cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr03779
title: ABC News/Washington Post War Poll #2, March 2003
title: Archival Version

description: This special topic poll, conducted March 23, 2003, was
 undertaken to assess public opinion on the war against Iraq.
 Respondents were asked whether they approved of the way President
 George W. Bush was handling the presidency, as well as the situation
 with Iraq and Iraqi President Saddam Hussein. Those polled were asked
 whether they supported or opposed the war, whether the war was going
 better or worse than expected, whether Iraqi resistance had been
 tougher or weaker than expected, and whether the battle for control of
 Baghdad would be difficult or easy. Views were sought on whether the
 United States and its allies had done the right thing, how long the
 war would last, whether there would be significant United States
 casualties, and whether everything was being done to avoid United
 States military and civilian casualties. A series of questions
 addressed anti-war demonstrations. Topics covered whether respondents
 had attended either anti-war or pro-war demonstrations, whether people
 had a right to demonstrate peacefully against the war, and whether
 anti-war demonstrations in other countries had affected respondents'
 support for the war. A final question probed whether respondents felt
 worried, proud, frightened, angry, or hopeful about the war.
 Background information on respondents includes age, gender, political
party, and Hispanic origin.
description: Households were selected by random-digit dialing. Within
 households, the respondent selected was the adult living in the
 household who last had a birthday and who was home at the time of the
interview.
description: Datasets: 
DS1: ABC News/Washington Post War Poll #2, March 2003
description: Persons aged 18 and over living in households with
telephones in the contiguous 48 United States.
description: (1) Additional information about sampling,
 interviewing, weighting, and sampling error may be found in the
 codebook. (2) The data are provided as an SPSS portable file. (3)
 This collection has not been processed by ICPSR staff. ICPSR is
 distributing the data and documentation for this collection in
 essentially the same form in which they were received. When
 appropriate, documentation has been converted to Portable Document
 Format (PDF), data files have been converted to non-platform-specific
 formats, and variables have been recoded to ensure respondents'
 anonymity. (4) The codebook is provided by ICPSR as a Portable
 Document Format (PDF) file. The PDF file format was developed by Adobe
 Systems Incorporated and can be accessed using PDF reader software,
 such as the Adobe Acrobat Reader. Information on how to obtain a copy
of the Acrobat Reader is provided on the ICPSR Web site.
DOI: 10.6084/m9.figshare.27935580
title: muhecloud.7z

description: Photos, six angular depth maps, point cloud maps, and color images of 20-60 cm <i>Schima superba</i> seedlings.
DOI: 10.6084/m9.figshare.27935580.v1
title: muhecloud.7z

description: Photos, six angular depth maps, point cloud maps, and color images of 20-60 cm <i>Schima superba</i> seedlings.
DOI: 10.15468/dl.tahva7
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "GadmGid is IND.15_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lantana camara L.",
 "Year 2015-2025"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004160-250221090833381/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.28478908
title: Punicalagin ameliorates lipopolysaccharide-induced inflammatory response in dental pulp cells via inhibition of the NF-κB/Wnt5a-ROR2 pathway

description: Punicalagin (PCG) is a major polyphenolic component with potent anti-inflammatory, anti-atherogenic, anti-cancer, and antioxidant activities. This study aimed to investigate the impact and underlying mechanisms of PCG on lipopolysaccharide (LPS)-induced dental pulpitis. A rat pulpitis model was constructed, and the infected pulp was covered with a PCG collagen sponge. In vitro, dental pulp cells (DPCs) were isolated, and the effects of LPS and PCG on cell viability were assessed. The expression levels of inflammation-related factors were investigated by qRT-PCR and ELISA. The Nuclear Factor kappa B (NF-κB) transcription factors and Wnt family member 5a-Receptor tyrosine kinase like Orphan Receptor 2 (Wnt5a-ROR2) levels were evaluated by immunofluorescence staining and Western blotting. We demonstrated that the PCG collagen sponge effectively reduced the infiltration of inflammatory cells in the pulp. PCG significantly alleviated the inflammatory response by reducing the mRNA expression levels of IL-1β, IL-6, IL-8, ICAM-1, and VCAM-1 and the secretion of IL-6 and IL-8 in a concentration-dependent manner. Immunofluorescence staining showed that the activation of the NF-κB pathway was hindered by PCG, which affected with the nuclear translocation of P65. PCG reduced the phosphorylation levels of P65 and IκBα and suppressed the expression levels of Wnt5a and ROR2 induced by LPS. The NF-κB inhibitor Bay11-7082 reduced the activation of the NF-κB/Wnt5a-ROR2 pathway and the inflammatory response; the application of PCG significantly augmented this inhibitory effect. PCG demonstrated an anti-inflammatory effect in LPS-induced DPCs by targeting the NF-κB/Wnt5a-ROR2 signaling pathway. This schematic illustration demonstrates that PCG ameliorates LPS-elicited pulpitis by decreasing the expression of the proteins associated with the NF-κB signaling pathway and its downstream Wnt5a/ROR2 pathway and by reducing the production of inflammatory molecules.
DOI: 10.6084/m9.figshare.28478908.v1
title: Punicalagin ameliorates lipopolysaccharide-induced inflammatory response in dental pulp cells via inhibition of the NF-κB/Wnt5a-ROR2 pathway

