cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4jwpsvxcs7
title: Visualization Results for MECS-Net: Robust Brain Tumor Segmentation under Modality Incompleteness

description: This repository provides visualization results only to support the study “Towards Robust Brain Tumor Segmentation under Modality Incompleteness: A Contribution-Optimized Edge-Enhanced Network (MECS-Net)”.

The uploaded materials consist of representative segmentation visualizations generated by MECS-Net under different modality settings. These results are intended to complement the qualitative analysis in the manuscript and to facilitate visual inspection of the model’s segmentation behavior.

No raw training data or executable code is included in this repository.
DOI: 10.17632/4jwpsvxcs7.1
title: Visualization Results for MECS-Net: Robust Brain Tumor Segmentation under Modality Incompleteness

description: This repository provides visualization results only to support the study “Towards Robust Brain Tumor Segmentation under Modality Incompleteness: A Contribution-Optimized Edge-Enhanced Network (MECS-Net)”.

The uploaded materials consist of representative segmentation visualizations generated by MECS-Net under different modality settings. These results are intended to complement the qualitative analysis in the manuscript and to facilitate visual inspection of the model’s segmentation behavior.

No raw training data or executable code is included in this repository.
DOI: 10.17632/bfc9g98297
title: Native postsynaptic density is a functional condensate formed via phase separation. Chen et al.

description: The data demonstrates that native PSD purified from mouse brain tissues is a functional protein condensate.
DOI: 10.17632/bfc9g98297.1
title: Native postsynaptic density is a functional condensate formed via phase separation. Chen et al.

description: The data demonstrates that native PSD purified from mouse brain tissues is a functional protein condensate.
DOI: 10.17632/d68p39bv39
title: Dual-ligand-modified cantharidin nanoparticles for the treatment of hepatocellular carcinoma via the inhibition of EphB4

description: In this study, we aimed to construct novel dual-ligand-modified cantharidin (CTD)-loaded solid lipid nanoparticles (GA-FA-CSLNs) for the targeted therapy of hepatocellular carcinoma. This design leverages the hepatocyte-targeting property of 18-GA-Suc (GA) and the long-circulation characteristic of FA-PEG3500-DSPE (FA). Nanoparticles were prepared via the emulsification-ultrasonic method and characterized. GA-FA-CSLNs not only exhibited excellent tumor-targeting ability but also markedly enhanced CTD’s inhibitory effect on Huh-7 cells and promoted Huh-7 cell apoptosis. In vivo experiments showed that GA-FA-CSLNs significantly inhibited tumor growth, downregulated the expression of EphB4 and Bcl-2 proteins, and upregulated the expression of caspase 3 and Bax proteins—thus suppressing tumor cell proliferation and inducing tumor cell apoptosis—with favorable in vivo safety. Pharmacokinetic studies revealed that GA-FA-CSLNs exhibited prolonged in vivo circulation time and improved bioavailability. Collectively, GA-FA-CSLNs emerge as a promising actively targeted nanocarrier system that enhances the specificity and efficacy of CTD while reducing its adverse effects, providing a potential strategy for the targeted therapy of HCC.
DOI: 10.17632/d68p39bv39.1
title: Dual-ligand-modified cantharidin nanoparticles for the treatment of hepatocellular carcinoma via the inhibition of EphB4

description: In this study, we aimed to construct novel dual-ligand-modified cantharidin (CTD)-loaded solid lipid nanoparticles (GA-FA-CSLNs) for the targeted therapy of hepatocellular carcinoma. This design leverages the hepatocyte-targeting property of 18-GA-Suc (GA) and the long-circulation characteristic of FA-PEG3500-DSPE (FA). Nanoparticles were prepared via the emulsification-ultrasonic method and characterized. GA-FA-CSLNs not only exhibited excellent tumor-targeting ability but also markedly enhanced CTD’s inhibitory effect on Huh-7 cells and promoted Huh-7 cell apoptosis. In vivo experiments showed that GA-FA-CSLNs significantly inhibited tumor growth, downregulated the expression of EphB4 and Bcl-2 proteins, and upregulated the expression of caspase 3 and Bax proteins—thus suppressing tumor cell proliferation and inducing tumor cell apoptosis—with favorable in vivo safety. Pharmacokinetic studies revealed that GA-FA-CSLNs exhibited prolonged in vivo circulation time and improved bioavailability. Collectively, GA-FA-CSLNs emerge as a promising actively targeted nanocarrier system that enhances the specificity and efficacy of CTD while reducing its adverse effects, providing a potential strategy for the targeted therapy of HCC.
DOI: 10.17632/jts6srmmfm
title: Dataset for Cluster Subtype Analysis of Pediatric Adenoidal Hypertrophy (Age, Immune Indicators, Dust Mite Allergen)

