set containing 613 images meticulously labeled into four distinct classes, allowing for in-depth model training and understanding.
Testing Set (315 Images): Evaluate the model's performance on a diverse range of 315 images, each belonging to one of the four well-defined lung cancer classes.
Validation Set (72 Images): A curated validation set of 72 images, essential for fine-tuning models and ensuring generalizability.
Classes:
Class 1: adenocarcinoma
Class 2: large.cell.carcinoma
Class 3: normal
DOI: 10.17632/zp8yfhvndv
title: AISSLab Breast Cancer Dataset: Toward General AI Harmonization with Real Mammogram Imaging

description: The AISSLab Breast Cancer Dataset is a collection of mammogram images by experts from the Ma'amon's Diagnostic Centre Mammogram Images for Breast Cancer (MDCMI-BC) in Yemen. It is designed to support advancements in breast cancer research and computer-aided diagnosis (CAD) systems. To facilitate research in breast cancer detection, focusing on harmonizing AI with diverse imaging data. This dataset emphasizes improving diagnostic accuracy and is available for academic and clinical research applications.

If you are using this dataset for research purpose kindly cite the following papers: 

[1] A. M. Al-Hejri, R. M. Al-Tam, M. Fazea, A. H. Sable, S. Lee, and M. A. Al-antari, “ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images,” Diagnostics, vol. 13, no. 1, p. 89, Dec. 2022, doi: 10.3390/diagnostics13010089.

[2] R. M. Al-Tam, A. M. Al-Hejri, S. S. Alshamrani, M. A. Al-antari, and S. M. Narangale, “Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images,” Biocybern. Biomed. Eng., vol. 44, no. 3, pp. 731–758, Jul. 2024, doi: 10.1016/j.bbe.2024.08.007.
DOI: 10.17632/zp8yfhvndv.2
title: AISSLab Breast Cancer Dataset: Toward General AI Harmonization with Real Mammogram Imaging

description: The AISSLab Breast Cancer Dataset is a collection of mammogram images by experts from the Ma'amon's Diagnostic Centre Mammogram Images for Breast Cancer (MDCMI-BC) in Yemen. It is designed to support advancements in breast cancer research and computer-aided diagnosis (CAD) systems. To facilitate research in breast cancer detection, focusing on harmonizing AI with diverse imaging data. This dataset emphasizes improving diagnostic accuracy and is available for academic and clinical research applications.

If you are using this dataset for research purpose kindly cite the following papers: 

[1] A. M. Al-Hejri, R. M. Al-Tam, M. Fazea, A. H. Sable, S. Lee, and M. A. Al-antari, “ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images,” Diagnostics, vol. 13, no. 1, p. 89, Dec. 2022, doi: 10.3390/diagnostics13010089.

[2] R. M. Al-Tam, A. M. Al-Hejri, S. S. Alshamrani, M. A. Al-antari, and S. M. Narangale, “Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images,” Biocybern. Biomed. Eng., vol. 44, no. 3, pp. 731–758, Jul. 2024, doi: 10.1016/j.bbe.2024.08.007.
DOI: 10.17632/2n2ttnpctw
title: Achi et al Raw Data (Fly Salt)

description: Raw Data for the paper: Amino Acid Substitutions in the Na+/K+-ATPase May Contribute to Salinity Tolerance in Insects by Achi et al.
DOI: 10.17632/2n2ttnpctw.2
title: Achi et al Raw Data (Fly Salt)

description: Raw Data for the paper: Amino Acid Substitutions in the Na+/K+-ATPase May Contribute to Salinity Tolerance in Insects by Achi et al.