description: Punicalagin (PCG) is a major polyphenolic component with potent anti-inflammatory, anti-atherogenic, anti-cancer, and antioxidant activities. This study aimed to investigate the impact and underlying mechanisms of PCG on lipopolysaccharide (LPS)-induced dental pulpitis. A rat pulpitis model was constructed, and the infected pulp was covered with a PCG collagen sponge. In vitro, dental pulp cells (DPCs) were isolated, and the effects of LPS and PCG on cell viability were assessed. The expression levels of inflammation-related factors were investigated by qRT-PCR and ELISA. The Nuclear Factor kappa B (NF-κB) transcription factors and Wnt family member 5a-Receptor tyrosine kinase like Orphan Receptor 2 (Wnt5a-ROR2) levels were evaluated by immunofluorescence staining and Western blotting. We demonstrated that the PCG collagen sponge effectively reduced the infiltration of inflammatory cells in the pulp. PCG significantly alleviated the inflammatory response by reducing the mRNA expression levels of IL-1β, IL-6, IL-8, ICAM-1, and VCAM-1 and the secretion of IL-6 and IL-8 in a concentration-dependent manner. Immunofluorescence staining showed that the activation of the NF-κB pathway was hindered by PCG, which affected with the nuclear translocation of P65. PCG reduced the phosphorylation levels of P65 and IκBα and suppressed the expression levels of Wnt5a and ROR2 induced by LPS. The NF-κB inhibitor Bay11-7082 reduced the activation of the NF-κB/Wnt5a-ROR2 pathway and the inflammatory response; the application of PCG significantly augmented this inhibitory effect. PCG demonstrated an anti-inflammatory effect in LPS-induced DPCs by targeting the NF-κB/Wnt5a-ROR2 signaling pathway. This schematic illustration demonstrates that PCG ameliorates LPS-elicited pulpitis by decreasing the expression of the proteins associated with the NF-κB signaling pathway and its downstream Wnt5a/ROR2 pathway and by reducing the production of inflammatory molecules.
DOI: 10.5281/zenodo.14921069
title: Neural Style Transfer: Content and Style Image Dataset

description: This dataset contains two sets of images designed for Neural Style Transfer (NST) research and experiments. It includes:



Content Images (17 images): These images represent the base content that will be stylized.

Style Images (16 images): These images provide artistic styles to be transferred onto the content images.


The dataset is intended for use in machine learning, deep learning, and AI-based artistic style transfer models. It can be used for tasks such as style transfer experiments, image transformation, and AI art generation.

File Format: .zip (contains organized folders for content and style images)
DOI: 10.5281/zenodo.14921070
title: Neural Style Transfer: Content and Style Image Dataset

description: This dataset contains two sets of images designed for Neural Style Transfer (NST) research and experiments. It includes:



Content Images (17 images): These images represent the base content that will be stylized.

Style Images (15 images): These images provide artistic styles to be transferred onto the content images.


The dataset is intended for use in machine learning, deep learning, and AI-based artistic style transfer models. It can be used for tasks such as style transfer experiments, image transformation, and AI art generation.

File Format: .zip (contains organized folders for content and style images)
DOI: 10.5281/zenodo.14921182
title: Neural Style Transfer: Content and Style Image Dataset

description: This dataset contains two sets of images designed for Neural Style Transfer (NST) research and experiments. It includes:



Content Images (17 images): These images represent the base content that will be stylized.

Style Images (16 images): These images provide artistic styles to be transferred onto the content images.


The dataset is intended for use in machine learning, deep learning, and AI-based artistic style transfer models. It can be used for tasks such as style transfer experiments, image transformation, and AI art generation.