description: This dataset corresponds to a retrospective clinical study conducted between 2023 and 2025 at the Department of Otolaryngology, Changzhou Children’s Hospital. The study enrolled 535 pediatric patients diagnosed with adenoidal hypertrophy. Data collection involved:
Recording basic clinical information (e.g., age) of eligible patients;
Detecting immune indicators (including complement C3, immunoglobulin A/IgG) via immunoturbidimetry;
Measuring allergen-specific IgE levels (e.g., Dermatophagoides pteronyssinus, Dermatophagoides farinae) using immunoblotting;
Collecting adenoidal-nasopharyngeal ratio data via nasal endoscopy.
All data were anonymized after collection to protect patient privacy, and the study was approved by the local ethics committee.
DOI: 10.17632/jts6srmmfm.1
title: Dataset for Cluster Subtype Analysis of Pediatric Adenoidal Hypertrophy (Age, Immune Indicators, Dust Mite Allergen)

description: This dataset corresponds to a retrospective clinical study conducted between 2023 and 2025 at the Department of Otolaryngology, Changzhou Children’s Hospital. The study enrolled 535 pediatric patients diagnosed with adenoidal hypertrophy. Data collection involved:
Recording basic clinical information (e.g., age) of eligible patients;
Detecting immune indicators (including complement C3, immunoglobulin A/IgG) via immunoturbidimetry;
Measuring allergen-specific IgE levels (e.g., Dermatophagoides pteronyssinus, Dermatophagoides farinae) using immunoblotting;
Collecting adenoidal-nasopharyngeal ratio data via nasal endoscopy.
All data were anonymized after collection to protect patient privacy, and the study was approved by the local ethics committee.
DOI: 10.17632/64kmzzhr4s
title: Harnessing carlina oxide scaffold for the management of vector-borne diseases: synthesis and structure-activity relationship
DOI: 10.25574/32022
title: Chandra X-ray Observatory ObsId 32022

description: HRC-I Checkout Observation
DOI: 10.17632/64kmzzhr4s.1
title: Harnessing carlina oxide scaffold for the management of vector-borne diseases: synthesis and structure-activity relationship
DOI: 10.25574/32044
title: Chandra X-ray Observatory ObsId 32044

description: Studying the Si K-edge of Cygnus X-2
DOI: 10.17632/782ctjjsbn
title: The role of arachidonic acid metabolites in the subtype classification and pathogenesis of primary aldosteronism

description: The effect of arachidonic acid and/or sh-TRPC3 in primary adrenal cortical cells and the effect of high-salt diet in the adrenal gland of mice and rat.
DOI: 10.17632/782ctjjsbn.1
title: The role of arachidonic acid metabolites in the subtype classification and pathogenesis of primary aldosteronism

description: The effect of arachidonic acid and/or sh-TRPC3 in primary adrenal cortical cells and the effect of high-salt diet in the adrenal gland of mice and rat.
DOI: 10.17632/4kbmt4ht46
title: Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 3

description: Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions.

Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears.

The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours.

The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance.
Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition.

This dataset provides the Part 3 of data collected during the experimental campaign.
DOI: 10.17632/4kbmt4ht46.1
title: Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 3

description: Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions.

Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears.

The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours.

The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance.
Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition.

This dataset provides the Part 3 of data collected during the experimental campaign.
DOI: 10.17632/w76ff4r2jh
title: Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 2

description: Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions.

Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears.

The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours.

The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance.
Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition.