File Format: .zip (contains organized folders for content and style images)
DOI: 10.15468/dl.97hew5
title: Occurrence Download

description: A dataset containing 736884 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Echinoidea"
 ]
}

 The dataset includes 736884 records from 1168 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004161-250221090833381/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.26922613
title: 视频数据

description: 仅部分测试数据，完整数据请通过邮件联系
DOI: 10.6084/m9.figshare.26922613.v1
title: 视频数据

description: 仅部分测试数据，完整数据请通过邮件联系
DOI: 10.5281/zenodo.14921266
title: HDSNE Chest X-ray Dataset

description: Description:







The continuous release of medical image databases, often featuring overlapping or identical categories, poses a significant challenge for the development of autonomous Computer-Aided Diagnostics (CAD) systems. These systems are essential for creating truly comprehensive medical diagnostics. However, one of the main obstacles lies in the frequent bulk release of HDSNE Chest X-ray Dataset, which commonly suffer from two critical issues: image duplication and data corruption.

The Problem of Dataset Redundancy

Repeated releases of the same categories often fail to integrate or deduplicate similar images across databases, which can severely impact the effectiveness of machine learning models. Data duplication not only reduces the efficiency of learning models but also leads to overfitting, wastes computational resources, and increases the carbon footprint due to the energy required for training complex models.

 







Download Dataset

 









Proposed Solution: Global Data Aggregation Model

In response to these challenges, we introduce a global data aggregation model that intelligently combines data from six distinct and reputable medical imaging databases. Each database was carefully curated to ensure the elimination of redundancies while preserving data diversity. Two robust algorithms were employed:



Hash MD5 Algorithm: This algorithm generates unique hash values for each image, helping in the effective detection and elimination of duplicate images.

t-SNE Algorithm: This technique is used for dimensionality reduction, with a tunable perplexity parameter to ensure accurate representation of high-dimensional data.


Dataset Categories

The final dataset includes an equal number of samples from three key categories of chest X-ray images:



Normal

Pneumonia

COVID-19


This uniform distribution ensures that the dataset is balanced, avoiding class imbalance—a common issue that can skew results in medical image analysis.

Dataset Application & Model Evaluation

The dataset was applied to the Inception V3 pre-trained model, a leading convolutional neural network (CNN) architecture known for its excellence in image classification tasks. The evaluation was conduct using the following performance metrics:



Accuracy: An exceptional accuracy rate of 98.48% was achieve.

Precision, Recall, and F1-score: The dataset showed strong performance across these metrics, reducing both false positives and false negatives.

Statistical Validation: A t-test was conduct to validate the results, and the t-values and p-values confirm the statistical significance of the model’s performance.


Conclusion

The HDSNE Chest X-ray Dataset offers a novel and effective approach to data aggregation, tackling the issues of redundancy and data duplication that have long plagued the field of medical imaging. By maintaining a balance class distribution and eliminating unnecessary data, this dataset provides a cleaner and more efficient resource for training machine learning models.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14921267
title: HDSNE Chest X-ray Dataset

description: Description:







The continuous release of medical image databases, often featuring overlapping or identical categories, poses a significant challenge for the development of autonomous Computer-Aided Diagnostics (CAD) systems. These systems are essential for creating truly comprehensive medical diagnostics. However, one of the main obstacles lies in the frequent bulk release of HDSNE Chest X-ray Dataset, which commonly suffer from two critical issues: image duplication and data corruption.

The Problem of Dataset Redundancy

Repeated releases of the same categories often fail to integrate or deduplicate similar images across databases, which can severely impact the effectiveness of machine learning models. Data duplication not only reduces the efficiency of learning models but also leads to overfitting, wastes computational resources, and increases the carbon footprint due to the energy required for training complex models.

 







Download Dataset

 









Proposed Solution: Global Data Aggregation Model

In response to these challenges, we introduce a global data aggregation model that intelligently combines data from six distinct and reputable medical imaging databases. Each database was carefully curated to ensure the elimination of redundancies while preserving data diversity. Two robust algorithms were employed:



Hash MD5 Algorithm: This algorithm generates unique hash values for each image, helping in the effective detection and elimination of duplicate images.

t-SNE Algorithm: This technique is used for dimensionality reduction, with a tunable perplexity parameter to ensure accurate representation of high-dimensional data.


Dataset Categories

The final dataset includes an equal number of samples from three key categories of chest X-ray images:



Normal

Pneumonia

COVID-19


This uniform distribution ensures that the dataset is balanced, avoiding class imbalance—a common issue that can skew results in medical image analysis.

Dataset Application & Model Evaluation

The dataset was applied to the Inception V3 pre-trained model, a leading convolutional neural network (CNN) architecture known for its excellence in image classification tasks. The evaluation was conduct using the following performance metrics:



Accuracy: An exceptional accuracy rate of 98.48% was achieve.