This dataset provides the Part 2 of data collected during the experimental campaign.
DOI: 10.17632/w76ff4r2jh.1
title: Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 2

description: Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions.

Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears.

The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours.

The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance.
Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition.

This dataset provides the Part 2 of data collected during the experimental campaign.
DOI: 10.17632/3n94byb6fk
title: Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 1

description: Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions.

Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears.

The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours.

The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance.
Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition.

This dataset provides the Part 1 of data collected during the experimental campaign.
DOI: 10.17632/3n94byb6fk.1
title: Run-to-failure vibration dataset of a three-stages planetary gearbox - PART 1

description: Data sourced from real operating conditions constitutes a crucial asset for research in machine diagnostics and prognostics, particularly in the context of predictive maintenance and the increasing adoption of artificial intelligence (AI) methods. For fault detection and prognostic applications, datasets must capture the evolution of degradation phenomena over time, enabling both the assessment of machinery health and the prediction of its remaining useful life (RUL). These objectives require long-term, high-quality run-to-failure data collected under realistic and variable working conditions.

Planetary gearboxes are widely employed in industrial and automotive systems and are subjected to complex loading and dynamic interactions among gears.

The Department of Engineering at the University of Ferrara conducted an extensive experimental campaign aimed at documenting the temporal evolution of vibration signals over the entire operational life of a three-stage planetary gearbox. The experiment was performed on a dedicated back-to-back test bench operating under variable speed and torque conditions. A mono-axial accelerometer was mounted on the gearbox housing to acquire radial vibration signals at hourly intervals. The system was operated continuously until the third-stage sun gear experienced complete mechanical failure after 1006 operating hours.

The resulting dataset provides the complete run-to-failure history of the gearbox and offers a comprehensive record of its degradation progression. This dataset represents a valuable resource for both academic research and industrial applications in the fields of diagnostics, prognostics, and predictive maintenance.
Data is provided in .mat format and, due to repository size limitations, is divided into three separate parts, each containing approximately one-third of the total experiment. Each file contains the radial acceleration signal, the sampling frequency, and the operating condition corresponding to the acquisition.

This dataset provides the Part 1 of data collected during the experimental campaign.
DOI: 10.17632/j46rc3445f
title: Echinodermata. Biology. Ecology. Taxonomy, Systematics and Anatomy (Species Inner Body Architecture)

description: Echinodermata.
Descriptions :
Biology. Ecology. Taxonomy, Systematics and Anatomy (Species Inner Body Architecture)
DOI: 10.17632/j46rc3445f.1
title: Echinodermata. Biology. Ecology. Taxonomy, Systematics and Anatomy (Species Inner Body Architecture)

description: Echinodermata.
Descriptions :
Biology. Ecology. Taxonomy, Systematics and Anatomy (Species Inner Body Architecture)
DOI: 10.17632/kjgt3x2nw7
title: Sustainable rigid pavement concretes based on recycled aggregates of different types: structural and mechanical assessment

description: Raw unprocessed results for mechanical characteristics, scanning electron microscopy, mercury intrusion porosimetry and termoanalysis, obtained during the tests, are presented in the dataset.
DOI: 10.17632/kjgt3x2nw7.1
title: Sustainable rigid pavement concretes based on recycled aggregates of different types: structural and mechanical assessment

description: Raw unprocessed results for mechanical characteristics, scanning electron microscopy, mercury intrusion porosimetry and termoanalysis, obtained during the tests, are presented in the dataset.
DOI: 10.34740/kaggle/dsv/14132791
title: RLCS season 2021-Now
DOI: 10.15468/dl.5mthmz
title: Occurrence Download

description: A dataset containing 1700 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-5.644041 39.878721, -5.646004 39.916803, -5.696114 39.916803, -5.694134 39.878721, -5.644041 39.878721))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1700 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050040-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzq334
title: Occurrence Download

description: A dataset containing 3208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Kentropyx calcarata"
 ]
}

 The dataset includes 3208 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050041-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.esab6q
title: Occurrence Download

description: A dataset containing 968 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 "GadmGid is IDN.7_1",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 968 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050042-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.