Precision, Recall, and F1-score: The dataset showed strong performance across these metrics, reducing both false positives and false negatives.

Statistical Validation: A t-test was conduct to validate the results, and the t-values and p-values confirm the statistical significance of the model’s performance.


Conclusion

The HDSNE Chest X-ray Dataset offers a novel and effective approach to data aggregation, tackling the issues of redundancy and data duplication that have long plagued the field of medical imaging. By maintaining a balance class distribution and eliminating unnecessary data, this dataset provides a cleaner and more efficient resource for training machine learning models.

This dataset is sourced from Kaggle.
DOI: 10.21227/hqj7-rd94
title: RGBT234

description: This dataset is a large-scale video benchmark constructed for RGB-Thermal (RGB-T) object tracking tasks, featuring the following key characteristics: 1. **Scale & Diversity**  - Contains 234,000 total frames, with sequences up to 8,000 frames  - Covers diverse scenarios and complex environmental conditions  - Currently the largest publicly available RGB-T dataset in the field   2. **Precise Multimodal Alignment**  - Strict spatiotemporal synchronization between RGB and thermal sequences  - Requires no pre/post-processing for direct usage  - Ensures cross-modal data consistency for reliable comparisons   3. **Granular Annotation System**  - Frame-level bounding box annotations  - Specially labeled occlusion levels for tracked objects  - Enables occlusion-sensitive analysis and robustness evaluation   4. **Core Innovations**  - Breaks scale limitations of existing datasets for comprehensive evaluation  - First benchmark with pixel-level multimodal alignment accuracy  - Introduces quantitative occlusion analysis as a new dimension  - Provides standardized validation for multimodal fusion algorithms   The dataset addresses longstanding evaluation challenges in RGB-T tracking by offering:  - Precisely aligned cross-modal data streams  - Fine-grained occlusion annotations  - Large-scale sample capacity   It enables in-depth research on:  - Effectiveness verification of multimodal feature fusion  - Sustained performance assessment for long-term tracking  - Algorithm robustness testing under varying occlusion levels  - Benchmark comparisons for cross-modal representation learning   As the first comprehensive RGB-T tracking benchmark, it establishes new research paradigms and reliability validation standards, significantly advancing visible-thermal fusion tracking technologies.
DOI: 10.15468/dl.hk82x6
title: Occurrence Download

description: A dataset containing 1624756 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "Year 2015-2015"
 ]
}

 The dataset includes 1624756 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004162-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v3vx24
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "TaxonKey is Lantana camara L.",
 "Year 2015-2015"
 ]
}

 The dataset includes 26 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004163-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2yy2ur
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Atelopus quimbaya Ruiz-Carranza & Osorno-Muñoz, 1994"
 ]
}

 The dataset includes 20 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004164-250221090833381/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.27619773
title: Meta-data from Evaluation of Hepatitis B core related antigen (HBcrAg) as a biomarker in cohorts from the United Kingdom and South Africa

description: This is the metadata for our paper 'Evaluation of Hepatitis B core related antigen (HBcrAg) as a biomarker in cohorts from the United Kingdom and South Africa' and will be published on acceptance of the manuscript for publication. This manuscript describes HBcrAg in two populations and looks at the ability of HBcrAg to predict liver disease outcomes in these two groups.<br>
DOI: 10.6084/m9.figshare.27619773.v2
title: Meta-data from Evaluation of Hepatitis B core related antigen (HBcrAg) as a biomarker in cohorts from the United Kingdom and South Africa

description: This is the metadata for our paper 'Evaluation of Hepatitis B core related antigen (HBcrAg) as a biomarker in cohorts from the United Kingdom and South Africa' and will be published on acceptance of the manuscript for publication. This manuscript describes HBcrAg in two populations and looks at the ability of HBcrAg to predict liver disease outcomes in these two groups.<br>
DOI: 10.6084/m9.figshare.27619773.v1
title: Meta-data from Evaluation of Hepatitis B core related antigen (HBcrAg) as a biomarker in cohorts from the United Kingdom and South Africa

description: This is the metadata for our paper 'Evaluation of Hepatitis B core related antigen (HBcrAg) as a biomarker in cohorts from the United Kingdom and South Africa' and will be published on acceptance of the manuscript for publication. This manuscript describes HBcrAg in two populations and looks at the ability of HBcrAg to predict liver disease outcomes in these two groups.<br>
DOI: 10.15156/bio/sh1046400.10fu
title: SH1046400.10FU

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